WO2022265949A1 - Groupement d'utilisateurs pour mimo multi-utilisateurs - Google Patents

Groupement d'utilisateurs pour mimo multi-utilisateurs Download PDF

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Publication number
WO2022265949A1
WO2022265949A1 PCT/US2022/033165 US2022033165W WO2022265949A1 WO 2022265949 A1 WO2022265949 A1 WO 2022265949A1 US 2022033165 W US2022033165 W US 2022033165W WO 2022265949 A1 WO2022265949 A1 WO 2022265949A1
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WIPO (PCT)
Prior art keywords
ues
subset
beams
rbg
srs
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PCT/US2022/033165
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English (en)
Inventor
Krishna Srikanth Gomadam
Po Han Huang
Brett Eric Schein
Praveen Kumar Gopala
Djordje Tujkovic
Mustafa Emin Sahin
Anoop Tomar TOMAR
Original Assignee
Meta Platforms Technologies, Llc
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Priority claimed from US17/491,307 external-priority patent/US11476903B1/en
Application filed by Meta Platforms Technologies, Llc filed Critical Meta Platforms Technologies, Llc
Publication of WO2022265949A1 publication Critical patent/WO2022265949A1/fr

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0452—Multi-user MIMO systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming

Definitions

  • This disclosure generally relates to wireless networks, and in particular, related to Multiple-Input Multiple-Output (MIMO) technologies.
  • MIMO Multiple-Input Multiple-Output
  • Multi-user MIMO is a set of MIMO technologies for multipath wireless communication, in which multiple users or terminals, each radioing over one or more antennas, communicate with one another.
  • single-user MIMO involves a single multi-antenna-equipped user or terminal communicating with precisely one other similarly equipped node.
  • MU-MIMO algorithms enhance MIMO systems where connections among users count greater than one.
  • MU-MIMO may be generalized into two categories: MIMO broadcast channels (MIMO BC) and MIMO multiple-access channels (MIMO MAC) for downlink and uplink situations, respectively.
  • MIMO BC MIMO broadcast channels
  • MIMO MAC MIMO multiple-access channels
  • Examples of advanced- transmit processing for downlink MU-MIMO are interference-aware precoding and space- division multiple access (SDMA)-based downlink user scheduling.
  • SDMA space- division multiple access
  • For advanced-transmit processing channel state information has to be known at the transmitter. Knowledge of channel state information allows throughput improvement. Thus, methods to obtain channel state information at the transmitter become of significant importance.
  • Downlink MU-MIMO systems have an outstanding advantage over SU-MIMO systems, especially when the number of antennas at the transmitter is larger than the number of antennas at each receiver (user).
  • a base station is the transmitter, and a user is the receiver for downlink connections, and vice versa for uplink connections.
  • One of the primary goals may be to maximize the MU-MIMO capacity by placing as many parallel layers of user data as possible in the same Resource Block Group (RBG) in the same Transmit Time Interval (TTI). Reaching a high number of parallel layers may require selecting a set of appropriate users based on their instant channel conditions and performing precoding/beamforming in such a way to orthogonalize transmissions to those users as much as possible.
  • the system may perform channel estimation, find the UE channels with the least correlation, group the users accordingly, and calculate the precoder/beamformer.
  • LI is a stateless entity not keeping any UE state information in its memory
  • L2 may need to keep UE specific information in its memory including an amount of pending data, served throughput, Quality of Service (QoS) data, and hybrid automatic repeat request (HARQ) information. Therefore, L1/L2 cross layer optimizations may be desired for highly efficient user grouping/beamforming algorithms. When an Ll/L2-cross-layer-optimized user grouping and beamforming algorithm is designed, special attention may need to be paid to two aspects. The one may be a computational complexity.
  • Another aspect may be an amount of data to be transferred between the Distributed Unit (DU) and the Radio Unit (RU).
  • the fronthaul between DU and RU has a finite capacity.
  • the amount of data the LI conveys to the RU for precoding / beamforming purposes over the fronthaul may be important factor to consider.
  • a DU associated with a gNB may comprise one or more computing devices.
  • An RU associated with the gNB may receive sounding reference signal (SRS) from a plurality of UEs associated with the gNB.
  • the DU may receive the SRS from the RU.
  • the DU may compute an SRS-based channel matrix for each of the plurality of UEs by performing SRS-based downlink channel estimations with the received SRS.
  • the DU may estimate strengths or SNRs for a plurality of pre-determined beams for each of the plurality of UEs by performing a two-dimensional discrete Fourier transform (2D-DFT) on the SRS- based channel matrix for each of the plurality of UEs.
  • 2D-DFT two-dimensional discrete Fourier transform
  • the DU may select a subset of the plurality of UEs to which downlink data is to be transmitted for a resource block group (RBG) in a transmission time interval (TTI).
  • the subset may be selected based on the estimated strengths or SNRs of the plurality of pre-determined beams, the CQIs, and downlink traffic information associated with each of the plurality of UEs.
  • the DU may establish a massive MIMO (maMIMO) channel matrix between UEs in the selected subset and transmission antenna ports associated with the gNB based on the SRS-based channel matrices.
  • the DU may compute a precoding matrix for the RBG by performing a regularized zero forcing (RZF) on the maMIMO channel matrix.
  • RZF regularized zero forcing
  • the DU may determine modulation and coding scheme (MCS) for each of the plurality of UEs for the TTI.
  • MCS modulation and coding scheme
  • the DU may prepare multi-layered UE data for the RBG based on the selected subset of the plurality of UEs, the computed precoding matrix, determined MCS values corresponding to the plurality of UEs, and a number of layers for each of the plurality of UEs.
  • the DU may send the multi-layered UE data and the precoding matrix for the RBG to the RU.
  • the RU may transmit pre-coded multi-layered UE data to UEs in the selected subset for the RBG using MIMO technologies.
  • an RU associated with a gNB may receive SRS from a plurality of UEs associated with the gNB.
  • the RU may send the SRS to a DU associated with the gNB.
  • the DU may compute an SRS-based channel matrix for each of the plurality of UEs by performing SRS-based downlink channel estimations with the received SRS.
  • the DU may estimate strengths or SNRs for a plurality of pre-determined beams for each of the plurality of UEs by performing a 2D-DFT on the SRS-based channel matrix for each of the plurality of UEs.
  • the DU may select a subset of the plurality of UEs to which downlink data is to be transmitted for an RBG in a TTI.
  • the subset may be selected based on the estimated strengths or SNRs of the plurality of pre-determined beams, the CQIs, and downlink traffic information associated with each of the plurality of UEs.
  • the DU may determine modulation and coding scheme (MCS) for each of the plurality of UEs for the TTI.
  • MCS modulation and coding scheme
  • the DU may associate a beam to each UE in the selected subset for the RBG.
  • the associated beam for a UE is a beam with a highest strength for the UE.
  • the RU may receive information regarding the selected subset, the beams associated with UEs in the selected subset, and data to be transmitted to the UEs in the selected subset from the DU.
  • the RU may establish a maMIMO channel matrix by calculating the IDFT of the DFT vectors corresponding to the beams associated with the UEs in the selected subset.
  • the RU may compute a precoding matrix for the RBG by RZF-ing the maMIMO matrix.
  • the RU may prepare pre-coded multi-user data by applying the precoding matrix to the UE data.
  • the RU may transmit the pre-coded data to the UEs in the selected subset for the RBG using MU-MIMO technologies.
  • a method comprising, by a computing device associated with a base station of a wireless network: sending, to a distributed unit (DU) associated with the base station, sounding reference signal (SRS) received from a plurality of user equipments (UEs) associated with the base station; receiving, from the DU, information regarding a subset of the plurality of UEs selected for downlink data transmissions for a resource block group (RBG), multi-user data to be transmitted to UEs in the subset, and identities of selected beams among a plurality of pre determined beams to be associated with the UEs in the subset, wherein each of the plurality of pre-determined beams corresponds to a discrete Fourier transform (DFT) vector; computing a precoding matrix for the RBG based on inverse-DFT (IDFT) vectors corresponding to the selected beams; preparing pre-coded multi-user data by applying the precoding matrix to the multi user data; and transmitting the pre-coded multi
  • DFT discrete Fourier transform
  • computing a precoding matrix for the RBG may comprise: establishing a maMIMO channel matrix by calculating the IDFT of the DFT vectors corresponding to the selected beams; calculating a regularized pseudo-inverse of the maMIMO channel matrix; and normalizing power of each column of the regularized pseudo-inverse of the maMIMO channel matrix such that a transmit power level for each UE in the selected subset equals to each other.
  • calculating an IDFT vector may be calculating a complex conjugate of the DFT vector.
  • the subset of the plurality of UEs may be selected based on estimated strengths or signal-to-noise ratios (SNRs) for the pre-determined beams for each of the plurality of UEs.
  • SNRs signal-to-noise ratios
  • estimating strengths or SNRs for the pre-determined beams for a UE may comprise: computing a channel matrix for corresponding to the UE by performing SRS-based downlink channel estimations based on the received SRS, wherein the channel matrix is between an antenna array for the UE and an antenna array for the base station; and estimating, for each of the pre-determined beams, a strength or an SNR for the UE by performing a two-dimensional discrete Fourier transform (2D-DFT) on the computed channel matrix.
  • 2D-DFT two-dimensional discrete Fourier transform
  • the SRS-based downlink channel estimation may be an SRS-based least squares channel estimation.
  • the method may further comprise sending channel quality indicators (CQIs) received from the plurality of UEs to the DU.
  • CQIs channel quality indicators
  • the subset of the plurality of UEs may be selected further based on the CQIs.
  • the subset of the plurality of UEs may be selected further based on downlink traffic information associated with each of the plurality of UEs.
  • the downlink traffic information associated with a UE may comprise traffic class type, hybrid automatic repeat request (HARQ) retransmission information, and any pre-scheduled persistent or semi-persistent transmission allocations.
  • HARQ hybrid automatic repeat request
  • one or more computer- readable non-transitory storage media embodying software that is operable when executed, by one or more computer devices associated with a base station of a wireless network, to: send, to a distributed unit (DU) associated with the base station, sounding reference signal (SRS) received from a plurality of user equipments (UEs) associated with the base station; receive, from the DU, information regarding a subset of the plurality of UEs selected for downlink data transmissions for a resource block group (RBG), multi-user data to be transmitted to UEs in the subset, and identities of selected beams among a plurality of pre determined beams to be associated with the UEs in the subset, wherein each of the plurality of pre-determined beams corresponds to a discrete Fourier transform (DFT) vector; compute a precoding matrix for the RBG based on inverse-DFT (IDFT) vectors corresponding to the selected beams; prepare pre-coded multi-user data by applying
  • DFT discrete Fourier transform
  • computing a precoding matrix for the RBG may comprise: establishing a maMIMO channel matrix by calculating the IDFT vectors corresponding to the selected beams; calculating a regularized pseudo-inverse of the maMIMO channel matrix; and normalizing power of each column of the regularized pseudo-inverse of the maMIMO channel matrix such that a transmit power level for each UE in the selected subset equals to each other.
  • calculating an IDFT vector may be calculating a complex conjugate of the DFT vector.
  • the subset of the plurality of UEs may be selected based on estimated strengths or signal-to-noise ratios (SNRs) for the pre-determined beams for each of the plurality of UEs.
  • SNRs signal-to-noise ratios
  • estimating strengths or SNRs for the pre-determined beams for a UE may comprise: computing a channel matrix for corresponding to the UE by performing SRS-based downlink channel estimations based on the received SRS, wherein the channel matrix is between an antenna array for the UE and an antenna array for the base station; and estimating, for each of the pre-determined beams, a strength or an SNR for the UE by performing a two-dimensional discrete Fourier transform (2D-DFT) on the computed channel matrix.
  • 2D-DFT two-dimensional discrete Fourier transform
  • the SRS-based downlink channel estimation may be an SRS-based least squares channel estimation.
  • the one or more computer-readable non-transitory storage media may embody software that is operable when executed, by one or more computer devices associated with a base station of a wireless network, to send channel quality indicators (CQIs) received from the plurality of UEs to the DU.
  • CQIs channel quality indicators
  • the subset of the plurality of UEs may be selected further based on the CQIs.
  • the subset of the plurality of UEs may be selected further based on downlink traffic information associated with each of the plurality of UEs.
  • a system associated with a base station of a wireless network comprising: one or more processors; and one or more computer-readable non-transitory storage media coupled to one or more of the processors and comprising instructions operable when executed by one or more of the processors to cause the system to: send, to a distributed unit (DU) associated with the base station, sounding reference signal (SRS) received from a plurality of user equipments (UEs) associated with the base station; receive, from the DU, information regarding a subset of the plurality of UEs selected for downlink data transmissions for a resource block group (RBG), multi-user data to be transmitted to UEs in the subset, and identities of selected beams among a plurality of pre determined beams to be associated with the UEs in the subset, wherein each of the plurality of pre-determined beams corresponds to a discrete Fourier transform (DFT) vector; compute a precoding matrix for the RBG based on inverse-D
  • DFT discrete Fourier transform
  • any subject matter resulting from a deliberate reference back to any previous claims can be claimed as well, so that any combination of claims and the features thereof are disclosed and can be claimed regardless of the dependencies chosen in the attached claims.
  • the subj ect-matter which can be claimed comprises not only the combinations of features as set out in the attached claims but also any other combination of features in the claims, wherein each feature mentioned in the claims can be combined with any other feature or combination of other features in the claims.
  • any of the embodiments and features described or depicted herein can be claimed in a separate claim and/or in any combination with any embodiment or feature described or depicted herein or with any of the features of the attached claims.
  • FIG. 1 illustrates an example architecture for open Radio Access Network
  • FIG. 2 illustrates an example logical structure for a Regularized Zero Forcing (RZF)-based user grouping for downlink MU-MIMO.
  • RZF Regularized Zero Forcing
  • FIG. 3 illustrates an example flow for RZF-based user grouping for downlink MU-MIMO.
  • FIG. 4 illustrates an example logical structure for a 2D-DFT-based user grouping for downlink MU-MIMO.
  • FIG. 5 illustrates an example flow for 2D-DFT-based user grouping for downlink MU-MIMO.
  • FIG. 6 illustrates an example logical structure for a 2D-DFT+RZF-based user grouping for downlink MU-MIMO.
  • FIG. 7 illustrates an example flow for 2D-DFT+RZF-based user grouping for downlink MU-MIMO.
  • FIG. 8 illustrates an example alternative flow for 2D-DFT+RZF-based user grouping for downlink MU-MIMO.
  • FIG. 9 illustrates an example logical structure for computing a precoding matrix based on IDFT vectors corresponding to UEs in the selected subset for downlink MU-MIMO.
  • FIG. 10A illustrates an example flow for computing a precoding matrix based on IDFT vectors corresponding to UEs in the selected subset for downlink MU-MIMO.
  • FIG. 10B illustrates an alternative example flow for computing a precoding matrix based on IDFT vectors corresponding to UEs in the selected subset for downlink MU- MIMO.
  • FIG. 11 illustrates an example method 1100 for computing a precoding matrix for downlink MU-MIMO by performing an RZF on a MIMO channel matrix between selected UEs and transmission antenna ports associated with the base station.
  • FIG. 12 illustrates an example method 1200 for computing a precoding matrix for downlink MU-MIMO by performing an RZF on a MIMO channel matrix established based on IDFT vectors corresponding to selected beams.
  • FIG. 13 illustrates example comparisons between the presented L1/L2 cross layer optimization algorithms.
  • FIG. 14 illustrates example results for simulations comparing performance of the algorithms.
  • FIG. 15 illustrates an example computer system.
  • FIG. 1 illustrates an example architecture for open RAN.
  • a functional split RAN architecture has been proposed for 5G networks, where each of one or more radio units (RUs) 110 is connected with a distributed unit (DU) 120 via a fronthaul 115.
  • An RU 110 may handle the digital front end (DFE) and parts of the physical (PHY) layer as well as the digital beamforming functionality.
  • a DU 120 may locate close to the RU 110 and may comprise a layer-2 (L2) module that runs radio link control (RLC) and media access control (MAC) layers and a layer-1 (LI) module that runs parts of the PHY layer.
  • the DU 120 may include a subset of eNB / gNB functions depending on the functional split option.
  • the DU 120 may be controlled by a CU 130.
  • the CU 130 may be a centralized unit that runs radio resource control (RRC) and packet data convergence protocol (PDCP) layers.
  • RRC radio resource control
  • PDCP packet data convergence protocol
  • a typical gNB may consist of a CU 130 and a DU 120, which are connected via F-C and F-U interfaces 125 for control plane and user plane respectively, and one or more RUs 110.
  • a CU 130 with multiple DUs 120 may support multiple gNBs.
  • the CU 130 may be connected to the core network 140 via NG interface 135.
  • Functional split between RU 110 and DU 120 may bring a number of benefits: (1) cost reduction - less intelligent RU may cost less, (2) ability to look at a sector of RUs 110 at once - the ability may help to enable features like Coordinated Multipoint (CoMP), and (3) resource pooling gains - computationally expensive processing may be pooled in a DU 120.
  • the fronthaul 115 between the RU 110 and the DU 120 may be a bottleneck. In a typical open RAN, fronthaul latency may be constrained to 100 microsections.
  • RZF-based user grouping may require full channel state information in L2 of the DU 120.
  • the DU 120 may perform an almost exhaustive search to find a combination of users that may maximize the capacity. In the process of the search, the DU 120 may calculate the precoding matrix a number of times.
  • the RZF-based user grouping may result in minimum inter-layer interference, and hence, maximized signal-to-interference-and-noise- ratio (SINR), the RZF-based user grouping is a most computationally expensive algorithm.
  • SINR signal-to-interference-and-noise- ratio
  • a DU 120 associated with a gNB may comprise one or more computing devices.
  • An RU 110 associated with the gNB may receive SRS from a plurality of user equipments (UEs) associated with the gNB.
  • the DU 120 may receive the SRS from the RU 110.
  • the DU 120 may compute SRS-based channel estimates for each of the plurality of UEs by performing SRS-based downlink channel estimations with the received SRS.
  • the DU 120 may select a subset of the plurality of UEs to which downlink data is to be transmitted for an RBG in a TTI.
  • the subset may be selected based on the SRS-based channel estimates, the CQIs, and downlink traffic information associated with each of the plurality of UEs.
  • a process for selecting the subset may also comprise computing an RZF precoding matrix to be used for the RBG.
  • the DU 120 may determine modulation and coding scheme (MCS) for each of the plurality of UEs for the TTI.
  • MCS modulation and coding scheme
  • the DU 120 may prepare multi-layered UE data for the RBG based on the selected subset of the plurality of UEs, the computed precoding matrix, determined MCS values corresponding to the plurality of UEs, and a number of layers for each of the plurality of UEs.
  • the DU 120 may send the multi-layered UE data and the precoding matrix for the RBG to the RU 110.
  • the RU 110 may transmit pre-coded multi-layered UE data to UEs in the selected subset for the RBG using MIMO technologies.
  • FIG. 2 illustrates an example logical structure for an RZF-based user grouping for downlink MU-MIMO.
  • an RU 210 associated with a gNB may receive SRS from a plurality of UEs 205 associated with the gNB at step 251.
  • the LI 221 of the DU 220 may receive SRS from the RU 210.
  • the RU 210 may also receive channel quality indicators (CQIs) from the plurality of UEs 205 at step 251.
  • the LI 221 may also receive the CQIs from the RU 210.
  • CQIs channel quality indicators
  • a channel estimation unit 221A of the LI 221 may compute SRS-based channel estimates for each of the plurality of UEs 205 by performing SRS- based downlink channel estimations with the received SRS.
  • An SRS-based channel estimate may be represented by a channel matrix.
  • An SRS-based downlink channel estimation may be an SRS-based least squares channel estimation.
  • the LI may convey the computed SRS-based channel estimates and CQIs for the plurality of UEs 205 to an L2 223 of the DU 220 over an L1/L2 interface.
  • the L2 223 may select a subset of the plurality of UEs 205 to which downlink data is to be transmitted for a RBG in a TTI.
  • the subset may be selected based on the SRS-based channel estimates, the CQIs, and downlink traffic information associated with each of the plurality of UEs 205.
  • the downlink traffic information associated with a UE 205 may comprise traffic class type, HARQ retransmission information, and any pre-scheduled persistent or semi-persistent transmission allocations.
  • a downlink traffic information unit 223D may comprise downlink UE traffic queues and an HARQ module.
  • a process for selecting the subset may also comprise computing a precoding matrix to be used for the RBG.
  • this disclosure describes selecting a subset of UEs to which downlink data is to be transmitted for a RBG in a TTI in a particular manner, this disclosure contemplates selecting a subset of UEs to which downlink data is to be transmitted for a RBG in a TTI in any suitable manner.
  • a pre-scheduler unit 223A in the L2223 of the DU 220 may calculate an achievable weighted data rate for each of the plurality of UEs 205.
  • the pre-scheduler unit 223A may calculate a signal-to-noise ratio (SNR) for each of the plurality of UEs 205 based on the SRS-based channel estimate and the CQI for the UE 205.
  • SNR signal-to-noise ratio
  • the pre-scheduler unit 223A may take traffic class types, HARQ retransmission information, and any pre-scheduled persistent / semi-persistent allocations as per-TTI input from the downlink traffic information unit 223D.
  • the pre-scheduler unit 223A may also take information regarding scheduled UEs and total pending bytes in the UE traffic queues as per-RBG input from the downlink traffic information unit 223D.
  • the pre-scheduler unit 223A may determine a scheduling priority per UE 205 by calculating user selection metrics based on the downlink traffic information including traffic class types, HARQ retransmission information, any pre-scheduled persistent / semi-persistent allocations, scheduled UEs, and total pending bytes in the UE traffic queues.
  • the user selection metrics may comprise proportional fairness metric, max CQI, round robin, or any suitable user selection metric.
  • the pre-scheduler unit 223A may determine a weight for each of the plurality of UEs 205 based on a scheduling priority associated with the UE 205.
  • the pre-scheduler unit 223A may calculate the achievable weighted data rate for the UE based on the calculated SNR and the determined weight.
  • this disclosure describes calculating an achievable weighted data rate for a UE in a particular manner, this disclosure contemplates calculating an achievable weighted data rate for a UE in any suitable manner.
  • a UE grouping / scheduling unit 223B in the L2223 of the DU 220 may determine a subset of the plurality of UEs that maximizes a sum of weighted data rate for the RBG. To determine the subset of the plurality of UEs, the UE grouping / scheduling unit 223B may sort the plurality of UEs 205 based on their corresponding achievable weighted data rate the UE grouping / scheduling unit 223B may prepare an empty candidate set. the UE grouping / scheduling unit 223B may move the first UE among the sorted UEs into the candidate set.
  • the UE grouping / scheduling unit 223B may repeat removing a candidate UE from the sorted UEs and adding the candidate UE into the candidate set when adding the candidate UE to the candidate set is determined to increase a sum of achievable weighted data rate for the candidate set.
  • the UE grouping / scheduling unit 223B may stop the repetition when a finishing condition is met.
  • the finishing condition may comprise (1) a size of the candidate set reaches a pre-determined size, or (2) a number of the repetition reaches a pre-determined number.
  • this disclosure describes determining a subset of the plurality of UEs that maximizes a sum of weighted data rate for the RBG in a particular manner, this disclosure contemplates determining a subset of the plurality of UEs that maximizes a sum of weighted data rate for the RBG in any suitable manner.
  • the UE grouping / scheduling unit 223B may determine the candidate UE among the sorted UEs.
  • the UE grouping / scheduling unit 223B may select first k UEs among the sorted UEs.
  • the UE grouping / scheduling unit 223B may calculate a sum of correlation values with UEs in the candidate set for each of the selected k UEs. Then, the UE grouping / scheduling unit 223B may determine a UE associated with a minimum sum of correlation values as the candidate UE.
  • this disclosure describes determining a candidate UE among the sorted UEs in a particular manner, this disclosure contemplates determining a candidate UE among the sorted UEs in any suitable manner.
  • the UE grouping / scheduling unit 223B may, in the repetition loop, determine whether adding the candidate UE to the candidate set increases a sum of achievable weighted data rate for the candidate set.
  • the UE grouping / scheduling unit 223B may calculate a sum of achievable weighted data rate if the candidate UE is added to the candidate set.
  • the UE grouping / scheduling unit 223B may define a temporary set with the candidate UE and all the UEs in the candidate set.
  • the UE grouping / scheduling unit 223B may determine whether adding the candidate UE to the candidate set increases a sum of achievable weighted data rate for the candidate set by comparing the sum of achievable weighted data rate for the temporary set and a previously-calculated sum of achievable weighted data rate for the candidate set. To calculate the sum of achievable weighted data rate for the temporary set, the UE grouping / scheduling unit 223B may calculate a regularized pseudo-inverse of a massive MIMO (maMIMO) channel matrix between UEs in the temporary set and transmission antenna ports associated with the base station. The UE grouping / scheduling unit 223B may obtain a regularized zero forcing (RZF) precoding matrix by normalizing the calculated regularized pseudo-inverse of the maMIMO channel matrix.
  • RZF regularized zero forcing
  • the UE grouping / scheduling unit 223B may calculate a sum of achievable weighted data rate for the temporary set based on the RZF precoding matrix.
  • this disclosure describes determining whether adding the candidate UE to the candidate set increases a sum of achievable weighted data rate for the candidate set in a particular manner, this disclosure contemplates determining whether adding the candidate UE to the candidate set increases a sum of achievable weighted data rate for the candidate set in any suitable manner.
  • a link adaptation unit 223C of the LI 223 in the DU 220 may determine modulation and coding scheme (MCS) for each of the plurality of UEs 205 for the TTI.
  • MCS modulation and coding scheme
  • the link adaptation unit 223C may calculate an initial (rough) MCS as a function of an effective SINR (subject to an upper limit set by CQI) for a UE.
  • the link adaptation unit 223C may determine a Transport Block (TB) size as a function of the initial MCS and a number of resource blocks allocated to the UE.
  • the link adaptation unit 223 C may adjust the effective SINR based on HARQ responses acquired from the downlink traffic information unit 223D.
  • the link adaptation unit 223C may optimize the MCS in a recursive fashion while sticking to a block error rate (BLER) target.
  • BLER block error rate
  • the L2 223 may convey the selected subset of UEs, corresponding precoding matrix per RBG to the LI 221.
  • the L2223 may also convey the determined MCS and a number of layers for each of the UEs per TTI to the LI 221.
  • a codeword generation / layer mapping unit 221B of the L2221 in the DU 220 may prepare multi-layered UE data for the RBG based on the selected subset of the plurality of UEs, the computed precoding matrix, the determined MCS values corresponding to the plurality of UEs, and a number of layers for each of the plurality of UEs.
  • the codeword generation / layer mapping unit 22 IB may generate codewords using information associated with the selected subset of the plurality of UEs, the determined MCS value for each of the plurality of UEs, and the number of layers for each of the plurality of UEs.
  • the codeword generation / layer mapping unit 221B may map user data onto layers allocated to the UE for each of the plurality of UEs based on the computed precoding matrix.
  • the LI 221 of the DU 220 may send the multi-layered UE data and the precoding matrix for the RBG to the RU 210 over the fronthaul 115.
  • a precoding unit 215 of the RU 210 may perform a precoding on the multi-layered UE data using the precoding matrix.
  • the RU 210 may transmit pre-coded multi-layered UE data to UEs 205 in the selected subset for the RBG using MU- MIMO technologies.
  • this disclosure describes transmitting UE data to the UEs in a selected subset of plurality of UEs using MU-MIMO technologies in a particular manner, this disclosure contemplates transmitting UE data to the UEs in a selected subset of plurality of UEs using MU-MIMO technologies in any suitable manner.
  • FIG. 3 illustrates an example flow for RZF-based user grouping for downlink MU-MIMO.
  • the RU 210 may send SRS and CQI received from the plurality of UEs 205 to the LI 221 of the DU 220.
  • the LI 221 may perform SRS-based channel estimation.
  • the LI 221 may convey the channel estimates for the plurality of UEs 205 and CQIs to the L2 223 of the DU 220.
  • the L2 223 may perform the user grouping / scheduling based on the received channel estimates, CQIs and downlink traffic information.
  • a precoding matrix may be generated in the process of the user grouping / scheduling.
  • the L2223 may also perform a link adaptation procedure.
  • the L2223 may convey information regarding the scheduled UE, the precoding matrix, determined MCSs, and a number of layers assigned to each UE to the LI 221.
  • the LI 221 may perform the codeword generation and layer mapping.
  • the LI 221 may send the multi layered UE data and the precoding matrix to the RU 210.
  • the RU 210 may perform precoding on the multi-layered UE data using the precoding matrix.
  • the RU 210 may send the pre-coded UE data to the scheduled UEs.
  • 2D-DFT-based user grouping algorithm may select a subset of UEs whose beams are geometrically distinguishable from each other.
  • the 2D-DFT-based user grouping algorithm may require only the knowledge of beams with highest strength for each of the plurality of UEs.
  • the algorithm may require relatively lower computing power because the 2D- DFT-based user grouping is much simpler than RZF-based user grouping algorithm.
  • the 2D- DFT-based user grouping may be vulnerable to inter-layer interference.
  • the UE When a beams with the highest strength is associated with a UE, the UE may be vulnerable to directions of secondary beams for the UE. When transmissions to other UEs in those directions are made, the UE may experience an interference. Information regarding secondary beams for the UE may also be used for user grouping to mitigate the inter-layer interference.
  • a DU 120 associated with a gNB may comprise one or more computing devices.
  • An RU 110 associated with the gNB may receive sounding reference signal (SRS) from a plurality of user equipments (UEs) associated with the gNB.
  • the DU 120 may receive the SRS from the RU 110.
  • the DU 120 may compute an SRS-based channel matrix for each of the plurality of UEs by performing SRS-based downlink channel estimations with the received SRS.
  • the DU 120 may estimate strengths or SNRs for a plurality of pre-determined beams for each of the plurality of UEs by performing a 2D-DFT on the SRS- based channel matrix for each of the plurality of UEs.
  • the DU 120 may select a subset of the plurality of UEs to which downlink data is to be transmitted for an RBG in a TTI.
  • the subset may be selected based on the estimated strengths or SNRs of the plurality of pre-determined beams, the CQIs, and downlink traffic information associated with each of the plurality of UEs.
  • the DU 120 may determine modulation and coding scheme (MCS) for each of the plurality of UEs for the TTI.
  • MCS modulation and coding scheme
  • the DU 120 may associate a beam to each UE in the selected subset for the RBG.
  • the associated beam for a UE is a beam with a highest strength for the UE.
  • the DU 120 may send information regarding the beams associated with UEs in the selected subset and data to be transmitted to the UEs in the selected subset to the RU 110.
  • the RU 110 may transmit beamformed data to the UEs in the selected subset for the RBG using their associated beams.
  • FIG. 4 illustrates an example logical structure for a 2D-DFT-based user grouping for downlink MU-MIMO.
  • an RU 410 associated with a gNB may receive SRS from a plurality of UEs 405 associated with the gNB at step 451.
  • LI 421 of the DU 420 may receive SRS from the RU 410.
  • the RU 410 may also receive CQIs from the plurality of UEs 405 at step 451.
  • the LI 421 may also receive the CQIs from the RU 410.
  • the channel estimation unit 421A of the LI 421 of the DU 420 may compute a channel matrix for each of the plurality of UEs 405 by performing SRS-based downlink channel estimations with the received SRS.
  • the channel matrix may be between an antenna array for the UE 405 and an antenna array for the gNB.
  • An SRS-based downlink channel estimation may be an SRS-based least squares channel estimation.
  • a 2D-DFT unit 42 IB of the LI 421 in the DU 420 may estimate strengths or SNRs for a plurality of pre-determined beams for each of the plurality of UEs based on the computed channel matrices.
  • the 2D-DFT unit 421B may perform a 2D- DFT on the channel matrix for the UE to estimate strengths or SNRs for the plurality of pre determined beams.
  • the antenna array size may play a direct role in a beam resolution.
  • the 2D-DFT unit 421B may perform oversampling of DFT vectors for beher granularity.
  • the LI 421 may convey indices of one or more beams with highest strength and their corresponding SNRs, and CQI for each of the plurality of UEs 405 to the L2423 over an L1/L2 interface.
  • this disclosure describes estimating strengths or SNRs for a plurality of pre-determined beams for a UE in a particular manner, this disclosure contemplates estimating strengths or SNRs for a plurality of pre-determined beams for a UE in any suitable manner.
  • the L2 423 may select a subset of the plurality of UEs to which downlink data is to be transmitted for a RBG in a TTI based on the one or more beams with highest strength and their corresponding SNRs, the CQIs, and downlink traffic information associated with each of the plurality of UEs.
  • the downlink traffic information associated with a UE 405 may comprise traffic class type, HARQ retransmission information, and any pre-scheduled persistent or semi-persistent transmission allocations.
  • a downlink traffic information unit 423 C may comprise downlink UE traffic queues and an HARQ module.
  • this disclosure describes selecting a subset of the plurality of UEs to which downlink data is to be transmitted for a RBG in a TTI in a particular manner, this disclosure contemplates selecting a subset of the plurality of UEs to which downlink data is to be transmitted for a RBG in a TTI in any suitable manner.
  • a UE grouping / scheduling unit 423A of the L2423 in the DU 420 may calculate a user selection metric for each of the plurality of UEs.
  • the user selection metric may comprise a proportional fair metric. Calculating a PF metric for a UE may be based on a beam with the highest strength for the UE and its corresponding SNR, and the downlink traffic information associated with the UE.
  • this disclosure describes calculating a user selection metric for a UE in a particular manner, this disclosure contemplates calculating a user selection metric for a UE in any suitable manner.
  • the UE grouping / scheduling unit 423A may determine a subset of the plurality of UEs that maximizes an estimated system capacity for the RBG.
  • the UE grouping / scheduling unit 423A may sort the plurality of UEs 405 based on their corresponding PF metrics.
  • the UE grouping / scheduling unit 423A may prepare an empty candidate set.
  • the UE grouping / scheduling unit 423A may prepare an empty list of reserved beams.
  • the UE grouping / scheduling unit 423 A may add the first UE among the sorted UEs into the candidate set.
  • the UE grouping / scheduling unit 423A may repeat removing a first UE from the sorted UEs as a candidate UE and adding the candidate UE into the candidate set when (1) a beam with the highest strength for the candidate UE is not in the list of reserved beams and (2) adding the candidate UE to the candidate set is determined not to decrease the estimated system capacity for UEs in the candidate set.
  • the UE grouping / scheduling unit 423A may stop the repetition when a finishing condition is met.
  • the finishing condition may comprise a size of the candidate set reaches a pre-determined size, or no more UE exists in the sorted UEs.
  • Adding a UE into the candidate set may comprise adding a beam with the highest strength for the UE to the list of reserved beams.
  • this disclosure describes determining a subset of the plurality of UEs that maximizes an estimated system capacity for the RBG in a particular manner, this disclosure contemplates determining a subset of the plurality of UEs that maximizes an estimated system capacity for the RBG in any suitable manner.
  • the LI 421 may perform oversampling of DFT vectors for better granularity.
  • using an oversampled set of basis vectors may improve system performance due to higher granularity, an oversampled version of DFT vectors does not constitute an orthogonal Eigenbasis.
  • an energy in one beam may be seen leaking to a number of other beams.
  • the UE grouping / scheduling unit 423 A may also add k adjacent beams to the beam with the highest strength for the UE to the list of reserved beams, where k may be an oversampling factor.
  • the UE grouping / scheduling unit 423 A may add kll adjacent beams in each side of the beam with the highest strength to the list of reserved beams.
  • most UEs may have secondary beams along with the beam with the highest strength due to multipath. If a transmission is made to a direction that collides with the secondary beams of the UE, the UE may experience an interference. To mitigate this type of interferences, the UE grouping / scheduling unit 423A may add n beams with next highest strength for the UE beyond the beam with the highest strength for the UE to the list of reserved beams. The LI 221 may need to convey information regarding the n beams for the UE to the L2223 at step 453 for this feature. Although this disclosure describes avoiding interferences caused by secondary beams in a particular manner, this disclosure contemplates avoiding interferences caused by secondary beams in any suitable manner.
  • the UE grouping / scheduling unit 423A may repeatedly determine in a loop whether adding the candidate UE to the candidate set decreases an estimated system capacity for UEs in the candidate set.
  • the UE grouping / scheduling unit 423A may define a temporary set as UEs in the candidate set and the candidate UE.
  • the UE grouping / scheduling unit 423 A may adjust a transmission signal power based on a number of UEs in the temporary set.
  • the UE grouping / scheduling unit 423A may calculate an estimated system capacity for UEs in the temporary set based on an assumption that a beam with the highest strength for each UE is used for a transmission.
  • the UE grouping / scheduling unit 423A may determine whether adding the candidate UE to the candidate set decreases an estimated system capacity for the candidate set by comparing the estimated system capacity for UEs in the temporary set and a previously-calculated estimated system capacity for UEs in the candidate set. Although this disclosure describes determining whether adding the candidate UE to the candidate set decreases an estimated system capacity for UEs in the candidate set in a particular manner, this disclosure contemplates determining whether adding the candidate UE to the candidate set decreases an estimated system capacity for UEs in the candidate set in any suitable manner.
  • a link adaptation unit 423B of the LI 423 in the DU 420 may determine MCS for each of the plurality of UEs 405 for the TTI.
  • the link adaptation unit 423B may calculate an initial MCS as a function of an effective SINR (subject to an upper limit set by CQI) for a UE.
  • the link adaptation unit 423B may determine a Transport Block (TB) size as a function of the initial MCS and a number of resource blocks allocated to the UE.
  • the link adaptation unit 423B may adjust the effective SINR based on HARQ responses acquired from the downlink traffic information unit 423C.
  • the link adaptation unit 423B may optimize the MCS in a recursive fashion while sticking to a BLER target.
  • the L2 423 may convey the selected subset of UEs per RBG to the LI 421.
  • the L2423 may also convey the determined MCS and a number of layers for each of the UEs per TTI to the LI 421.
  • a beam mapping unit 421C of the LI 421 in the DU 420 may associate a beam to each UE in the selected subset for the RBG.
  • the associated beam for a UE is a beam with the highest strength for the UE.
  • the LI 421 may send information regarding the beams associated with UEs in the selected subset and data to be transmitted to the UEs in the selected subset per RBG to RU 410 over the fronthaul 115.
  • a beamforming unit 415 of the RU 410 may perform beamforming on the UE data using a beam associated with the UE.
  • the RU 410 may transmit the beamformed UE data to UEs 405 in the selected subset for the RBG using MU-MIMO technologies.
  • this disclosure describes transmitting UE data to the UEs in a selected subset of plurality of UEs using MU-MIMO technologies in a particular manner, this disclosure contemplates transmitting UE data to the UEs in a selected subset of plurality of UEs using MU-MIMO technologies in any suitable manner.
  • FIG. 5 illustrates an example flow for 2D-DFT-based user grouping for downlink MU-MIMO.
  • the RU 410 may send SRS and CQI received from the plurality of UEs 405 to the LI 421 of the DU 420.
  • the LI 421 may compute a channel matrix for each of the plurality of UEs 405 based on the SRS and estimate strengths or SNRs of a plurality of pre-determined beams for the UE by performing 2D-DFT on the channel matrix.
  • the LI 421 may convey information regarding one or more beams with highest strength for each UE and their corresponding SNRs, and CQIs to the L2423 of the DU 420.
  • the L2423 may perform the user grouping / scheduling based on the received information regarding the one or more beams with highest strength, their corresponding SNRs, CQIs and downlink traffic information for each UE.
  • the L2 423 may also perform a link adaptation procedure to determine an MCS value for each UE per TTI.
  • the L2423 may convey information regarding the scheduled UEs, determined MCS, and a number of layers assigned to each UE to the LI 421.
  • the LI 421 may map a beam to a UE in the scheduled UEs.
  • the LI 421 may send the UE data and an index of the beam associated with each UE to the RU 410.
  • the RU 410 may perform beamforming on the UE data using the beam associated with the UE.
  • the RU 410 may send the beamformed UE data to the scheduled UEs.
  • this disclosure describes a particular flow for downlink MU-MIMO downlink based on the 2D-DFT-based user group algorithm, this disclosure contemplates any suitable flow for downlink MU-MIMO downlink based on the 2D-DFT-based user group algorithm.
  • 2D-DFT+RZF-based user grouping algorithm may select a subset of UEs for an RBG in a same manner with the 2D-DFT-based user grouping algorithm.
  • the LI may be able to establish a maMIMO channel matrix between the selected UEs and transmission antenna ports associated with the gNB.
  • the LI may compute a precoding matrix for an RBG by performing an RZF on the maMIMO channel matrix. While user grouping may require relatively simple computations based on the knowledge of beam(s) with highest strength for each UE, the computed precoding matrix may reduce inter-layer interferences. Consequently, the 2D-DFT+RZF-based user grouping algorithm may achieve performance close to the RZF-based user grouping algorithm.
  • the computational load may be mostly on the LI of the DU.
  • a DU 120 associated with a gNB may comprise one or more computing devices.
  • An RU 110 associated with the gNB may receive SRS from a plurality of UEs associated with the gNB.
  • the DU 120 may receive the SRS from the RU 110.
  • the DU 120 may compute an SRS-based channel matrix for each of the plurality of UEs by performing SRS-based downlink channel estimations with the received SRS.
  • the DU 120 may estimate strengths or SNRs for a plurality of pre-determined beams for each of the plurality of UEs by performing a 2D-DFT on the SRS-based channel matrix for each of the plurality of UEs.
  • the DU 120 may select a subset of the plurality of UEs to which downlink data is to be transmitted for an RBG in a TTI.
  • the subset may be selected based on the estimated strengths or SNRs of the plurality of pre-determined beams, the CQIs, and downlink traffic information associated with each of the plurality of UEs.
  • the DU 120 may establish a maMIMO channel matrix between UEs in the selected subset and transmission antenna ports associated with the gNB based on the SRS-based channel matrices.
  • the DU 120 may compute a precoding matrix for the RBG by RZF-ing the maMIMO channel matrix.
  • the DU 120 may determine modulation and coding scheme (MCS) for each of the plurality of UEs for the TTI.
  • MCS modulation and coding scheme
  • the DU 120 may prepare multi-layered UE data for the RBG based on the selected subset of the plurality of UEs, the computed precoding matrix, determined MCS values corresponding to the plurality of UEs, and a number of lay ers for each of the plurality of UEs.
  • the DU 120 may send the multi-layered UE data and the precoding matrix for the RBG to the RU 110.
  • the RU 110 may transmit pre- coded multi-layered UE data to UEs in the selected subset for the RBG using MIMO technologies.
  • this disclosure describes performing a 2D-DFT+RZF-based user grouping for downlink MU-MIMO in a particular manner, this disclosure contemplates performing a 2D-DFT+RZF-based user grouping for downlink MU-MIMO in any suitable manner.
  • FIG. 6 illustrates an example logical structure for a 2D-DFT+RZF-based user grouping for downlink MU-MIMO.
  • an RU 610 associated with a gNB may receive SRS from a plurality of UEs 605 associated with the gNB at step 651.
  • LI 621 of the DU 620 may receive SRS from the RU 610.
  • the RU 610 may also receive CQIs from the plurality of UEs 605 at step 651.
  • the LI 621 may also receive the CQIs from the RU 610.
  • the channel estimation unit 621A of the LI 621 of the DU 620 may compute a channel matrix for each of the plurality of UEs 605 by performing SRS-based downlink channel estimations with the received SRS.
  • the channel matrix may be between an antenna array for the UE 605 and an antenna array for the gNB.
  • An SRS-based downlink channel estimation may be an SRS-based least squares channel estimation.
  • a 2D-DFT unit 62 IB of the LI 621 in the DU 620 may estimate strengths or SNRs for a plurality of pre-determined beams for each of the plurality of UEs 605 based on the computed channel matrices.
  • the 2D-DFT unit 62 IB may perform a 2D-DFT on the channel matrix for the UE to estimate strengths or SNRs for the plurality of pre-determined beams.
  • the 2D-DFT unit 621B may perform oversampling of DFT vectors for better granularity.
  • the LI 621 may convey indices of one or more beams with highest strength and their corresponding SNRs, and CQI for each of the plurality of UEs 605 to the L2 623 over an L1/L2 interface.
  • this disclosure describes estimating strengths or SNRs for a plurality of pre determined beams for a UE in a particular manner, this disclosure contemplates estimating strengths or SNRs for a plurality of pre-determined beams for a UE in any suitable manner.
  • the L2 623 may select a subset of the plurality of UEs to which downlink data is to be transmitted for a RBG in a TTI based on the one or more beams with highest strength and their corresponding SNRs, the CQIs, and downlink traffic information associated with each of the plurality of UEs.
  • the downlink traffic information associated with a UE 605 may comprise traffic class type, HARQ retransmission information, and any pre-scheduled persistent or semi-persistent transmission allocations.
  • a downlink traffic information unit 623 C may comprise downlink UE traffic queues and an HARQ module.
  • this disclosure describes selecting a subset of the plurality of UEs to which downlink data is to be transmitted for a RBG in a TTI in a particular manner, this disclosure contemplates selecting a subset of the plurality of UEs to which downlink data is to be transmitted for a RBG in a TTI in any suitable manner.
  • a UE grouping / scheduling unit 623A of the L2623 in the DU 620 may calculate a user selection metric for each of the plurality of UEs.
  • the user selection metric may comprise a proportional fair metric. Calculating a PF metric for a UE may be based on a beam with the highest strength for the UE and its corresponding SNR, and the downlink traffic information associated with the UE.
  • this disclosure describes calculating a user selection metric for a UE in a particular manner, this disclosure contemplates calculating a user selection metric for a UE in any suitable manner.
  • the UE grouping / scheduling unit 623A may determine a subset of the plurality of UEs that maximizes an estimated system capacity for the RBG.
  • the UE grouping / scheduling unit 623A may sort the plurality of UEs 605 based on their corresponding PF metrics.
  • the UE grouping / scheduling unit 623A may prepare an empty candidate set.
  • the UE grouping / scheduling unit 623A may prepare an empty list of reserved beams.
  • the UE grouping / scheduling unit 623 A may add the first UE among the sorted UEs into the candidate set.
  • the UE grouping / scheduling unit 623A may repeat removing a first UE from the sorted UEs as a candidate UE and adding the candidate UE into the candidate set when (1) a beam with the highest strength for the candidate UE is not in the list of reserved beams and (2) adding the candidate UE to the candidate set is determined not to decrease the estimated system capacity for UEs in the candidate set.
  • the UE grouping / scheduling unit 623A may stop the repetition when a finishing condition is met.
  • the finishing condition may comprise a size of the candidate set reaches a pre-determined size, or no more UE exists in the sorted UEs.
  • Adding a UE into the candidate set may comprise adding a beam with the highest strength for the UE to the list of reserved beams.
  • this disclosure describes determining a subset of the plurality of UEs that maximizes an estimated system capacity for the RBG in a particular manner, this disclosure contemplates determining a subset of the plurality of UEs that maximizes an estimated system capacity for the RBG in any suitable manner.
  • the UE grouping / scheduling unit 623A may also add k adjacent beams to the beam with the highest strength for the UE to the list of reserved beams in order to avoid interferences caused by an oversampling, where k may be an oversampling factor.
  • the UE grouping / scheduling unit 623 A may add k/2 adjacent beams in each side of the beam with the highest strength to the list of reserved beams.
  • the UE grouping / scheduling unit 623A may add n beams with next highest strength for the UE beyond the beam with the highest strength for the UE to the list of reserved beams to mitigate this type of interferences.
  • the LI 621 may need to convey information regarding the n beams for the UE to the L2623 at step 653 for this feature.
  • the UE grouping / scheduling unit 623A may repeatedly determine in a loop whether adding the candidate UE to the candidate set decreases an estimated system capacity for UEs in the candidate set.
  • the UE grouping / scheduling unit 623A may define a temporary set as UEs in the candidate set and the candidate UE.
  • the UE grouping / scheduling unit 623 A may adjust a transmission signal power based on a number of UEs in the temporary set.
  • the UE grouping / scheduling unit 623A may calculate an estimated system capacity for UEs in the temporary set based on an assumption that a beam with the highest strength for each UE is used for a transmission.
  • the UE grouping / scheduling unit 623A may determine whether adding the candidate UE to the candidate set decreases an estimated system capacity for the candidate set by comparing the estimated system capacity for UEs in the temporary set and a previously-calculated estimated system capacity for UEs in the candidate set. Although this disclosure describes determining whether adding the candidate UE to the candidate set decreases an estimated system capacity for UEs in the candidate set in a particular manner, this disclosure contemplates determining whether adding the candidate UE to the candidate set decreases an estimated system capacity for UEs in the candidate set in any suitable manner.
  • the L2623 may convey the information regarding the selected subset of the plurality of UEs for the RBG and a number of layers for each of the UEs per TTI to the LI 611 over an L1/L2 interface.
  • a precoder calculation unit 621C of the LI in the DU 620 may compute a precoding matrix for the RBG based on the selected subset.
  • the precoder calculation unit 621 C may establish a massive MIMO (maMIMO) channel matrix between UEs in the selected subset and transmission antenna ports associated with the gNB based on the SRS-based channel estimates.
  • the precoder calculation unit 621C may calculate a regularized pseudo-inverse of the maMIMO channel matrix.
  • the precoder calculation unit 621C may obtain a regularized zero forcing (RZF) precoding matrix by normalizing power of each column of the regularized pseudo-inverse of the maMIMO channel matrix.
  • RZF regularized zero forcing
  • a transmit power level for each UE in the selected subset may equal to each other.
  • this disclosure describes computing a precoding matrix for the RBG in a particular manner, this disclosure computing a precoding matrix for the RBG in any suitable manner.
  • the LI 621 may convey the computed precoding matrix to the L2623 over the L1/L2 interface.
  • a link adaptation unit 623B of the LI 623 in the DU 620 may determine MCS for each of the plurality of UEs 605 for the TTI.
  • the link adaptation unit 623B may calculate an initial MCS as a function of an effective SINR (subject to an upper limit set by CQI) for a UE.
  • the link adaptation unit 623B may determine a Transport Block (TB) size as a function of the initial MCS and a number of resource blocks allocated to the UE.
  • the link adaptation unit 623B may adjust the effective SINR based on HARQ responses acquired from the downlink traffic information unit 623C.
  • the link adaptation unit 623B may optimize the MCS in a recursive fashion while sticking to a BLER target.
  • the L2623 may convey the determined MCS for each of the UEs per TTI to the LI 621.
  • a codeword generation / layer mapping unit 621D of the L2 621 in the DU 620 may prepare multi-layered UE data for the RBG based on the selected subset of the plurality of UEs, the computed precoding matrix, the determined MCS, and a number of layers for each of the plurality of UEs.
  • the codeword generation / layer mapping unit 62 ID may generate codewords using information associated with the selected subset of the plurality of UEs, the determined MCS value for each of the plurality of UEs, and the number of layers for each of the plurality of UEs.
  • the codeword generation / layer mapping unit 621D may map user data onto layers allocated to the UE for each of the plurality of UEs based on the computed precoding matrix.
  • the LI 621 may send the multi-layered UE data and the precoding matrix for the RBG to the RU 210 over the fronthaul 115.
  • a precoding unit 615 of the RU 610 may perform a precoding on the multi-layered UE data using the precoding matrix.
  • the RU 210 may transmit pre-coded multi-layered UE data to UEs 605 in the selected subset for the RBG using MU- MIMO technologies.
  • this disclosure describes transmitting UE data to the UEs in a selected subset of plurality of UEs using MU-MIMO technologies in a particular manner, this disclosure contemplates transmitting UE data to the UEs in a selected subset of plurality of UEs using MU-MIMO technologies in any suitable manner.
  • FIG. 7 illustrates an example flow for 2D-DFT+RZF-based user grouping for downlink MU-MIMO.
  • the RU 610 may send SRS and CQI received from the plurality of UEs 605 to the LI 621 of the DU 620.
  • the LI 621 may compute a channel matrix for each of the plurality of UEs 605 based on the SRS and estimate strengths or SNRs of a plurality of pre-determined beams for the UE by performing 2D-DFT on the channel matrix.
  • the LI 621 may convey information regarding one or more beams with highest strength for each UE and their corresponding SNRs, and CQIs to the L2623 of the DU 620.
  • the L2623 may perform the user grouping / scheduling based on the received information regarding the one or more beams with highest strength, their corresponding SNRs, CQIs and downlink traffic information for each UE.
  • the L2 623 may convey information regarding the selected subset of UEs for the RBG and a number of layers for each of the UEs per TTI to the LI 621.
  • the LI 621 may compute a precoding matrix for the RBG based on the selected subset.
  • the LI 621 may convey the computed precoding matrix to the L2 623.
  • the L2 623 may perform a link adaptation procedure to determine MCS for each of the plurality of UEs per TTI.
  • the L2623 may convey the MCS for each UE per TTI to the LI 621.
  • the LI 621 may perform the codeword generation and layer mapping.
  • the LI 621 may send the multi layered UE data and the precoding matrix to the RU 610.
  • the RU 610 may perform precoding on the multi-layered UE data using the precoding matrix.
  • the RU 610 may send the pre-coded UE data to the UEs in the selected subset for the RBG.
  • this disclosure describes a particular flow for downlink MU-MIMO downlink based on the 2D-DFT+RZF- based user group algorithm
  • this disclosure contemplates any suitable flow for downlink MU- MIMO downlink based on the 2D-DFT+RZF-based user group algorithm.
  • FIG. 7 illustrates an example alternative flow for 2D-DFT+RZF-based user grouping for downlink MU-MIMO.
  • the RU 610 may send SRS and CQI received from the plurality of UEs 605 to the LI 621 of the DU 620.
  • the LI 621 may compute a channel matrix for each of the plurality of UEs 605 based on the SRS and estimate strengths or SNRs of a plurality of pre-determined beams for the UE by performing 2D-DFT on the channel matrix.
  • the LI 621 may convey information regarding one or more beams with highest strength for each UE and their corresponding SNRs, and CQIs to the L2 623 of the DU 620.
  • the LI 621 may also convey the channel estimates (in forms of channel matrices) for all of the plurality of UEs.
  • the L2623 may perform the user grouping / scheduling based on the received information regarding the one or more beams with highest strength, their corresponding SNRs, CQIs and downlink traffic information for each UE.
  • the L2 623 may compute a precoding matrix for the RBG based on the selected subset by performing an RZF on a maMIMO matrix between the UEs in the selected subset and transmission antenna ports associated with the gNB.
  • the L2623 may perform a link adaptation procedure to determine MCS for each of the plurality of UEs per TTI.
  • the L2623 may convey information regarding the selected subset of UEs and the computed precoding matrix for the RBG to the LI 621.
  • the L2623 may also convey the MCS and a number of layers for each UE per TTI to the LI 621.
  • the LI 621 may perform the codeword generation and layer mapping.
  • the LI 621 may send the multi-layered UE data and the precoding matrix to the RU 610.
  • the RU 610 may perform precoding on the multi-layered UE data using the precoding matrix.
  • the RU 610 may send the pre-coded UE data to the UEs in the selected subset for the RBG.
  • this disclosure describes a particular flow for downlink MU-MIMO downlink based on the 2D-DFT+RZF -based user group algorithm
  • this disclosure contemplates any suitable flow for downlink MU-MIMO downlink based on the 2D- DFT+RZF-based user group algorithm.
  • Another alternative optimization algorithm may select a subset of UEs for an RBG in a same manner with the 2D-DFT-based user grouping algorithm.
  • the RU 110 may establish a maMIMO channel matrix by conjugating DFT vectors corresponding to the subset of UEs.
  • the conjugated DFT vectors are IDFT vectors of the DFT vectors.
  • the RU 110 may compute a precoding matrix by performing an RZF on the maMIMO channel matrix.
  • the RU 110 may not need to have channel knowledge for computing the precoding matrix.
  • the algorithm may be particularly effective for high level of DFT oversampling, where significant level of interference between the DFT vectors of the UEs in the selected subset.
  • a computational complexity for this alternative algorithm may be slightly higher than the 2D-DFT-based user grouping algorithm.
  • the amount of data transferred over the fronthaul 115 may be identical to that of the 2D- DFT-based user grouping algorithm. But the performance gain may be significant in many cases.
  • an RU 110 associated with a gNB may comprise one or more computing devices.
  • the RU 110 may receive SRS from a plurality of UEs associated with the gNB.
  • the RU may send the SRS to a DU 120 associated with the gNB.
  • the DU 120 may compute an SRS-based channel matrix for each of the plurality of UEs by performing SRS-based downlink channel estimations with the received SRS.
  • the DU 120 may estimate strengths or SNRs for a plurality of pre-determined beams for each of the plurality of UEs by performing a 2D-DFT on the SRS-based channel matrix for each of the plurality of UEs.
  • the DU 120 may select a subset of the plurality of UEs to which downlink data is to be transmitted for an RBG in a TTI.
  • the subset may be selected based on the estimated strengths or SNRs of the plurality of pre-determined beams, the CQIs, and downlink traffic information associated with each of the plurality of UEs.
  • the DU 120 may determine modulation and coding scheme (MCS) for each of the plurality of UEs for the TTI.
  • MCS modulation and coding scheme
  • the DU 120 may associate a beam to each UE in the selected subset for the RBG.
  • the associated beam for a UE is a beam with a highest strength for the UE.
  • the RU 110 may receive information regarding the selected subset, the beams associated with UEs in the selected subset, and data to be transmitted to the UEs in the selected subset from the DU 120.
  • the RU 110 may establish a maMIMO channel matrix by calculating the IDFT of the DFT vectors corresponding to the beams associated with the UEs in the selected subset.
  • the RU 110 may compute a precoding matrix for the RBG by RZF-ing the maMIMO matrix.
  • the RU 110 may prepare pre-coded multi-user data by applying the precoding matrix to the UE data.
  • the RU 110 may transmit the pre-coded data to the UEs in the selected subset for the RBG using MU-MIMO technologies.
  • this disclosure describes computing a precoding matrix based on IDFT vectors corresponding to UEs in the selected subset for downlink MU-MIMO in a particular manner, this disclosure contemplates computing a precoding matrix based on IDFT vectors corresponding to UEs in the selected subset for downlink MU-MIMO in any suitable manner.
  • FIG. 9 illustrates an example logical structure for computing a precoding matrix based on IDFT vectors corresponding to UEs in the selected subset for downlink MU-MIMO.
  • an RU 910 associated with a gNB may receive SRS from a plurality of UEs 905 associated with the gNB at step 951.
  • LI 921 of the DU 920 may receive SRS from the RU 910.
  • the RU 910 may also receive CQIs from the plurality of UEs 905 at step 951.
  • the LI 921 may also receive the CQIs from the RU 910.
  • the channel estimation unit 921A of the LI 921 of the DU 920 may compute a channel matrix for each of the plurality of UEs 905 by performing SRS-based downlink channel estimations with the received SRS.
  • the channel matrix may be between an antenna array for the UE 905 and an antenna array for the gNB.
  • An SRS-based downlink channel estimation may be an SRS-based least squares channel estimation.
  • a 2D-DFT unit 921B of the LI 921 in the DU 920 may estimate strengths or SNRs for a plurality of pre-determined beams for each of the plurality of UEs based on the computed channel matrices.
  • the 2D-DFT unit 921B may perform a 2D- DFT on the channel matrix for the UE to estimate strengths or SNRs for the plurality of pre determined beams.
  • the antenna array size may play a direct role in a beam resolution.
  • the 2D-DFT unit 921B may perform oversampling of DFT vectors for better granularity.
  • this disclosure describes estimating strengths or SNRs for a plurality of pre determined beams for a UE in a particular manner, this disclosure contemplates estimating strengths or SNRs for a plurality of pre-determined beams for a UE in any suitable manner.
  • the LI 921 may convey indices of one or more beams with highest strength and their corresponding SNRs, and CQI for each of the plurality of UEs 905 to the L2923 over an L1/L2 interface.
  • the L2 923 may select a subset of the plurality of UEs to which downlink data is to be transmitted for a RBG in a TTI based on the one or more beams with highest strength and their corresponding SNRs, the CQIs, and downlink traffic information associated with each of the plurality of UEs.
  • the downlink traffic information associated with a UE 905 may comprise traffic class type, HARQ retransmission information, and any pre-scheduled persistent or semi-persistent transmission allocations.
  • a downlink traffic information unit 923 C may comprise downlink UE traffic queues and an HARQ module.
  • this disclosure describes selecting a subset of the plurality of UEs to which downlink data is to be transmitted for a RBG in a TTI in a particular manner, this disclosure contemplates selecting a subset of the plurality of UEs to which downlink data is to be transmitted for a RBG in a TTI in any suitable manner.
  • a UE grouping / scheduling unit 923A of the L2923 in the DU 920 may calculate a user selection metric for each of the plurality of UEs.
  • the user selection metric may comprise a proportional fair metric. Calculating a PF metric for a UE may be based on a beam with the highest strength for the UE and its corresponding SNR, and the downlink traffic information associated with the UE.
  • this disclosure describes calculating a user selection metric for a UE in a particular manner, this disclosure contemplates calculating a user selection metric for a UE in any suitable manner.
  • the UE grouping / scheduling unit 923A may determine a subset of the plurality of UEs that maximizes an estimated system capacity for the RBG.
  • the UE grouping / scheduling unit 923A may sort the plurality of UEs 905 based on their corresponding PF metrics.
  • the UE grouping / scheduling unit 923A may prepare an empty candidate set.
  • the UE grouping / scheduling unit 923A may prepare an empty list of reserved beams.
  • the UE grouping / scheduling unit 923 A may add the first UE among the sorted UEs into the candidate set.
  • the UE grouping / scheduling unit 923A may repeat removing a first UE from the sorted UEs as a candidate UE and adding the candidate UE into the candidate set when (1) a beam with the highest strength for the candidate UE is not in the list of reserved beams and (2) adding the candidate UE to the candidate set is determined not to decrease the estimated system capacity for UEs in the candidate set.
  • the UE grouping / scheduling unit 923A may stop the repetition when a finishing condition is met.
  • the finishing condition may comprise a size of the candidate set reaches a pre-determined size, or no more UE exists in the sorted UEs.
  • Adding a UE into the candidate set may comprise adding a beam with the highest strength for the UE to the list of reserved beams.
  • this disclosure describes determining a subset of the plurality of UEs that maximizes an estimated system capacity for the RBG in a particular manner, this disclosure contemplates determining a subset of the plurality of UEs that maximizes an estimated system capacity for the RBG in any suitable manner.
  • the UE grouping / scheduling unit 923A may also add k adjacent beams to the beam with the highest strength for the UE to the list of reserved beams to avoid interferences caused by an oversampling, where k may be an oversampling factor.
  • the UE grouping / scheduling unit 923 A may add k/2 adjacent beams in each side of the beam with the highest strength to the list of reserved beams.
  • the UE grouping / scheduling unit 923A may add n beams with next highest strength for the UE beyond the beam with the highest strength for the UE to the list of reserved beams to mitigate interferences caused by secondary beams.
  • the LI 221 may need to convey information regarding the n beams for the UE to the L2 223 at step 953 for this feature.
  • the UE grouping / scheduling unit 923A may repeatedly determine in a loop whether adding the candidate UE to the candidate set decreases an estimated system capacity for UEs in the candidate set.
  • the UE grouping / scheduling unit 923A may define a temporary set as UEs in the candidate set and the candidate UE.
  • the UE grouping / scheduling unit 923 A may adjust a transmission signal power based on a number of UEs in the temporary set.
  • the UE grouping / scheduling unit 923A may calculate an estimated system capacity for UEs in the temporary set based on an assumption that a beam with the highest strength for each UE is used for a transmission.
  • the UE grouping / scheduling unit 923A may determine whether adding the candidate UE to the candidate set decreases an estimated system capacity for the candidate set by comparing the estimated system capacity for UEs in the temporary set and a previously-calculated estimated system capacity for UEs in the candidate set. Although this disclosure describes determining whether adding the candidate UE to the candidate set decreases an estimated system capacity for UEs in the candidate set in a particular manner, this disclosure contemplates determining whether adding the candidate UE to the candidate set decreases an estimated system capacity for UEs in the candidate set in any suitable manner.
  • a link adaptation unit 923B of the LI 923 in the DU 920 may determine MCS for each of the plurality of UEs 905 for the TTI.
  • the link adaptation unit 923B may calculate an initial MCS as a function of an effective SINR (subject to an upper limit set by CQI) for a UE.
  • the link adaptation unit 923B may determine a Transport Block (TB) size as a function of the initial MCS and a number of resource blocks allocated to the UE.
  • the link adaptation unit 923B may adjust the effective SINR based on HARQ responses acquired from the downlink traffic information unit 923C.
  • the link adaptation unit 923B may optimize the MCS in a recursive fashion while sticking to a BLER target.
  • the L2 923 may convey the selected subset of UEs per RBG to the LI 921.
  • the L2 923 may also convey the determined MCS and a number of layers for each of the UEs per TTI to the LI 921.
  • a beam mapping unit 921C of the LI 921 in the DU 920 may associate a beam to each UE in the selected subset for the RBG.
  • the associated beam for a UE is a beam with the highest strength for the UE.
  • the LI 921 may send information regarding the selected subset for the RBG, multi-user data to be transmitted to UEs in the selected subset, and identities of selected beams among a plurality of pre-determined beams to be associated with the UEs in the subset to RU 910 over the fronthaul 115.
  • Each of the plurality of pre-determined beams may correspond to a discrete Fourier transform (DFT) vector.
  • DFT discrete Fourier transform
  • a precoding calculation unit 913 of the RU 910 may compute a precoding matrix for the RBG based on inverse-DFT (IDFT) vectors corresponding to the selected beams.
  • the precoding calculation unit 913 may establish a maMIMO channel matrix by calculating the IDFT of the DFT vectors corresponding to the selected beams. Calculating an IDFT of a DFT vector may be calculating a complex conjugate of the DFT vector.
  • the precoding calculation unit 913 may calculate a regularized pseudo-inverse of the maMIMO channel matrix.
  • the precoding calculation unit 913 may normalize power of each column of the regularized pseudo-inverse of the maMIMO channel matrix such that a transmit power level for each UE in the selected subset equals to each other.
  • this disclosure describes computing a precoding matrix for an RBG based on IDFT vectors corresponding to the selected beams in a particular manner, this disclosure contemplates computing a precoding matrix for an RBG based on IDFT vectors corresponding to the selected beams in any suitable manner.
  • a precoding unit 915 of the RU 910 may perform a precoding on the multi-user data using the computed precoding matrix.
  • the RU 910 may transmit pre-coded multi-user data to UEs 205 in the selected subset for the RBG using MU- MIMO technologies.
  • this disclosure describes transmitting user data to the UEs in a selected subset of plurality of UEs using MU-MIMO technologies in a particular manner, this disclosure contemplates transmitting user data to the UEs in a selected subset of plurality of UEs using MU-MIMO technologies in any suitable manner.
  • FIG. 10A illustrates an example flow for computing a precoding matrix based on IDFT vectors corresponding to UEs in the selected subset for downlink MU-MIMO.
  • the RU 910 may send SRS and CQI received from the plurality of UEs 905 to the LI 921 of the DU 920.
  • the LI 921 may compute a channel matrix for each of the plurality of UEs 905 based on the SRS and estimate strengths or SNRs of a plurality of pre determined beams for the UE by performing 2D-DFT on the channel matrix.
  • the LI 921 may convey information regarding one or more beams with highest strength for each UE and their corresponding SNRs, and CQIs to the L2923 of the DU 920.
  • the L2 923 may perform the user grouping / scheduling based on the received information regarding the one or more beams with highest strength, their corresponding SNRs, CQIs and downlink traffic information for each UE.
  • the L2 923 may also perform a link adaptation procedure to determine an MCS value for each UE per TTI.
  • the L2 923 may convey information regarding the scheduled UEs, determined MCS, and number of layers assigned to each UE to the LI 921.
  • the LI 921 may map a beam to a UE in the scheduled UEs.
  • the LI 921 may send the UE data and an index of the beam associated with each UE to the RU 910.
  • the RU 910 may compute a precoding matrix for the RBG based on IDFT vectors corresponding to the selected beams.
  • the RU 910 may also perform a precoding on the multi-user data using the computed precoding matrix.
  • the RU 910 may send the pre-coded multi-user data to UEs 205 in the selected subset for the RBG using MU-MIMO technologies.
  • this disclosure describes a particular flow for computing a precoding matrix based on IDFT vectors corresponding to UEs in the selected subset for downlink MU-MIMO, this disclosure contemplates any suitable flow for computing a precoding matrix based on IDFT vectors corresponding to UEs in the selected subset for downlink MU-MIMO.
  • FIG. 10B illustrates an alternative example flow for computing a precoding matrix based on IDFT vectors corresponding to UEs in the selected subset for downlink MU-MIMO.
  • the RU 910 instead of the LI 921 of the DU 920, may compute a channel matrix for each of the plurality of UEs 905 and estimate strengths or SNRs of a plurality of pre-determined beams for the UE by performing 2D-DFT on the channel matrix.
  • the RU 910 may compute a channel matrix for each of the plurality of UEs 905 based on SRS received from each of the plurality of UEs 905 and estimate strengths or SNRs of a plurality of pre-determined beams for the UE by performing 2D-DFT on the channel matrix.
  • the RU 910 may send information regarding one or more beams with highest strength for each UE and their corresponding SNRs, and CQIs to the LI 921 of the DU 920.
  • the LI 921 may forward the received information to the L2923 of the DU 920.
  • the L2 923 may perform the user grouping / scheduling based on the received information regarding the one or more beams with highest strength, their corresponding SNRs, CQIs and downlink traffic information for each UE.
  • the L2 923 may also perform a link adaptation procedure to determine an MCS value for each UE per TTI.
  • the L2 923 may convey information regarding the scheduled UEs, determined MCS, and number of layers assigned to each UE to the LI 921.
  • the LI 921 may forward the received information to the RU 910.
  • the RU 910 may map a beam to a UE in the scheduled UEs.
  • the RU 910 may compute a precoding matrix for the RBG based on IDFT vectors corresponding to the selected beams.
  • the RU 910 may also perform a precoding on the multi user data using the computed precoding matrix.
  • the RU 910 may send the pre-coded multi user data to UEs 205 in the selected subset for the RBG using MU-MIMO technologies.
  • this disclosure describes a particular flow for computing a precoding matrix based on IDFT vectors corresponding to UEs in the selected subset for downlink MU-MIMO, this disclosure contemplates any suitable flow for computing a precoding matrix based on IDFT vectors corresponding to UEs in the selected subset for downlink MU-MIMO.
  • FIG. 11 illustrates an example method 1100 for computing a precoding matrix for downlink MU-MIMO by performing an RZF on a MIMO channel matrix between selected UEs and transmission antenna ports associated with the base station.
  • the method may begin at step 1110, where a DU associated with a base station may receive SRS received from a plurality of UEs associated with the base station from an RU associated with the base station.
  • the DU may estimate strengths or SNRs for pre-determined beams for each of the plurality of UEs based on the received SRS.
  • the DU may select a subset of the plurality of UEs to which downlink data is to be transmitted for an RBG in a TTI based on the estimated strengths or SNRs of the pre-determined beams for the plurality of UEs.
  • the DU may compute a precoding matrix for the RBG based on the selected subset.
  • Computing the precoding matrix may comprise establishing a maMIMO channel matrix between UEs in the selected subset and transmission antenna ports associated with the base station, calculating a regularized pseudo-inverse of the maMIMO channel matrix, and normalizing power of each column of the regularized pseudo-inverse of the maMIMO channel matrix.
  • the DU may prepare multi-layered UE data for the RBG based on the selected subset and the computed precoding matrix.
  • the DU may send the multi layered UE data and the precoding matrix for the RBG to the RU.
  • the RU may transmit pre- coded multi-layered UE data to the UEs in the subset using MIMO technologies. Particular embodiments may repeat one or more steps of the method of FIG. 11, where appropriate.
  • this disclosure describes and illustrates an example method for computing a precoding matrix for downlink MU-MIMO by performing an RZF on a MIMO channel matrix between selected UEs and transmission antenna ports associated with the base station including the particular steps of the method of FIG. 11, this disclosure contemplates any suitable method for computing a precoding matrix for downlink MU-MIMO by performing an RZF on a MIMO channel matrix between selected UEs and transmission antenna ports associated with the base station including any suitable steps, which may include all, some, or none of the steps of the method of FIG. 11, where appropriate. Furthermore, although this disclosure describes and illustrates particular components, devices, or systems carrying out particular steps of the method of FIG. 11, this disclosure contemplates any suitable combination of any suitable components, devices, or systems carrying out any suitable steps of the method of FIG. 11.
  • FIG. 12 illustrates an example method 1200 for computing a precoding matrix for downlink MU-MIMO by performing an RZF on a MIMO channel matrix established based on IDFT vectors corresponding to selected beams.
  • the method may begin at step 1210, where an RU may send SRS received from a plurality of UEs associated with the base station to a DU associated with the base station.
  • the RU may receive information regarding a subset of the plurality of UEs selected for downlink data transmissions for an RBG, multi-user data to be transmitted to UEs in the subset, and identities of selected beams among a plurality of pre-determined beams to be associated with the UEs in the subset from the DU.
  • Each of the plurality of pre-determined beams may correspond to a DFT vector.
  • the RU may compute a precoding matrix for the RBG based on IDFT vectors corresponding to the selected beams.
  • the RU may prepare pre-coded multi-user data by applying the precoding matrix to the multi-user data.
  • the RU may transmit the pre-coded multi-user data to the UEs in the subset for the RBG using Multiple-Input Multiple-Output (MIMO) technologies.
  • MIMO Multiple-Input Multiple-Output
  • Particular embodiments may repeat one or more steps of the method of FIG. 12, where appropriate.
  • this disclosure describes and illustrates an example method for computing a precoding matrix for downlink MU-MIMO by performing an RZF on a MIMO channel matrix established based on IDFT vectors corresponding to selected beams including the particular steps of the method of FIG. 12, this disclosure contemplates any suitable method for computing a precoding matrix for downlink MU-MIMO by performing an RZF on a MIMO channel matrix established based on IDFT vectors corresponding to selected beams including any suitable steps, which may include all, some, or none of the steps of the method of FIG. 12, where appropriate. Furthermore, although this disclosure describes and illustrates particular components, devices, or systems carrying out particular steps of the method of FIG. 12, this disclosure contemplates any suitable combination of any suitable components, devices, or systems carrying out any suitable steps of the method of FIG. 12. Evaluation
  • FIG. 13 illustrates example comparisons between the presented L1/L2 cross layer optimization algorithms.
  • the values are computed based on a set of assumptions applied to all the algorithms.
  • CMACs complex multiplier-accumulators
  • a number of antenna ports at the base station is assumed to be 64.
  • a size of sliding window that determines a set of potential candidates is assumed to be 16.
  • a number of active UEs associated with the base station is assumed to be 64.
  • a number of UEs to be selected in a subset is assumed to be 16.
  • a median number of iterations per layer is assumed to be 3.
  • the RZF- based user grouping algorithm requires the most computations while the 2D-IDF-based user grouping algorithm requires the least computations.
  • Both 2D-DFT+RZF-based algorithm and the RZF of IDFT algorithm require slightly more computations than the 2D-IDF-based user grouping algorithm. Considering that both 2D-DFT+RZF-based algorithm and the RZF of IDFT algorithm are based on the 2D-IDF-based user grouping algorithm, their own computational load would not be high. As for the maximum fronthaul data rate for the algorithms, comparison, the RZF-based user grouping algorithm and the 2D-DFT+RZF-based algorithm requires significantly more fronthaul bandwidth compared to the 2D-IDF-based user grouping algorithm and the RZF of IDFT algorithm.
  • the LI of the DU transmits entire precoder information over the fronthaul while the LI of the DU, for the 2D-IDF-based user grouping algorithm and the RZF of IDFT algorithm, transmits only index of the selected beam for a UE.
  • FIG. 14 illustrates example results for simulations comparing performance of the algorithms.
  • clustered delay line (CDL) channel models are employed where each channel path has a certain delay, path gain, as well as an azimuth and elevation of arrival.
  • CDL-A channel model is utilized.
  • CDF curves indicating the average UE throughput obtained using the 4 algorithms disclosed in this application are plotted.
  • the plots in FIG. 14 are obtained for 30dB SNR.
  • a plot 1401 indicates the average UE throughput for the 2D-DFT-based user grouping algorithm without oversampling.
  • a plot 1402 indicates the average UE throughput for 2D-DFT-based user grouping algorithm with azimuthal oversampling of 4.
  • a plot 1403 indicates the average UE throughput for RZF of IDFT algorithm with azimuthal oversampling of 8.
  • a plot 1404 indicates the average UE throughput for 2D DFT+RZF algorithm.
  • a plot 1405 indicates the average UE throughput for the RZF-based user grouping algorithm.
  • the 2D-DFT+RZF algorithm achieves comparable throughput to the RZF-based user grouping algorithm while requires much less computations.
  • the RZF of IDFT algorithm requires significantly less computations and smaller bandwidth for the fronthaul between the DU and the RU compared to the RZF-based user grouping algorithm. Considering those resource requirements, the RZF of IDFT algorithm may be useful in certain circumstances.
  • FIG. 15 illustrates an example computer system 1500.
  • one or more computer systems 1500 perform one or more steps of one or more methods described or illustrated herein.
  • one or more computer systems 1500 provide functionality described or illustrated herein.
  • software running on one or more computer systems 1500 performs one or more steps of one or more methods described or illustrated herein or provides functionality described or illustrated herein.
  • Particular embodiments include one or more portions of one or more computer systems 1500.
  • reference to a computer system may encompass a computing device, and vice versa, where appropriate.
  • reference to a computer system may encompass one or more computer systems, where appropriate.
  • computer system 1500 may be an embedded computer system, a system-on-chip (SOC), a single-board computer system (SBC) (such as, for example, a computer-on-module (COM) or system-on-module (SOM)), a desktop computer system, a laptop or notebook computer system, an interactive kiosk, a mainframe, a mesh of computer systems, a mobile telephone, a personal digital assistant (PDA), a server, a tablet computer system, an augmented/virtual reality device, or a combination of two or more of these.
  • SOC system-on-chip
  • SBC single-board computer system
  • COM computer-on-module
  • SOM system-on-module
  • computer system 1500 may include one or more computer systems 1500; be unitary or distributed; span multiple locations; span multiple machines; span multiple data centers; or reside in a cloud, which may include one or more cloud components in one or more networks.
  • one or more computer systems 1500 may perform without substantial spatial or temporal limitation one or more steps of one or more methods described or illustrated herein.
  • one or more computer systems 1500 may perform in real time or in batch mode one or more steps of one or more methods described or illustrated herein.
  • One or more computer systems 1500 may perform at different times or at different locations one or more steps of one or more methods described or illustrated herein, where appropriate.
  • computer system 1500 includes a processor 1502, memory 1504, storage 1506, an input/output (I/O) interface 1508, a communication interface 1510, and a bus 1512.
  • processor 1502 memory 1504
  • storage 1506 storage 1506
  • I/O interface 1508 input/output interface 1508
  • communication interface 1510 communication interface 1510
  • bus 1512 bus 1512.
  • processor 1502 includes hardware for executing instructions, such as those making up a computer program.
  • processor 1502 may retrieve (or fetch) the instructions from an internal register, an internal cache, memory 1504, or storage 1506; decode and execute them; and then write one or more results to an internal register, an internal cache, memory 1504, or storage 1506.
  • processor 1502 may include one or more internal caches for data, instructions, or addresses. This disclosure contemplates processor 1502 including any suitable number of any suitable internal caches, where appropriate.
  • processor 1502 may include one or more instruction caches, one or more data caches, and one or more translation lookaside buffers (TLBs).
  • TLBs translation lookaside buffers
  • Instructions in the instruction caches may be copies of instructions in memory 1504 or storage 1506, and the instruction caches may speed up retrieval of those instructions by processor 1502.
  • Data in the data caches may be copies of data in memory 1504 or storage 1506 for instructions executing at processor 1502 to operate on; the results of previous instructions executed at processor 1502 for access by subsequent instructions executing at processor 1502 or for writing to memory 1504 or storage 1506; or other suitable data.
  • the data caches may speed up read or write operations by processor 1502.
  • the TLBs may speed up virtual-address translation for processor 1502.
  • processor 1502 may include one or more internal registers for data, instructions, or addresses. This disclosure contemplates processor 1502 including any suitable number of any suitable internal registers, where appropriate. Where appropriate, processor 1502 may include one or more arithmetic logic units (ALUs); be a multi- core processor; or include one or more processors 1502. Although this disclosure describes and illustrates a particular processor, this disclosure contemplates any suitable processor.
  • ALUs
  • memory 1504 includes main memory for storing instructions for processor 1502 to execute or data for processor 1502 to operate on.
  • computer system 1500 may load instructions from storage 1506 or another source (such as, for example, another computer system 1500) to memory 1504.
  • Processor 1502 may then load the instructions from memory 1504 to an internal register or internal cache.
  • processor 1502 may retrieve the instructions from the internal register or internal cache and decode them.
  • processor 1502 may write one or more results (which may be intermediate or final results) to the internal register or internal cache.
  • Processor 1502 may then write one or more of those results to memory 1504.
  • processor 1502 executes only instructions in one or more internal registers or internal caches or in memory 1504 (as opposed to storage 1506 or elsewhere) and operates only on data in one or more internal registers or internal caches or in memory 1504 (as opposed to storage 1506 or elsewhere).
  • One or more memory buses (which may each include an address bus and a data bus) may couple processor 1502 to memory 1504.
  • Bus 1512 may include one or more memory buses, as described below.
  • one or more memory management units reside between processor 1502 and memory 1504 and facilitate accesses to memory 1504 requested by processor 1502.
  • memory 1504 includes random access memory (RAM). This RAM may be volatile memory, where appropriate.
  • this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, where appropriate, this RAM may be single-ported or multi-ported RAM. This disclosure contemplates any suitable RAM.
  • Memory 1504 may include one or more memories 1504, where appropriate. Although this disclosure describes and illustrates particular memory, this disclosure contemplates any suitable memory.
  • storage 1506 includes mass storage for data or instructions.
  • storage 1506 may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disc, a magneto-optical disc, magnetic tape, or a Universal Serial Bus (USB) drive or a combination of two or more of these.
  • Storage 1506 may include removable or non-removable (or fixed) media, where appropriate.
  • Storage 1506 may be internal or external to computer system 1500, where appropriate.
  • storage 1506 is non-volatile, solid-state memory.
  • storage 1506 includes read-only memory (ROM).
  • this ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM), or flash memory or a combination of two or more of these.
  • This disclosure contemplates mass storage 1506 taking any suitable physical form.
  • Storage 1506 may include one or more storage control units facilitating communication between processor 1502 and storage 1506, where appropriate. Where appropriate, storage 1506 may include one or more storages 1506. Although this disclosure describes and illustrates particular storage, this disclosure contemplates any suitable storage.
  • I/O interface 1508 includes hardware, software, or both, providing one or more interfaces for communication between computer system 1500 and one or more I/O devices.
  • Computer system 1500 may include one or more of these I/O devices, where appropriate.
  • One or more of these I/O devices may enable communication between a person and computer system 1500.
  • an I/O device may include a keyboard, keypad, microphone, monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet, touch screen, trackball, video camera, another suitable I/O device or a combination of two or more of these.
  • An I/O device may include one or more sensors. This disclosure contemplates any suitable I/O devices and any suitable I/O interfaces 1508 for them.
  • I/O interface 1508 may include one or more device or software drivers enabling processor 1502 to drive one or more of these I/O devices.
  • I/O interface 1508 may include one or more I/O interfaces 1508, where appropriate.
  • communication interface 1510 includes hardware, software, or both providing one or more interfaces for communication (such as, for example, packet-based communication) between computer system 1500 and one or more other computer systems 1500 or one or more networks.
  • communication interface 1510 may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wire-based network or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as a WI-FI network.
  • NIC network interface controller
  • WNIC wireless NIC
  • WI-FI network wireless network
  • computer system 1500 may communicate with an ad hoc network, a personal area network (PAN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), or one or more portions of the Internet or a combination of two or more of these.
  • PAN personal area network
  • LAN local area network
  • WAN wide area network
  • MAN metropolitan area network
  • computer system 1500 may communicate with a wireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FI network, a WI-MAX network, a cellular telephone network (such as, for example, a Global System for Mobile Communications (GSM) network), or other suitable wireless network or a combination of two or more of these.
  • WPAN wireless PAN
  • WI-FI wireless personal area network
  • WI-MAX wireless personal area network
  • WI-MAX wireless personal area network
  • cellular telephone network such as, for example, a Global System for Mobile Communications (GSM) network
  • GSM Global System
  • bus 1512 includes hardware, software, or both coupling components of computer system 1500 to each other.
  • bus 1512 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT) interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBAND interconnect, a low-pin-count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCIe) bus, a serial advanced technology attachment (SATA) bus, a Video Electronics Standards Association local (VLB) bus, or another suitable bus or a combination of two or more of these.
  • Bus 1512 may include one or more buses 1512, where appropriate.
  • a computer-readable non-transitory storage medium or media may include one or more semiconductor-based or other integrated circuits (ICs) (such, as for example, field-programmable gate arrays (FPGAs) or application-specific ICs (ASICs)), hard disk drives (HDDs), hybrid hard drives (HHDs), optical discs, optical disc drives (ODDs), magneto-optical discs, magneto-optical drives, floppy diskettes, floppy disk drives (FDDs), magnetic tapes, solid-state drives (SSDs), RAM-drives, SECURE DIGITAL cards or drives, any other suitable computer-readable non-transitory storage media, or any suitable combination of two or more of these, where appropriate.
  • ICs such, as for example, field-programmable gate arrays (FPGAs) or application-specific ICs (ASICs)
  • HDDs hard disk drives
  • HHDs hybrid hard drives
  • ODDs optical disc drives
  • magneto-optical discs magneto-optical drives
  • references in the appended claims to an apparatus or system or a component of an apparatus or system being adapted to, arranged to, capable of, configured to, enabled to, operable to, or operative to perform a particular function encompasses that apparatus, system, component, whether or not it or that particular function is activated, turned on, or unlocked, as long as that apparatus, system, or component is so adapted, arranged, capable, configured, enabled, operable, or operative. Additionally, although this disclosure describes or illustrates particular embodiments as providing particular advantages, particular embodiments may provide none, some, or all of these advantages.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Radio Transmission System (AREA)

Abstract

Selon un mode de réalisation, un procédé consiste à envoyer un SRS reçu en provenance d'une pluralité d'UE associés à la station de base à une DU associée à la station de base, à recevoir des informations concernant un sous-ensemble de la pluralité d'UE sélectionné pour des transmissions de données de liaison descendante pour un RBG, des données multi-utilisateurs à transmettre à des UE dans le sous-ensemble et des identités de faisceaux sélectionnés parmi une pluralité de faisceaux prédéfinis à associer aux UE dans le sous-ensemble à partir de la DU, chacun de la pluralité de faisceaux prédéfinis correspondant à un vecteur de DFT, à calculer une matrice de précodage pour le RBG sur la base de vecteurs d'IDFT correspondant aux faisceaux sélectionnés, à préparer les données multi-utilisateurs précodées par l'application de la matrice de précodage aux données multi-utilisateurs et à transmettre les données multi-utilisateurs précodées aux UE dans le sous-ensemble pour le RBG à l'aide de technologies MIMO.
PCT/US2022/033165 2021-06-17 2022-06-12 Groupement d'utilisateurs pour mimo multi-utilisateurs WO2022265949A1 (fr)

Applications Claiming Priority (4)

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US202163211978P 2021-06-17 2021-06-17
US63/211,978 2021-06-17
US17/491,307 US11476903B1 (en) 2021-06-17 2021-09-30 User grouping for multi-user MIMO
US17/491,307 2021-09-30

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170264355A1 (en) * 2014-11-19 2017-09-14 Samsung Electronics Co., Ltd. Method and apparatus for transmitting and receiving reference signal and for scheduling
US20200389880A1 (en) * 2017-12-27 2020-12-10 Telefonaktiebolaget Lm Ericsson (Publ) Methods, Systems and Units of Distributed Base Station System for Handling Downlink Communication

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170264355A1 (en) * 2014-11-19 2017-09-14 Samsung Electronics Co., Ltd. Method and apparatus for transmitting and receiving reference signal and for scheduling
US20200389880A1 (en) * 2017-12-27 2020-12-10 Telefonaktiebolaget Lm Ericsson (Publ) Methods, Systems and Units of Distributed Base Station System for Handling Downlink Communication

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