Patent Description:
Massive MIMO (mMIMO) technology is an important technology to improve the spectral efficiency of <NUM>th generation (<NUM>) and <NUM> cellular networks. A number of antennas in mMIMO is typically much larger than the number of user equipment (UE), which allows base station (BS) to perform multi-user downlink (DL) beamforming to schedule parallel data transmission on the same time-frequency resources. However, performance of mMIMO depends heavily on the quality of CSI at a BS. It has been recently verified that the multi user-MIMO (MU-MIMO) performance degrades according to UE mobility.

In the <NUM> system, Hybrid FSK and QAM Modulation (FQAM) and sliding window superposition coding (SWSC) as an advanced coding modulation (ACM), and filter bank multi carrier(FBMC), non-orthogonal multiple access(NOMA), and sparse code multiple access (SCMA) as an advanced access technology have been developed.

<CIT> discloses a channel prediction method to perform more efficient beamforming.

<CIT> is concerned with a method allowing for estimating a channel more accurately and with less complexity based on an unscented Kalman filter.

<NPL> addresses the problem of channel tracking and equalization for MIMO time-varying frequency-selective channels, wherein a time gap between channel estimates produced by the Kalman filter and those needed for the MIMO DFE adaptation was bridged by using a simple prediction module.

The channel state information in a legacy wireless communication system is quickly out-of-date for a mMIMO (massive MIMO) base station (BS) which relies on sounding reference signal sent by a user equipment (UE), This greatly reduces the performance of mMIMO DL MU-MIMO (multi-user MIMO) transmission with mobile UEs. Therefore, there is a need for a new channel estimation and tracking method for mMIMO CSI acquisitions.

The present disclosure relates to wireless communication systems and, more specifically, the present disclosure relates to parameter tracking for CSI estimation.

According the embodiments of the present invention, Methods of channel estimation and tracking are provided. According to the methods, several benefits, such as reduced complexity, improved numerical stability and alogorithmic flexibility, can be achieved.

The term "controller" means any device, system, or part thereof that controls at least one operation.

<FIG> below describe various embodiments implemented in wireless communications systems and with the use of orthogonal frequency division multiplexing (OFDM) or orthogonal frequency division multiple access (OFDMA) communication techniques. The descriptions of <FIG> are not meant to imply physical or architectural limitations to the manner in which different embodiments may be implemented. Different embodiments of the present disclosure may be implemented in any suitably-arranged communications system.

As shown in <FIG>, the wireless network includes a gNB <NUM> (e.g., base station, BS), a gNB <NUM>, and a gNB <NUM>. The gNB <NUM> communicates with the gNB <NUM> and the gNB <NUM>. The gNB <NUM> also communicates with at least one network <NUM>, such as the Internet, a proprietary Internet Protocol (IP) network, or other data network.

The gNB <NUM> provides wireless broadband access to the network <NUM> for a first plurality of UEs within a coverage area <NUM> of the gNB <NUM>. The first plurality of UEs includes a UE <NUM>, which may be located in a small business; a UE <NUM>, which may be located in an enterprise (E); a UE <NUM>, which may be located in a WiFi hotspot (HS); a UE <NUM>, which may be located in a first residence (R); a UE <NUM>, which may be located in a second residence (R); and a UE <NUM>, which may be a mobile device (M), such as a cell phone, a wireless laptop, a wireless PDA, or the like. The gNB <NUM> provides wireless broadband access to the network <NUM> for a second plurality of UEs within a coverage area <NUM> of the gNB <NUM>. The second plurality of UEs includes the UE <NUM> and the UE <NUM>. In some embodiments, one or more of the gNBs <NUM>-<NUM> may communicate with each other and with the UEs <NUM>-<NUM> using <NUM>/NR, LTE, LTE-A, WiMAX, WiFi, or other wireless communication techniques.

Depending on the network type, the term "base station" or "BS" can refer to any component (or collection of components) configured to provide wireless access to a network, such as transmit point (TP), transmit-receive point (TRP), an enhanced base station (eNodeB or eNB), a <NUM>/NR base station (gNB), a macrocell, a femtocell, a WiFi access point (AP), or other wirelessly enabled devices. Base stations may provide wireless access in accordance with one or more wireless communication protocols, e.g., <NUM>/NR 3GPP new radio interface/access (NR), long term evolution (LTE), LTE advanced (LTE-A), high speed packet access (HSPA), Wi-Fi <NUM>. 11a/b/g/n/ac, etc. For the sake of convenience, the terms "BS" and "TRP" are used interchangeably in this patent document to refer to network infrastructure components that provide wireless access to remote terminals. Also, depending on the network type, the term "user equipment" or "UE" can refer to any component such as "mobile station," "subscriber station," "remote terminal," "wireless terminal," "receive point," or "user device. " For the sake of convenience, the terms "user equipment" and "UE" are used in this patent document to refer to remote wireless equipment that wirelessly accesses a BS, whether the UE is a mobile device (such as a mobile telephone or smartphone) or is normally considered a stationary device (such as a desktop computer or vending machine).

As described in more detail below, one or more of the UEs <NUM>-<NUM> include circuitry, programing, or a combination thereof for UEs. In certain embodiments, and one or more of the gNBs <NUM>-<NUM> includes circuitry, programing, or a combination thereof for UEs.

The controller/processor <NUM> can include one or more processors or other processing devices that control the overall operation of the gNB <NUM>. For example, the controller/processor <NUM> could control the reception of forward channel signals and the transmission of reverse channel signals by the RF transceivers 210a-210n, the RX processing circuitry <NUM>, and the TX processing circuitry <NUM> in accordance with well-known principles. The controller/processor <NUM> could support additional functions as well, such as more advanced wireless communication functions. For instance, the controller/processor <NUM> could support beam forming or directional routing operations in which outgoing/incoming signals from/to multiple antennas 205a-205n are weighted differently to effectively steer the outgoing signals in a desired direction. Any of a wide variety of other functions could be supported in the gNB <NUM> by the controller/processor <NUM>.

For example, when the gNB <NUM> is implemented as part of a cellular communication system (such as one supporting <NUM>/NR, LTE, or LTE-A), the interface <NUM> could allow the gNB <NUM> to communicate with other gNBs over a wired or wireless backhaul connection.

Part of the memory <NUM> could include a RAM, and another part of the memory <NUM> could include a flash memory or other ROM.

As shown in <FIG>, the UE <NUM> includes an antenna <NUM>, a radio frequency (RF) transceiver <NUM>, TX processing circuitry <NUM>, a microphone <NUM>, and RX processing circuitry <NUM>.

The processor <NUM> is also capable of executing other processes and programs resident in the memory <NUM>, such as processes for beam management. The processor <NUM> can move data into or out of the memory <NUM> as required by an executing process. In some embodiments, the processor <NUM> is configured to execute the applications <NUM> based on the OS <NUM> or in response to signals received from gNBs or an operator. The processor <NUM> is also coupled to the I/O interface <NUM>, which provides the UE <NUM> with the ability to connect to other devices, such as laptop computers and handheld computers. The I/O interface <NUM> is the communication path between these accessories and the processor <NUM>.

To meet the demand for wireless data traffic having increased since deployment of <NUM> communication systems and to enable various vertical applications, efforts have been made to develop and deploy an improved <NUM>/NR or pre-<NUM>/NR communication system. Therefore, the <NUM>/NR or pre-<NUM>/NR communication system is also called a "beyond <NUM> network" or a "post LTE system. " The <NUM>/NR communication system is considered to be implemented in higher frequency (mmWave) bands, e.g., <NUM> or <NUM> bands, so as to accomplish higher data rates or in lower frequency bands, such as <NUM>, to enable robust coverage and mobility support. Aspects of the present disclosure may also be applied to deployment of <NUM> communication system, <NUM> or even later release which may use terahertz (THz) bands. To decrease propagation loss of the radio waves and increase the transmission distance, the beamforming, massive multiple-input multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antenna, an analog beam forming, large scale antenna techniques are discussed in <NUM>/NR communication systems.

In addition, in <NUM>/NR communication systems, development for system network improvement is under way based on advanced small cells, cloud radio access networks (RANs), ultra-dense networks, device-to-device (D2D) communication, wireless backhaul, moving network, cooperative communication, coordinated multi-points (CoMP), reception-end interference cancellation and the like.

A communication system includes a downlink (DL) that refers to transmissions from a base station or one or more transmission points to UEs and an uplink (UL) that refers to transmissions from UEs to a base station or to one or more reception points.

A time unit for DL signaling or for UL signaling on a cell is referred to as a slot and can include one or more symbols. A symbol can also serve as an additional time unit. A frequency (or bandwidth (BW)) unit is referred to as a resource block (RB). One RB includes a number of sub-carriers (SCs). For example, a slot can have duration of <NUM> milliseconds or <NUM> millisecond, include <NUM> symbols and an RB can include <NUM> SCs with inter-SC spacing of <NUM> or <NUM>, and so on.

DL signals include data signals conveying information content, control signals conveying DL control information (DCI), and reference signals (RS) that are also known as pilot signals. A gNB transmits data information or DCI through respective physical DL shared channels (PDSCHs) or physical DL control channels (PDCCHs). A PDSCH or a PDCCH can be transmitted over a variable number of slot symbols including one slot symbol. For brevity, a DCI format scheduling a PDSCH reception by a UE is referred to as a DL DCI format and a DCI format scheduling a physical uplink shared channel (PUSCH) transmission from a UE is referred to as an UL DCI format.

A gNB transmits one or more of multiple types of RS including channel state information RS (CSI-RS) and demodulation RS (DMRS). A CSI-RS is primarily intended for UEs to perform measurements and provide channel state information (CSI) to a gNB. For channel measurement, non-zero power CSI-RS (NZP CSI-RS) resources are used. For interference measurement reports (IMRs), CSI interference measurement (CSI-IM) resources associated with a zero power CSI-RS (ZP CSI-RS) configuration are used. A CSI process consists of NZP CSI-RS and CSI-IM resources.

A UE can determine CSI-RS transmission parameters through DL control signaling or higher layer signaling, such as radio resource control (RRC) signaling, from a gNB. Transmission instances of a CSI-RS can be indicated by DL control signaling or be configured by higher layer signaling. A DMRS is transmitted only in the BW of a respective PDCCH or PDSCH and a UE can use the DMRS to demodulate data or control information.

<FIG> and <FIG> illustrate example wireless transmit and receive paths according to this disclosure. In the following description, a transmit path <NUM> may be described as being implemented in an gNB (such as gNB <NUM>), while a receive path <NUM> may be described as being implemented in a UE (such as UE <NUM>). However, it may be understood that the receive path <NUM> can be implemented in an gNB and that the transmit path <NUM> can be implemented in a UE. In some embodiments, the receive path <NUM> is configured to support the codebook design and structure for systems having 2D antenna arrays as described in embodiments of the present disclosure.

The transmit path <NUM> as illustrated in <FIG> includes a channel coding and modulation block <NUM>, a serial-to-parallel (S-to-P) block <NUM>, a size N inverse fast Fourier transform (IFFT) block <NUM>, a parallel-to-serial (P-to-S) block <NUM>, an add cyclic prefix block <NUM>, and an up-converter (UC) <NUM>. The receive path <NUM> as illustrated in <FIG> includes a down-converter (DC) <NUM>, a remove cyclic prefix block <NUM>, a serial-to-parallel (S-to-P) block <NUM>, a size N fast Fourier transform (FFT) block <NUM>, a parallel-to-serial (P-to-S) block <NUM>, and a channel decoding and demodulation block <NUM>.

As illustrated in FIGURE <NUM>, the channel coding and modulation block <NUM> receives a set of information bits, applies coding (such as a low-density parity check (LDPC) coding), and modulates the input bits (such as with quadrature phase shift keying (QPSK) or quadrature amplitude modulation (QAM)) to generate a sequence of frequency-domain modulation symbols.

The serial-to-parallel block <NUM> converts (such as de-multiplexes) the serial modulated symbols to parallel data in order to generate N parallel symbol streams, where N is the IFFT/FFT size used in the gNB <NUM> and the UE <NUM>. The size N IFFT block <NUM> performs an IFFT operation on the N parallel symbol streams to generate time-domain output signals. The parallel-to-serial block <NUM> converts (such as multiplexes) the parallel time-domain output symbols from the size N IFFT block <NUM> in order to generate a serial time-domain signal. The add cyclic prefix block <NUM> inserts a cyclic prefix to the time-domain signal. The up-converter <NUM> modulates (such as up-converts) the output of the add cyclic prefix block <NUM> to an RF frequency for transmission via a wireless channel. The signal may also be filtered at baseband before conversion to the RF frequency.

A transmitted RF signal from the gNB <NUM> arrives at the UE <NUM> after passing through the wireless channel, and reverse operations to those at the gNB <NUM> are performed at the UE <NUM>.

As illustrated in <FIG>, the down-converter <NUM> down-converts the received signal to a baseband frequency, and the remove cyclic prefix block <NUM> removes the cyclic prefix to generate a serial time-domain baseband signal. The serial-to-parallel block <NUM> converts the time-domain baseband signal to parallel time domain signals. The size N FFT block <NUM> performs an FFT algorithm to generate N parallel frequency-domain signals. The parallel-to-serial block <NUM> converts the parallel frequency-domain signals to a sequence of modulated data symbols. The channel decoding and demodulation block <NUM> demodulates and decodes the modulated symbols to recover the original input data stream.

Each of the gNBs <NUM>-<NUM> may implement a transmit path <NUM> as illustrated in <FIG> that is analogous to transmitting in the downlink to UEs <NUM>-<NUM> and may implement a receive path <NUM> as illustrated in <FIG> that is analogous to receiving in the uplink from UEs <NUM>-<NUM>. Similarly, each of UEs <NUM>-<NUM> may implement the transmit path <NUM> for transmitting in the uplink to gNBs <NUM>-<NUM> and may implement the receive path <NUM> for receiving in the downlink from gNBs <NUM>-<NUM>.

Each of the components in <FIG> and <FIG> can be implemented using only hardware or using a combination of hardware and software/firmware. As a particular example, at least some of the components in <FIG> and <FIG> may be implemented in software, while other components may be implemented by configurable hardware or a mixture of software and configurable hardware. For instance, the FFT block <NUM> and the IFFT block <NUM> may be implemented as configurable software algorithms, where the value of size N may be modified according to the implementation.

Furthermore, although described as using FFT and IFFT, this is by way of illustration only and may not be construed to limit the scope of this disclosure. Other types of transforms, such as discrete Fourier transform (DFT) and inverse discrete Fourier transform (IDFT) functions, can be used. It may be appreciated that the value of the variable N may be any integer number (such as <NUM>, <NUM>, <NUM>, <NUM>, or the like) for DFT and IDFT functions, while the value of the variable N may be any integer number that is a power of two (such as <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, or the like) for FFT and IFFT functions.

Although <FIG> and <FIG> illustrate examples of wireless transmit and receive paths, various changes may be made to <FIG> and <FIG>. For example, various components in <FIG> and <FIG> can be combined, further subdivided, or omitted and additional components can be added according to particular needs. Also, <FIG> and <FIG> are meant to illustrate examples of the types of transmit and receive paths that can be used in a wireless network. Any other suitable architectures can be used to support wireless communications in a wireless network.

<FIG> illustrates an example beam forming architecture <NUM> according to embodiments of the present disclosure. An embodiment of the beam forming architecture <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

For mmWave bands, although the number of antenna elements can be larger for a given form factor, the number of CSI-RS ports -which can correspond to the number of digitally precoded ports - tends to be limited due to hardware constraints (such as the feasibility to install a large number of analog-to-digital converts/digital-to-analog converts (ADCs/DACs) at mmWave frequencies) as illustrated by beamforming architecture <NUM> in <FIG>.

In this case, one CSI-RS port is mapped onto a large number of antenna elements which can be controlled by a bank of analog phase shifters <NUM>. One CSI-RS port can then correspond to one sub-array which produces a narrow analog beam through analog beamforming <NUM>. This analog beam can be configured to sweep across a wider range of angles <NUM> by varying the phase shifter bank across symbols or subframes or slots (wherein a subframe or a slot comprises a collection of symbols and/or can comprise a transmission time interval). The number of sub-arrays (equal to the number of RF chains) is the same as the number of CSI-RS ports N CSI-PORT. A digital beamforming unit <NUM> performs a linear combination across N CSI-PORT analog beams to further increase precoding gain. While analog beams are wideband (hence not frequency-selective), digital precoding can be varied across frequency sub-bands or resource blocks.

The channel state information is quickly out-of-date for a mMIMO BS which relies on sounding reference signal sent by a UE in a network. This greatly reduces the performance of mMIMO DL MU-MIMO transmission with mobile UEs.

The present disclosure discloses a new channel estimation and tracking method for mMIMO CSI acquisitions. The apparatus comprises a control unit that determines configuration and triggers operation for different parameter tracking modules, and a processor to update channel and parameter buffers, estimate channel parameters, and predict channel coefficients. The channel model comprises channel parameters that represent signal strength, signal delay and Doppler shift. A sequential parameter update scheme is disclosed, where signal strength, signal delay and Doppler shift are updated sequentially based on past and new channel measurements. Predicted channel coefficients comprise prediction from the estimated multipath model as well as adaptively filtered residual signal, which is the difference between the input CSI and reconstructed CSI from the multipath model. The provided embodiments have several benefits, such as reduced complexity, improved numerical stability and algorithmic flexibility.

The present disclosure provides new methods and apparatus for mMIMO CSI acquisitions. A mMIMO BS for CSI estimation comprises a transceiver configured to receive sounding reference signal (SRS) and DMRS from PUSCH from a UE, a control unit that determines configuration and triggers operation for different parameter tracking modules, and a processor to update channel and parameter buffers, estimate channel parameters, and predict channel coefficients. The overall architecture is illustrated in <FIG>.

<FIG> illustrates an example CSI estimation architecture <NUM> according to embodiments of the present disclosure. An embodiment of the CSI estimation architecture <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

As illustrated in <FIG>, CSI estimation and tracking control in <NUM> receives new CSI measurements from SRS, PUCCH or PUSCH at time to, and then adds new information into the CSI buffer. Configuration and triggers operation are determined for different processing modules. The updated channel parameters and predicted CSI are stored into designated buffers, when updating from channel parameter tracking unit <NUM> and channel coefficient prediction unit <NUM>.

Channel parameter tracking unit <NUM> uses stored CSI and previous path parameters, triggers a series of parameter tracking modules based on control signals from <NUM>, calculates new channel path parameters and residual channel response, and outputs updated information to <NUM>. Channel coefficient prediction unit <NUM> uses residual channel response and updated path parameters, triggers adaptive scaling of residual signal, predicts the channel coefficient, and outputs predicted CSI to <NUM>.

In time division duplexing (TDD) mMIMO systems, one method for a BS to obtain DL CSI is to utilize channel reciprocity. The predicted DL channel can be used by other functional blocks in the BS to improve system performance. For example, the predicted DL channel helps the scheduler to optimize resource allocation between different UEs, and to increase the accuracy of DL precoder and performance of DL MU-MIMO transmission by reducing the inter-user interference.

In one embodiment, CSI estimation and tracking control module takes uplink channel information, decides configurations of different parameter tracking modules, triggers the operations of different parameter tracking modules and update data buffers that hold channel and parameter estimates.

In one embodiment, the channel parameter tracking unit tracks the difference between input CSI and reconstructed CSI based on a parametric model. Channel coefficient prediction unit further filters the difference based on control signals and combines with prediction from a parametric model.

In one embodiment, the BS implements a filtering method to track channel path weights (e.g., Gamma) based on past CSI and path parameters, and outputs updated path weights needed in other parameter tracking modules and the channel coefficient prediction module.

One embodiment of the parametric channel model is a multipath channel model. The parameters are updated upon receiving, by a BS, new SRS measurements. One embodiment of the present disclosure adopts a multipath channel model, where the time-frequency channel response h ( t, f) is modeled as a sum of contributions from several multipath components (MPC). The model assumes the channel is constructed on a sum of basis waveforms. P sinusoidal waveforms, indexed by p = <NUM>, <NUM>,. , P, are used. Waveform p is parameterized by signal delays τp and signal Doppler shifts νp, which spans both the time and frequency domain. Then, the channel at time t and frequency f on antenna k is a linear combination of the P basis waveforms: <MAT>.

The parameter set in this embodiment is antenna-dependent path weight { γk,p}, path delay { τp}, and path Doppler { νp}. Both path delay and path Doppler are considered common across different antennas.

One embodiment considers a vectorized signal model for SRS measurement, which denotes as s(τ, ν, γ). If the vectorization of H performs first along the frequency domain and secondly along the time domain s(τ, ν, γ) is expressed by: s(τ, ν, γ) = vec{H} = B(τ, ν)·γ = Btf(τ, ν)◇ Bf(τ)·γ where parameter vectors τ, ν∈RP, and path weights γ ∈ CP.

Also, operator ◇ represents Khatri-Rao product, which is a column-wise Kronecker product. An example of the Khatri-Rao product between two <NUM> X <NUM> matrices is given by: <MAT>. Bf(τ) is a matrix-valued function, RP → CNvbX P and represents the difference among sub-bands due to path delay, and p-th column can be expressed by: [Bf(τ)]p = e-j<NUM>πx<NUM>δfτp, where x<NUM> = [<NUM>, <NUM>,. , Nrb - <NUM>]T δf denotes the frequency spacing of RB. In the case of fullband SRS sounding, Nrb is replaced by <MAT>, which is <NUM> for <NUM> bandwidth and <NUM> for <NUM> bandwidth in LTE. In the case of subband hopping SRS, Nrb is <NUM> for a quarter band of <NUM> total bandwidth and <NUM> for a quarter band of <NUM> total bandwidth. Similarly, Btf(τ, ν) is also a matrix-valued function.

In one embodiment where SRS is updated on a fraction of the total bandwidth every Δt seconds, Btf() represents the inter-band SRS response over time, which depends on both delay τ and Doppler ν in the hopping SRS case, and only Doppler ν in the full-band SRS case.

For the hopping SRS, the input and output mapping of Btf(τ, ν) is (RP,RP)→CNsrs×P, and p-th column can be expressed by:.

For the full-band SRS, the input and output mapping of Btf(ν) is RP → CNsrs×P and p-th column can be expressed by: [Btf(ν)]p = ej2πnΔtvp.

To extend the vectorized signal model for multiple BS antennas. It may be assumed that path delays and Dopplers are common across Nant antennas. Therefore, the following channel model is considered: s(τ, ν, Γ) = Γ ✧ Btf(τ, ν) ✧ Bf(τ) ·<NUM>, where Γ is a path weight matrix with dimension NantX P, and each row of Γ contains path weights for one antenna. <NUM> stands for an all-one column vector with dimension P × <NUM>. Similarly, Btf(τ, ν) is replaced by Btf(ν) for the fullband SRS. The received SRS is corrupted with additive white complex Gaussian noise, which models the uplink thermal noise and interference.

ysrs = s(τ, ν, Γ) + n<NUM>, here n<NUM> is the noise vector and follows a zero-mean complex Gaussian distribution with a covariance matrix Rn. In current algorithm development, Rn is configured to be a diagonal matrix <MAT>. The parameter <MAT> depends on uplink noise and interference power level, which is assumed to be fed by external modules. In further optimization, Rn can be computed dynamically based on the residual power level at the n-th time instant.

As illustrated in <FIG>, one embodiment of CSI estimation and control unit <NUM> is to trigger all parameter tracking modules in <NUM> with default parameters. Another embodiment is to configure the tracking units to use a smaller number of time domain SRS samples, when sudden changes occur in channel statistics such as average power, delay spread or Doppler spread calculated based on CSI. Another embodiment of <NUM> is to configure (e.g., <NUM> as illustrated in <FIG>), to operate in least square method when the first tracking snapshot or the previous Gamma tracking is skipped. Another embodiment of <NUM>, as illustrated in <FIG>, is to skip delay tracking (e.g., <NUM> as illustrated in <FIG>), as illustrated in <FIG>, when SRS or DMRS may have limited frequency domain samples.

One embodiment of channel parameter tracking unit <NUM> is in <FIG>. Path parameters are divided in three subsets, which are path weights (Gamma), path delay and path Doppler. The parameter-tracking unit comprises tracking modules to update one particular subset of parameters at one time, while fixing the other subsets of parameters. The parameter-tracking unit operates in a sequential manner where Gamma is updated first, delay is updated second, and Doppler is updated last. The parameter-tracking unit responds to external input control signals from <NUM>, so that different algorithm configurations can be applied or certain tracking modules can be turned off.

<FIG> illustrates an example CSI estimation operation <NUM> according to embodiments of the present disclosure. An embodiment of the CSI estimation operation <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

As illustrated in <FIG>, the inputs are previous parameters and stored CSI. In this embodiment, the parameter update happens in a sequential order with Gamma update, delay update and Doppler update, as illustrated by operations <NUM>, <NUM>, and <NUM> in <FIG>. One embodiment to perform residual signal tracking <NUM> calculates the difference between the reconstructed channel at the last SRS snapshot, ĥk,m,n and the last SRS snapshot yk,m,n = (hk,m,n + nk,m,n), where nk,m,n is noise. The expression for the residual signal at time n is given by: <MAT>.

As illustrated in <FIG>, for Gamma tracking (e.g., operation <NUM>), one embodiment uses adaptive filtering methods, such as Kalman filter (KF). The aforementioned embodiment first predicts state and error covariance matrix based on state transition model and past state information, secondly calculates the correction of state variables based on the predicted state variables and new data and generates the final estimated variables. This embodiment updates Gamma (path weight) per antenna basis for the P tracked paths concurrently.

Denote γk(n) = [γk,<NUM>···γk,P]Tthe path weights at SRS capture instance n for antenna k. It may be assumed that the below Gauss-Markov model for path weight evaluation is given by: γk(n) = Aγ · γk(n - <NUM>) + Bγ · u(n), where Aγ and Bγ are fixed matrices of dimension P X P and are common to all antennas. It may be assumed that the state noise u(n) is independent from SRS capture to capture and is uncorrelated among paths, e.g., <MAT>. Note that in practice, u(n) can be correlated in both time and path, since u(n) is a combined effect of dense multipath components (DMC) that are not captured in the tracked paths.

The state transition matrix is <MAT>, where Dj(i) is a diagonal matrix representing the theoretical phase progression due to Doppler effect, based on Doppler estimation at time j, between two SRS captures with time gap i:
Dj(i) = diag(ej·<NUM>πv<NUM>,jΔti···ej·<NUM>πvP,jΔti). That is: <MAT>.

In the above, it may be assumed that during the duration of Δt the path Doppler remains constant and is approximated by estimate νp,n-<NUM>. For example, for Δt = <NUM>, path Doppler may change about <NUM> Hz, that is only about <NUM> degrees difference.

The scaling factor <MAT> can be tuned based on real measurement, and currently <MAT> is chosen to reflect the fact that the path weight gain remains roughly the same between two adjacent SRS captures.

Measurement equation for γk(n) is linear given known path delay and Doppler. The channel response at frequency f and latest SRS capture instance n is: <MAT>, where f = f<NUM>(n), ··· , fNrb(n) and fm(n) = f<NUM>(n) + (m - <NUM>) · Δfrb is the frequency of the received Nrb RBs of SRS at time n. nk,f is the noise.

Write in matrix format: hk(n) = Bn(τ) · γk(n) + nk,n, where nk,n = [nk,<NUM>,n···nk,Nrb,n]T and the m th row of Bn(τ) is [Bn(τ)]m = [e-j2πfm(n)τ1, ··· , e-j<NUM>πfm(n)τP]. The meaning of Bn(τ) is frequency response basis matrix at time n, and only depends on the path delays τ = [τ<NUM>,···, τP]T and the starting RB frequency f<NUM>(n). In reality, the path delays can only be known from the previous estimates. To make this explicit dependency, denote Bn(τi) the frequency response basis matrix using the path delay estimates τi obtained at time i. Therefore, the following equation are given by: [Bn(τi)]m = [e-j2πfm(n)τ<NUM>,i, ··· , e-j<NUM>πfm(n)τP,i].

In path weight update, it may be observed that including a few past SRS signal is necessary according to extensive evaluation using field captures, especially for sub-band SRS. One reason may be that only using the latest SRS signal may not provide sufficient measurements to stably update γk(n). Consider Nr SRSs being used, i.e., SRS captured at n, n - <NUM>,···, n - Nγ + <NUM>. Certain assumption has to be made to relate current γk(n) to the past SRS signals. For n - i, the signal model for SRS n - i is:<MAT>.

Note that in the above equation, the complex path weight is always referenced to the current SRS capture instance n. and the past path weights at n - i, i = <NUM>···Nγ - <NUM>, are assumed to have only phase evolution according to Doppler effect but the amplitude remains the same. Also, the path delay for all SRS captures is assumed to be identical to the latest estimate at n - <NUM> (for SRS at n the path delay is not used for updating delay and Doppler at this point of time).

The overall measurement equation with Nr SRS can be written in a matrix format: <MAT>, where hk,Nγ(n) = [hk(n);···; hk(n - Nγ + <NUM>)] and nk = [nk,n; ···; nk,n-Nγ+<NUM>] is the measurement noise. In reality, the measurement noise includes both additive white Gaussian noise (AWGN) noise and the residual un-captured DMC power, which in general is correlated in both time and frequency: <MAT>.

Denote γk(n) = [γk,<NUM>···γk,P]T the estimated path weight for antenna k with using SRS n. The difference between γ̂k(n) and γk(n) is that the former is the estimated value and the latter is true value. Since only estimated value is available, for simplicity γk(n) is used to represent the estimated value in the sequel.

<FIG> illustrates a flowchart of a method <NUM> for adaptive filtering according to embodiments of the present disclosure. An embodiment of the method <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

Assume initial path parameter acquisition is completed at n = O. Now a KF based method is described for γk(n), n ≥ <NUM>. As illustrated in <FIG>, upon receiving SRS signal n, do the following step <NUM> to step <NUM> in <FIG>.

In step <NUM>, state vector prediction is performed: γk(n|n - <NUM>) = Aγ · γk(n - <NUM>). Note that Aγ is common to all antennas.

In step <NUM>, the covariance of prediction error is computed in γk(n|n - <NUM>): <MAT>. Note that Mγ(n|n - <NUM>) is common to all antennas.

In step <NUM>, Kalman is calculated gain: <MAT>.

The above formula is not suitable for computation due to large matrix inversion size. Use Matrix inversion lemma and some approximation to simplify a calculation.

Assume the measurement noise is uncorrelated: <MAT>, the above equation may be simplified as: <MAT> which is used as Kalman gain calculation. Note that Kγ(n) is common to all antennas.

In step <NUM>, state vector correction is performed: <MAT>.

In step <NUM>, the covariance of estimation error is computed in γk(n): <MAT>. Note that Mγ(n) is common to all antennas.

Parameter choices are provided as shown following.

In one example, for n = <NUM>, initial path weights γk(<NUM>) and measurement matrix BNγ(τ<NUM>) are from initial path parameter acquisition.

In one example, for n ≥ <NUM>, BNγ(τn-<NUM>) is calculated based on delay and Doppler estimated during tracking stage with update from SRS at time n - <NUM>.

In one example, <MAT> can include both additive noise and interference as well as the DMCs not captured by the tracked paths. Based on experiment, <MAT> can be relative value to the tracked path power and can be further optimized based on actual tuning.

In one example, <MAT> can be relative value to the tracked path power and can be configured based on actual tuning.

Another embodiment is to use least square (LS) based method. A switch to LS based method from KF based method can be easily configured. The procedures to configure KF based tracking to LS are provided here. In this embodiment, step <NUM> is skipped. In step <NUM>, the covariance of prediction error in γk(n|n - <NUM>) is set as a zero matrix. In step <NUM>, Kalman gain is calculated. In this case, the LS normal equation is calculated (with a regularization factor): <MAT>. In step <NUM>, state vector correction is performed: γk(n) = γk(n|n - <NUM>) + Kγ(n) (hk,Nγ(n) - BNγ(τn-<NUM>)γk(n|n - <NUM>)) = Kγ(n)hk,Nγ(n). In step <NUM>, the covariance of estimation error is computed in γk(n): skipped.

For delay tracking (e.g., operation <NUM>), one embodiment uses adaptive filtering methods, such as extended Kalman filter (EKF). The aforementioned embodiment first predicts state and error covariance matrix based on state transition model and past state information, secondly calculates the correction of state variables based on the predicted state variables and new data and generates the final estimated variables. Path delay are common across different antennas and P tracked delays are updated concurrently. Other path parameters such as path weights and path Doppler are fixed while updating path delay.

The new information along with the prior knowledge about the channel parameters such as path delay, path Doppler and path weights are combined to update path delay. One embodiment of delay update is an EKF-based estimation framework. A dynamic state space model is assumed for path delay, where it may be assumed that the dynamic state space model follows a random walk process and is perturbed by independent and identical distributed (i. ) random Gaussian noise wτ,n at each time instant: τn = τn-<NUM> + wτ,n.

The observation equation uses the following the signal model.

If path Doppler ν and path weights Γ are fixed, there is a nonlinear mapping from path delay τ to the observation vector. A traditional KF may not work with a nonlinear observation equation, and one has to rely on EKF to linearize the observation equation around the predicted values of the state vector. The path weight matrix Γ can be constructed by stacking path weight vector <MAT> for k-th antenna in the row direction, which has a dimension of Nant × P: <MAT>.

In the delay tracking/update function block (e.g., operation <NUM>), the state vector consists of path delays τ from P paths. At n-th time instant, the inputs to this function block are previous path delays τn-<NUM>, prior error covariance matrix Pτ,n-<NUM>, updated path weights Γn and previous path Doppler νn-<NUM>. The outputs of this function block are updated path delays τn and error covariance matrix Pτ,n.

<FIG> illustrates a flowchart of a method <NUM> for path delay correction according to embodiments of the present disclosure. An embodiment of the method <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

The main steps of path delay tracking follow <FIG>, which are summarized as follows.

In step <NUM>, predicted path delay and error covariance matrix are generated. τn|n-<NUM> = Φττn-<NUM>; <MAT> where Φτ is the state transition matrix with dimension P × P. In one embodiment, Φτ is an identity matrix and can be configurable in other embodiments. Qτ is the state noise covariance matrix for path delay with dimension P × P, and computed by Qτ = ατΔtIP. ατ represents the state noise of path delay and is assumed to be the same among P paths. The parameter qτ is set to 1e-<NUM> in one embodiment, while the parameter qτ is configurable in other embodiments.

In step <NUM>, corrected path delay and error covariance matrix are generated: <MAT> ; Δτn = Pτ,nqτ(ysrs,n; τn|n-<NUM>,νn-<NUM>, Γn,Rn); τn = τn|n-<NUM> + Δτn. qτ(ysrs,n; τn|n-<NUM>,νn-<NUM>, Γn, Rn) is the score-function with dimension P × <NUM>, and can be computed by <MAT>. Jτ(τn|n-<NUM>,νn-<NUM>, Γn, Rn) is Fisher information matrix (FIM) with dimension P × P, which can be computed by <MAT>. Rn is the residual error covariance matrix of the measurement process, which is common among path delay tracking and Doppler tracking. Rn is set to <MAT> by default. Dτ denotes the Jacobian matrix and is computed as <MAT>.

The partial derivatives are defined as: <MAT>; and <MAT>.

Because the dimension of the state vector P is much smaller than the dimension of the observation vector N tot. The alternative form of EKF is considered here, which is also known as the information form.

In some embodiments, there may require some preprocessing for τn-<NUM> if SRS measurements are contaminated by timing offset correction initiated by UE. The delay state vector in the previous iteration, τn-<NUM>, was updated using the SRS channel estimate buffer. The state vector may also be updated by the amount of TO correction using: τn-<NUM> ← τn-<NUM> + lTOTS · <NUM>πδf · <NUM>, where <NUM> is a column vector with all elements being <NUM> and with the same dimension of τn-<NUM>, lTOTS is the amount of TO correction in time unit and <NUM>πδf is the normalization factor. TS is the basic time unit.

For Doppler tracking (e.g., <NUM>), one embodiment uses adaptive filtering methods, such as EKF. The aforementioned embodiment first predicts state and error covariance matrix based on state transition model and past state information, secondly calculates the correction of state variables based on the predicted state variables and new data and generates the final estimated variables. Path Doppler are common across different antennas and P tracked Dopplers are updated concurrently. Other path parameters such as path weights and path delay are fixed while updating path Doppler.

The path Doppler tracking is preceded by the path delay tracking. The input to this function block are previous path Doppler shifts νn-<NUM>, prior error covariance matrix Pν,n-<NUM>, updated path weights Γn and path Delays τn. The output of this function block are updated path Doppler shifts νn and error covariance matrix Pν,n.

<FIG> illustrates a flowchart of a method <NUM> for path doppler correction according to embodiments of the present disclosure. An embodiment of the method <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

The main steps of path Doppler tracking follow <FIG> are summarized as following. In step <NUM>, predicted path Doppler and error covariance matrix are generated: νn|n-<NUM> = Φνn-<NUM> = νn-<NUM>; Pν,n|n-<NUM> = ΦPν,n-<NUM>ΦT + Qν = Pτ,n-<NUM> + Qv, where Qν is the state noise covariance matrix for path Doppler with dimension P × P, and is computed by Qν = ανΔtIP. αν represents the state noise of path delay and is assumed to be the same among P paths. In step <NUM>, updated path Doppler and error covariance matrix are generated: <MAT>; Δνn = Pν,nqν(ysrs,n; τn, νn|n-<NUM>, Γn, Rn); and νn = νn|n-<NUM> + Δνn.

qν(ysrs,n; νn|n-<NUM>) is the score-function with dimension P × <NUM>, and can be computed by <MAT>. Jν(τn, νn|n-<NUM>, Γ,Rn) is the Fisher information matrix with dimension P × P, which can be computed by <MAT>. Dν denotes the Jacobian matrix and is computed as <MAT>.

The partial derivative is defined as: <MAT>.

In one embodiment, channel coefficient prediction module <NUM> in <FIG> predicts channel coefficients for different frequency points and antennas, which is used by a BS to generate BF weights for DL MU-MIMO transmission. The channel coefficient prediction module generates predicted channel coefficients by adding up prediction based on updated path parameters and adaptively scaled residual channel response.

For a particular time transmission interval (TTI), the predicted channel is defined by <MAT>, and covers the entire bandwidth with <MAT> being the total number of RBs. This step follows the following equation: ĥ = ĥpath + ĥres.

It generates ĥpath based on the parametric channel model and updated path parameters (Γ, τ, ν). ĥpath = Γ ✧ B̃(τ, ν, nTTI)·<NUM>, where B̃(τ, ν, nTTI) is a modified basis matrix defined by: [B̃(τ, ν, nTTI)]p = e-j<NUM>π(x·Δfrbτp-<NUM>·nTTItTTIvp), where [B̃(τ, ν, nTTI)]p denotes the p-th column of B̃(τ, ν, nTTI), x is a RB index vector that ranges the entire (or interested) RB, tTTI is the time duration of TTI in seconds, and nTTI is a targeting TTI index that is relative to the TTI in which the most recent SRS has been received.

It then calculates ĥres by scaling the difference between the reconstructed channel at the last SRS snapshot, ĥk,m,n and the last SRS snapshot yk,m,n = (hk,m,n + nk,m,n), where n k,m,n is noise. The expression for the residual signal at time n is given by: <MAT>. The total residue power, totPwr, is computed as: <MAT>.

The noise power, noisePwr, is approximated as: <MAT>.

The scaling factor, scale, is computed as: <MAT>. The scaled residue signal. <MAT>, is computed as: <MAT>.

The parameter update framework has shown a better numerical stability over the other joint parameter update in simulations. The following figure shows the comparison of matrix condition numbers between two parameter update frameworks. The right subfigure uses the framework outlined in <NUM>, which shows a much smaller condition number and better numerical stability compared to the joint parameter update in the left subfigure.

<FIG> illustrates a flowchart of a method <NUM> for parameter tracking for CSI estimation according to embodiments of the present disclosure, as may be performed by a UE (e.g., <NUM>-<NUM> as illustrated in <FIG>). An embodiment of the method <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

As illustrated in <FIG>, the method <NUM> begins at step <NUM>. In step <NUM>, a BS receives information of uplink transmissions.

Subsequently, the BS in step <NUM> stores the received information.

Next, the BS in step <NUM> performs, based on the received information, channel parameter tracking operations to generate channel parameters, wherein the channel parameter tracking operations are configured with different configuration parameters.

Finally, the BS in step <NUM> performs, based on the channel parameters, a channel coefficient prediction operation to generate channel state information (CSI).

In on embodiment, the BS stores the channel parameters and the CSI in at least one buffer, wherein the CSI is predicted CSI based on the received information of uplink transmission and the channel parameters are updated channel parameters based on the received information of uplink transmission and identifies, based on the received information of the uplink transmissions, the different configuration parameters for the channel parameter tracking operations, the uplink transmissions comprising sounding reference signals (SRSs), a physical uplink channel (PUCCH), or a physical uplink shared channel (PUSCH).

In such embodiment, the different configuration parameters are identified to selectively track and update the channel parameters based on the received information of the uplink transmissions, the different configuration parameters being adjusted for the channel parameter tracking operations.

In one embodiment, the BS, in response to generating the channel parameters and the CSI, updates the at least one buffer with the channel parameters and the CSI, wherein the at least one buffer includes previously stored channel parameters and CSI.

In one embodiment, the BS generates, based on the received information of the uplink transmissions, control information including different configuration parameters for the channel parameter tracking operations and tracks, by the channel parameter tracking operations, differences between the CSI and currently received CSI based on a parametric model including a multipath channel model.

In one embodiment, the BS sequentially triggers the channel parameter tracking operations based on the control information, wherein the channel parameter tracking operations is sequentially ordered in a gamma tracking operation including an adaptive filter, a delay tracking operation, a Doppler tracking operation, and a residual signal tracking operation and calculates the channel parameters and a residual channel response to generate the channel parameters.

In one embodiment, the BS identifies, using the channel parameters and the CSI, a scaling factor based on the residual channel response determined from the residual signal tracking operation and performs, based on the scaling factor, the channel coefficient prediction operation to generate the CSI.

In one embodiment, the BS performs filtering and scaling, using the scaling factor on differences between the CSI and the currently received CSI based on the control information, combines the differences between the CSI and the currently received CSI with the channel parameters and a parametric channel model, and identifies, based on the combined differences, channel path weights that are used for the channel coefficient prediction operation and the channel parameter tracking operations.

The above flowcharts illustrate example methods that can be implemented in accordance with the principles of the present disclosure and various changes could be made to the methods illustrated in the flowcharts herein. For example, while shown as a series of steps, various steps in each figure could overlap, occur in parallel, occur in a different order, or occur multiple times. In another example, steps may be omitted or replaced by other steps.

Claim 1:
A base station, BS, in a wireless communication system, the BS comprising:
a transceiver; and
a processor coupled with the transceiver and configured to:
receive (<NUM>) information of uplink transmissions,
store (<NUM>) the received information,
perform (<NUM>), based on the received information, channel parameter tracking operations to generate channel parameters and a residual channel response, wherein the channel parameter tracking operations are configured with different configuration parameters, and
perform (<NUM>), based on the channel parameters and the residual channel response, a channel coefficient prediction operation to generate channel state information, CSI,
wherein the residual channel response comprises a difference between last uplink transmission of the uplink transmissions and a reconstructed channel at the last uplink transmission according to the channel parameters.