CN112823479A

CN112823479A - Non-linear precoding procedure

Info

Publication number: CN112823479A
Application number: CN201880098587.0A
Authority: CN
Inventors: 宋暖
Original assignee: Nokia Shanghai Bell Co Ltd; Nokia Networks Oy
Current assignee: Nokia Shanghai Bell Co Ltd; Nokia Oyj
Priority date: 2018-10-12
Filing date: 2018-10-12
Publication date: 2021-05-18
Anticipated expiration: 2038-10-12
Also published as: WO2020073342A1; EP3834296A4; CN112823479B; EP3834296A1

Abstract

There are methods, apparatus, and computer-readable media for a unified non-linear precoding process. The method (400) comprises: transmitting channel information about a channel between the network device and the terminal device to the network device; receiving, from the network device, an indication of a reception mode for the terminal device to decode the precoded data and the precoded reference signal, the reception mode being determined based on a precoding mode for precoding the data and the reference signal for the terminal device, the precoding mode indicating a non-linear precoding scheme used by the network device and being determined based on a set of parameters determined by the network device based on the channel information; and decoding the precoded data based on the precoded reference signal and the indication.

Description

Non-linear precoding procedure

Technical Field

Embodiments of the present disclosure relate generally to the field of telecommunications, and more particularly, to methods, devices, and computer-readable storage media for a non-linear precoding process.

Background

As one of the advanced transmission schemes in NR MIMO, nonlinear precoding shows a promising advantage of achieving significantly enhanced system performance and supporting more users, compared to linear precoding. By using the full CSI at the transmitter side, a pre-reduced "dirty paper" coding (DPC) technique that relies on non-causal known interference can achieve the maximum sum rate of the system and provide the maximum diversity order. There are generally two types of non-linear precoding techniques, thomlinson-Harashima precoding (THP) and Vector Perturbation (VP). They are simplified, efficient versions of DPC, which have lower computational requirements and are therefore more attractive to practical implementations.

Disclosure of Invention

In general, example embodiments of the present disclosure provide methods, devices, and computer-readable storage media for a non-linear precoding process.

In a first aspect, a method implemented at a network device is provided. The method comprises the following steps: determining, at the network device, a set of parameters based on channel information about a channel between the network device and the terminal device; determining a precoding pattern for precoding data and reference signals for the terminal device based on the set of parameters, the precoding pattern comprising one of THP and VP; based on the precoding mode, a reception mode for the terminal device to decode the precoded data and the precoded reference signal is determined, and an indication of the reception mode is sent to the terminal device.

In a second aspect, a method implemented at a terminal device is provided. The method comprises the following steps: transmitting channel information about a channel between the network device and the terminal device to the network device; receiving, from the network device, an indication of a reception mode for the terminal device to decode precoded data and precoded reference signals, the reception mode being determined based on a precoding mode for precoding data and reference signals for the terminal device, the precoding mode comprising one of THP and VP and being determined based on a set of parameters determined by the network device based on channel information; and decoding the precoded data based on the precoded reference signal and the indication.

In a third aspect, a terminal device is provided. The apparatus includes at least one processor; and at least one memory including computer program code. The at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to perform the method according to the first aspect.

In a fourth aspect, a network device is provided. The apparatus includes at least one processor; and at least one memory including computer program code. The at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to perform the method according to the second aspect.

In a fifth aspect, there is provided an apparatus comprising means for performing the steps of the method according to the first aspect.

In a sixth aspect, an apparatus is provided, comprising means for performing the steps of the method according to the second aspect.

In a seventh aspect, a computer-readable medium is provided, having a computer program stored thereon, which, when executed by at least one processor of an apparatus, causes the apparatus to perform the method according to the first aspect.

In an eighth aspect, a computer-readable medium having stored thereon a computer program is provided, which, when executed by at least one processor of an apparatus, causes the apparatus to perform the method according to the second aspect.

It should be understood that this summary is not intended to identify key or essential features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become readily apparent from the following description.

Drawings

The above and other objects, features and advantages of the present disclosure will become more apparent from the following more detailed description of some exemplary embodiments of the present disclosure in which:

FIG. 1 illustrates an example communication system 100 in which example embodiments of the present disclosure may be implemented;

fig. 2 illustrates a diagram of an example process 200 for a non-linear precoding process, according to some example embodiments of the present disclosure;

fig. 3A and 3B respectively illustrate graphs of cell throughput according to some example embodiments of the present disclosure;

fig. 4 illustrates a flowchart of an example method 400 for NLP processes, according to some example embodiments of the present disclosure;

fig. 5 illustrates a flowchart of an example method 500 for NLP procedures, according to some example embodiments of the present disclosure; and

fig. 6 is a simplified block diagram of a device suitable for implementing an example embodiment of the present disclosure.

Throughout the drawings, the same or similar reference numbers refer to the same or similar elements.

Detailed Description

The principles of the present disclosure will now be described with reference to a few exemplary embodiments. It is understood that these embodiments are described for illustrative purposes only and are presented to aid those skilled in the art in understanding and enabling the present disclosure without suggesting any limitation on the scope of the present disclosure. The present disclosure described herein may be implemented in various ways other than those described below.

In the following description and claims, unless defined otherwise, all 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.

As used herein, the term "communication network" refers to a network that conforms to any suitable communication standard or protocol, such as Long Term Evolution (LTE), LTE-advanced (LTE-a), and 5G NR, and employs any suitable communication technology, including, for example, multiple-input multiple-output (MIMO), OFDM, Time Division Multiplexing (TDM), Frequency Division Multiplexing (FDM), Code Division Multiplexing (CDM), bluetooth, ZigBee, Machine Type Communication (MTC), eMBB, MTC, and urrllc technologies. For purposes of discussion, in some embodiments, an LTE network, an LTE-a network, a 5G NR network, or any combination thereof are examples of communication networks.

As used herein, the term "network device" refers to any suitable device on the network side of a communication network. The network device may comprise any suitable device in an access network of a communication network, including, for example, a Base Station (BS), a relay, an Access Point (AP), a node B (NodeB or NB), an evolved NodeB (eNodeB or eNB), a gigabit NodeB (gnb), a remote radio module (RRU), a Radio Head (RH), a Remote Radio Head (RRH), a low power node (such as a femto, piconet), and so forth. For discussion purposes, in some embodiments, an eNB is taken as an example of a network device.

The network devices may also include any suitable device in the core network, including, for example, multi-standard radio (MSR) radios such as MSR BSs, network controllers such as Radio Network Controllers (RNCs) or Base Station Controllers (BSCs), multi-cell/Multicast Coordination Entities (MCEs), Mobile Switching Centers (MSCs) and MMEs, operations and management (O & M) nodes, Operations Support Systems (OSS) nodes, self-organizing networks (SON) nodes, location nodes such as enhanced serving mobile location centers (E-SMLCs), and/or Mobile Data Terminals (MDTs).

As used herein, the term "terminal device" refers to a device that is capable of, configured to, arranged to, and/or operable to communicate with a network device or another terminal device in a communication network. Communication may involve the transmission and/or reception of wireless signals using electromagnetic signals, radio waves, infrared signals, and/or other types of signals suitable for conveying information over the air. In some embodiments, the terminal device may be configured to transmit and/or receive information without direct human interaction. For example, when triggered by an internal or external event, or in response to a request from the network side, the terminal device may transmit information to the network device in a predetermined schedule.

Examples of end devices include, but are not limited to, User Equipment (UE) such as a smart phone, a wireless-enabled tablet, a laptop embedded device (LEE), a laptop installation device (LME), and/or a wireless Customer Premises Equipment (CPE). For purposes of discussion, some embodiments will be described below with reference to a UE as an example of a terminal device, and the terms "terminal device" and "user equipment" (UE) may be used interchangeably within the context of the present disclosure.

As used herein, the term "cell" refers to an area covered by radio signals transmitted by a network device. Terminal devices within a cell may be served by and access a communication network via a network device.

As used herein, the term "circuitry" may refer to one or more or all of the following:

(a) a purely hardware circuit implementation (such as an implementation in analog and/or digital circuitry only), and

(b) a combination of hardware circuit(s) with software, such as (if applicable): (i) a combination of analog and/or digital hardware circuit(s) and software/firmware, and (ii) any portion of hardware processor(s) with software (including digital signal processor(s), software, and memory(s) that work together to cause a device such as a mobile phone or server to perform various functions), and

(c) hardware circuit(s) and/or processor(s), such as microprocessor(s) or a portion of microprocessor(s), that require software (e.g., firmware) to operate, but which may not be present when it is not required for operation.

This definition of circuitry applies to all uses of the term in this application, including all uses in any claims. As a further example, as used in this application, the term circuitry also encompasses implementations in hardware circuitry only or a processor (or multiple processors) or a portion of a hardware circuitry or a processor and its (or their) accompanying software and/or firmware. By way of example, and where applicable to particular claim elements, the term circuitry also encompasses baseband or processor integrated circuits for mobile devices, or similar integrated circuits in servers, cellular network devices, or other computing or network devices.

As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The term "including" and its variants are to be read as open-ended terms, which mean "including, but not limited to". The term "based on" should be read as "based, at least in part, on. The terms "one embodiment" and "an embodiment" should be read as "at least one embodiment". The term "another embodiment" should be read as "at least one other embodiment". Other definitions, whether explicit or implicit, may be included below.

Fig. 1 illustrates a communication network 100 in which embodiments of the present disclosure may be implemented. Communication network 100 includes network device (e.g., gNB)110 and terminal devices (e.g., UEs) 120-1, 120-K and 120 '-1, 120' -K (hereinafter collectively referred to as terminal devices 120 or UEs 120) in communication therewith. The gNB 100 and the UEs 120-1, 120-K and 120 '-1, 120' -K may communicate with each other via a channel 130 between the gNB 100 and the UEs 120-1, 120-K and 120 '-1, 120' -K.

It should be understood that the number of network devices and terminal devices are shown for illustrative purposes only and no limitation is suggested. Communication network 100 may include any suitable number of network devices and terminal devices. Communication between network device 110 and terminal device 120 may utilize any suitable technology that already exists or will be developed in the future.

As one of the advanced transmission schemes in NR MIMO, nonlinear precoding (NLP) shows considerable advantages compared to linear precoding, achieving significantly enhanced system performance and supporting more users. There are several types of non-linear precoding techniques, such as THP and VP.

Most existing non-linear precoding procedures may only support THP-based precoding. THP continuously handles transmitter side interference by applying modulo arithmetic in the feedback loop and avoids transmit power boosting. Another scheme is VP, where the signals to be transmitted to all receivers are perturbed jointly by another vector to minimize the transmit power from the extended constellation. The VP scheme can further enhance performance with slightly higher complexity compared to THP.

Generally, for THP schemes, the signal at the transmitter undergoes a feedback loop and modulo operation to suppress interference between layers and reduce transmit power. It is then precoded with a feed forward filter and sent to the UE. On the UE side, when the UE has multiple antennas, reception combining is required to map the received signals from the antennas to the respective layers. Each UE should be able to estimate an effective channel via an appropriate DMRS and weighting coefficients for data demodulation. For the VP scheme, a power normalization factor is utilized at the transmitter

The signal is perturbed, precoded and scaled. Therefore, the UE should also estimate the effective channel via a specific DMRS to obtain reception combining. But unlike THP, if the UE performs precoding using the VP scheme, the UE should pass the weighting that is the same for all UEs and related to the power normalization factor at the transmitter

To scale the data stream.

For both schemes, modulo arithmetic is required at the receiver side to recover the desired signal. However, to support THP and VP based schemes and enhance system performance, one key technical challenge is: different transmission procedures for THP and VP result in different reception procedures at the UE.

Some approaches attempt to support both THP and VP. For example, it is proposed that spatially multiplexed DMRS undergo non-linear precoding together with data. In this way, spatially multiplexed DMRSs may be transmitted in the same resource to reduce overhead. However, the gNB and the UE require a DMRS corrector and a disturbance vector adder, respectively, which brings high implementation complexity at both ends. Furthermore, in this method, how to design reception combining when the UE has multiple antennas is not considered.

Furthermore, with respect to existing VP algorithms for MU MIMO systems, such as Geometric Mean Decomposition (GMD), receive combining is obtained by the GMD, and the perturbation vector is optimized conditioned on the receive combiner. Such a scheme brings difficulties for the UE to calculate the receive combiner from the estimate of the effective channel via the DMRS. Therefore, this receiving scheme is not applicable to THP, since the unified process requires the same receiving process for both THP and VP. Furthermore, the proposed algorithm will result in a large performance degradation if the number of receive antennas is larger than the number of ports at the transmitter. This is due to the limitations of the pure ZF algorithm.

As shown in fig. 1, a non-linear precoding (NLP) process may be performed at the gNB 110. The precoding process may be performed based on the THP precoder 111-1 or the VP precoder 111-2. It should be appreciated that the gNB110 may determine the NLP pattern and precode data streams for multiple users through corresponding precoders. Although in the example of fig. 1, the gNB110 includes NLP precoders 111-1 and 111-2, those skilled in the art will appreciate that the gNB110 may include only one of the NLP precoders 111-1 and 111-2. After undergoing the NLP procedure, the data streams and corresponding reference signals (e.g., demodulation reference signals (DMRS)) may be transmitted to the corresponding UEs via the channel 130.

As shown in FIG. 1, there are K UEs in the system, each having

An antenna (). At gNB there is M_TAn antenna and N_TOne port and total

Wherein gNB will r_kOne stream is sent to UE k. For the THP-based NLP process (NLP process performed by precoder 111-1), the matrix

Is a feedforward filter, and a matrix

Corresponding to the feedback filter used for interference pre-processing. Data of

Subject to conventional THP-based NLP procedures, while DMRS

Through a feedback loop and a feedforward filter. On the UE (UE 120-1, …, 120-K) side, the receive processing consists primarily of a linear combiner

Weighting process

And demodulating and decoding the modulo operation Mod (·). Each UE measures its effective channel via DMRS and obtains a receive combiner W _k And a data stream D_kThe weight of (c).

Further, for a VP-based NLP process (i.e., the NLP process performed by precoder 111-2), the data is perturbed by a vector τ l, where τ is a real number and l is an r-dimensional complex vector a + ib where a and b are integers. The DMRS d of the VP scheme is not subject to vector perturbation, but is precoded by P together with the data and by a power normalization factor

Scaling is performed. At the UE side, UEs 120 '-1, …, 120' -K, each UE also measures its effective channel via DMRS and recovers the reception combining weights W_kAnd power normalization factor for data modulation

Channel with a plurality of channels

Is beamformed CSI, where beamforming (e.g., eigenbeamformer) will be N_TMapping of ports to M_TAn antenna element, and

is the total number of receive antennas from all UEs.

Since different NLP procedures may be performed at the gNB, a corresponding decoding procedure must be performed at the UE. As described above, most existing non-linear precoding processes may only support THP-based precoding. Therefore, incorporating VPs into the system to allow further performance enhancement with minimal impact on transceiver implementation will be discussed in this disclosure.

The principles and implementations of the present disclosure will be described in detail below with reference to fig. 2, where fig. 2 illustrates a process 200 according to an example embodiment of the present disclosure. For discussion purposes, the process 200 will be described with reference to fig. 1. Process 200 may relate to a random access procedure.

As shown in fig. 2, at 210, the gNB110 receives channel information from the UE 120. This channel information may be considered Channel State Information (CSI), which characterizes the channel between the gNB110 and the UE 120. At 220, the gNB110 determines a set of parameters based on the received channel information to design a precoding scheme, i.e., a coding mode for the precoded data and reference signals for the UE 120. As mentioned above, the precoding mode indicates the non-linear precoding scheme used by the network device.

Alternatively, the receive combining matrix W may be considered independent of the precoding design. In some example embodiments, the gNB110 may obtain valid CSI from the channel information. In this case, the gNB110 may determine a matrix for characterizing the channel from the valid CSI and determine a set of parameters based on the matrix, the matrix corresponding to the number N of ports of the gNB110_TNumber of antennas M of gNB110_TNumber of antennas of UE120

And the combining matrix W of UE 120.

In some example embodiments, if a parameter set is associated with THP, the parameter set may include a precoding matrix P and a feedback matrix B^-1At least one of (a).

In some example embodiments, if a parameter set is associated with a VP, the parameter set may include a precoding matrix P, a power normalization factor

And a disturbance vector/.

For example, the total receive combining matrix W is a block diagonal, which can be written as follows in equation (1):

where blkding {. cndot } represents a building block diagonal matrix.

For the VP precoding scheme, it is assumed that a Maximum Ratio Combining (MRC) receiver is applied and a reception combiner for each UE can be obtained in equation (2) as follows:

W_k＝H_k (2)

then, based on the channel

Or the entire channel

The VP precoder (e.g., precoder 111-2 of fig. 1) is designed.

A VP scheme based on Zero Forcing (ZF) is considered and a precoding matrix P can be obtained by equation (3) as follows:

for this case, the key point is to select the optimal l that is used to perturb the data vector. The vector/is chosen to minimize the following cost function in equation (4), as follows:

where x is s + τ l' (4)

Where | · | | represents the euclidean norm. This is an r-dimensional integer lattice least squares problem that can be effectively solved on columns of P by the Lenstra-Lenstra-Lov-sz (LLL) algorithm.

The transmitted signal should be passed through the factor using equation (5)

Normalized as follows:

γ＝||P(s+τl)||² (5)

for the THP precoding scheme, similar to VP, an MRC receiver may be applied to each UE based on equation (2) and according to a channel

The THP precoding is designed. By computing the channel

The LQ decomposition above, equation (6) can be obtained as follows:

where L is a lower triangular matrix and Q is a unitary matrix. The feedforward and feedback filters for the THP algorithm can be obtained in equations (7) - (9), respectively, as follows:

P＝O^H (7)

B＝DL (8)

and

D＝diag{L^-1(1，1)，...，L^-1(r，r)} (9)

where L (i, i) is the ith diagonal element of the matrix L.

Alternatively, the receive combining matrix W may be considered to be designed jointly with precoding. In some example embodiments, the gNB110 may obtain the full CSI from the channel information. In general, full CSI may refer to CSI at the antenna level for the base station and the terminal.

In this case, the gNB110 may determine a matrix for characterizing the channel from the complete CSI and determine a set of parameters based on the matrix, the matrix corresponding to the number N of ports of the gNB110_TNumber of antennas M of gNB110_TAnd number of antennas of UE120

And (4) associating.

In some example embodiments, if the parameter set is associated with THP, the parameter set may include a precoding matrix P and a feedback matrix B for UE120^-1And at least one of the merging matrices W.

In some example embodiments, if a parameter set is associated with a VP, the parameter set may include a precoding matrix P, a power normalization factor for the UE120

At least one of a disturbance vector l and a combining matrix W.

In case of jointly designing the receive combining with NLP, for the VP precoding scheme, the precoding and combining matrices should follow equation (10) according to the ZF criterion as follows:

W^HHP＝I (10)

where I denotes the identity matrix and VP precoding design is to select the optimal perturbation vector that minimizes the cost function in equation (4) with the obtained P.

To implement matrix decomposition and satisfy the ZF criterion in equation (10), it is proposed to compute the receive combining matrix by equation (11) via some matrix decomposition (such as GMD) as follows:

W^HHF＝L (11)

where L is the lower triangular matrix.

In this case, the channel matrix H in equation (11) may be updated through successive decomposition based on at least one of Geometric Mean Decomposition (GMD), Singular Value Decomposition (SVD), or Generalized Trigonometric Decomposition (GTD). Equation (11) may be reconstructed as equation (12) as follows:

W^HH(FL^-1)＝I (12)

equation (12) satisfies the ZF criterion in equation (10). The above mentioned algorithm for the VP precoding scheme may be named as continuous decomposition VP (SD-VP).

For example, taking GMD as an example, the continuous matrix decomposition of H of equation (11) is implemented. The correlation matrix can be written in equation (13) as follows:

wherein

Receive combining, beamformed channels, feed forward filters, and equivalent lower triangular channels corresponding to users from K to K. For the first UE (e.g., UE 120' -1 of FIG. 1), receive beamforming and feed forward filters may be constructed by applying a GMD algorithm to construct a lower triangular matrix

To obtain wherein W is₁And F₁Including orthogonal columns. In addition, to ensure that the first UE does not interfere with the remaining scheduled UEs, i.e. the first UE does not interfere with the remaining scheduled UEs

By elimination of F₁Will have an influence on

Projected as

And obtain another lower triangular equivalent channel

This can be similarly addressed by the GMD. The total lower triangular matrix in equation (13) may be calculated xi₁Is composed of

To construct. Then, the matrix may be performed for the second to Kth UEs (e.g., UEs 120' -K of FIG. 1) in the same manner

Further decomposition of (a). SVD and GTD may be similarly applied in addition to GMD.

Then, the precoding matrix can be defined by equation (14) as follows:

P＝FL^-1 (14)

the transmission signal should be formed by the factor of equation (5)

To normalize.

In case of joint design receive combining with NLP, for THP precoding scheme, THP based on block diagonal GMD is applied, i.e. by channel merging

Constructed as a lower triangular structure in equation (15), as follows:

W^HHP＝L (15)

the block diagonal GMD-THP algorithm may be implemented recursively to obtain W_k、P_kAnd L_k。

Referring back to fig. 2, at 220, the gNB110 also determines a reception mode for the UE120 to decode the precoded data and the precoded reference signals based on the precoding mode.

In some example embodiments, the gNB110 may perform a non-linear process on the data and a linear process on the reference signal, and then precode the processed data and reference signal based on the precoding pattern. For example, as shown in fig. 1, for a THP-based NLP process, the data undergoes a conventional THP-based NLP process while the DMRS passes through a feedback loop and a feedforward filter, while for a VP-based NLP process, the data is perturbed by a vector while the DMRS does not undergo vector perturbation

As shown in fig. 2, at 230, the gNB110 sends an indication of the reception mode to the UE 120.

At 240, upon receiving the indication of the receive mode, the UE120 decodes the precoded data based on the precoded reference signal and the indication. The precoded reference signals generated by the gNB110 are considered to be a unified indication for indicating a unified decoding mode to the UE 120.

In some example embodiments, the UE120 may determine the weights associated with the precoding mode by decoding the precoded reference signals based on the indication. For THP precoding schemes, the weights may include a weighting procedure

For the VP precoding scheme, the weights may include a power normalization factor

In some example embodiments, UE120 may also determine a comb matrix W for UE120 based on the precoded reference signals if the gNB110 obtains full CSI.

As mentioned above, the reference signals (e.g., DMRSs) are only linearly precoded and undergo different transmission processing than the data. In case of THP procedure, DMRS goes through feedback loop B^-1And a feedforward filter P representing an equivalent channel to be measured at the UE by equation (16), as follows:

H_e＝HPB^-1 (16)

in case of the VP procedure, DMRS is precoded only by ZF precoder P as shown in equation (14), and the measured equivalent channel is coded in equation (17) via the DMRS as follows:

regardless of the precoding type (i.e., THP or VP), the UE receives the unified non-linear precoding mode indication and performs the same estimation and reception procedure to demodulate the data.

For the VP precoding procedure, the received DMRS may be represented in equation (18) as follows:

where n is a signal having a covariance σ²Additive White Gaussian Noise (AWGN).

Accordingly, the DMRS after the reception combiner may be expressed in equation (18) as:

in the case where the receive combining matrix W is independent of the precoding design, the receive combining matrix W may be calculated by equation (2) using the beamformed channel H. The precoding matrix P from equation (3) is inserted into equation (19), resulting in equation (20) as:

at each stream, the equivalently received DMRS is represented in equation (21):

wherein

Is the AWGN after receiving the combining. The received data stream should also be scaled back by a power normalization factor, which can be estimated directly from equation (21) via DMRS. The weight for each stream is calculated by estimating the inverse of the power scaling factor.

In case of jointly designing the receive combining W with NLP, the received DMRS in equation (18) is reformulated as equation (22) as follows:

at each stream, the equivalently received DMRS is represented in equation (23):

wherein w_jIs the jth column of the receive combining matrix W. Therefore, v will be estimated simply_j(by

Marking) and recovering the receive combining matrix as

And a power scaling factor of

For the THP precoding procedure, if the receive combining matrix W is independent of the precoding design, then the MRC receiver is applied and on the active channel

The LQ decomposition in THP is performed, which can be expressed in equation (24):

wherein P is Q^H，B＝L·diag{L^-1(1，1)，...，L^-1(r，r)}， (24)

Wherein diag {. is denoted to construct a diagonal matrix.

The DMRS received after the MRC combiner is represented as:

the received DMRS for each data stream is denoted by equation (26):

the weight for the data stream at each UE may be calculated by taking the inverse of the estimation coefficient, i.e.,

in the case of designing the receive combining W jointly with NLP, assuming that GMD-THP is applied for this case, the received DMRS before the receive combining is calculated by equation (27) according to equation (11) P ═ F:

r_d＝HPB^-1d+n＝W·diag{L(1，1)，...，L(r，r)}d+n (27)

the received DMRS for each data stream is denoted by equation (28):

thus, estimation by normalization

To obtain a receive combiner for each UE, i.e.

The weighting of the streams should be the inverse of L (j, j), i.e., according to equation (28)

According to embodiments of the present disclosure, when the gNB is to perform a precoding procedure including the THP and VP schemes, the implementation of the transceiver is simplified. By including VP precoding functionality, system performance can be enhanced with a small amount of modification to the non-linear precoding process. The UE does not have to know the type of non-linear precoding method, i.e. UE transparent, which simplifies the procedure and additional signaling to the UE.

Further, according to embodiments of the present disclosure, the gbb is allowed to dynamically switch between non-linear coding schemes in order to improve system and UE performance.

In both cases, the cell throughput performance of the proposed non-linear precoding procedure for THP and VP algorithms was evaluated, i.e. the receive combining W was designed to be independent of the NLP and designed jointly with the NLP case. Detailed simulation parameters can be found in table 1.

Table 1: simulation setup

Fig. 3A and 3B show the Cumulative Distribution Function (CDF) of cell throughput in the above two cases. FIG. 3A shows a situation, M_T＝32，N_T8, K8, while fig. 3B shows another case, M_T＝64，N_T16, and 16. It can be observed that the proposed VP-based algorithms (i.e., curves 330 and 340 in fig. 3A and curves 330 'and 340' in fig. 3B) outperform their THP controls (i.e., curves 310 and 320 in fig. 3A and curves 310 'and 320' in fig. 3B) for both cases. The "SD-VP" scheme (i.e., curve 340 in fig. 3A and 340 'in fig. 3B) proposed in the case of receiving the merge W in conjunction with NLP design shows performance enhancement compared to the VP method (i.e., curve 330 in fig. 3A and 330' in fig. 3B) in the case of receiving the merge W designed to be independent of NLP.

Further details of example embodiments according to the present disclosure will be described with reference to fig. 4-5.

Fig. 4 illustrates a flowchart of an example method 300 for NLP procedures, according to some example embodiments of the present disclosure. Method 400 may be implemented at a gNB110 as shown in fig. 1. For discussion purposes, the method 400 will be described with reference to fig. 1.

At 410, network device (gNB)110 determines a set of parameters based on channel information about a channel between the network device and the terminal device.

In some example embodiments, network device 110 may obtain valid channel state information, CSI, from the channel information. Network device 110 may also determine a first matrix for characterizing the channel from the valid CSI and determine a set of parameters based on the first matrix, the first matrix being associated with a number of antennas of the network device, a number of ports of the network device, a number of antennas of the terminal device, and a combining matrix of the terminal device.

In some example embodiments, the parameter set is associated with the THP and comprises at least one of: a precoding matrix; and a feedback matrix.

In some example embodiments, a parameter set is associated with a VP and includes at least one of: a precoding matrix; a power normalization factor; and a disturbance vector

In some example embodiments, network device 110 may obtain the complete channel state information CSI from the channel information. Network device 110 may also determine a second matrix for characterizing the channel from the complete CSI and determine a set of parameters based on the second matrix, the second matrix being associated with a number of antennas of the network device, a number of ports of the network device, and a number of antennas of the terminal device.

In some example embodiments, the parameter set is associated with the THP and comprises at least one of: a precoding matrix; a feedback matrix and a combining matrix for the terminal device.

In some example embodiments, a parameter set is associated with a VP and includes at least one of: a precoding matrix; a power normalization factor; a perturbation vector and a combining matrix for the terminal device.

In some example embodiments, if a parameter set is associated with a VP, network device 110 may decompose the GMD based on geometric means; singular Value Decomposition (SVD); and generalized trigonometric decomposition, GTD, updating the second matrix by successively decomposing the second matrix, and determining the parameter set based on the updated second matrix.

At 420, the network device 110 determines a precoding pattern for precoding the data and reference signals for the terminal device based on the set of parameters, the precoding pattern indicating a non-linear precoding scheme used by the network device.

At 430, network device 110 determines a reception mode for the terminal device to decode the precoded data and the precoded reference signal based on the precoding mode.

At 440, network device 110 sends an indication of a receive mode to the terminal device.

In some example embodiments, network device 110 may also perform a non-linear process for the data and a linear process for the reference signal and precode the processed data and reference signal based on the precoding pattern.

Fig. 5 illustrates a flowchart of an example method 500 for NLP procedures, according to some example embodiments of the present disclosure. The method 500 may be implemented at the UE120 as shown in fig. 1. For discussion purposes, the method 500 will be described with reference to fig. 1.

At 510, terminal device (UE)120 sends channel information about a channel between the network device and the terminal device to network device 110.

At 520, terminal device 120 receives, from network device 110, an indication of a receive mode for the terminal device to decode the precoded data and the precoded reference signal, the receive mode determined based on a precoding mode used to precode the data and the reference signal for the terminal device, the precoding mode indicating a non-linear precoding scheme used by the network device and determined based on a set of parameters determined by the network device based on the channel information.

At 530, the terminal device 120 decodes the precoded data based on the precoded reference signal and the indication.

In some example embodiments, terminal device 120 may determine the weights associated with the precoding mode by decoding the precoded reference signal based on the indication; and decoding the precoded data based on the weights.

In some example embodiments, terminal device 120 may determine a combining matrix for the terminal device based on the precoded reference signals if network device 110 obtains complete CSI.

In some example embodiments, an apparatus capable of performing method 400 (e.g., gNB 110) may include means for performing various steps of method 400. The component may be implemented in any suitable form. For example, the components may be implemented in a circuit or a software module.

In some example embodiments, the apparatus comprises: means for determining, at the network device, a set of parameters based on channel information about a channel between the network device and the terminal device; means for determining a precoding pattern for precoding data and reference signals for the terminal device based on the set of parameters, the precoding pattern indicating a non-linear precoding scheme used by the network device; means for determining a reception mode for the terminal device to decode the precoded data and the precoded reference signal based on the precoding mode; and means for sending an indication of the reception mode to the terminal device.

In some example embodiments, the means for determining the set of parameters may comprise means for obtaining valid channel state information, CSI, from the channel information; means for determining a first matrix for characterizing a channel based on the valid CSI, the first matrix being associated with a number of antennas of the network device, a number of ports of the network device, a number of antennas of the terminal device, and a combining matrix for the terminal device; and means for determining a set of parameters based on the first matrix.

In some example embodiments, a parameter set is associated with a VP and includes at least one of: a precoding matrix; a power normalization factor; and a disturbance vector.

In some example embodiments, the means for determining the set of parameters may comprise: means for obtaining complete channel state information, CSI, from the channel information; means for determining a second matrix for characterizing the channel from the complete CSI, the second matrix being associated with a number of antennas of the network device, a number of ports of the network device, and a number of antennas of the terminal device; and means for determining a set of parameters based on the second matrix.

In some example embodiments, a parameter set is associated with a VP, and means for determining the parameter set may include: means for updating the second matrix by successively decomposing the second matrix based on at least one of: decomposing GMD by geometric mean; singular Value Decomposition (SVD); and generalized trigonometric decomposition GTD; and means for determining a set of parameters based on the updated second matrix.

In some example embodiments, the apparatus may further include: means for performing a non-linear process on the data and a linear process on the reference signal; and means for precoding the processed data and reference signals based on the precoding pattern.

In some example embodiments, an apparatus (e.g., UE 120) capable of performing method 500 may include means for performing the various steps of method 500. The component may be implemented in any suitable form. For example, the components may be implemented in a circuit or a software module.

In some example embodiments, the apparatus comprises: means for transmitting channel information about a channel between the network device and the terminal device to the network device; means for receiving, from a network device, an indication of a reception mode for a terminal device to decode precoded data and precoded reference signals, the reception mode being determined based on a precoding mode used to precode data and reference signals for the terminal device, the precoding mode indicating a non-linear precoding scheme used by the network device and being determined based on a set of parameters determined by the network device based on the channel information; and means for decoding the precoded data based on the precoded reference signal and the indication.

In some example embodiments, the means for decoding may comprise: means for determining weights associated with a precoding mode by decoding a precoded reference signal based on the indication; and means for decoding the precoded data based on the weights.

In some example embodiments, the means for decoding may comprise means for determining a combining matrix for the terminal device based on the precoded reference signals if the network device obtains the complete channel state information, CSI.

Fig. 6 is a simplified block diagram of a device 600 suitable for implementing an example embodiment of the present disclosure. Device 600 may be viewed as another example implementation of a gNB110 as shown in fig. 1. Thus, the apparatus 600 may be implemented at the UE120 or as at least a portion of the UE 120.

As shown, device 600 includes a processor 610, a memory 620 coupled to processor 610, a suitable Transmitter (TX) and Receiver (RX)640 coupled to processor 610, and a communication interface RX640 coupled to TX. Memory 610 stores at least a portion of program 630. TX/RX 640 is used for bi-directional communication. TX/RX 640 has at least one antenna to facilitate communication, but in practice an access node referred to in this application may have several antennas. The communication interface may represent any interface required for communication with other network elements, such as an X2 interface for bidirectional communication between enbs, an S1 interface for communication between a Mobility Management Entity (MME)/serving gateway (S-GW) and an eNB, a Un interface for communication between an eNB and a Relay Node (RN), or a Uu interface for communication between an eNB and a terminal device.

The programs 630 are assumed to include program instructions that, when executed by the associated processor 610, enable the device 600 to operate in accordance with example embodiments of the present disclosure, as discussed herein with reference to fig. 2-5. The example embodiments herein may be implemented by computer software executable by the processor 610 of the device 600, or by hardware, or by a combination of software and hardware. The processor 610 may be configured to implement various example embodiments of the present disclosure. Further, the combination of the processor 610 and the memory 610 may form a processing component 650 suitable for implementing various example embodiments of the present disclosure.

The memory 610 may be of any type suitable to a local technology network and may be implemented using any suitable data storage technology, such as non-transitory computer-readable storage media, semiconductor-based memory devices, magnetic memory devices and systems, optical memory devices and systems, fixed memory and removable memory, as non-limiting examples. Although only one memory 610 is shown in device 600, there may be several physically separate memory modules in device 600. The processor 610 may be of any type suitable to the local technology network and may include one or more of general purpose computers, special purpose computers, microprocessors, Digital Signal Processors (DSPs) and processors based on a multi-core processor architecture, as non-limiting examples. Device 600 may have multiple processors, such as application specific integrated circuit chips that are time-dependent from a clock synchronized to the main processor.

In general, the various embodiments of the disclosure may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device. While various aspects of the embodiments of the disclosure are illustrated and described as block diagrams, flow charts, or using some other pictorial representation, it is well understood that the blocks, apparatus, systems, techniques or methods described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.

The present disclosure also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product comprises computer executable instructions, such as those included in program modules, executed in a device on a target real or virtual processor to perform the processes or methods described above with reference to any of figures 2 to 5. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In various embodiments, the functionality of the program modules may be combined or divided among the program modules as desired. Machine-executable instructions for program modules may be executed within a local device or within a distributed device. In a distributed facility, program modules may be located in both local and remote memory storage media.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram block or blocks to be performed. The program code may execute entirely on the computer, partly on the computer, as a stand-alone software package, partly on the computer and partly on a remote computer or entirely on the remote computer or server.

In the context of the present disclosure, computer program code or related data may be carried by any suitable carrier to enable a device, apparatus or processor to perform various processes and operations as described above. Examples of the carrier include a signal, computer readable medium.

The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a computer-readable storage medium would include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable reader-read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In some scenarios, multitasking and parallel processing may be advantageous. Also, while the above discussion contains several specific implementation details, these should not be construed as limitations on the scope of the disclosure, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.

Although the disclosure has been described in language specific to structural features and/or methodological acts, it is to be understood that the disclosure defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of the claims.

Claims

1. A method implemented at a network device, comprising:

determining, at the network device, a set of parameters based on channel information about a channel between the network device and a terminal device;

determining a precoding pattern for precoding data and reference signals for the terminal device based on the set of parameters, the precoding pattern indicating a non-linear precoding scheme used by the network device;

determining a reception mode for the terminal device to decode the precoded data and the precoded reference signal based on the precoding mode; and

and sending the indication of the receiving mode to the terminal equipment.

2. The method of claim 1, wherein determining the set of parameters comprises:

obtaining effective Channel State Information (CSI) from the channel information;

determining a first matrix for characterizing the channel based on the valid CSI, the first matrix being associated with at least one of:

a number of antennas of the network device;

a number of ports of the network device;

the number of antennas of the terminal device; and

a merging matrix of the terminal device; and

determining the set of parameters based on the first matrix.

3. The method of claim 2, wherein the set of parameters is associated with thomlinson-halauximab precoding THP and comprises at least one of:

a precoding matrix; and

and (5) feedback matrix.

4. The method of claim 2, wherein the set of parameters is associated with a Vector Perturbation (VP) and comprises at least one of:

a precoding matrix;

a power normalization factor; and

and (5) disturbing the vector.

5. The method of claim 1, wherein determining the set of parameters comprises:

obtaining complete Channel State Information (CSI) from the channel information;

determining a second matrix for characterizing the channel from the complete CSI, the second matrix being associated with at least one of:

a number of antennas of the network device;

a number of ports of the network device; and

the number of antennas of the terminal device; and

determining the set of parameters based on the second matrix.

6. The method of claim 5, wherein the set of parameters is associated with Thomlinson-Harashima precoding (THP) and comprises at least one of:

a precoding matrix;

a feedback matrix; and

a combining matrix for the terminal device.

7. The method of claim 5, wherein the set of parameters is associated with a Vector Perturbation (VP) and comprises at least one of:

a precoding matrix;

a power normalization factor;

a disturbance vector; and

a combining matrix for the terminal device.

8. The method of claim 5, wherein the parameter set is associated with a Vector Perturbation (VP), and determining the parameter set comprises:

updating the second matrix by successively decomposing the second matrix based on at least one of:

the geometric mean decomposition GMD is performed,

singular value decomposition SVD, and

GTD is decomposed by generalized trigonometric decomposition; and

determining the parameter set based on the updated second matrix.

9. The method of claim 1, further comprising:

performing a non-linear process on the data and a linear process on the reference signal;

and precoding the processed data and the reference signal based on the precoding mode.

10. A method implemented at a terminal device, comprising:

transmitting channel information about a channel between the network device and the terminal device to a network device;

receiving, from the network device, an indication of a receive mode for the terminal device to decode precoded data and precoded reference signals, the receive mode determined based on a precoding mode used to precode data and reference signals for the terminal device, the precoding mode indicating a non-linear precoding scheme used by the network device; and

decoding the precoded data based on the precoded reference signal and the reception mode.

11. The method of claim 10, wherein decoding the precoded data comprises:

determining weights associated with the precoding mode by decoding the precoded reference signal based on the reception mode; and

decoding the precoded data based on the weights.

12. The method of claim 10, further comprising:

determining a combing matrix for the terminal device based on the precoded reference signals.

13. A network device, comprising:

at least one processor; and

at least one memory including computer program code;

the at least one memory and the computer program code configured to, with the at least one processor, cause the terminal device at least to:

and sending the indication of the receiving mode to the terminal equipment.

14. The network device of claim 13, wherein the network device is caused to determine the set of parameters by:

a number of antennas of the network device;

a number of ports of the network device;

the number of antennas of the terminal device; and

a merging matrix of the terminal device; and

determining the set of parameters based on the first matrix.

15. The network device of claim 14, wherein the set of parameters is associated with thomlinson-halauximab THP and comprises at least one of:

a precoding matrix; and

and (5) feedback matrix.

16. The network device of claim 14, wherein the set of parameters is associated with a Vector Perturbation (VP) and comprises at least one of:

a precoding matrix;

a power normalization factor; and

and (5) disturbing the vector.

17. The network device of claim 13, wherein the network device is caused to determine the set of parameters by:

determining a second matrix for characterizing a channel from the complete CSI, the second matrix being associated with at least one of:

a number of antennas of the network device;

a number of ports of the network device; and

the number of antennas of the terminal device; and

determining the set of parameters based on the second matrix.

18. The network device of claim 17, wherein the set of parameters is associated with thomlinson-halauximab precoding THP and comprises at least one of:

a precoding matrix;

a feedback matrix; and

a combining matrix for the terminal device.

19. The network device of claim 17, wherein the set of parameters is associated with a Vector Perturbation (VP) and comprises at least one of:

a precoding matrix;

a power normalization factor;

a disturbance vector; and

a combining matrix for the terminal device.

20. The network device of claim 17, wherein the set of parameters is associated with a Vector Perturbation (VP), and determining the set of parameters comprises:

the geometric mean decomposition GMD is performed,

singular value decomposition SVD, and

GTD is decomposed by generalized trigonometric decomposition; and

determining the parameter set based on the updated second matrix.

21. The network device of claim 13, wherein the network device is further caused to:

22. A terminal device, comprising:

at least one processor; and

at least one memory including computer program code;

the at least one memory and the computer program code configured to, with the at least one processor, cause the network device at least to:

transmitting channel information about a channel between the network device and the terminal device to a network device; and

receiving, from the network device, an indication of a reception mode for the terminal device to decode precoded data and precoded reference signals, the reception mode being determined based on a precoding mode used to precode data and reference signals for the terminal device, the precoding mode indicating a non-linear precoding scheme used by the network device and being determined based on a set of parameters determined by the network device based on the channel information; and

decoding the precoded data based on the precoded reference signal and the indication.

23. The terminal device of claim 22, wherein the terminal device is caused to decode the precoded data by:

determining weights associated with the precoding mode by decoding the precoded reference signal based on the indication; and

decoding the precoded data based on the weights.

24. The terminal device of claim 22, wherein the complete channel state information, CSI, is obtained by the network device and further causes the terminal device to:

25. An apparatus for a non-linear precoding process, comprising:

means for determining, at a network device, a set of parameters based on channel information about a channel between the network device and a terminal device;

means for determining a precoding pattern for precoding data and reference signals for the terminal device based on the set of parameters, the precoding pattern indicating a non-linear precoding scheme used by the network device;

means for determining a reception mode for the terminal device to decode precoded data and precoded reference signals based on the precoding mode; and

means for sending an indication of the reception mode to the terminal device.

26. An apparatus for a non-linear precoding process, comprising:

means for transmitting channel information about a channel between the network device and the terminal device to the network device; and

means for receiving, from the network device, an indication of a reception mode for the terminal device to decode precoded data and precoded reference signals, the reception mode determined based on a precoding mode used to precode data and reference signals for the terminal device, the precoding mode indicating a non-linear precoding scheme used by the network device and determined based on a set of parameters determined by the network device based on the channel information; and

means for decoding the precoded data based on the precoded reference signal and the indication.

27. A non-transitory computer readable medium comprising program instructions for causing an apparatus to perform at least the method of any of claims 1-9.

28. A non-transitory computer readable medium comprising program instructions for causing an apparatus to perform at least the method of any of claims 10-12.