WO2021073543A1

WO2021073543A1 - Channel state information reporting method, base station, and user equipment

Info

Publication number: WO2021073543A1
Application number: PCT/CN2020/120955
Authority: WO
Inventors: Salah Eddine HAJRI; Mohamad Assaad
Original assignee: Tcl Communication Limited; Centralesupelec
Priority date: 2019-10-14
Filing date: 2020-10-14
Publication date: 2021-04-22
Also published as: CN114586435A

Abstract

A channel state information reporting method is executed in a system with a central controller, a plurality of transmission and reception points (TRPs), and user equipments (UEs) in a cloud radio access network (CRAN). The present invention exploits macro-diversity of the CRAN to address the difficulties of CSI feedback in large antenna systems. A base station estimates channel autocorrelation coefficients and large scale channel fading coefficients and derives CSI feedback configuration for the UEs. Each of the UEs reports CSI feedback or not according to the configuration. The base station predicts CSI that are not reported by the UEs.

Description

CHANNEL STATE INFORMATION REPORTING METHOD, BASE STATION, AND USER EQUIPMENT

Technical Field

The present disclosure relates to the field of communication systems, and more particularly, to improved channel state information (CSI) acquisition in frequency division duplexing (FDD) cloud radio access networks (CRAN) .

Background Art

Wireless communication systems, such as the third-generation (3G) of mobile telephone standards and technology are well known. Such 3G standards and technology have been developed by the Third Generation Partnership Project (3GPP) . The 3rd generation of wireless communications has generally been developed to support macro-cell mobile phone communications. Communication systems and networks have developed towards being a broadband and mobile system. In cellular wireless communication systems, user equipment (UE) is connected by a wireless link to a radio access network (RAN) . The RAN comprises a set of base stations (BSs) which provide wireless links to the UEs located in cells covered by the base station, and an interface to a core network (CN) which provides overall network control. As will be appreciated the RAN and CN each conduct respective functions in relation to the overall network. The 3rd Generation Partnership Project has developed the so-called Long Term Evolution (LTE) system, namely, an Evolved Universal Mobile Telecommunication System Territorial Radio Access Network, (E-UTRAN) , for a mobile access network where one or more macro-cells are supported by a base station known as an eNodeB or eNB (evolved NodeB) . More recently, LTE is evolving further towards the so-called 5G or NR (new radio) systems where one or more cells are supported by a base station known as a gNB.

With the proliferation of smart devices and emergence of new capacity demanding services, wireless networks face a whole new set of challenges in terms of technologies and business models.

Indeed, the next-generation mobile networks must meet diversified demands with different key performance indicators (KPIs) . 5G mobile networks enable three major categories of emerging services: enhanced mobile broadband (eMBB) , ultra-reliable and low-latency communications (uRLLC) , and massive machine type communications (mMTC) .

Technical Problem

Current mobile network architecture has been proven to be insufficient to address the requirements of 5G services. Previous network generations were specifically designed to meet requirements for voice and conventional mobile broadband services. Since 5G networks are expected to provide diversified services, support current standards including LTE and wireless local area network (WLAN) , and coordinate different site types, a more flexible and distributed service-driven architecture is needed.

Cloud radio access network (CRAN) /virtualized radio access network (VRAN) technology is envisaged to be a key technology for next generation networks. A CRAN provides the needed flexibility to adapt to an ever-evolving traffic volume with its increasingly dynamic nature. Coupled with a large number of antennas at the access points (APs) , CRAN/VRAN are able to provide the energy and spectral efficiency gains in large antenna systems. Additionally, leveraging centralized pools of baseband units (BBUs) and network functions virtualization (NFV) , CRAN/VRAN enables the fast deployment of additional value-add mobile services and a lower network operation cost.

Massive CRAN/VRAN systems, however, requires CSI acquisition in large antenna systems. CSI estimation overhead increases with the number of antennas at both the receiver and transmitter. To adapt adequate CSI estimation schemes characterized by a low estimation/feedback overhead for CRAN/VRAN architecture is desirable.

In frequency division duplexing (FDD) systems, since an uplink (UL) and a downlink (DL) may utilize different frequency bands, CSI of both links need to be estimated. UL CSI is obtained by enabling user equipments to send different pilot sequences. In the DL, CSI estimates are obtained using DL training followed by explicit or implicit CSI feedback. As the number of base station (BS) antennas increases, FDD channel estimation becomes highly problematic since CSI feedback overhead increases linearly with the number of system antennas.

Different schemes have been developed to address the problem of CSI estimation in TDD and FDD large antenna systems. For a FDD system, joint spatial and division multiplexing (JSDM) for multi-user multiple input multiple output (MU-MIMO) DL was investigated to address the problem of the CSI feedback bottleneck. JSDM is a scheme that aims to serve user equipments by clustering UEs into groups such that user equipments within a group have approximately similar channel covariances, while user equipments across groups have near orthogonal covariance eigenspaces.

Additionally, a chordal distance based K-mean clustering was proposed for FDD systems with JSDM. A wide range of similarity measures, such as weighted likelihood, subspace projection and Fubini-Study based similarity measures were also investigated. Two clustering methods, namely hierarchical and K-medoids clustering were proposed for UE grouping with the aforementioned proximity measures.

A comparison of the proposed grouping methods was performed and the combination that achieves the largest capacity was derived. A new similarity measure coupled with a novel clustering method has achieved appropriate user equipment grouping based on the channels second order statistics. Using the same principle of two stage beamforming as in JSDM, a graph theory based UE grouping approach has been developed to mitigates the shortcomings of previously proposed UE clustering methods. Spatial properties of the channel were leveraged to obtain CSI estimates in the mm-wave range. Some solutions have been proposed to implicate time correlation between two sequential block frames in the procedure owing to the special set of challenges that mm-waves impose. Reducing the feedback overhead can also be achieved using compressed sensing and channel sparsity. Sparse channel modeling was used in order to show that Compressed sensing (CS) can efficiently save DL time-frequency training resources.

Generally speaking, a majority of these schemes exploit the wireless channel properties, such as sparsity and low rank, to reduce the needed feedback overhead to acquire CSI at the transmitter. When a CRAN/VRAN is considered, an additional property of the wireless channels can be exploited to reduce CSI estimation overhead.

Technical Solution

An object of the present disclosure is to propose a channel state information (CSI) method, a base station, and a user equipment.

A first aspect of the disclosure provides a channel state information (CSI) reporting method executable in a central controller of a base station, comprising:

estimating channel autocorrelation coefficients and large scale channel fading coefficients for all channels between a user equipment (UE) u and a group of M distributed radio nodes, wherein M is a positive integer;

deriving CSI feedback configuration for the UE u, wherein the CSI feedback configuration comprises a set of channel feedback index parameters represented by a variable x _u, m that indicates whether CSI of a channel C _u, m between the UE u and a radio node m is to be predicted or fed back, and the radio node m is one of the M distributed radio nodes; transmitting the CSI feedback configuration comprising the set of channel feedback index parameters represented by a variable x _u, m to the UE u;

receiving CSI of the channel C _u, m in a specific time slot when the variable x _u, m indicates CSI of the channel C _u, m is to be fed back; and

obtaining the CSI of the channel C _u, m in the specific time slot through CSI prediction when the variable x _u, m indicates CSI of the channel C _u, m is to be predicted.

A second aspect of the disclosure provides a channel state information (CSI) reporting method executable in a user equipment (UE) , comprising:

receiving CSI feedback configuration for a user equipment (UE) u, wherein the CSI feedback configuration comprises a set of channel feedback index parameters represented by a variable x _u, m that indicates whether CSI of a channel C _u, m between the UE u and a radio node m is to be predicted or fed back, and the radio node m is one of M distributed radio nodes of a base station;

transmitting CSI of the channel C _u, m in a specific time slot when the variable x _u, m indicates CSI of the channel C _u, m is to be fed back; and

not transmitting the CSI of the channel C _u, m in the specific time slot when the variable x _u, m indicates CSI of the channel C _u, m is to be predicted.

A third aspect of the disclosure provides a channel state information (CSI) reporting method executable in a user equipment (UE) , comprising:

estimating channel autocorrelation coefficients and large scale channel fading coefficients for all channels between a user equipment (UE) u and a group of M distributed radio nodes of a base station, wherein M is a positive integer; deriving CSI feedback configuration for the UE u, wherein the CSI feedback configuration comprises a set of channel feedback index parameters represented by a variable x _u, m that indicates whether CSI of a channel C _u, m between the UE u and a radio node m is to be predicted or fed back, and the radio node m is one of the M distributed radio nodes; transmitting CSI of the channel C _u, m in a specific time slot when the variable x _u, m indicates CSI of the channel C _u, m is to be fed back; and

A fourth aspect of the disclosure provides a base station comprising a transceiver and a processor connected with the transceiver. The processor is configured to execute the following steps comprising:

A fifth aspect of the disclosure provides a user equipment comprising a transceiver and a processor connected with the transceiver. The processor is configured to execute the following steps comprising:

A sixth aspect of the disclosure provides a user equipment comprising a transceiver and a processor connected with the transceiver. The processor is configured to execute the following steps comprising:

The disclosed method may be implemented in a chip. The chip may include a processor, configured to call and run a computer program stored in a memory, to cause a device in which the chip is installed to execute the disclosed method.

The disclosed method may be programmed as computer executable instructions stored in non-transitory computer readable medium. The non-transitory computer readable medium, when loaded to a computer, directs a processor of the computer to execute the disclosed method.

The non-transitory computer readable medium may comprise at least one from a group consisting of: a hard disk, a CD-ROM, an optical storage device, a magnetic storage device, a Read Only Memory, a Programmable Read Only Memory, an Erasable Programmable Read Only Memory, EPROM, an Electrically Erasable Programmable Read Only Memory and a Flash memory.

The disclosed method may be programmed as computer program product, that causes a computer to execute the disclosed method.

The disclosed method may be programmed as computer program, that causes a computer to execute the disclosed method.

Advantageous Effects

CRANs enable users to enjoy diverse services with high energy efficiency, high spectral efficiency and lower the cost of the network operations. CRANs, however, face many technical challenges due to the requirements of massive user connectivity, increasingly severe spectrum scarcity and energy-constrained devices. When the number of antennas becomes large, CRAN/VRANs face the same challenge of channel state information (CSI) estimation as in conventional massive MIMO systems. In a frequency division duplex (FDD) mode, the problem of CSI estimation becomes critical as the feedback overhead increases with the number of transmit and receive antennas. The present invention provides an optimized CSI acquisition scheme for FDD CRAN/VRAN in order to enable massive connectivity in high-density scenario. The main goal of the proposed scheme is to reduce the needed CSI feedback overhead by means of linear prediction at the network side and opportunistic feedback at the user side. By leveraging the macro-diversity of the cell-less architecture of CRAN/VRAN, UE devices are allowed to perform limited CSI feedback, and the network complement CSI through linear prediction of slow changing channels.

Description of Drawings

In order to more clearly illustrate the embodiments of the present disclosure or related art, the following figures will be described in the embodiments are briefly introduced. It is obvious that the drawings are merely some embodiments of the present disclosure, a person having ordinary skill in this field can obtain other figures according to these figures without paying the premise.

FIG. 1 is a schematic diagram showing a telecommunication system.

FIG. 2 is a schematic diagram showing a CRAN with a baseband unit pool, remote radio heads, and UEs.

FIG. 3 is a schematic diagram showing a disclosed method executed at a base station side according to an embodiment of the disclosure.

FIG. 4 is a schematic diagram showing a disclosed method executed at a UE side according to an embodiment of the disclosure.

FIG. 5 is a schematic diagram showing a disclosed method executed for a plurality of UEs according to an embodiment of the disclosure.

FIG. 6 is a schematic diagram showing a disclosed method with channel feedback indices are determined by both a base station and a UE according to another embodiment of the disclosure.

FIG. 7 is a schematic diagram showing impact of the disclosed method on spectral efficiency (SE) .

FIG. 8 is another schematic diagram showing impact of the disclosed method on spectral efficiency (SE) .

FIG. 9 is a block diagram of a system for wireless communication according to an embodiment of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

Embodiments of the disclosure are described in detail with the technical matters, structural features, achieved objects, and effects with reference to the accompanying drawings as follows. Specifically, the terminologies in the embodiments of the present disclosure are merely for describing the purpose of the certain embodiment, but not to limit the disclosure.

Leveraging software-defined networking (SDN) and network functions virtualization (NFV) , cloud network architecture can efficiently address the diversified 5G services with various KPIs. Cloud radio access networks (CRANs) with an option of flexible base station functional splits have been proposed to address the limitations of conventional network architecture.

UE signals are subject to heterogeneous Doppler spreads at the antennas of different APs. This means that the channels between a given UE and serving APs do not evolve at the same rate. In this context, slow changing channels may be accurately predicted without CSI feedback. The present invention is based on this observation to providing a flexible CSI feedback method in which the network and UEs limits the amount of CSI feedback and performs CSI prediction.

With reference to FIG. 1, a telecommunication system including a group 100a of a plurality of UEs, a base station (BS) 200a, and a network entity device 300 executes the disclosed method according to an embodiment of the present disclosure. The group 100a of a plurality of UEs may include a UE 10a, a UE 10b, and other UEs. FIG. 1 is shown for illustrative not limiting, and the system may comprise more UEs, BSs, and CN entities. Connections between devices and device components are shown as lines and arrows in the FIGs. Connections between devices may be realized by wireless connections. Connections between device components may be realized by wirelines, buses, traces, cables or optical fabrics. The UE 10a may include a processor 11a, a memory 12a, and a transceiver 13a. The UE 10b may include a processor 11b, a memory 12b, and a transceiver 13b. The base station 200a may include a baseband unit (BBU) 204a. The base band unit 204a may include a processor 201a, a memory 202a, and a transceiver 203a. The network entity device 300 may include a processor 301, a memory 302, and a transceiver 303. Each of the

processors

11a, 11b, 201a, and 301 may be configured to implement proposed functions, procedures and/or methods described in the description. Layers of radio interface protocol may be implemented in the

processors

11a, 11b, 201a, and 301. Each of the

memory

12a, 12b, 202a, and 302 operatively stores a variety of program and information to operate a connected processor. Each of the

transceiver

13a, 13b, 203a, and 303 is operatively coupled with a connected processor, transmits and/or receives radio signals or wireline signals. The UE 10a may be in communication with the UE 10b through a sidelink. The base station 200a may be an eNB, a gNB, or one of other types of radio nodes.

Each of the

processor

11a, 11b, 201a, and 301 may include a central processing unit (CPU) , an application-specific integrated circuits (ASICs) , other chipsets, logic circuits and/or data processing devices. Each of the

memory

12a, 12b, 202a, and 302 may include a read-only memory (ROM) , a random access memory (RAM) , a flash memory, a memory card, a storage medium and/or other storage devices. Each of the

transceiver

13a, 13b, 203a, and 303 may include baseband circuitry and radio frequency (RF) circuitry to process radio frequency signals. When the embodiments are implemented in software, the techniques described herein can be implemented with modules, units, procedures, functions, entities and so on, that perform the functions described herein. The modules can be stored in a memory and executed by the processors. The memory can be implemented within a processor or external to the processor, in which those can be communicatively coupled to the processor via various means are known in the art.

The network entity device 300 may be a node in a CN. CN may include LTE CN or 5G core (5GC) which includes user plane function (UPF) , session management function (SMF) , mobility management function (AMF) , unified data management (UDM) , policy control function (PCF) , control plane (CP) /user plane (UP) separation (CUPS) , authentication server (AUSF) , network slice selection function (NSSF) , and the network exposure function (NEF) .

With reference to FIG. 2, base station 200b is an embodiment of the base station 200a and includes a central controller (CC) 210, access points 211-1, 211-2, …and 211-M. M is a positive integer. The central controller 210 may be implemented into a central unit (CU) , and may include a BBU, such as BBU 204a, in connection with the access points (APs) 211-1, 211-2, …and 211-M. Each of the access points 211-1, 211-2, …and 211-M may be implemented into a radio node, a remote unit (RU) , or a remote radio head (RRH) , and may include a transmission and reception point (TRP) . The access points 211-1, 211-2, …and 211-M may be located in different locations.

The central controller 210 receives wireless signals from a group 100b of V user equipments (UEs) through a group of M distributed radio nodes. V is a positive integer. The group of V user equipments includes UEs 10-1, 10-2, 10-3, and …10-V. The UEs 10-1, 10-2, 10-3, and …10-V may be located in different locations.

An embodiment of the disclosure processes uplinks from V UEs to M single antenna access points (APs) . At each time slot, each AP performs uplink channel estimation independently.

The APs 211-1, 211-2, …and 211-M are distributed within a coverage area and are managed by the central controller 210 that contains a centralized baseband unit (BBU) pool and handles operations of a physical layer and a medium access control (MAC) layer, such as data decoding and encoding, scheduling, and power allocation. The APs are linked to the central controller 210 through high performance transport links known as fronthaul. Fronthaul may be implemented by optical cables or high bandwidth wireless channels. The system in FIG. 2 including the base station 200b and the UEs is a simplified example of a CRAN. The APs 211-1, 211-2, …and 211-M perform channel estimation and the link level transmission chain until equalization. The central controller 210 performs signal decoding, encoding, modulation, demodulation, scheduling and MAC layer operations.

In an FDD CRAN system, user equipments are required to obtain CSI of channels to the different APs through downlink training and then feedback the CSI. This task can be quite complicated as the CSI feedback overhead increases with the number of APs and user equipments antennas. Nevertheless, serving user equipments from a plurality of APs has been proven to provide high performance gains that mainly result from favorable propagation and channel hardening which are the direct consequence of a large number of transmit antennas.

The present invention facilitates CSI reporting in large-scale CRAN systems operating in FDD mode. As CSI reporting is a fundamental component of a telecommunication system with a high number of antenna elements, an efficient reporting procedure that minimizes the feedback overhead is required in addition to reducing CSI calculation at the UE side.

The present invention includes mainly of two parts: channel feedback selection and an adapted linear channel prediction at the base station.

The main idea in this invention is to exploit the macro-diversity of CRAN systems to reduce CSI feedback overhead. The system in FIG. 2 has distributed antenna provides macro diversity as an additional degree of freedom when compared with conventional communication networks. Macro diversity is the diversity of slow changing channel statistics to each AP.

Doppler spread, or equivalently, channel autocorrelation in time may be utilized to exploit such macro diversity and reduce the CSI feedback overhead. Doppler spread reflects a rate at which the channel changes and can be precisely predicted with minimum error. Experienced Doppler spread characterizes correlation of channels between two given time slots.

An embodiment of the invention exploits different rates of channel aging in order to reduce volume of CSI feedback. Features of the embodiment of the invention includes:

● The user equipments feedback, at each CSI reporting occasion, a limited number of channel coefficients, that is CSI.

● Any channel coefficients that were not reported by the user equipments are predicted by the base station.

● The indexes of the channel coefficients that are fed back and that are predicted are derived at both the CC and UEs. Leveraging a low complexity optimization problem, the CC and UE are able to derive, at each slot, CSI feedback and prediction that the total mean squared error of channel prediction is minimized. In an alternative embodiment, the CSI feedback and prediction are performed only by the CC and then communicated to the UEs.

● At each slot, the CC generates channel feedback index parameters with respect to a maximum feedback capacity.

An embodiment of the invention leverages information of Doppler spread at the user equipment and the base station to minimize signaling overhead. Doppler spread between each AP and the user equipment can be estimated efficiently based on a simple correlation procedure on the cyclic prefix of each orthogonal frequency division multiplexing (OFDM) symbol.

With reference to FIG. 3 and FIG. 4, a base station and a user equipment (UE) u executes a channel state information (CSI) reporting method. An example of the UE u in the description may include one of the UE 10a or UE 10b. An example of the base station in the description may include the

base station

200a or 200b. The following steps may be executed or controlled by a central controller of the base station.

The base station estimates channel autocorrelation coefficients and large scale channel fading coefficients for all channels between a user equipment (UE) u and a group of M distributed radio nodes 211-1, 211-2, …and 211-M. M is a positive integer (block 310) .

The base station derives CSI feedback configuration for the UE u (block 311) . The CSI feedback configuration comprises a set of channel feedback index parameters represented by a variable x _u, m that indicates whether CSI of a channel C _u, m between the UE u and a radio node m is to be predicted or fed back. The radio node m is one of the M distributed radio nodes. The variable x _u, m may be a binary variable. For example, as shown in Table 1, value 0 of the variable x _u, m may indicate CSI of a channel C _u, m between the UE u and a radio node m is to be predicted, and value 1 of the variable x _u, m may indicate CSI of a channel C _u, m between the UE u and a radio node m is to be fed back.

Table 1

Value of the variable x _u, m	CSI reporting operation by UE
0	Not transmitting CSI feedback
1	Transmitting CSI feedback

The base station transmits the CSI feedback configuration comprising the set of channel feedback index parameters represented by a variable x _u, m to the UE u (block 312) .

The UE u receives the CSI feedback configuration and performs CSI reporting according to the CSI feedback configuration (block 320) . Specifically, the UE u transmits CSI of the channel C _u, m in a specific time slot when the variable x _u, m indicates CSI of the channel C _u, m is to be fed back (block 321) . The UE u does not transmit the CSI of the channel C _u, m in the specific time slot when the variable x _u, m indicates CSI of the channel C _u, m is to be predicted (block 322) .

The base station receives CSI of the channel C _u, m in a specific time slot when the variable x _u, m indicates CSI of the channel C _u, m is to be fed back (block 313) . The base station obtains the CSI of the channel C _u, m in the specific time slot through CSI prediction when the variable x _u, m indicates CSI of the channel C _u, m is to be predicted (block 314) .

The UE u is one of the V UEs 10-1, 10-2, 10-3, and …10-V served by the base station through the group of M distributed radio nodes. The V is a positive integer. Similarly, the base station may perform the aforementioned steps to each of the V UEs. With reference to FIG. 5, the base station derives CSI feedback configuration for all of the V UEs (block 330) , and transmits CSI feedback configuration to all of the V UEs (block 331) . Each of the V UEs receives the CSI feedback configuration and reports CSI to the base station according to received configuration. The base station receives CSI of channels of the V UEs that are fed back (block 332) , and predicts CSI of channels of the V UEs that are not fed back (block 333) . The channel state information prediction may be performed using a p-th order Wiener linear predictor.

Values of the channel feedback index parameters in the specific time slot are solutions of the variable x _u, m in the following optimization problem:

wherein B represents limited feedback capacity of B bits;

B _m is feedback capacity of the radio node m; and

is a mean squared-error of CSI prediction.

P1 is an optimization problem with a constraint C1. The problem indicates that the CSI feedback procedure aims at minimizing the mean squared-error of channel prediction. In CSI prediction, a channel vector h _u, m (t _n+1) is calculated as the CSI of the channel C _u, m the specific time slot t _n+1 according to the following formula:

wherein

ρ denotes a channel autocorrelation coefficient in the channel autocorrelation coefficients;

β _u, m denotes a large scale channel fading coefficient of the channel C _u, m in the large scale channel fading coefficients;

t _n+1 denotes a slot index at which the CSI of the channel C _u, m is predicted; and

is the CSI of the channel C _u, m predicted till the time slot t _n+1.

The channel vector

may be initialized. For example, the central controller assigns values of the channel vector

to the last CSI values of the channel C _u, m. The ρ is a constant representing a channel autocorrelation coefficient regardless of time slot index. The element

in the matrix is ρ to the power t _n+1+1-t _n-p. The first row of the matrix may be

where the exponents of elements in the row ascend in a step of one. Arrangement of elements in the first row of the matrix is left shifted to form arrangement of elements in the second row of the matrix, arrangement of elements in the second row of the matrix is left shifted to form arrangement of elements in the third row of the matrix, and so on. Left shifting of matrix elements in a row is left rotation of matrix elements in the row where leftmost element is rearranged to the rightmost. The first column of the matrix may be

where the exponents of elements in the column ascend in a step of one.

The problem P1 is solved based on a known

The mean squared-error of CSI prediction

may be updated according to the following formula:

where tr (. ) represents the trace operator; and

the star sign “*” represents a matrix conjugate transpose operator.

The mean squared-error of CSI prediction

may be updated using the optimal p-th order Wiener linear predictor. Note that the prediction error increases as a function of the delay between the prediction slot t _n+1 and a time slot of the explicit reported channel feedback. This means that the performance of channel prediction decays over time if no explicit feedback is performed. Channel prediction reduces CSI feedback overhead, but results in increased channel estimation error over time. The trade-off should be addressed.

The limited CSI feedback optimization problem (P1) is to minimize the mean squared-error of channel prediction. Such optimization problem involves evolution of the prediction error over time. The

is updated at each slot and take into consideration the performance decay of CSI prediction with aging of the last feedback channel.

The disclosed method changes the feedback decisions from one slot to the next in order to balance between the constraint of the limited feedback capacity and the error from channel prediction. Note that the system is suitable to predict the channel coefficients with high time-correlation as circumstances are characterized by low prediction error.

An embodiment of the invention determines the indexes of channel with predicted channel coefficients with consideration of the prediction error increase.

An embodiment of the invention addresses the problem of deriving the optimized CSI feedback decisions using the Doppler spread knowledge at the APs. Taking into consideration the constrained feedback capacity of the system, the base station and UE select the channels that are to be fed back. When a channel coefficient is not transmitted to the base station, the base station predicts the channel coefficient using a linear p-th order Wiener predictor. That is, the base station predicts CSI using a linear combination of the last p feedbacks of the channel coefficient in question.

In an embodiment, the aforementioned problem is solved by the CC and the resulting solution is communicated to the V UEs. Alternatively, both the CC and the UE may solve the problem so that the signaling required to communicate the CSI feedback configuration is not required since both the CC and UE solve the same problem and obtain the same solution. A flow chart of such embodiment is given hereafter.

With reference to FIG. 6, the base station and the UE u estimate channel autocorrelation coefficients and large scale channel fading coefficients for all channels between a user equipment (UE) u and a group of M distributed radio nodes 211-1, 211-2, …and 211-M. M is a positive integer (block 340) . The base station and the UE u derive CSI feedback configuration for the UE u (block 341) . The base station need not to transmit the CSI feedback configuration to the UE u. The UE u transmits CSI of the channel C _u, m in a specific time slot when the variable x _u, m indicates CSI of the channel C _u, m is to be fed back (block 342) . The UE u does not transmit the CSI of the channel C _u, m in the specific time slot when the variable x _u, m indicates CSI of the channel C _u, m is to be predicted (block 343) .

Results of numerical simulations show the effects of the proposed invention. The performance of the proposed CSI reporting method is compared with a conventional explicit CSI feedback framework taken as a baseline. The same limited feedback capacity is set in the two case. The proposed method circumvents feedback capacity limitation by selecting CSI feedback indices such that the total CSI mean square error (MSE) is minimized.

In an example, a distributed antenna system containing 12 access points, each with 10 antennas, serves 5 UE devices that share the same time-frequency ressources. APs and UEs are distributed within a disc of radius 300 m. UE devices are moving according to randomly generated directions and speed with a maximum velocity of 500 Km/h.

FIG. 7 shows a comparison of the average achievable spectral efficiency. A considerable improvement to spectral efficiency can be obtained by the disclosure. The gain results from the optimized feedback and prediction framework that efficiently circumvent the limited feedback capacity. The baseline approach cannot serve the UEs with all APs since CSI is not acquired for all APs. On the other hand, the proposed method in this invention can optimize feedback and prediction decisions so that more comprehensive CSI is available for all APs

FIG. 8 shows a comparison of the cumulative spectral efficiency over 36 time slots, that is, 36 prediction and feedback occasions. Again, considerable improvement to spectral efficiency is obtained by the disclosure. As the gain in efficiency is maintained over a long period of time, the proposed feedback selection and prediction framework works well with channel aging effects.

FIG. 9 is a block diagram of an example system 700 for wireless communication according to an embodiment of the present disclosure. Embodiments described herein may be implemented into the system using any suitably configured hardware and/or software. FIG. 9 illustrates the system 700 including a radio frequency (RF) circuitry 710, a baseband circuitry 720, a processing unit 730, a memory/storage 740, a display 750, a camera 760, a sensor 770, and an input/output (I/O) interface 780, coupled with each other as illustrated.

The processing unit 730 may include a circuitry, such as, but not limited to, one or more single-core or multi-core processors. The processors may include any combinations of general-purpose processors and dedicated processors, such as graphics processors and application processors. The processors may be coupled with the memory/storage and configured to execute instructions stored in the memory/storage to enable various applications and/or operating systems running on the system.

The baseband circuitry 720 may include a circuitry, such as, but not limited to, one or more single-core or multi-core processors. The processors may include a baseband processor. The baseband circuitry may handle various radio control functions that enable communication with one or more radio networks via the RF circuitry. The radio control functions may include, but are not limited to, signal modulation, encoding, decoding, radio frequency shifting, etc. In some embodiments, the baseband circuitry may provide for communication compatible with one or more radio technologies. For example, in some embodiments, the baseband circuitry may support communication with 5G NR, LTE, an evolved universal terrestrial radio access network (EUTRAN) and/or other wireless metropolitan area networks (WMAN) , a wireless local area network (WLAN) , a wireless personal area network (WPAN) . Embodiments in which the baseband circuitry is configured to support radio communications of more than one wireless protocol may be referred to as multi-mode baseband circuitry. In various embodiments, the baseband circuitry 720 may include circuitry to operate with signals that are not strictly considered as being in a baseband frequency. For example, in some embodiments, baseband circuitry may include circuitry to operate with signals having an intermediate frequency, which is between a baseband frequency and a radio frequency.

The RF circuitry 710 may enable communication with wireless networks using modulated electromagnetic radiation through a non-solid medium. In various embodiments, the RF circuitry may include switches, filters, amplifiers, etc. to facilitate the communication with the wireless network. In various embodiments, the RF circuitry 710 may include circuitry to operate with signals that are not strictly considered as being in a radio frequency. For example, in some embodiments, RF circuitry may include circuitry to operate with signals having an intermediate frequency, which is between a baseband frequency and a radio frequency.

In various embodiments, the transmitter circuitry, control circuitry, or receiver circuitry discussed above with respect to the UE, eNB, or gNB may be embodied in whole or in part in one or more of the RF circuitries, the baseband circuitry, and/or the processing unit. As used herein, “circuitry” may refer to, be part of, or include an Application Specific Integrated Circuit (ASIC) , an electronic circuit, a processor (shared, dedicated, or group) , and/or a memory (shared, dedicated, or group) that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable hardware components that provide the described functionality. In some embodiments, the electronic device circuitry may be implemented in, or functions associated with the circuitry may be implemented by, one or more software or firmware modules. In some embodiments, some or all of the constituent components of the baseband circuitry, the processing unit, and/or the memory/storage may be implemented together on a system on a chip (SOC) .

The memory/storage 740 may be used to load and store data and/or instructions, for example, for system. The memory/storage for one embodiment may include any combination of suitable volatile memory, such as dynamic random access memory (DRAM) ) , and/or non-volatile memory, such as flash memory. In various embodiments, the I/O interface 780 may include one or more user interfaces designed to enable user interaction with the system and/or peripheral component interfaces designed to enable peripheral component interaction with the system. User interfaces may include, but are not limited to a physical keyboard or keypad, a touchpad, a speaker, a microphone, etc. Peripheral component interfaces may include, but are not limited to, a non-volatile memory port, a universal serial bus (USB) port, an audio jack, and a power supply interface.

In various embodiments, the sensor 770 may include one or more sensing devices to determine environmental conditions and/or location information related to the system. In some embodiments, the sensors may include, but are not limited to, a gyro sensor, an accelerometer, a proximity sensor, an ambient light sensor, and a positioning unit. The positioning unit may also be part of, or interact with, the baseband circuitry and/or RF circuitry to communicate with components of a positioning network, e.g., a global positioning system (GPS) satellite. In various embodiments, the display 750 may include a display, such as a liquid crystal display and a touch screen display. In various embodiments, the system 700 may be a mobile computing device such as, but not limited to, a laptop computing device, a tablet computing device, a netbook, an ultrabook, a smartphone, etc. In various embodiments, system may have more or less components, and/or different architectures. Where appropriate, methods described herein may be implemented as a computer program. The computer program may be stored on a storage medium, such as a non-transitory storage medium.

The embodiment of the present disclosure is a combination of techniques/processes that can be adopted in 3GPP specification to create an end product.

A person having ordinary skill in the art understands that each of the units, algorithm, and steps described and disclosed in the embodiments of the present disclosure are realized using electronic hardware or combinations of software for computers and electronic hardware. Whether the functions run in hardware or software depends on the condition of application and design requirement for a technical plan. A person having ordinary skill in the art can use different ways to realize the function for each specific application while such realizations should not go beyond the scope of the present disclosure. It is understood by a person having ordinary skill in the art that he/she can refer to the working processes of the system, device, and unit in the above-mentioned embodiment since the working processes of the above-mentioned system, device, and unit are basically the same. For easy description and simplicity, these working processes will not be detailed.

It is understood that the disclosed system, device, and method in the embodiments of the present disclosure can be realized with other ways. The above-mentioned embodiments are exemplary only. The division of the units is merely based on logical functions while other divisions exist in realization. It is possible that a plurality of units or components are combined or integrated in another system. It is also possible that some characteristics are omitted or skipped. On the other hand, the displayed or discussed mutual coupling, direct coupling, or communicative coupling operate through some ports, devices, or units whether indirectly or communicatively by ways of electrical, mechanical, or other kinds of forms.

The units as separating components for explanation are or are not physically separated. The units for display are or are not physical units, that is, located in one place or distributed on a plurality of network units. Some or all of the units are used according to the purposes of the embodiments. Moreover, each of the functional units in each of the embodiments can be integrated in one processing unit, physically independent, or integrated in one processing unit with two or more than two units.

If the software function unit is realized and used and sold as a product, it can be stored in a readable storage medium in a computer. Based on this understanding, the technical plan proposed by the present disclosure can be essentially or partially realized as the form of a software product. Or, one part of the technical plan beneficial to the conventional technology can be realized as the form of a software product. The software product in the computer is stored in a storage medium, including a plurality of commands for a computational device (such as a personal computer, a server, or a network device) to run all or some of the steps disclosed by the embodiments of the present disclosure. The storage medium includes a USB disk, a mobile hard disk, a read-only memory (ROM) , a random access memory (RAM) , a floppy disk, or other kinds of media capable of storing program codes.

The present invention optimizes the CSI feedback in cell-less networks operating in FDD mode to reduce the CSI estimation overhead, resulting ultimately improved efficiency.

The pivotal part of the present invention is to exploit macro-diversity of CRAN/VRAN architecture to address the difficulties of CSI feedback in large antenna systems. Leveraging the diversity of experienced channel conditions, especially Doppler spread, and a linear prediction framework for slow changing channels, the disclosure proposes an efficient CSI feedback framework for FDD CRAN/VRAN.

The disclosure reduces CSI feedback overhead, as the network is enabled to predict slow changing channels instead of requiring their implicit or explicit CSI feedback.

While the present disclosure has been described in connection with what is considered the most practical and preferred embodiments, it is understood that the present disclosure is not limited to the disclosed embodiments but is intended to cover various arrangements made without departing from the scope of the broadest interpretation of the appended claims.

Claims

A channel state information (CSI) reporting method executable in a central controller of a base station, comprising: estimating channel autocorrelation coefficients and large scale channel fading coefficients for all channels between a user equipment (UE) u and a group of M distributed radio nodes, wherein M is a positive integer;

deriving CSI feedback configuration for the UE u, wherein the CSI feedback configuration comprises a set of channel feedback index parameters represented by a variable x _u, m that indicates whether CSI of a channel C _u, m between the UE u and a radio node m is to be predicted or fed back, and the radio node m is one of the M distributed radio nodes;

transmitting the CSI feedback configuration comprising the set of channel feedback index parameters represented by a variable x _u, m to the UE u;

receiving CSI of the channel C _u, m in a specific time slot when the variable x _u, m indicates CSI of the channel C _u, m is to be fed back; and

obtaining the CSI of the channel C _u, m in the specific time slot through CSI prediction when the variable x _u, m indicates CSI of the channel C _u, m is to be predicted.
The channel state information reporting method of claim 1, wherein UE u is one of V UEs served by the base station through the group of M distributed radio nodes, and V is a positive integer, the method further comprises:

deriving CSI feedback configuration for all of the V UEs;

transmitting CSI feedback configuration to all of the V UEs;

receiving CSI of channels of the V UEs that are fed back; and

predicting CSI of channels of the V UEs that are not fed back.
The channel state information reporting method of claim 1, wherein the channel prediction is performed using a p-th order Wiener linear predictor.
The channel state information reporting method of claim 1, wherein values of the channel feedback index parameters in the specific time slot are solutions of the variable x _u, m in the following optimization problem:

subject to

wherein B represents limited feedback capacity of B bits;

B _m is feedback capacity of the radio node m; and

is a mean squared-error of CSI prediction.
The channel state information reporting method of claim 4, wherein a channel vector h _u, m (t _n+1) is calculated as the CSI of the channel C _u, m the specific time slot t _n+1 according to the following formula:

wherein

ρ denotes a channel autocorrelation coefficient in the channel autocorrelation coefficients;

β _u, m denotes a large scale channel fading coefficient of the channel C _u, m in the large scale channel fading coefficients;

t _n+1 denotes a slot index at which the CSI of the channel C _u, m is predicted; and

is the CSI of the channel C _u, m predicted till the time slot t _n+1.
The channel state information reporting method of claim 5, wherein the mean squared-error of CSI prediction
is updated according to the following formula:

where tr (.) represents the trace operator; and

the star sign represents a matrix conjugate transpose operator.
A channel state information (CSI) reporting method executable in a user equipment (UE) , comprising:

receiving CSI feedback configuration for a user equipment (UE) u, wherein the CSI feedback configuration comprises a set of channel feedback index parameters represented by a variable x _u, m that indicates whether CSI of a channel C _u, m between the UE u and a radio node m is to be predicted or fed back, and the radio node m is one of M distributed radio nodes of a base station;

transmitting CSI of the channel C _u, m in a specific time slot when the variable x _u, m indicates CSI of the channel C _u, m is to be fed back; and

not transmitting the CSI of the channel C _u, m in the specific time slot when the variable x _u, m indicates CSI of the channel C _u, m is to be predicted.
The channel state information reporting method of claim 7, wherein channel prediction of the CSI of the channel C _u, m is performed using a p-th order Wiener linear predictor.
A channel state information (CSI) reporting method executable in a user equipment (UE) , comprising:

estimating channel autocorrelation coefficients and large scale channel fading coefficients for all channels between a user equipment (UE) u and a group of M distributed radio nodes of a base station, wherein M is a positive integer;

deriving CSI feedback configuration for the UE u, wherein the CSI feedback configuration comprises a set of channel feedback index parameters represented by a variable x _u, m that indicates whether CSI of a channel C _u, m between the UE u and a radio node m is to be predicted or fed back, and the radio node m is one of the M distributed radio nodes;

transmitting CSI of the channel C _u, m in a specific time slot when the variable x _u, m indicates CSI of the channel C _u, m is to be fed back; and

not transmitting the CSI of the channel C _u, m in the specific time slot when the variable x _u, m indicates CSI of the channel C _u, m is to be predicted.
The channel state information reporting method of claim 9, wherein channel prediction of the CSI of the channel C _u, m is performed using a p-th order Wiener linear predictor.
The channel state information reporting method of claim 9, wherein values of the channel feedback index parameters in the specific time slot are solutions of the variable x _u, m in the following optimization problem:

subject to

wherein B represents limited feedback capacity of B bits;

B _m is feedback capacity of the radio node m; and

is a mean squared-error of CSI prediction.
The channel state information reporting method of claim 11, wherein a channel vector h _u, m (t _n+1) is calculated as the CSI of the channel C _u, m the specific time slot t _n+1 according to the following formula:

wherein

ρ denotes a channel autocorrelation coefficient in the channel autocorrelation coefficients;

β _u, m denotes a large scale channel fading coefficient of the channel C _u, m in the large scale channel fading coefficients;

t _n+1 denotes a slot index at which the CSI of the channel C _u, m is predicted; and

is the CSI of the channel C _u, m predicted till the time slot t _n+1.
The channel state information reporting method of claim 12, wherein the mean squared-error of CSI prediction
is updated according to the following formula:

where tr (.) represents the trace operator; and

the star sign represents a matrix conjugate transpose operator.
A base station comprising:

a transceiver; and

a processor connected with the transceiver and configured to execute the following steps comprising:

estimating channel autocorrelation coefficients and large scale channel fading coefficients for all channels between a user equipment (UE) u and a group of M distributed radio nodes, wherein M is a positive integer;

deriving CSI feedback configuration for the UE u, wherein the CSI feedback configuration comprises a set of channel feedback index parameters represented by a variable x _u, m that indicates whether CSI of a channel C _u, m between the UE u and a radio node m is to be predicted or fed back, and the radio node m is one of the M distributed radio nodes;

transmitting the CSI feedback configuration comprising the set of channel feedback index parameters represented by a variable x _u, m to the UE u;

receiving CSI of the channel C _u, m in a specific time slot when the variable x _u, m indicates CSI of the channel C _u, m is to be fed back; and

obtaining the CSI of the channel C _u, m in the specific time slot through CSI prediction when the variable x _u, m indicates CSI of the channel C _u, m is to be predicted.
The base station of claim 14, wherein UE u is one of V UEs served by the base station through the group of M distributed radio nodes, and V is a positive integer, and the processor further executes:

deriving CSI feedback configuration for all of the V UEs;

transmitting CSI feedback configuration to all of the V UEs;

receiving CSI of channels of the V UEs that are fed back; and

predicting CSI of channels of the V UEs that are not fed back.
The base station of claim 14, wherein the channel prediction is performed using a p-th order Wiener linear predictor.
The base station of claim 14, wherein values of the channel feedback index parameters in the specific time slot are solutions of the variable x _u, _m in the following optimization problem:

subject to

wherein B represents limited feedback capacity of B bits;

B _m is feedback capacity of the radio node m; and

is a mean squared-error of CSI prediction.
The base station of claim 17, wherein a channel vector h _u, m (t _n+1) is calculated as the CSI of the channel C _u, m the specific time slot t _n+1 according to the following formula:

wherein

ρ denotes a channel autocorrelation coefficient in the channel autocorrelation coefficients;

β _u, m denotes a large scale channel fading coefficient of the channel C _u, m in the large scale channel fading coefficients;

t _n+1 denotes a slot index at which the CSI of the channel C _u, m is predicted; and

is the CSI of the channel C _u, m predicted till the time slot t _n+1.
The base station of claim 18, wherein the mean squared-error of CSI prediction
is updated according to the following formula:

where tr (.) represents the trace operator; and

the star sign represents a matrix conjugate transpose operator.
A user equipment (UE) comprising:

a transceiver; and

a processor connected with the transceiver and configured to execute the following steps comprising:

receiving CSI feedback configuration for a user equipment (UE) u, wherein the CSI feedback configuration comprises a set of channel feedback index parameters represented by a variable x _u, m that indicates whether CSI of a channel C _u, m between the UE u and a radio node m is to be predicted or fed back, and the radio node m is one of M distributed radio nodes of a base station;

transmitting CSI of the channel C _u, m in a specific time slot when the variable x _u, m indicates CSI of the channel C _u, m is to be fed back; and

not transmitting the CSI of the channel C _u, m in the specific time slot when the variable x _u, m indicates CSI of the channel C _u, m is to be predicted.
The user equipment of claim 20, wherein channel prediction of the CSI of the channel C _u, m is performed using a p-th order Wiener linear predictor.
A user equipment (UE) , comprising:

a transceiver; and

a processor connected with the transceiver and configured to execute the following steps comprising:

estimating channel autocorrelation coefficients and large scale channel fading coefficients for all channels between a user equipment (UE) u and a group of M distributed radio nodes of a base station, wherein M is a positive integer;

deriving CSI feedback configuration for the UE u, wherein the CSI feedback configuration comprises a set of channel feedback index parameters represented by a variable x _u, m that indicates whether CSI of a channel C _u, m between the UE u and a radio node m is to be predicted or fed back, and the radio node m is one of the M distributed radio nodes;

transmitting CSI of the channel C _u, m in a specific time slot when the variable x _u, m indicates CSI of the channel C _u, m is to be fed back; and

not transmitting the CSI of the channel C _u, m in the specific time slot when the variable x _u, m indicates CSI of the channel C _u, m is to be predicted.
The user equipment of claim 22, wherein channel prediction of the CSI of the channel C _u, m is performed using a p-th order Wiener linear predictor.
The user equipment of claim 22, wherein values of the channel feedback index parameters in the specific time slot are solutions of the variable x _u, m in the following optimization problem:

subject to

wherein B represents limited feedback capacity of B bits;

B _m is feedback capacity of the radio node m; and

is a mean squared-error of CSI prediction.
The user equipment of claim 24, wherein a channel vector h _u, m (t _n+1) is calculated as the CSI of the channel C _u, m the specific time slot t _n+1 according to the following formula:

wherein

ρ denotes a channel autocorrelation coefficient in the channel autocorrelation coefficients;

β _u, m denotes a large scale channel fading coefficient of the channel C _u, m in the large scale channel fading coefficients;

t _n+1 denotes a slot index at which the CSI of the channel C _u, m is predicted; and

is the CSI of the channel C _u, m predicted till the time slot t _n+1.
The user equipment of claim 25, wherein the mean squared-error of CSI prediction
is updated according to the following formula:

where tr (.) represents the trace operator; and

the star sign represents a matrix conjugate transpose operator.
A chip, comprising:

a processor, configured to call and run a computer program stored in a memory, to cause a device in which the chip is installed to execute any of the methods of claims 1 to 6.
A chip, comprising:

a processor, configured to call and run a computer program stored in a memory, to cause a device in which the chip is installed to execute any of the methods of claims 7 to 13.
A computer readable storage medium, in which a computer program is stored, wherein the computer program causes a computer to execute any of the methods of claims 1 to 6.
A computer readable storage medium, in which a computer program is stored, wherein the computer program causes a computer to execute any of the methods of claims 7 to 13.
A computer program product, comprising a computer program, wherein the computer program causes a computer to execute any of the methods of claims 1 to 6.
A computer program product, comprising a computer program, wherein the computer program causes a computer to execute any of the methods of claims 7 to 13.
A computer program, wherein the computer program causes a computer to execute any of the methods of claims 1 to 6.
A computer program, wherein the computer program causes a computer to execute any of the methods of claims 7 to 13.