EP4690692A1

EP4690692A1 - Methods and apparatus for channel aging estimation

Info

Publication number: EP4690692A1
Application number: EP23929160.2A
Authority: EP
Inventors: Yipeng ZHANG; Rui He
Original assignee: Telefonaktiebolaget LM Ericsson AB
Current assignee: Telefonaktiebolaget LM Ericsson AB
Priority date: 2023-03-28
Filing date: 2023-03-28
Publication date: 2026-02-11
Also published as: WO2024197559A1

Abstract

Methods and apparatus for channel aging estimation are provided. The method (400) may include deriving, based on channel estimations for a channel at different times, a time correlation coefficient representing a channel aging estimation of the channel for all beams formed with an antenna system including at least two antenna elements in the network node (1200).

Description

METHODS AND APPARATUS FOR CHANNEL AGING ESTIMATION

TECHNICAL FIELD

The present disclosure relates generally to the field of communication networks, and more specifically to techniques for Signal-to-Noise Ratio (SNR) -resistant channel aging estimation.

BACKGROUND

Recent technology developments have made advanced antenna systems (AAS) a viable option for large scale deployments in 4G and 5G mobile networks. AAS enables beamforming and Multi-Input Multi-Output (MIMO) techniques that are powerful tools for improving end-user experience, capacity and coverage. In other words, AAS is featured in multi-antenna techniques including beamforming and MIMO. Specifically, AAS utilizes an antenna array including a plurality of antenna elements to form beams and make it possible to steer those beams over a range of angles. When each data stream can be beamformed, spatial multiplexing, also referred to as MIMO, can be achieved to transmit multiple data streams using the same time and frequency resource.
Knowledge of the radio channels between the antennas of the user and those of the base station is a key enabler for beamforming and MIMO, both for UL reception and DL transmission. This allows the AAS to adapt the number of layers and determine how to beamform them. Channel aging, i.e., time variation of a channel, which is caused by mobility of users or user equipments (UEs) , has also been a critical issue for MIMO transmissions, which impacts validity of acquired channel information.
When a UE reports its channel information back to a network node (e.g., base station, or gNB) , or when a network node receives reciprocity channel information, the channel information would be invalid and mismatched when it is used for next PDSCH transmission, as the channel may vary a lot due to the Doppler spread caused by the mobility of the UE.
To identify UE with mobility, adjust transmission strategies or do further prediction and compensation, it is important to estimation how much a channel varies with time. Herein, time correlation coefficient is a very classic measurement, which embodies the variation of a channel with time by comparing channel information at different times. Based on the measurement of UE mobility, a network node could adopt a more proper and robust scheduling strategy to get a higher throughput.
While using time correlation coefficients to estimate/measure channel aging due to mobility is very effective way, there could be some issues when implementing such estimation in case of beamforming and/or MIMO. For example, it is difficult to calculate time correlation coefficients for AAS with multiple beams. Also, the calculation for time correlation coefficients may have a low resistance to low SNR. For some cases such as antenna switching, where channel estimation for different ports may have different channel aging levels, it is hard to calculate an integrated value for the base station when calculating precoders for PDSCH. Further, for some cases such as calculation based on Sounding Reference Signal (SRS) channel estimation, there would be random phase jump after UL-DL or DL-UL switching, resulting in that the channel estimations at two time slots have different phase differences on different frequency samples.
SUMMARY
In view of at least the above problems, embodiments of the present disclosure provide techniques for channel aging estimation that is compatible with AAS commonly having multiple beams, has a good resistance to low SNR and high delay spread, and can be implemented in 3GPP architecture with corresponding reference signals.
In some embodiments, a method of channel aging estimation in a network node is provided. The method may include deriving, based on channel estimations for a channel at different times, a time correlation coefficient representing a channel aging estimation of the channel for all beams formed with an antenna system including at least two antenna elements in the network node.
In some other embodiments, a method for scheduling signal transmission in a network node is provided. The method may include deriving, based on channel estimations for a channel at different times, a time correlation coefficient representing a channel aging estimation of the channel for all beams formed with an antenna system including at least two antenna elements in the network node; and scheduling signal transmission over the channel based on the derived time correlation coefficient.
In still other embodiments, a network node may include a processor; and a memory storing instructions that, when executed by the processor, cause the network node to perform any one of the above methods.
In still other embodiments, a network node may include: a channel estimator configured to obtain channel estimations for a channel at different times based on received Reference Signals (RSs) ; and an auto-correlation calculator including a correlation calculation unit configured to derive, based on the channel estimations from the channel estimator, a time correlation coefficient representing a channel aging estimation of the channel for all beams formed with an antenna system including at least two antenna elements in the network node.
In still other embodiments, a computer-readable storage medium having computer-readable instructions stored therein is provided. The computer-readable instructions, when executed by a processor of a network node, may configure the network node to perform any one of the above methods.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features, and advantages of the present disclosure will become apparent upon reading the following Detailed Description in view of the Drawings briefly described below.
Figure 1 shows an exemplary communication system in which embodiments of the present disclosure is applicable.
Figure 2 shows a schematic block diagram of a network node according to some embodiments of the present disclosure.
Figure 3 is a block diagram of a virtualization environment in which functions implemented by some embodiments of the present disclosure may be virtualized.
Figure 4A is a flowchart illustrating an exemplary method of channel aging estimation in a network node according to some embodiments of the present disclosure.
Figure 4B is a flowchart illustrating an exemplary method for scheduling signal transmission in a network node according to some embodiments of the present disclosure.
Figure 5 schematically shows a procedure of obtaining channel aging estimation for scheduling according to some embodiments of the present disclosure.
Figure 6 schematically shows details of channel estimator and ACF calculator according to some embodiments of the present disclosure.
Figure 7 is an exemplary flow of a procedure of obtaining channel aging estimation for scheduling according to some embodiments of the present disclosure.
Figures 8 to 12 show simulation results of time correlation coefficient comparison between the classic scheme and the proposed scheme according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

Embodiments briefly summarized above will now be described more fully with reference to the accompanying drawings. These descriptions are provided by way of example to explain the subject matter to those skilled in the art and should not be construed as limiting the scope of the subject matter to only the embodiments described herein. More specifically, examples are provided below that illustrate the operation of various embodiments according to the advantages discussed above.
Generally, all terms used herein are to be interpreted according to their ordinary meaning in the relevant technical field, unless a different meaning is clearly given and/or is implied from the context in which it is used. All references to a/an/the element, apparatus, component, means, step, etc. are to be interpreted openly as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any methods and/or procedures disclosed herein do not have to be performed in the exact order disclosed, unless a step is explicitly described as following or preceding another step and/or where it is implicit that a step must follow or precede another step. Any feature of any of the embodiments disclosed herein can be applied to any other embodiment, wherever appropriate. Likewise, any advantage of any of the embodiments can apply to any other embodiments, and vice versa. Other objects, features and advantages of the disclosed embodiments will be apparent from the following description.
Examples of network nodes are NodeB, base station (BS) , multi-standard radio (MSR) radio node such as MSR BS, eNodeB, gNodeB, MeNB, SeNB, location measurement unit (LMU) , integrated access backhaul (IAB) node, network controller, radio network controller (RNC) , base station controller (BSC) , relay, donor node controlling relay, base transceiver station (BTS) , Central Unit (e.g. in a gNB) , Distributed Unit (e.g. in a gNB) , Baseband Unit, Centralized Baseband, C-RAN, access point (AP) , transmission points, transmission nodes, transmission reception point (TRP) , RRU, RRH, nodes in distributed antenna system (DAS) , core network node (e.g. MSC, MME etc) , O&M, OSS, SON, positioning node (e.g. E-SMLC) , etc.
The non-limiting term UE refers to any type of wireless device communicating with a network node and/or with another UE in a cellular or mobile communication system. Examples of UE are target device, device to device (D2D) UE, vehicular to vehicular (V2V) , machine type UE, MTC UE or UE capable of machine to machine (M2M) communication, Reduced Capability (RedCap) UE, PDA, tablet, mobile terminals, smart phone, laptop embedded equipment (LEE) , laptop mounted equipment (LME) , USB dongles, fixed wireless access (FWA) node, customer premises equipment (CPE) , integrated access backhaul mobile terminal (IAB-MT) , network-controlled repeater mobile terminal (NCR-MT) etc.
The term “radio access technology” , or RAT, may refer to any RAT e.g. UTRA, E-UTRA, narrow band internet of things (NB-IoT) , WiFi, Bluetooth, next generation RAT, New Radio (NR) , 4G, 5G, etc. Any of the equipment denoted by the term node, network node or radio network node may be capable of supporting a single or multiple RATs.
The term “signal” or “radio signal” used herein may be any physical signal or physical channel. Examples of DL physical signals are reference signal (RS) such as PSS, SSS, CSI-RS, DMRS signals in SS/PBCH block (SSB) , discovery reference signal (DRS) , CRS, PRS etc. Examples of UL physical signals are reference signal such as SRS, DMRS etc. The term physical channel refers to any channel carrying higher layer information e.g. data, control etc. Examples of physical channels are PBCH, NPBCH, PDCCH, PDSCH, sPUCCH, sPDSCH, sPUCCH, sPUSCH, MPDCCH, NPDCCH, NPDSCH, E-PDCCH, PUSCH, PUCCH, NPUSCH etc.
Figure 1 shows an example of a communication system 1000 in which some embodiments of the present disclosure may be applied. As shown in Figure 1, the communication system 1000 includes a telecommunication network 1002 that includes an access network 1004, such as a radio access network (RAN) , and a core network 1006, which includes one or more core network nodes 1008. The access network 1004 includes one or more access network nodes, such as network nodes 1010A and 1010B (one or more of which may be generally referred to as network nodes 1010) , or any other 3GPP access node or non-3GPP access point. The network nodes 1010 facilitate direct or indirect connection of UE, such as by connecting UEs 1012A, 1012B, 1012C and 1012D (one or more of which may be generally referred to as UEs 1012) to the core network 1006 over one or more wireless connections.
Example wireless communications over a wireless connection include transmitting and/or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and/or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 1000 may include any number of wired or wireless networks, network nodes, UEs, and/or any other components or systems that may facilitate or participate in the communication of data and/or signals whether via wired or wireless connections. The communication system 1000 may include and/or interface with any type of communication, telecommunication, data, cellular, radio network, and/or other similar type of system.
The UEs 1012 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and/or operable to communicate wirelessly with the network nodes 1010 and other communication devices. Similarly, the network nodes 1010 are arranged, capable, configured, and/or operable to communicate directly or indirectly with the UEs 1012 and/or with other network nodes or equipment in the telecommunication network 1002 to enable and/or provide network access, such as wireless network access, and/or to perform other functions, such as administration in the telecommunication network 1002.
In the depicted example, the core network 1006 connects the network nodes 1010 to one or more hosts, such as host 1016. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 1006 includes one more core network nodes (e.g., core network node 1008) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and/or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 1008. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC) , Mobility Management Entity (MME) , Home Subscriber Server (HSS) , Access and Mobility Management Function (AMF) , Session Management Function (SMF) , Authentication Server Function (AUSF) , Subscription Identifier De-concealing function (SIDF) , Unified Data Management (UDM) , Security Edge Protection Proxy (SEPP) , Network Exposure Function (NEF) , and/or a User Plane Function (UPF) .
The host 1016 may be under the ownership or control of a service provider other than an operator or provider of the access network 1004 and/or the telecommunication network 1002, and may be operated by the service provider or on behalf of the service provider. The host 1016 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio/video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.
As a whole, the communication system 1000 of Figure 1 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM) ; Universal Mobile Telecommunications System (UMTS) ; Long Term Evolution (LTE) , and/or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G) ; wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802. 11 standards (WiFi) ; and/or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax) , Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and/or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.
In some examples, the telecommunication network 1002 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 1002 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 1002. For example, the telecommunications network 1002 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and/or Massive Machine Type Communication (mMTC) /Massive IoT services to yet further UEs.
In some examples, the UEs 1012 are configured to transmit and/or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 1004 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 1004. Additionally, a UE may be configured for operating in single-RAT or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e., being configured for multi-radio dual connectivity (MR-DC) , such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio -Dual Connectivity (EN-DC) .
In the example, the hub 1014 communicates with the access network 1004 to facilitate indirect communication between one or more UEs (e.g., UE 1012c and/or 1012d) and network nodes (e.g., network node 1010b) . In some examples, the hub 1014 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 1014 may be a broadband router enabling access to the core network 1006 for the UEs. As another example, the hub 1014 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 1010, or by executable code, script, process, or other instructions in the hub 1014. As another example, the hub 1014 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 1014 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 1014 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 1014 then provides to the UE either directly, after performing local processing, and/or after adding additional local content. In still another example, the hub 1014 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy IoT devices.
The hub 1014 may have a constant/persistent or intermittent connection to the network node 1010b. The hub 1014 may also allow for a different communication scheme and/or schedule between the hub 1014 and UEs (e.g., UE 1012c and/or 1012d) , and between the hub 1014 and the core network 1006. In other examples, the hub 1014 is connected to the core network 1006 and/or one or more UEs via a wired connection. Moreover, the hub 1014 may be configured to connect to an M2M service provider over the access network 1004 and/or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 1010 while still connected via the hub 1014 via a wired or wireless connection. In some embodiments, the hub 1014 may be a dedicated hub -that is, a hub whose primary function is to route communications to/from the UEs from/to the network node 1010b. In other embodiments, the hub 1014 may be a non-dedicated hub -that is, a device which is capable of operating to route communications between the UEs and network node 1010b, but which is additionally capable of operating as a communication start and/or end point for certain data channels.
Figure 2 shows a network node 1200 in accordance with some embodiments of the present disclosure. The network node may refer to equipment capable, configured, arranged and/or operable to communicate directly or indirectly with a UE and/or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points) , base stations (BSs) (e.g., radio base stations, NodeBs, evolved NodeBs (eNBs) , and NR NodeBs (gNBs) ) .
Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units and/or remote radio units (RRUs) , sometimes referred to as Remote Radio Heads (RRHs) . Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS) .
Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs) , base transceiver stations (BTSs) , transmission points, transmission nodes, multi-cell/multicast coordination entities (MCEs) , Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs) ) , and/or Minimization of Drive Tests (MDTs) .
As shown in Figure 2, the network node 1200 includes a processing circuitry 1202, a memory 1204, a communication interface 1206, and a power source 1208. The network node 1200 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc. ) , which may each have their own respective components. In certain scenarios in which the network node 1200 comprises multiple separate components (e.g., BTS and BSC components) , one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 1200 may be configured to support multiple radio access technologies (RATs) . In such embodiments, some components may be duplicated (e.g., separate memory 1204 for different RATs) and some components may be reused (e.g., a same antenna 1210 may be shared by different RATs) . The network node 1200 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1200, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 1200.
The processing circuitry 1202 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and/or encoded logic operable to provide, either alone or in conjunction with other network node 1200 components, such as the memory 1204, to provide network node 1200 functionality.
In some embodiments, the processing circuitry 1202 includes a system on a chip (SOC) . In some embodiments, the processing circuitry 1202 includes one or more of radio frequency (RF) transceiver circuitry 1212 and baseband processing circuitry 1214. In some embodiments, the radio frequency (RF) transceiver circuitry 1212 and the baseband processing circuitry 1214 may be on separate chips (or sets of chips) , boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 1212 and baseband processing circuitry 1214 may be on the same chip or set of chips, boards, or units.
The memory 1204 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM) , read-only memory (ROM) , mass storage media (for example, a hard disk) , removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD) ) , and/or any other volatile or non-volatile, non-transitory device-readable and/or computer-executable memory devices that store information, data, and/or instructions that may be used by the processing circuitry 1202. The memory 1204 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and/or other instructions capable of being executed by the processing circuitry 1202 and utilized by the network node 1200. The memory 1204 may be used to store any calculations made by the processing circuitry 1202 and/or any data received via the communication interface 1206. In some embodiments, the processing circuitry 1202 and memory 1204 are integrated.
The communication interface 1206 may be used in wired or wireless communication of signaling and/or data between a network node, access network, and/or UE. As illustrated, the communication interface 1206 comprises port (s) /terminal (s) 1216 to send and receive data, for example to and from a network over a wired connection. The communication interface 1206 also includes radio front-end circuitry 1218 that may be coupled to, or in certain embodiments a part of, the antenna 1210. Radio front-end circuitry 1218 comprises filters 1220 and amplifiers 1222. The radio front-end circuitry 1218 may be connected to the antenna 1210 and processing circuitry 1202. The radio front-end circuitry 1218 may be configured to condition signals communicated between the antenna 1210 and processing circuitry 1202. The radio front-end circuitry 1218 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 1218 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 1220 and/or amplifiers 1222. The radio signal may then be transmitted via the antenna 1210. Similarly, when receiving data, the antenna 1210 may collect radio signals which are then converted into digital data by the radio front-end circuitry 1218. The digital data may be passed to the processing circuitry 1202. In other embodiments, the communication interface 1216 may comprise different components and/or different combinations of components.
In certain alternative embodiments, the network node 1200 does not include separate radio front-end circuitry 1218, instead, the processing circuitry 1202 includes radio front-end circuitry and is connected to the antenna 1210. Similarly, in some embodiments, all or some of the RF transceiver circuitry 1212 is part of the communication interface 1206. In still other embodiments, the communication interface 1206 includes one or more ports or terminals 1216, the radio front-end circuitry 1218, and the RF transceiver circuitry 1212, as part of a radio unit (not shown) , and the communication interface 1206 communicates with the baseband processing circuitry 1214, which is part of a digital unit (not shown) .
The antenna 1210 may include one or more antennas, or antenna arrays, configured to send and/or receive wireless signals. The antenna 1210 may be coupled to the radio front-end circuitry 1218 and may be any type of antenna capable of transmitting and receiving data and/or signals wirelessly. In certain embodiments, the antenna 1210 is separate from the network node 1200 and connectable to the network node 1200 through an interface or port. In some embodiments, the antenna 1210 may include AAS having an antenna array of a plurality of antenna elements.
The antenna 1210, the communication interface 1206, and/or the processing circuitry 1202 may be configured to perform any receiving operations and/or certain obtaining operations described herein as being performed by the network node. Any information, data and/or signals may be received from a UE, another network node and/or any other network equipment. Similarly, the antenna 1210, the communication interface 1206, and/or the processing circuitry 1202 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and/or signals may be transmitted to a UE, another network node and/or any other network equipment.
The power source 1208 provides power to the various components of network node 1200 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component) . The power source 1208 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 1200 with power for performing the functionality described herein. For example, the network node 1200 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 1208. As a further example, the power source 1208 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.
Embodiments of the network node 1200 may include additional components beyond those shown in Figure 2 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and/or any functionality necessary to support the subject matter described herein. For example, the network node 1200 may include user interface equipment to allow input of information into the network node 1200 and to allow output of information from the network node 1200. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 1200.
Figure 3 is a block diagram illustrating a virtualization environment 1400 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 1400 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host) , then the node may be entirely virtualized.
Applications 1402 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc. ) are run in the virtualization environment 1400 to implement some of the features, functions, and/or benefits of some of the embodiments disclosed herein.
Hardware 1404 includes processing circuitry, memory that stores software and/or instructions executable by hardware processing circuitry, and/or other hardware devices as described herein, such as a network interface, input/output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 1406 (also referred to as hypervisors or virtual machine monitors (VMMs) ) , provide VMs 1408A and 1408B (one or more of which may be generally referred to as VMs 1408) , and/or perform any of the functions, features and/or benefits described in relation with some embodiments described herein. The virtualization layer 1406 may present a virtual operating platform that appears like networking hardware to the VMs 1408.
The VMs 1408 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 1406. Different embodiments of the instance of a virtual appliance 1402 may be implemented on one or more of VMs 1408, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV) . NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.
In the context of NFV, a VM 1408 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 1408, and that part of hardware 1404 that executes that VM, be it hardware dedicated to that VM and/or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 1408 on top of the hardware 1404 and corresponds to the application 1402.
Hardware 1404 may be implemented in a standalone network node with generic or specific components. Hardware 1404 may implement some functions via virtualization. Alternatively, hardware 1404 may be part of a larger cluster of hardware (e.g., such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 1410, which, among others, oversees lifecycle management of applications 1402. In some embodiments, hardware 1404 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 1412 which may alternatively be used for communication between hardware nodes and radio units.
In various embodiments, virtualization environment 1400 may be configured to host various network functions (NFs) and application functions (AFs) . In other words, these NFs and AFs can be implemented in respective virtual nodes 1402 based on underlying hardware 1404.
Embodiments of the present disclosure will be described below to provide methods and apparatuses for channel aging estimation. Figure 4A is a flowchart illustrating an exemplary method 400 of channel aging estimation in a network node according to some embodiments of the present disclosure. The method 400 may include an operation S402 of deriving, based on channel estimations for a channel at different times, a time correlation coefficient representing a channel aging estimation of the channel for all beams formed with an antenna system including at least two antenna elements in the network node. In an example, the antenna system, such as AAS, may utilize an antenna array including a plurality of antenna elements to form multiple beams. In some embodiments, the time correlation coefficient may be derived for each subcarrier and each polarization of the antenna system, by calculating a correlation of the channel estimations for each beam, and averaging the calculated correlations over all the beams to obtain the time correlation coefficient. In an example, the calculated correlations may be averaged over all the beams by getting a Euclidean norm of each of the channel estimations over all the beams, and dividing a sum of the calculated correlations by a product of the Euclidean norms of the channel estimations. In a more specific example, the time correlation coefficients may be each calculated as an absolute value for each subcarrier and each polarization separately by using Equation (1) :
whereindicating the norm-2 or Euclidean norm ofis channel estimation after denoising for thei-th beam, is the number of beams per polarization, means channel estimations of all the beams at the scs-th subcarrier and the pol-th polarization, denotes absolute ofandanddenotes channel estimations for different times t1 and t2. Here, denotes conjugate transpose for
By deriving the time correlation coefficients for all the beams and averaging them over all frequency samples, it is possible to be compatible with the antenna system, for example, AAS systems, which commonly have multiple beams, thereby making it compatible with realistic systems based on 3GPP. It also is possible to remove the impact of random phase jumps caused by DL-UL/UL-DL switch and the impact of higher delay spread.
The derived time correlation coefficients may be used for scheduling signal/data transmission over the estimated channel, as shown in Figure 4B. Figure 4B is a flowchart illustrating an exemplary method 400’ for scheduling signal transmission in a network node according to some embodiments of the present disclosure. The method 400’ may include an operation S404 of deriving, based on channel estimations for a channel at different times, a time correlation coefficient representing a channel aging estimation of the channel for all beams formed with an antenna system including at least two antenna elements in the network node. This operation S404 is the same as the operation S402 of Figure 4A. The method 400’ may further include an operation S406 of scheduling signal transmission over the channel based on the derived time correlation coefficient.
In some embodiments, before using the time correlation coefficient to perform scheduling in the network node, the method 400 (and/or 400’) may further include an operation of correcting the time correlation coefficient with a correction factor based on noise power for the channel, in case of low-SNR correction. For example, when low-SNR enhancement is set or needed as the calculation of the time correlation coefficient has a low resistance to low SNR, which may lead to poor performance in channel estimation and data transmission scheduling, the time correlation coefficient may be modified to remove the impact of noise. In such embodiments, the correction factor may be calculated for each subcarrier and each polarization of the antenna system, based on the noise power for the channel and the channel estimations at different times. Here, the channel estimations may include last channel estimation at a previous time t1 and current channel estimation at a current time t2, and the noise power for the channel may be a noise power obtained for the last channel estimation at the previous time t1. Here, the noise power may be the estimated noise power obtained after channel estimation, more specifically, estimated noise power per beam per polarization obtained after window filtering. On the other hand, for those channel estimators based on a filtering window, such as channel estimator with a DCT-based denoising filter, it is preferred to calculate an effective noise power before filtered. In an example, for each subcarrier and each polarization of the antenna system, the effective noise power may be calculated per beam based on the estimated noise power per beam per polarization obtained after window filtering, the number of received reference signals (RSs) in the frequency domain, a size of a received pilot after match filtering (shown in Figure 6) , and a window length for the window filtering. Here, the size of the pilot and the window length may logged when applying the window filter for channel estimation, in case of the low-SNR correction is set or needed. In a more specific example, the effective noise power per beam may be calculated by dividing a product of the estimated noise power per beam per polarization and the window length by a product of the number of reference signals and a difference between the size of the pilot and the window length. An exemplary Equation (2) for calculating the effective noise powermay be as follows:
where σ² (beam, pol) is the estimated noise power after window filtering per beam per polarization for the last channel estimation, N_sc is the number of RSs in the frequency domain, N_full is the size of the pilot after match filter, and N_opt is filter window length.
With the effective noise powerthe correction factor may be calculated by using Equation (3) :
where η (pol, scs) denotes the correction factor per polarization per each subcarrier, denotes the effective noise power per beam per polarization per each subcarrier for the last channel estimation, i denotes the beam, denotes the number of the beams, denotes a sum of the effective noise power for all the beams, anddenote the channel estimations at different times t1 and t2, andwherein denotes an absolute of
Then, the time correlation coefficients for two polarizations may be corrected with corresponding correction factors, and may be further modified by combining the corrected time correlation coefficients for both polarizations, and averaging the combination result over all subcarriers. An exemplary Equation (4) for such correction and modification may be as follows:
where R denotes the corrected and modified time correlation coefficient, Nscs denotes the number of the subcarriers in the estimated channel, i denotes the i-th subcarrier, j denotes the j-th polarization, denotes channel estimation for the current time t2, denotes the derived time correlation coefficient for the j-th polarization and the i-th subcarrier as in Equation (1) , and η (j, i) denotes the correction factor for the j-th polarization and the i-th subcarrier as in Equation (3) .
In some other embodiments where low SNR correction is not set or needed, the method 400 (and/or 400’) may further include modifying the derived time correlation coefficients by combining the time correlation coefficients for both polarizations of the antenna system, and averaging the combination result over all subcarriers, for example by using the Equation (4) without the correct factor.
In some embodiments, the method 400 (and/or 400’) may further include an operation of performing a memory update by storing, in a memory, the modified time correlation coefficient in place of last time correlation coefficient previously stored. For example the time correlation coefficient R for the channel estimation at the current time may be stored in memory to replace the time correlation coefficient for the channel estimation at the previous time.
In some embodiments, the method 400 (and/or 400’) may further include an operation of applying memory filtering to the modified time correlation coefficient based on the last time correlation coefficient derived for the channel at a previous time, in case of memory filter. For example, when the memory filter is set or needed, the modified time correlation coefficient above mentioned may be further manipulated taking into account the last time correlation coefficient stored in the memory, for example, by calculating a weighted sum of the modified time correlation coefficient and the last time correlation coefficient. An exemplary Equation (5) for such memory filtering may be as follows:
R′=βR + (1 -β) R_last (5)
where R is obtained as in Equation (4) , R_last denotes the last time correlation coefficient, and β is a memory factor between 0 and 1. More specifically, when memory filter is off, β=1.
Equations (4) and (5) may be combined as follows:
Also, the memory update may be performed to store memory-filtered time correlation coefficient R′. For sounding reference signal (SRS) , symbol 1 is the last channel estimation with a memory filter, and symbol 2 is the current channel estimation. The memory update may be performed after calculation of the time correlation coefficient.
With the correction and modification in case of low SNR enhancement, the impact of noise may be removed or reduced, and the resultant time correlation coefficient can have a high resistance to low SNR, and can be consequently used for scheduling as in the operation S406.
In some embodiments, before using the time correlation coefficient to perform scheduling in the network node, the method 400 (and/or 400’) may further include an operation of calculating a port correlation coefficient for each of the ports based on the derived time correlation coefficient, and integrating the port correlation coefficients for all the ports to obtain an equivalent time correlation coefficient to represent the channel aging estimation, in case of multiple ports with antenna switching. For example, there are some cases such as antenna switching, where channel estimation for different ports may have different channel aging levels, and it is thus hard to calculate an integrated value for the network node when calculating precoders for PDSCH. However, with the above operation, it is possible to integrate the time correlation coefficients for different times as an equivalent value, when antenna switching is used with multiple different ports (where channel information for different ports may arrives at the network node at different times and only a part of ports of channel information arrives at one time) . In this operation, the port correlation coefficient for each of the ports may be obtained by calculating an effective Doppler spread for the corresponding port based on the derived time correlation coefficient and a period of Reference Signal (RS) received at the corresponding port, and calculating the port correlation coefficient based on the effective Doppler spread and a time interval between a reception time of the RS at the corresponding port and a time for next scheduled downlink transmission. In an example, for port i whose time correlation coefficient has been calculated before a downlink (DL) slot, the effective Doppler spreadmay be calculated by using Equation (7) with Jake’s model J₀:
where T_srs is the period of SRS received at the port i, and R denotes the above calculated R or R’. |R| denotes an absolute of R.
Then, for the DL slot, the port correlation coefficient or its absolute for the port i may be calculated by using Equation (8) with Jake’s model J₀:
where T_i is the time interval between SRS reception at the port i and DL transmission slot.
Next, the time correlation coefficients for all the ports may be integrated by averaging the port correlation coefficients over all the ports, for example, dividing a sum of the port correlation coefficients by the number of the ports. More specifically, the equivalent time correlation coefficient for channel aging may be obtained by averaging over all the ports as in Equation (9) :
where N_ports denotes the number of ports, and i is a integer from 0 to N_ports -1.
The equivalent time correlation coefficient |α| may be consequently used for scheduling.
Hereafter, a procedure of obtaining channel aging estimation for scheduling according to some embodiments of the present disclosure will be described with reference to Figures 5 to 7. Figure 5 schematically shows a modularized process of channel aging estimation according to some embodiments of the present disclosure, which may include blocks of OFDM demodulation 502, beam space processing 504, reference signal extraction 506, channel estimation 508, auto-correlation function (ACF) estimation 510 and scheduling 512. Figure 6 schematically shows details of channel estimator 602 corresponding to the channel estimation block 508 and the auto-correlation calculator 604 corresponding to the ACF estimation block 510 according to some embodiments of the present disclosure. The dash-line blocks in Figure 6 denote optional modules including blocks of transformation 6022, detransformation 6026, correction factor calculation 6040, correction and memory filter 6044, and equivalence filter 6046. Figure 7 shows an exemplary flow of a procedure of obtaining channel aging estimation for scheduling according to some embodiments of the present disclosure.
The OFDM demodulation block 502 may demodulate orthogonal frequency division multiplexing (OFDM) symbols from received signals through an antenna array based on OFDM parameters, e.g., FFT length, CP length, and the number of right and left guard subcarriers. This block may supports 5G new radio (NR) standard, long term evolution (LTE) , wireless local area network (WLAN) , WiMAX, digital video broadcast (DVB) , digital audio broadcast (DAB) standards, etc. The beam space processing block 504 may perform spatial processing on the demodulated OFDM symbols to obtain a plurality of spatially separated beams. The reference signal extraction 506 may extract reference signals from the demodulated OFDM symbols for the plurality of beams, shown as step 702 of Figure 7, and provide the extracted reference signals to the channel estimation block 508 as shown in Figure 6. The channel estimation block 508 may perform channel estimation using the reference signals. The channel estimator 602 may include blocks of match filter 6020, transformation 6022, window filtering 6024, detransformation 6026 and memory 6028. At the match filter block 6020, the reference signals may be applied with a match filter to get raw estimation. This is also shown in step 704 of Figure 7. Then at the window filtering block 6024, the raw estimation may be applied with a window filter for more accurate estimation. This is also shown in step 706 of Figure 7. If the window filter is a Discrete Cosine Transform (DCT) -based denoising filter, the raw estimation needs to be processed at the transformation block 6022 to obtain a DCT-transformed estimation. In this case, the estimation after the window filtering needs to be detransformed at the detransformation block 6026. Here it is noted that the transformation and detransformation may be optional depending on the algorithm implemented in the channel estimator 602. Also, as shown in step 708, post-processing may be applied to the estimation after window filtering, depending on the algorithm implemented in the channel estimator 602 and get current channel estimation. The current channel estimation and the last channel estimation stored in the memory block 6028 may be outputted to the auto-correlation calculator 604 as shown in Figure 6, while the current channel estimation may be stored in the memory block 6028. This is also shown in step 710 of Figure 7.
The auto-correlation calculator 604 (corresponding to the ACF estimation block 510) may calculate/estimate and output an ACF factor to the scheduler corresponding to the scheduling block 512 for future utilization. Here, the ACF factor corresponds to time correlation coefficients representing channel aging. According to some embodiments of the present disclosure, the auto-correlation calculator 604 may include blocks of correction factor calculation 6040, correlation calculation 6042, correction and memory filter 6044, equivalence filter 6046 and memory 6048.
The auto-correlation calculator 604 may calculate, at the correlation calculation block 6042, a preliminary ACF factor for each subcarrier and each polarization with respect to all beams formed by the antenna system including a plurality of antenna elements. This is also shown in step 712 of Figure 7, where the preliminary ACF factor may be calculated for each subcarrier and each polarization based on the current and last channels estimations from the channel estimator 602. For more details, reference may be made to the operation S402 or S404 in Figure 4A or 4B depicting calculation of time correlation coefficients.
At step 714, it may be determined whether low-SNR correction is set or needed. If the low-SNR correction is set or needed, the auto-correlation calculator 604 may correct, at the correction and memory filter 6044, the preliminary ACF factor or time correlation coefficient using a correction factor based on noise power, for improving performance of estimation at low SNR. Here, the correction factor may be calculated at the correction factor calculation block 6040, which is also shown in step 716 of Figure 7. There may be also a step 718 of logging, in case of low-SNR correction being set or needed, the full size of pilots and the window length of the filtering window, for calculation of the correction factor at step 716. The correction factor calculation block 6040 may calculate the correction factor and output it to the correction and memory filter 6044. The calculation of the correction factor may include calculating equivalent noise power based on the estimated noise power, and the logged pilot size and window length, and then calculating the correction factor based on the equivalent noise power and the last and current channel estimations from the channel estimator 602. Although the step 714 of determining whether low-SNR correction is set or needed is depicted repeatedly in Figure 7, it may be just for better and easier illustration, and it may be understood as a single step.
If low-SNR correction is not set or needed in step 714, the flow may go to step 720 of determining whether memory filter is set or needed. If memory filtered is not set or needed, the flow may further go to step 724 where a final ACF factor is calculated by weighting the preliminary ACF factors over both polarizations and averaging over all the subcarriers. This calculation may be performed at the correlation calculation block 6042, for example, by using Equation (4) without the correct factor.
On the other hand, if low-SNR correction is on while the memory filter is off, the flow may go from step 716 to step 724 where the optional low-SNR correction may be applied to the preliminary ACF factors by using with the correction factor from the correction factor calculation block 6040, and then the corrected preliminary ACF factors may be combined and averaged over the polarizations and subcarriers to obtain the final ACF factor. This may be performed at the correction and memory filter 6044, for example, by using Equation (4) .
In some embodiments where both of low-SNR correction and memory filter are set or needed, the flow may further go to step 722 of outputting the last ACF factor from memory (e.g., memory block 6048) to the correction and memory filter 6044. Then, at step 724, the preliminary ACF factors may be combined and averaged over the polarizations and subcarriers, with the optional low-SNR correction and memory filter being applied, for example, by using Equations (4) and (5) or by using Equation (6) to obtain the final ACF factor.
The above obtained final ACF factor may be stored in memory to replace the previous ACF factor.
Then the flow may go to step 726 of determining whether antenna switching with multiple ports are used or set. If the answer is no, the flow may go to step 730 where the final ACF factor may be provided to the scheduler for further utilization. If the answer is yes, the flow may go to step 728 of getting an equivalent ACF factor. Specifically, if there are multi-ports with antenna switching, especially when different ports of reference signals are scheduled in different time slots, the final ACT factor may be updated more accurately as an equivalent ACF factor by deriving the effective Doppler spread using the Jake’s model, calculating port correlation coefficients for all the ports based on the effective Doppler spread, and averaging the port correlation coefficients over all the ports. This may be performed at the equivalence filter block 6046, for example, by using Equations (7) to (9) . In this case, the equivalent ACF factor may be outputted from the equivalence filter block 6046 to the scheduler for further utilization at step 730.
Figures 8 to 12 show simulation results of time correlation coefficient comparison between the classic scheme and the proposed scheme according to some embodiments of the present disclosure. We have implemented the proposed solutions in the link simulator falcon. To illustrate the performance enhancement of the proposed solutions, we show the uplink (UL) correlation coefficients averaged over 500 frames in the following simulations. Table 1 lists parameters for the simulations.
Table 1
To embody advantages of the proposed solutions, we have a baseline method where time correlation coefficients are calculated per frequency sample and averaged over all frequency samples.
Low SNR Enhancement
Figure 8 shows UL averaging time correlation coefficients comparison between the classic scheme (broken lines denoted as 1b, 2b, 3b and 4b) and the proposed scheme (solid lines denoted as 1a, 2a, 3a and 4a) with CDL-C and 30 ns delay spread. Figure 9 shows UL averaging time correlation coefficients comparison between the classic scheme (broken lines denoted as 1b, 2b, 3b and 4b) and the proposed scheme (solid lines denoted as 1a, 2a, 3a and 4a) with CDL-E and 30 ns delay spread.
As is shown in Figures 8 and 9, there is a working SNR range for the classic scheme (broken lines) , which performance degrades badly at low SNR, and arrives at a stable value at 30dB DL SNR (4dB UL SNR) . The proposed scheme (solid lines) has a much better resistance to low SNR. At an ultra-low SNR, such -10 dB DL SNR, both of the two schemes can’t work well but the proposed scheme looks better.
Resistance to Frequency Selectivity (high delay spread)
Figure 10 shows UL averaging time correlation coefficients comparison between the classic scheme (broken lines) and the proposed scheme (solid lines) , without low SNR enhancement under CDL-C and 30 ns delay spread.
Figure 11 shows UL averaging time correlation coefficients comparison between the classic scheme (broken lines) and the proposed scheme (solid lines) , without low SNR enhancement under CDL-C and 100 ns delay spread.
Figure 12 shows UL averaging time correlation coefficients comparison between the classic scheme (broken lines) and the proposed scheme (solid lines) , without low SNR enhancement under CDL-C and 300 ns delay spread.
As shown in Figures 10 to 12, the UL averaging time correlation coefficients comparison between the classic scheme (broken lines) and the proposed scheme (solid lines) are given without low SNR enhancement and under CDL-C with different delay spreads. It is obvious that, for a certain value of speed, the proposed scheme gets the similar results under different delay spreads, while the classic scheme degrades for higher delay spread, which means the proposed scheme has a better resistance to higher delay spread (frequency selectivity) .
So far, embodiments of the present disclosure have been described to provide SNR-resistant channel aging estimation in the network node, which is compatible with AAS systems with multiple beams, has a good resistance to low SNR and high delay spread, and can be implemented in a 3GPP architecture with corresponding reference signals. The foregoing merely illustrates the principles of the disclosure. Various modifications and alterations to the described embodiments will be apparent to those skilled in the art in view of the teachings herein. It will thus be appreciated that those skilled in the art will be able to devise numerous systems, arrangements, and procedures that, although not explicitly shown or described herein, embody the principles of the disclosure and can be thus within the spirit and scope of the disclosure. Various embodiments can be used together with one another, as well as interchangeably therewith, as should be understood by those having ordinary skill in the art.
The term unit, as used herein, can have conventional meaning in the field of electronics, electrical devices and/or electronic devices and can include, for example, electrical and/or electronic circuitry, devices, modules, processors, memories, logic solid state and/or discrete devices, computer programs or instructions for carrying out respective tasks, procedures, computations, outputs, and/or displaying functions, etc., such as those that are described herein.
Any appropriate steps, methods, features, functions, or benefits disclosed herein may be performed through one or more functional units or modules of one or more virtual apparatuses. Each virtual apparatus may comprise a number of these functional units. These functional units may be implemented via processing circuitry, which may include one or more microprocessor or microcontrollers, as well as other digital hardware, which may include Digital Signal Processor (DSPs) , special-purpose digital logic, and the like. The processing circuitry may be configured to execute program code stored in memory, which may include one or several types of memory such as Read Only Memory (ROM) , Random Access Memory (RAM) , cache memory, flash memory devices, optical storage devices, etc. Program code stored in memory includes program instructions for executing one or more telecommunications and/or data communications protocols as well as instructions for carrying out one or more of the techniques described herein. In some implementations, the processing circuitry may be used to cause the respective functional unit to perform corresponding functions according one or more embodiments of the present disclosure.
As described herein, device and/or apparatus can be represented by a semiconductor chip, a chipset, or a (hardware) module comprising such chip or chipset; this, however, does not exclude the possibility that a functionality of a device or apparatus, instead of being hardware implemented, be implemented as a software module such as a computer program or a computer program product comprising executable software code portions for execution or being run on a processor. Furthermore, functionality of a device or apparatus can be implemented by any combination of hardware and software. A device or apparatus can also be regarded as an assembly of multiple devices and/or apparatuses, whether functionally in cooperation with or independently of each other. Moreover, devices and apparatuses can be implemented in a distributed fashion throughout a system, so long as the functionality of the device or apparatus is preserved. Such and similar principles are considered as known to a skilled person.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms used herein should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
In addition, certain terms used in the present disclosure, including the specification and drawings, can be used synonymously in certain instances (e.g., “data” and “information” ) . It should be understood, that although these terms (and/or other terms that can be synonymous to one another) can be used synonymously herein, there can be instances when such words can be intended to not be used synonymously. Further, to the extent that the prior art knowledge has not been explicitly incorporated by reference herein above, it is explicitly incorporated herein in its entirety. All publications referenced are incorporated herein by reference in their entireties.
Abbreviation Explanation
3GPP 3rd Generation Partnership Project
AAS Advance antenna system
ACF Auto-correlation function
CDL Clustered Delay Line
DCT Discrete cosine transform
DL Downlink
gNB gNodeB, the new base station in 5G
LOS Line of sight
NLOS Non line of sight
SNR Signal-to-Noise Ratio
SRS Sounding reference signal
UE User Equipment
UL Uplink

Claims

A method (400) of channel aging estimation in a network node (1200) , the method (400) comprising:

deriving (S402) , based on channel estimations for a channel at different times, a time correlation coefficient representing a channel aging estimation of the channel for all beams formed with an antenna system including at least two antenna elements in the network node (1200) .
The method (400) of claim 1, wherein deriving the time correlation coefficient comprises:

for each subcarrier and each polarization of the antenna system,

calculating a correlation of the channel estimations for each beam, and

averaging the calculated correlations over all the beams to obtain the time correlation coefficient.
The method (400) of claim 2, wherein averaging the calculated correlations over all the beams comprises:

getting a Euclidean norm of each of the channel estimations over all the beams, and

dividing a sum of the calculated correlations by a product of the Euclidean norms of the channel estimations.
The method (400) of any of claims 1 to 3, further comprising:

in case of a low signal-to-noise ratio (SNR) correction, correcting the time correlation coefficient with a correction factor based on noise power for the channel.
The method (400) of claim 4, further comprising:

in case of the low SNR correction, calculating the correction factor for each subcarrier and each polarization of the antenna system, based on the noise power for the channel and the channel estimations at different times.
The method (400) of claim 5, wherein the channel estimations includes last channel estimation at a previous time and current channel estimation at a current time,

wherein the noise power for the channel is a noise power obtained for the last channel estimation at the previous time, and

the noise power includes one of:

estimated noise power obtained after window filtering; or

effective noise power before window filtering.
The method (400) of claim 6, wherein for each subcarrier and each polarization of the antenna system, the effective noise power is calculated per beam based on estimated noise power per beam per polarization obtained after window filtering, a number of received reference signals, a size of a received pilot after match filtering, and a window length for the window filtering.
The method (400) of claim 7, wherein the effective noise power per beam is calculated by dividing a product of the estimated noise power per beam per polarization and the window length by a product of the number of reference signals and a difference between the size of the pilot and the window length.
The method (400) of claim 7 or 8, further comprising:

logging the size of the pilot and the window length in case of the low SNR correction.
The method of any of claims 6 to 9, wherein the correction factor is calculated as:

wherein η (pol, scs) denotes the correction factor per polarization per each subcarrier, denotes the effective noise power per beam per polarization per each subcarrier, i denotes the beam, denotes the number of the beams, denotes a sum of the effective noise power for all the beams, anddenote the channel estimations at different times, and normwhereindenotes absolute of
The method (400) of any of claims 4 to 10, further comprising:

modifying the corrected time correlation coefficients by combining the corrected time correlation coefficients for both polarizations of the antenna system, and averaging the combination result over all subcarriers.
The method (400) of any of claims 1 to 3, further comprising:

in case of no low signal-to-noise ratio (SNR) correction,

modifying the time correlation coefficients by combining the time correlation coefficients for both polarizations of the antenna system, and

averaging the combination result over all subcarriers.
The method (400) of claim 11 or 12, further comprising:

in case of memory filter, applying memory filtering to the modified time correlation coefficient based on last time correlation coefficient derived for the channel at a previous time.
The method (400) of claim 13, wherein applying memory filtering to the modified time correlation coefficient comprises:

calculating a weighted sum of the modified time correlation coefficient and the last time correlation coefficient.
The method (400) of any of claims 4 to 14, further comprising:

performing a memory update by storing the modified time correlation coefficient in place of last time correlation coefficient previously stored.
The method (400) of any of claims 1 to 15, further comprising:

in case of multiple ports with antenna switching,

calculating a port correlation coefficient for each of the ports based on the derived time correlation coefficient, and

integrating the port correlation coefficients for all the ports to obtain an equivalent time correlation coefficient to represent the channel aging estimation.
The method (400) of claim 16, wherein calculating the port correlation coefficient for each of the ports comprises:

calculating an effective Doppler spread for the corresponding port based on the derived time correlation coefficient and a period of Reference Signal (RS) received at the corresponding port; and

calculating the port correlation coefficient based on the effective Doppler spread and a time interval between a reception time of the RS at the corresponding port and a time for next scheduled downlink transmission.
The method (400) of claim 16 or 17, wherein integrating the time correlation coefficients for all the ports comprises:

averaging the port correlation coefficients over all the ports by dividing a sum of the port correlation coefficients by a number of the ports.
A method (400’) for scheduling signal transmission in a network node (1200) , the method comprising:

deriving (S404) , based on channel estimations for a channel at different times, a time correlation coefficient representing a channel aging estimation of the channel for all beams formed with an antenna system including at least two antenna elements in the network node (1200) ; and

scheduling (S406) signal transmission over the channel based on the derived time correlation coefficient.
A network node (1200) , comprising:

a processor (1202) ; and

a memory (1204) storing instructions that, when executed by the processor (1202) , cause the network node (1200) to perform a method (400, 400’) of any one of claims 1 to 19.
A network node, comprising:

a channel estimator (602) configured to obtain channel estimations for a channel at different times based on received Reference Signals (RSs) ; and

an auto-correlation calculator (604) including a correlation calculation unit configured to derive, based on the channel estimations from the channel estimator (602) , a time correlation coefficient representing a channel aging estimation of the channel for all beams formed with an antenna system including at least two antenna elements in the network node.
The network node of claim 21, wherein the auto-correlation calculator further comprises:

a correction factor calculation unit configured to, in case of a low signal-to-noise (SNR) correction, calculate a correction factor for each subcarrier and each polarization of the antenna system, based on a noise power for the channel and the channel estimations at different times.
The network node of claim 22, wherein the auto-correlation calculator further comprises a modification unit configured to,

in case of the low SNR correction, correct the time correlation coefficient with the correction factor from the correction factor calculation unit, and modify the corrected time correlation coefficient by combining the corrected time correlation coefficient for both polarizations of the antenna system, and averaging the combination result over all subcarriers; and

in case of no low SNR correction, modify the time correlation coefficient by combining the time correlation coefficient for both polarizations of the antenna system, and averaging the combination result over all subcarriers.
The network node of claim 23, wherein the modification unit is further configured to, in case of memory filter, applying memory filtering to the modified time correlation coefficient based on last time correlation coefficient derived for the channel at a previous time.
The network node of any of claims 21 to 24, wherein the auto-correlation calculator further comprises an equivalence filter unit configured to:

in case of multiple ports with antenna switching,

calculating a port correlation coefficient for each of the ports based on the derived time correlation coefficient, and

integrating the port correlation coefficients for all the ports to obtain an equivalent time correlation coefficient to represent the channel aging estimation.
The network node of any of claims 22 to 25, further comprising:

a scheduler configured to schedule signal transmission over the channel based on the time correlation coefficient from the auto-correlation calculator.
A computer-readable storage medium having computer-readable instructions stored therein, the computer-readable instructions, when executed by a processor (1202) of a network node (1200) , configure the network node (1200) to perform a method (400, 400’) of any one of claims 1 to 19.