KR102441253B1 - Apparatus and method for receiving signal in wireless communication system - Google Patents

Abstract

The present disclosure relates to a communication technique that converges a 5G communication system for supporting a higher data rate after a 4G system with IoT technology, and a system thereof. The present disclosure provides intelligent services (eg, smart home, smart building, smart city, smart car or connected car, healthcare, digital education, retail business, security and safety related services, etc.) based on 5G communication technology and IoT-related technology. ) can be applied to
In the coded multi-user MIMO wireless communication system according to the present disclosure, a receiver includes: a transceiver for receiving a received signal from a transmitter; and a processor that continuously performs an interference cancellation operation based on the received signal, and performs soft decision estimation-based directing MIMO detection based on the interference-removed signal according to the interference cancellation operation.

Description

APPARATUS AND METHOD FOR RECEIVING SIGNAL IN WIRELESS COMMUNICATION SYSTEM

The present disclosure relates to a wireless communication system, and more particularly, to an apparatus and method for receiving a signal in a multi-input multi-output (MIMO) wireless communication system.

Efforts are being made to develop an improved 5G communication system or pre-5G communication system in order to meet the increasing demand for wireless data traffic after commercialization of the 4G communication system. For this reason, the 5G communication system or the pre-5G communication system is called a system after the 4G network (Beyond 4G Network) communication system or the LTE system after (Post LTE). In order to achieve a high data rate, the 5G communication system is being considered for implementation in a very high frequency (mmWave) band (eg, such as a 60 gigabyte (60 GHz) band). In order to alleviate the path loss of radio waves and increase the propagation distance of radio waves in the very high frequency band, in the 5G communication system, beamforming, massive MIMO, and Full Dimensional MIMO (FD-MIMO) are used. ), array antenna, analog beam-forming, and large scale antenna technologies are being discussed. In addition, for network improvement of the system, in the 5G communication system, an evolved small cell, an advanced small cell, a cloud radio access network (cloud radio access network: cloud RAN), an ultra-dense network (ultra-dense network) , Device to Device communication (D2D), wireless backhaul, moving network, cooperative communication, Coordinated Multi-Points (CoMP), and interference cancellation Technology development is underway. In addition, in the 5G system, the advanced coding modulation (ACM) methods FQAM (Hybrid FSK and QAM Modulation) and SWSC (Sliding Window Superposition Coding), and advanced access technologies FBMC (Filter Bank Multi Carrier), NOMA (non orthogonal multiple access), and sparse code multiple access (SCMA) are being developed.

On the other hand, the Internet is evolving from a human-centered connection network where humans create and consume information to an Internet of Things (IoT) network that exchanges and processes information between distributed components such as objects. Internet of Everything (IoE) technology, which combines big data processing technology through connection with cloud servers, etc. with IoT technology, is also emerging. In order to implement IoT, technology elements such as sensing technology, wired and wireless communication and network infrastructure, service interface technology, and security technology are required. , M2M), and MTC (Machine Type Communication) are being studied. In the IoT environment, an intelligent IT (Internet Technology) service that collects and analyzes data generated from connected objects and creates new values in human life can be provided. IoT is a field of smart home, smart building, smart city, smart car or connected car, smart grid, health care, smart home appliance, advanced medical service, etc. can be applied to

Accordingly, various attempts are being made to apply the 5G communication system to the IoT network. For example, in technologies such as sensor network, machine to machine (M2M), and MTC (Machine Type Communication), 5G communication technology is implemented by techniques such as beam forming, MIMO, and array antenna. there will be The application of a cloud radio access network (cloud RAN) as the big data processing technology described above is an example of the convergence of 5G technology and IoT technology.

It is an object of the present disclosure to improve the error rate performance of the system by designing a high performance MIMO detector for an coded multi-user MIMO system.

The present disclosure discloses a receiver in a coded multi-user MIMO wireless communication system. The receiver may include: a transceiver for receiving a received signal from the transmitter; and a processor that continuously performs an interference cancellation operation based on the received signal, and performs soft decision estimation-based directing MIMO detection based on the interference-removed signal according to the interference cancellation operation.

The processor may perform continuous soft decision estimation on the transmission symbol based on the received signal from which the interference has been removed.

The processor may determine the constellation set corresponding to the soft decision estimate value so that the constellation set corresponding to the soft decision estimate value converges to the constellation set corresponding to the transmission symbol.

The processor may determine the constellation set corresponding to the soft decision estimate value so that the size of the constellation set decreases as the index of the detection iteration increases and constitutes a nested permutation of the sets.

The processor may determine the constellation set corresponding to the soft decision estimate value so that each element converges to an element of the constellation set of the transmission symbol while maintaining the size of the constellation set constant as the index of the detection iteration increases.

The processor may determine soft decision estimate values in a current detection iteration based on soft decision estimate values in a previous detection iteration.

The processor may continuously determine soft decision estimate values in the current detection iteration based on the soft decision estimate values determined in the current detection iteration.

The processor may successively determine the interference cancellation and the soft decision estimate and associated metrics and variances.

The processor may determine external information using a metric and variance determined through a continuous process.

The receiver according to an embodiment of the present disclosure can efficiently determine external information for each sign bit.

A receiver according to an embodiment of the present disclosure may have excellent error rate performance.

1 is a conceptual diagram illustrating a transmitter and a receiver in a wireless communication system according to an embodiment of the present disclosure.
2 is a block diagram illustrating a transmitter in a wireless communication system according to an embodiment of the present disclosure.
3 is a block diagram illustrating a receiver in a wireless communication system according to an embodiment of the present disclosure.
4 is a block diagram illustrating a processor of a receiver according to an embodiment of the present disclosure.
5 is a flowchart illustrating an operation sequence of a receiver in a wireless communication system according to an embodiment of the present disclosure.
6 is a conceptual diagram illustrating a constellation set of soft decision estimation values determined by a receiver in a wireless communication system according to an embodiment of the present disclosure.
7 is a conceptual diagram illustrating a constellation set of soft decision estimation values determined by a receiver in a wireless communication system according to an embodiment of the present disclosure.
8 is a graph (8×32 MIMO, 64-QAM) illustrating frame error rate performance of a reception method according to the prior art and a reception method according to the present disclosure.
9 is a graph (8×32 MIMO, 64-QAM) showing frame error rate performance of a reception method according to the prior art and a reception method according to the present disclosure.

In describing the embodiments of the present disclosure, descriptions of technical contents that are well known in the technical field to which the present disclosure pertains and are not directly related to the present disclosure will be omitted. This is to more clearly convey the gist of the present disclosure without obscuring the gist of the present disclosure by omitting unnecessary description.

For the same reason, some components are exaggerated, omitted, or schematically illustrated in the accompanying drawings. In addition, the size of each component does not fully reflect the actual size. In each figure, the same or corresponding elements are assigned the same reference numerals.

Advantages and features of the present disclosure, and a method for achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below, but may be implemented in a variety of different forms, and only the present embodiments allow the present disclosure to be complete, and those of ordinary skill in the art to which the present disclosure pertains. It is provided to fully understand the scope of the invention, and the present disclosure is only defined by the scope of the claims. Like reference numerals refer to like elements throughout.

At this time, it will be understood that each block of the flowchart diagrams and combinations of the flowchart diagrams may be performed by computer program instructions. These computer program instructions may be embodied in a processor of a general purpose computer, special purpose computer, or other programmable data processing equipment, such that the instructions performed by the processor of the computer or other programmable data processing equipment are not described in the flowchart block(s). It creates a means to perform functions. These computer program instructions may also be stored in a computer-usable or computer-readable memory that may direct a computer or other programmable data processing equipment to implement a function in a particular manner, and thus the computer-usable or computer-readable memory. It is also possible for the instructions stored in the flowchart block(s) to produce an article of manufacture containing instruction means for performing the function described in the flowchart block(s). The computer program instructions may also be mounted on a computer or other programmable data processing equipment, such that a series of operational steps are performed on the computer or other programmable data processing equipment to create a computer-executed process to create a computer or other programmable data processing equipment. It is also possible that instructions for performing the processing equipment provide steps for performing the functions described in the flowchart block(s).

Additionally, each block may represent a module, segment, or portion of code that includes one or more executable instructions for executing specified logical function(s). It should also be noted that in some alternative implementations it is also possible for the functions recited in the blocks to occur out of order. For example, it is possible that two blocks shown in succession are actually performed substantially simultaneously, or that the blocks are sometimes performed in the reverse order according to the corresponding function.

At this time, the term '~ unit' used in this embodiment means software or hardware components such as FPGA or ASIC, and '~ unit' performs certain roles. However, '-part' is not limited to software or hardware. '~unit' may be configured to reside on an addressable storage medium or may be configured to refresh one or more processors. Thus, as an example, '~' refers to components such as software components, object-oriented software components, class components, and task components, and processes, functions, properties, and procedures. , subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. The functions provided in the components and '~ units' may be combined into a smaller number of components and '~ units' or further separated into additional components and '~ units'. In addition, components and '~ units' may be implemented to play one or more CPUs in a device or secure multimedia card. Also, in an embodiment, '~ unit' may include one or more processors.

1 is a conceptual diagram illustrating a transmitter and a receiver in a wireless communication system according to an embodiment of the present disclosure.

Referring to FIG. 1 , a wireless communication system according to an embodiment of the present disclosure may include a transmitter 100 and a receiver 200 . For example, the wireless communication system may be an coded multi-user multi-input multi-output (MIMO) system.

In the encoded multi-user MIMO system, the transmitter 100 may transmit a transmission symbol including code bits of an error correction code to the receiver 200 . The receiver 200 may perform MIMO detection to reconstruct an information stream of a transmission symbol transmitted from the transmitter 100 . For example, the receiver 200 may use a high-power MIMO detector that calculates external information for each sign bit of a transmission symbol in order to maximize reception performance.

The receiver 200 may use a list sphere decoding method or a linear detection method for MIMO detection. For example, when the list construction decoding method is used, the receiver 200 may select a candidate group of symbol vectors having a close Euclidean distance in the signal space based on a tree search. In the list reconstruction decoding method, sub-optimal detection may be possible by selecting a sufficiently large list size. However, the list structure decoding method may have very high complexity.

The receiver 200 may use a linear detection method based on linear transformation of a vector of a received signal. When using the linear detection method, the receiver 200 may perform low-complexity detection by approximating the MIMO detection to the transmission/reception situation of the single transmitter 100 through linear transformation. However, the linear detection method may deteriorate the reception performance.

Accordingly, the receiver 200 may use a low-complexity, high-performance MIMO detection method capable of efficiently and more accurately calculating external information for each sign bit.

The coded multi-user MIMO system according to an embodiment of the present disclosure may include K transmitters 100 using a single transmit antenna and a receiver 200 including N (≥K) receive antennas. For example, the transmitter 100 may be a terminal. The receiver 200 may be a base station.

에 포함되며, [수학식 1]의 전력 제한을 만족하도록 설정될 수 있다.Each transmitter 100 may generate a code by performing channel encoding on an information stream for transmission. Each transmitter 100 may generate the generated code in units of Q bits. Each transmitter 100 may sequentially transmit through a single transmit antenna by mapping the code of each Q-bit unit to one transmit symbol. For example, the Q sign bits of the k-th user may be c _k,1 , c _k,2 ,..., c _k,Q . A transmission symbol corresponding to the sign bit may be s _k . Each transmit symbol s _k is a set of 2 ^Q -binary constellations

, and may be set to satisfy the power limit of [Equation 1].

라 정의될 수 있다. 여기서, s는 성좌 집합

에 속하는 원소가 될 수 있다.The transmission symbol vectors of the K transmitters 100 are

can be defined as where s is the constellation set

It can be an element belonging to

The receiver 200 may receive the signal vector of [Equation 2] through N antennas.

는 N×K 크기의 채널 행렬일 수 있다. n은

을 공분산 행렬로 갖는 백색 가우시안 잡음 벡터일 수 있다. 신호 대 잡음비는

로 정의될 수 있다. 채널 행렬 H는 수신기(200)에 기저장될 수 있다.Here, h _k may be a channel vector of length N indicating propagation between the k-th transmitter 100 and the receiver 200 .

may be an N×K channel matrix. n is

It may be a white Gaussian noise vector having as a covariance matrix. signal-to-noise ratio

can be defined as The channel matrix H may be pre-stored in the receiver 200 .

The receiver 200 may include a soft-output MIMO maximum likelihood (ML) detector. The directing power MIMO ML detector may calculate extrinsic information about the sign bit c _k,q expressed as in [Equation 3] for a given received signal vector y.

와

는 각각 부호 비트 c_k,q가 0 또는 1인 송신 심볼 벡터들의 집합을 의미할 수 있다. [수학식 4]와 같이 집합 상

에서

의 최소값을

라 정의할 수 있다.here

Wow

may mean a set of transmission symbol vectors in which the sign bits c _k,q are 0 or 1, respectively. As in [Equation 4], on a set

at

the minimum value of

can be defined as

는 최대-로그 근사를 통해 [수학식 5]와 같이 표현될 수 있다.External information about sign bits c _k,q

can be expressed as [Equation 5] through maximum-log approximation.

의 값을 계산해야 할 수 있다. 각

의 계산은 2^KQ-1가지의 가설(hypothesis)에 대한 열거(enumeration)를 필요로 하기 때문에 높은 계산 복잡도를 요구할 수 있다. 이를 해결하기 위해 다양한 저복잡도 MIMO 검출 방법들이 제안될 수 있다.For directing MIMO ML detection, all 3-tuples (k, q, b) are

It may be necessary to calculate the value of each

Calculation of may require high computational complexity because it requires enumeration of 2 ^KQ-1 hypotheses. To solve this problem, various low-complexity MIMO detection methods may be proposed.

가 짧은 L개의 후보 심볼 벡터들의 리스트 L을 결정할 수 있다. 그리고 이를 기반으로 [수학식 6]의 메트릭(metric)

을 계산한 후, [수학식 7]의 외부 정보를 계산할 수 있다.For example, list sphere decoding (LSD)-based MIMO detection is based on Euclidean distance through tree search.

may determine a list L of short L candidate symbol vectors. And based on this, the metric of [Equation 6]

After calculating , the external information of [Equation 7] can be calculated.

Although this list structure decoding-based MIMO detection method shows suboptimal performance by selecting a sufficiently large list size, complexity may be very high.

를 이용하여 선형 변환을 수행할 경우, [수학식 8]의 결정 통계(decision statistic)

를 얻을 수 있다.Meanwhile, linear MIMO detection may be performed based on a linear transformation with respect to a given received signal vector y. Specifically, the weight matrix

When performing linear transformation using

can get

은 간섭과 잡음의 합에 대한 선형 변환의 결과일 수 있다. 가중 행렬 B는 다양한 조건(criterion)을 기반으로 설계될 수 있다. z_k가 가우시안 분포를 따른다는 가정 하에서 부호 비트 c_k,q에 대한 외부 정보를 [수학식 9]과 같이 계산할 수 있다.here

may be the result of a linear transformation on the sum of interference and noise. The weight matrix B may be designed based on various criteria. Under the assumption that z _k follows a Gaussian distribution, external information about the sign bit c _k,q can be calculated as in [Equation 9].

는 q번째 비트가 b인 송신 심볼들의 집합을 의미할 수 있다.

는 z_k의 분산일 수 있다.here

may mean a set of transmission symbols in which the q-th bit is b.

may be the variance of z _k .

The present disclosure relates to a receiver in an coded multi-user MIMO system and a method of operating the same, wherein the receiver cancels interference for performance MIMO detection, performs soft decision detection, determines related metrics and variances, and By determining the information, it is possible to efficiently calculate external information for each sign bit and to have excellent error rate performance. For example, the transmitter 100 may have a structure as shown in FIG. 2 . The receiver 200 may have a structure as shown in FIG. 3 .

2 is a block diagram illustrating a transmitter in a wireless communication system according to an embodiment of the present disclosure.

Referring to FIG. 2 , the transmitter 100 may include a transceiver 110 , a processor 120 , and a memory 130 .

The transceiver 110 may include a plurality of antennas. For example, the transceiver 110 may transmit a signal to the receiver 200 through a plurality of antennas. The transceiver 110 may receive a signal from the receiver 200 through a plurality of antennas.

The memory 130 may store various commands for the operation of the transmitter 100 . Various instructions may be executed by the processor 120 .

The processor 120 may perform an overall control operation for the transmitter 100 . For example, the processor 120 may execute various instructions stored in the memory 130 .

3 is a block diagram illustrating a receiver in a wireless communication system according to an embodiment of the present disclosure.

Referring to FIG. 3 , the receiver 200 may include a transceiver 210 , a processor 220 , and a memory 230 .

The transceiver 210 may include a plurality of antennas. For example, the transceiver 210 may transmit a signal to the transmitter 100 through a plurality of antennas. The transceiver 210 may receive a signal from the transmitter 100 through a plurality of antennas.

The memory 230 may store various commands for the operation of the receiver 200 . Various instructions may be executed by processor 220 .

The processor 220 may perform an overall control operation on the receiver 200 . For example, the processor 220 may execute various instructions stored in the memory 130 . For example, the processor 220 may have a structure as shown in FIG. 4 .

4 is a block diagram illustrating a processor of a receiver according to an embodiment of the present disclosure.

Referring to FIG. 4 , the processor 220 of the receiver 200 may include a MIMO detector 221 , a channel decoder 222 , a re-encoder 223 , a signal regenerator 224 , and an interference canceller 225 . can The MIMO detector 201 may calculate external information for each sign bit based on a given received signal. The MIMO detector 221 may transmit the calculated external information to the channel decoder 222 . The channel decoder 222 may decode the information stream through a decoding algorithm based on the external information received from the MIMO detector 221 . The re-encoder 223 may perform a re-encoding operation based on the decoded information stream. The signal generator 224 may regenerate a signal based on the decoded information stream. The interference canceller 225 may remove an interference component from the received signal. The above-described MIMO detection, channel decoding, and interference cancellation processes may be repeatedly performed until a predetermined turbo repetition number I is reached or all information streams are successfully restored. The present disclosure focuses on the operation process of the directing power MIMO detector 221 .

Hard-decision detection in the present disclosure may mean limiting an estimated value of a symbol to a constellation set that a transmission symbol can have. On the other hand, soft-decision detection may mean selecting an estimated value of a symbol from a larger set including a constellation set.

에 대한 추정 값(estimate)

가 주어진 상황을 고려할 수 있다.

는 간섭 신호로 고려될 수 있다. 수신 신호 벡터 y에서 간섭을 제거하여 얻은 신호

는 [수학식 10]과 같이 정리될 수 있다.Before describing the MIMO detection process of the present disclosure in detail, a mathematical expression of external information may be looked at. First, it is possible to detect the transmission symbol s _k of the k-th transmitter, and the symbol of another transmitter

estimate for

can be considered in the given situation.

may be considered as an interference signal. The signal obtained by removing the interference from the received signal vector y

can be arranged as in [Equation 10].

를 이용한 선형 변환을 기반으로 [수학식 11]의 결정 통계(decision statistic)

를 얻을 수 있다.Similar to linear MIMO detection, the weighting matrix

Decision statistic of [Equation 11] based on a linear transformation using

can get

은 남아있는(residual) 간섭과 잡음의 합에 대한 선형 변환의 결과일 수 있다. 부호 비트 c_k,q에 대한 외부 정보는 [수학식 12]와 같이 계산될 수 있다.here

may be the result of a linear transformation on the sum of residual interference and noise. External information on the sign bit c _k,q may be calculated as in [Equation 12].

는 c_k,q=b의 사건과 관련한 메트릭일 수 있다.

는

의 분산이며,

일 수 있다. 다시 말해, 부호 비트 c_k,q에 관한 외부 정보는 간섭 심볼

에 관한 추정 값

를 기반으로

상에서 s_k를 탐색함으로써 결정될 수 있다.here

may be a metric related to the event of c _k,q =b.

Is

is the dispersion of

can be In other words, the external information about the sign bit c _k,q is the interference symbol

estimated value about

based on

It can be determined by searching for s _k in

와 분산

의 비(ratio)인

을 결정해야 할 수 있다. 본 개시의 연출력 MIMO 검출은 송신 심볼에 대한 연판정 추정 값

와 간섭이 제거된 수신 신호

를 연속적으로 갱신(update)하면서, 외부 정보의 계산을 위한 메트릭

와 분산

을 결정함으로써 수행될 수 있다. 그리고 연속적인 처리 과정에서 결정된

의 값들 중 최대값을 이용하여 외부 정보를 결정할 수 있다.To calculate the external information of [Equation 12], the metric

with dispersion

which is the ratio of

may have to decide The performance MIMO detection of the present disclosure is a soft decision estimation value for a transmission symbol

and interference-free received signal

while continuously updating the metric for the calculation of external information

with dispersion

This can be done by determining and determined in a continuous process

External information may be determined using the maximum value among the values of .

5 is a flowchart illustrating an operation sequence of a receiver in a wireless communication system according to an embodiment of the present disclosure.

Referring to FIG. 5 , steps S501 to S507 may represent the overall operation process of detecting MIMO performance according to an embodiment of the present disclosure. Here, I _DET is the number of iterations inside the detector, and t may be an index of detection iterations. The operation process of MIMO detection may be summarized as follows.

Step S501 may be initialization.

Step S502 may be a step of generating a reception signal from which interference is removed.

Step S503 may be a step of performing soft decision detection on the transmission symbol.

Step S504 may be a step of determining a relevant metric and variance.

Step S507 may be a step of determining external information.

는 각각 t번째 검출 반복에서 송신 심볼 s_k에 대한 연판정 추정 값, c_k,q=b 의 이벤트와 관련한 메트릭, 남아있는 간섭 및 잡음의 분산,

를 의미할 수 있다.Steps S501 to S504 may be repeatedly performed I _DET times. In steps S501 to S504

is the soft decision estimate for the transmitted symbol sk at the tth detection iteration, respectively, the metric related to the event of c _{k ,q} =b, the variance of the remaining interference and noise,

can mean

,

그리고

에 대한 초기값을 설정할 수 있다. 예를 들어, 검출기(501)는 초기 추정 값

을 다음과 같이 결정할 수 있다.The detector 221 may perform an initialization operation (S501). For example, the detector 221 is

,

and

You can set the initial value for . For example, the detector 501 may have an initial estimate

can be determined as

을 영 벡터로 결정할 수 있다. 예를 들어,

=0 일 수 있다.The detector 221 is an initial estimated value

can be determined as a zero vector. for example,

=0.

을 정규화된 정합 필터링을 통한 추정 값으로 결정할 수 있다. 예를 들어,

일 수 있다. 검출기(221)는 초기 추정 값

을 영점 강제(zero-forcing, ZF) 등화를 통한 비제약(unconstraint) ML 추정 값으로 결정할 수 있다. 예를 들어,

일 수 있다.The detector 221 is an initial estimated value

can be determined as an estimated value through normalized matched filtering. for example,

can be The detector 221 is an initial estimated value

can be determined as an unconstraint ML estimate through zero-forcing (ZF) equalization. for example,

can be

을 최소제곱평균오차(minimum mean-square error) 등화를 통한 추정 값으로 결정할 수 있다. 예를 들어,

일 수 있다.The detector 221 is an initial estimated value

can be determined as an estimated value through minimum mean-square error equalization. for example,

can be

의 값은 0으로 결정할 수 있다.The detector 221 is

The value of can be determined to be 0.

를 생성할 수 있다. 검출기(221)는 [수학식 13]을 통해 각각의 신호를 결정할 수 있다.The detector 221 may generate an interference-removed signal ( S302 ). For example, the detector 221 is an interference-removed received signal.

can create The detector 221 may determine each signal through [Equation 13].

를 결정할 수 있다. 검출기(221)는 [수학식 14]에 기초하여 연판정 추정 값

을 결정할 수 있다.The detector 221 may determine an estimated soft decision value ( S503 ). For example, the detector 221 may determine a soft decision estimate for the transmitted symbol.

can be decided The detector 221 is a soft decision estimated value based on [Equation 14].

can be decided

는 t번째 검출 반복에서 연판정 추정 값

가 위치하는 성좌 집합으로서, 최적화 문제의 탐색 공간일 수 있다.here

is the soft decision estimate at the tth detection iteration

As a constellation set in which is located, it may be a search space for an optimization problem.

일 수 있다. 이때, [수학식 14]는 경판정 검출을 위한 수학식일 수 있다. 만약 모든

에 대하여

를 만족할 경우, 모든 간섭이 제거된 상태이므로 단일 송신기(100)에 대한 송수신 상황과 동일할 수 있다. 검출기(221)는 [수학식 15]에 기초하여 정규화된 정합 필터(matched filter)를 통해 s_k에 대한 최적의 경판정 추정 값을 결정할 수 있다.for example,

can be In this case, [Equation 14] may be an equation for hard decision detection. if all

about

is satisfied, since all interference is removed, the transmission/reception situation for the single transmitter 100 may be the same. The detector 221 may determine an optimal hard decision estimation value for _sk through a normalized matched filter based on [Equation 15].

에 의해 검출 오류가 발생할 수 있다. 이때

는

의 분산을 가질 수 있다.Here, Q(X) may be a slicing or quantization function that maps x to a 2 ^Q -binary constellation set A. The hard decision detection is a linearly transformed noise

may cause a detection error. At this time

Is

can have a variance of

일 수 있다. 간섭이 잘못 제거된 수신 신호는 [수학식 16]와 같이 표현될 수 있다.Conversely, a case in which a hard decision estimation value for a specific j-th symbol is different from an actual transmission symbol may be considered. for example,

can be The received signal from which interference is erroneously removed may be expressed as [Equation 16].

에 대하여 가중 벡터

를 이용한 선형 변환을 수행할 경우 [수학식 17]에 기초하여 결정 통계

를 결정할 수 있다.Similarly, the detector 221 is

weight vector against

Decision statistics based on [Equation 17] when performing linear transformation using

can be decided

의 크기에 따라 달라질 수 있다. 예를 들어,

를 경판정 검출로써 성좌 집합 A에 포함시킬 경우,

가 가질 수 있는 최소 크기는 성좌 집합 A에 포함되는 서로 다른 두 심볼 사이의 최소 유클리드 거리의 두 배가 될 수 있다. 이를 통해 송신 심볼의 경판정 검출 과정에서 발생하는 최소 양자화 에러(quantization error)의 크기와 연관이 있음을 알 수 있다.In [Equation 17], the judgment error rate due to the remaining interference component is

may vary depending on the size of for example,

is included in constellation set A as a hard decision detection,

The minimum size that can have may be twice the minimum Euclidean distance between two different symbols included in the constellation set A. Through this, it can be seen that it is related to the magnitude of the minimum quantization error occurring in the hard decision detection process of the transmission symbol.

가 송신 심볼의 성좌 집합 에 포함되도록 추정 값의 성좌 집합

를 결정할 수 있다. 예를 들어, 검출기(221)는 [수학식 18]과 같이

가 t의 증가에 따라 크기를 감소시키면서 집합들의 내포된(nested) 순열을 구성하도록 결정할 수 있다.The detector 221 according to an embodiment of the present disclosure has an estimated value as the number of detection repetitions t increases.

The constellation set of the estimated value so that is included in the constellation set of the transmission symbol

can be decided For example, the detector 221 is as in [Equation 18]

It can be decided to construct nested permutations of sets while decreasing in size as t increases.

의 크기는 일정하지만,

의 각 원소가 A의 원소로 수렴하도록

를 결정할 수 있다. 예를 들어, 도 6 및 도 7은 그레이 라벨링된(Gray-labelled) 4-PAM 성좌에서 추정 값의 성좌 집합

을 도시한다.Also, as t increases, the detector 221

is the same size, but

so that each element of converges to an element of A.

can be decided For example, FIGS. 6 and 7 are constellation sets of estimated values in a gray-labeled 4-PAM constellation.

shows

에 기초하여 t번째 검출 반복에서의 연판정 추정 값

을 결정할 수 있다. 이 경우 검출기(221)는

를 모두 한 번에 결정할 수 있다. 반면, 검출기(221)는 이전 검출 반복에서의 연판정 추정 값

와 t번째 검출 반복에서의

에 기초하여 t번째 검출 반복에서의 연판정 추정 값

을 결정할 수 있다. 예를 들어, 검출기(221)는

를 순차적으로 결정할 수 있다.Detector 221 determines the soft decision estimate from previous detection iterations.

Soft decision estimate at the t-th detection iteration based on

can be decided In this case, the detector 221 is

can all be determined at once. On the other hand, the detector 221 calculates the soft decision estimate in the previous detection iteration.

and at the tth detection iteration

Soft decision estimate at the t-th detection iteration based on

can be decided For example, the detector 221 is

can be sequentially determined.

과 무관하게 t번째 검출 반복에서의 연판정 추정 값

을 결정할 수 있다. 또한, 검출기(221)는 [수학식 19]에 기초하여 새롭게 결정한 추정 값과

의 사이의 내분점을 할 수 있다.On the other hand, the detector 221 is a soft decision estimation value in the previous detection iteration.

The soft decision estimate at the tth iteration of detection irrespective of

can be decided In addition, the detector 221 is a newly determined estimated value based on [Equation 19] and

It is possible to make an internal division between

는 연판정 추정 값의 변화율을 나타내는 파라미터로서, 1에 가까운 값일수록 새롭게 결정한 추정 값이 더 반영될 수 있다.here

is a parameter representing the rate of change of the soft decision estimated value, and the closer the value is to 1, the more the newly determined estimated value may be reflected.

와 분산

을 결정할 수 있다. 이때, 검출기(221)는 S503 단계에서 송신 심볼에 대한 연판정 추정 값을 결정하는 것과는 달리 경판정 추정 값에 기초하여 메트릭 및 분산을 결정할 수 있다. 예를 들어, 검출기(221)는 [수학식 20] 및 [수학식 21]에 기초하여 메트릭

및 분산

을 결정할 수 있다.The detector 221 may determine the metric and variance (S304). For example, the detector 221 may

with dispersion

can be decided In this case, the detector 221 may determine the metric and variance based on the hard decision estimate value, unlike determining the soft decision estimate value for the transmission symbol in step S503 . For example, the detector 221 is a metric based on [Equation 20] and [Equation 21].

and dispersion

can be decided

를 결정할 수 있다. 검출기(221)는 S501 내지 S506 단계를 통해

와

을 모두 결정할 수 있다. 검출기(221)는 S507 단계에서 [수학식 22]에 기초하여

중

의 값을 결정할 수 있다.The detector 221 may determine external information (S507). For example, the detector 221 has external information about the sign bit c _k,q

can be decided The detector 221 through steps S501 to S506

Wow

can all be determined. The detector 221 is based on [Equation 22] in step S507

middle

can determine the value of

의 값들 중 최대값을 이용하여 외부 정보를 결정할 수 있다. 검출기(221)는 결정된

의 값에 기초하여 부호 비트 c_k,q에 대한 외부 정보

를 [수학식 23]을 이용하여 결정할 수 있다.Detector 221 determines in a continuous process

External information may be determined using the maximum value among the values of . The detector 221 is determined

External information about sign bits c _k,q based on the value of

can be determined using [Equation 23].

8 is a frame error rate (FER) performance graph in a range of 4 to 9 dB of an LSD-based receiver, an MMSE-SIC receiver, and an SPA receiver in a wireless communication system according to an embodiment of the present disclosure.

9 is a graph of FER performance in a range of 0 to 5 dB of an LSD-based receiver, an MMSE-SIC receiver, and an SPA receiver in a wireless communication system according to an embodiment of the present disclosure.

의 FER 성능 측정을 위해, 준정적 레일레이 페이딩 채널 모델이 적용될 수 있다. 또한, 채널 부호는 (1200, 600) NR LDPC 부호 및 16-비트 CRC가 사용될 수 있다. 본 개시의 일실시예에 따른 SPA 수신기는 LSD 기반 수신기보다 우수한 오율 성능을 가질 수 있다. 또한, 본 개시의 일실시예에 따른 SPA 수신기는 역행렬 연산을 수행하지 않으면서도 MMSE-SIC 수신기 보다 우수한 오율 성능을 가질 수 있다.8 and 9 , the wireless communication system may be an 8×32 and 8×64 MIMO system using 64-QAM modulation. LSD-based receiver (L=1024), MMSE-SIC receiver (parallel hard decision interference cancellation), and SPA receiver of the present disclosure

To measure the FER performance of , a quasi-static Rayleigh fading channel model may be applied. In addition, as the channel code, (1200, 600) NR LDPC code and 16-bit CRC may be used. The SPA receiver according to an embodiment of the present disclosure may have better error rate performance than the LSD-based receiver. In addition, the SPA receiver according to an embodiment of the present disclosure may have superior error rate performance than the MMSE-SIC receiver without performing an inverse matrix operation.

Claims (10)

In a coded multi-user MIMO wireless communication system,
a transceiver for receiving a received signal from the transmitter; and
a processor connected to the transceiver;
The processor is
Performing a continuous interference cancellation operation based on the received signal, performing MIMO detection based on a soft decision estimation value based on a signal from which interference has been removed according to the interference cancellation operation,
successively determining metrics and variances associated with the interference-removed signal and the soft decision estimate;
A receiver for determining external information based on a maximum value of the continuously determined ratios of the metric and the variance. The method of claim 1,
and the processor performs continuous soft decision estimation on the transmission symbol based on the received signal from which the interference has been removed. 3. The method of claim 2,
The processor is configured to determine a constellation set corresponding to the soft decision estimate value so that the constellation set corresponding to the soft decision estimate value converges to the constellation set corresponding to the transmission symbol. 4. The method of claim 3,
and the processor is configured to determine a constellation set corresponding to the soft decision estimate value so that a size of the constellation set decreases as an index of detection iteration increases and constitutes a nested permutation of the sets. 4. The method of claim 3,
The processor determines a constellation set corresponding to the soft decision estimate value so that each element converges to an element of the constellation set of a transmission symbol while maintaining a constant size of the constellation set as the index of the detection iteration increases. 3. The method of claim 2,
wherein the processor determines soft decision estimate values in a current detection iteration based on soft decision estimate values in a previous detection iteration. 3. The method of claim 2,
wherein the processor continuously determines soft decision estimate values in a current detection iteration based on the soft decision estimate values determined in the current detection iteration. The method of claim 1,
The processor is the signal from which the interference is removed

, and the soft decision estimate

A receiver for continuously determining the metric and the variance by continuously updating . The method of claim 1,
The processor determines the external information based on the following equation,
[Equation]

here

is the metric related to the event of c _k,q =b,

Is

is the dispersion of
The external information is the metric

and the dispersion

which is the ratio of

Based on the successively determined

A receiver determined based on a maximum value among the values of . A method of operating a receiver in a coded multi-user MIMO wireless communication system,
using a transceiver to receive a received signal from a transmitter; and
performing, by using a processor, a continuous interference cancellation operation based on the received signal;
performing MIMO detection based on a soft decision estimation value based on a signal from which interference has been removed according to the interference cancellation operation;
successively determining metrics and variances associated with the interference-removed signal and the soft decision estimate; and
and determining external information based on a maximum value among the continuously determined ratios of the metric and variance.

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