KR102056403B1 - Data-Aided Active User Detection for Massive Machine Type Communications - Google Patents

Abstract

Proposed in the present invention is a method for simultaneously estimating and detecting effective users, channels, and data for unauthorized wireless communication in a large-scale multi-input/multi-output system. According to the present invention, the method comprises the steps of: initializing an active vector of a user, an observed value of a pilot signal, an observed value of a data symbol, and the number of repetitions to perform a greed algorithm; estimating an active user after performing the initialization, and updating an index detected in the active vector corresponding to the active user; estimating a channel initial value and the data symbol using the active vector; updating a residual vector for estimating the active user; and determining whether all users have been identified and repeating from a step of detecting active users until all users have been identified.

Description

Data-Aided Active User Detection for Massive Machine Type Communications for Simultaneous User, Channel, and Data for Unlicensed Wireless Communication in a Large-scale Multiple Input / Output System

The present invention relates generally to methods and apparatus for detecting and recovering data, and more particularly to methods and apparatus for detecting and estimating active users and data utilizing data received observations for large scale communication systems.

Wireless and mobile communications establish communication between two or more devices or users, where data is transferred from one device to another. In order for communication to operate efficiently, the wireless communication system allocates resources efficiently and appropriately.

In the conventional fourth generation communication system, a high data rate and stable communication have been developed for a limited type of service for people-oriented communication. However, with the recent development of communication networks, the number of wireless communication devices connected to one base station has increased exponentially.

As communication devices increase exponentially, there is an increasing need for large-scale machine type communication while supporting human-oriented communication systems. There is a need for a communication network capable of supporting a very large number of devices or users. To support this random communication, the 3rd Generation Partnership Project (3GPP) Communication Standards Conference calls for the following three requirements for fifth-generation communication systems:

1) Enhanced Multimedia Broadband (eMMB)

2) Massive Machine Type Communication (mMTC)

3) Ultra-reliable Low-Latency Communication (uRLLC)

When using a permission-based communication method that communicates with the permission of a base station, as in the existing LTE system, much more computation and resource allocation is required. Therefore, in order to satisfy the requirements of the fifth generation communication, a grant-free communication technique for communicating without permission of a base station has been proposed. In the case of unauthorized communication, since the prior resource allocation by the base station is omitted in order to connect with the network, the required resources and delay time can be greatly reduced. Because only a small percentage of devices actually communicate in the mMTC system, for efficient unauthorized communication, the base station identifies the active user (ie, the user and device transmitting the data) and performs channel estimation. The process is necessary. For this purpose, in order to identify an active user and recover data, the user performs user detection and communication using pilot data known to the base station.

명의 사용자는 단일 안테나를 구비한 기지국과의 통신을 설정하기 위해 단일 안테나를 갖는다고 가정한다. 기지국과 통신하는

번째 사용자는 자신의 파일럿 심볼

과 랜덤 고유 사용자 식별 코드

를 가지고 있다고 가정한다. 하나의 전송 프레임에서 실제로 기지국으로 데이터를 전송하는 활성 사용자(active user)는

명이라고 가정한다.

는 활성화된 사용자를 표현하는 변수이며 활성 사용자에 대한 성분

, 비활성 사용자에 대한 성분

을 갖는다. 따라서, 셀 내의 모든

명의 사용자들의 활동성은 벡터

을 통해 표현 가능하다. 각 사용자가 단일 프레임을 통해 하나의 파일럿 심볼 및

개의 데이터 심볼을 전송하는 상황을 가정하면 기지국에서 수신한 관측 값

은 다음과 같이 표현 가능하다.Typically in mMTC unauthorized communication scenarios

It is assumed that two users have a single antenna to establish communication with a base station having a single antenna. Communicating with the base station

User is his pilot symbol

And random unique user identification code

Assume that we have An active user who actually sends data to a base station in one transmission frame

Assume the name

Is the variable representing the active user and the component for the active user

, Ingredients for inactive users

Has Thus, all within the cell

Activity of users

Can be expressed through Each user has one pilot symbol and one

Suppose the base station receives two data symbols

Can be expressed as:

(1)

(One)

는 복소 채널 이득,

은 관측 잡음을 나타낸다. 파일럿 심볼이

, 공동채널

,

는 같은 프레임내에서 변하지 않는다고 가정하면, (1)은 다음과 같이 표현 가능하다.here

The complex channel gain,

Represents observed noise. Pilot symbol

, Common channel

,

Assuming that does not change within the same frame, (1) can be expressed as

(2)

(2)

,

을 나타낸다.

번째 사용자는 자신의 사용자 식별 코드를 사용하여

번째 타임 슬롯에서 데이터 심볼

을 전송한다고 가정하면 기지국에서 수신한

번째 데이터 심볼에 대한 관측 값

은 다음과 같이 주어진다.From here

,

Indicates.

User uses his own user identification code

Symbol in the first time slot

Suppose we send a

Observed value for the first data symbol

Is given by

(3)

(3)

라고하면, 데이터에 대한 관측벡터

는 아래와 같이 다시 표현 가능하다.Data symbol vector here

If you say, the observation vector for the data

Can be rewritten as

(4)

(4)

은 잡음 벡터를 나타낸다. From here

Denotes a noise vector.

를 이용하여 공동 채널

및 사용자 활동 벡터

를 추정한다. 채널 다중화를 위해 비직교 코드를 사용하는 상황에서, 시스템 행렬

는 뚱뚱한 형태를 나타내어 유일한 해를 구할 수 없는 과소결정시스템(underdetermined system)을 갖게 된다. 일반적으로 과소결정시스템에서

의 정확한 추정은 불가하지만 사용자 활동이 희소한 특성 (

의 대부분의 요소가 0 임)을 이용하면 압축센싱기법을 이용하여 벡터

를 정확하게 복구가 가능하다. 기존의 AUD기법의 경우 파일럿 심볼

만을 사용하여 데이터 심볼에 대한 측정값을 효과적으로 이용하지 않았다.In previous studies, pilot observations were used for Active User Detection (AUD).

Using common channels

And user activity vectors

Estimate System matrix in the case of using non-orthogonal code for channel multiplexing

Has an underdetermined system in its fat form that cannot be uniquely solved. Generally in underdetermined systems

Although accurate estimates of the

Most elements of 0 are zero).

It is possible to recover accurately. Pilot symbol for the conventional AUD technique

Only the measurements of data symbols were not used effectively.

The technical problem to be achieved by the present invention is to provide an effective strategy for active user detection, channel estimation and data detection over conventional approaches without increasing resources such as code factor or multiple pilot transmission. In particular, it is intended to simultaneously estimate the data transmitted by a user and the activated user and channel by using a property having a common sparsity between the pilot and the data vector to improve active user detection.

In one aspect, an effective user, channel, and data simultaneous estimation and detection method for an unauthorized wireless communication in a large-scale multiple input / output system is a user's active vector, a pilot signal observation value, an observation value of a data symbol to perform a greed algorithm. And initializing a repetition number, estimating an active user after performing initialization, updating an index detected from an active vector corresponding to the active user, estimating a channel initial value and a data symbol using the active vector, and active Updating the residual vector for estimating the user and determining whether all the users have been identified and repeating the steps from detecting the active user until all the users have been identified.

After performing the initialization, estimating the active user and updating the index detected from the active vector corresponding to the active user assume that the observed value of the pilot signal and the observed value of the data symbol in the frame have the same sparse structure. Assuming they have a constant size, we estimate the active user without dependence on the data symbols.

Estimating channel initial values and data symbols using an active vector includes estimating channel initial values and data symbols using a block coordinate descent (BCD) technique to find a channel and data symbol that maximizes an objective function. The process repeats two processes of fixing the data symbol, optimizing the channel and correcting the channel by fixing the channel and optimizing the data symbol. At this time, the discretized data symbol is approximated, and since the channel and data symbol which are variables of the objective function are convex, the optimal estimation value is found through the BCD technique that converges to the optimal solution when the objective function is convex for all variables. .

In another aspect, an apparatus for simultaneously estimating and detecting an effective user, a channel, and data for an unauthorized wireless communication in a large-scale multiple input / output system is configured to perform an active vector of a user, observation values of a pilot signal, and data symbols to perform a greed algorithm. An initialization unit for initializing the observed value and the number of repetitions, estimating an active user after performing initialization, updating an index detected from an active vector corresponding to the active user, estimating a channel initial value and a data symbol using the active vector, A detection and estimating unit for updating the residual vector for estimating the active user, and determining whether all users have been identified, and determining from the step of detecting the active user until all users are identified.

According to embodiments of the present invention, the calculation time can be 4.15 times faster than that of the conventional method, and both pilot and data measurement can be used to simultaneously detect or support 75% more active users than the conventional method. The proposed method shows much better channel estimation performance than OMP and BSASP. Thus, the present invention allows a significant amount of performance improvement to be achieved with less computation time compared to existing approaches.

1 is a conceptual diagram illustrating an uplink of a large-scale machine communication system according to an embodiment of the present invention.
2 is a flowchart illustrating an active user detection and channel estimation strategy according to an embodiment of the present invention.
3 is a graph illustrating active user detection performance according to an embodiment of the present invention.
4 is a graph illustrating channel estimation performance according to an embodiment of the present invention.
5 is a diagram illustrating a configuration of an active user detection and channel estimation apparatus according to an embodiment of the present invention.

을 이용하여 대규모 기계 유형 시스템에서 활성 사용자 검출, 채널 및 데이터의 공동 검출 및 추정을 향상시킬 수 있는 방법을 제안한다. 이하, 본 발명의 실시 예를 첨부된 도면을 참조하여 상세하게 설명한다. In the present invention, data symbols

We propose a method that can improve the joint detection and estimation of active user detection, channel and data in a large machine type system. Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a conceptual diagram illustrating an uplink of a large-scale machine communication system according to an embodiment of the present invention.

As shown in FIG. 1, a situation in which the device in the coverage 110 is communicated randomly with some of the inactive user 120 and the active user 130 is included. The users participating in the communication transmit pilot and data symbols using a unique code of a specific frame and estimate the active user 130 using the received measurement. When the active user 130 is detected and identified through this, the corresponding data, channel estimation, and residual value update are performed in stages.

및

에 기초하여 사용자 활동변수 벡터

, 채널 변수

및 데이터 심볼

을 동시에 추정해야 한다. 본 발명에서는 최대 우도 (Maximuim likelihood, ML) 추정을 기반으로 이들 변수들을 동시에 추정하고자 했으며, 아래와 같이 나타낼 수 있다.Measurement values for communication with users in the mMTC system

And

Activity variable vector based on

, Channel variables

And data symbols

Should be estimated at the same time. In the present invention, these variables were simultaneously estimated based on the maximum likelihood (ML) estimation, which can be expressed as follows.

(5)

(5)

(6)

(6)

는 아래 수식을 이용하여 추정 가능하다.Calculating (6) here is very difficult to use because it requires a very large amount of computation. Greed algorithms such as OMP are known as methods to reduce the amount of computation required for estimation, and optimal estimation is possible in a specific environment. Using greed algorithm, user's activity variable

Can be estimated using the equation below.

(7)

(7)

와

에 대한

의 모든 후보에 대한 비용함수 값을 순차적으로 계산하여 최대값을 추정한다. 본 발명에서는 탐욕 알고리즘을 이용하여 상당한 연산량을 줄이고 효율적으로 처리할 수 있는 방법을 제안한다.Using the greed algorithm

Wow

For

The maximum value is estimated by sequentially calculating the cost function values for all candidates. The present invention proposes a method that can reduce the amount of computation and process efficiently using greed algorithm.

2 is a flowchart illustrating an active user detection and channel estimation strategy according to an embodiment of the present invention.

The present invention performs Data-Aided Active User Detection (DA-AUD) using data observation values based on the conventional greed algorithm. The process of estimating the active user based on greed algorithm is performed according to the following process.

The proposed active user detection and channel estimation method initializes the active vector of the user, the observed value of the pilot signal, the observed value of the data symbol, and the number of repetitions to perform the greed algorithm (210). And updating the index detected in the active vector corresponding to the active user (220), estimating a channel initial value and a data symbol using the active vector (230), and updating the residual vector for estimating the active user. And 240 determining whether all users have been identified, and repeating from detecting active users until all users have been identified.

In step 210, the user's active vector, the observed value of the pilot signal, the observed value of the data symbol and the number of repetitions are initialized to perform the greed algorithm. Data reception and Fourier transform 211 are performed, and resource demapping 212 is performed.

과 파일럿에 대한 잔여벡터

, 데이터에 대한 잔여벡터를

, 반복횟수

를 아래와 같이 초기화한다. First, the user's active vector to perform the greed algorithm

and Residual Vector for Pilot

, The residual vector for the data

, Repeat count

Initialize as follows.

(8)

(8)

In step 220, after performing initialization, the active user is estimated, and the index detected in the active vector corresponding to the active user is updated. It is assumed that the observed value of the pilot signal and the observed value of the data symbol in the frame have the same sparse structure, and the active user is estimated without dependence on the data symbol by assuming that the data symbols have a constant size.

에서 검출된 인덱스에 해당되는

로 업데이트한다. 여기에서,

는 검출된 사용자의 인덱스를 나타낸다. 활성사용자를 검출하는 자세한 방법은 명세서에서 후술한다. Detect active user with dominant influence and active vector corresponding to user

For the index detected by

Update to. From here,

Represents the index of the detected user. Detailed methods for detecting active users are described later in the specification.

In step 230, the channel initial value and data symbol are estimated using the active vector.

를 이용하여

를 최대화하는

를 추정한다. 자세한 추정방법은 후술한다.Given in step 220

Using

To maximize

Estimate A detailed estimation method will be described later.

와

를 아래와 같이 업데이트한다.In step 240, the residual vector for estimating the active user is updated. Residual vector to estimate the next active user

Wow

Update to

(10)

10

(11)

(11)

이다. here

to be.

In step 250, it is determined whether all users have been identified, and from step 220 of detecting active users until all users have been identified.

만 이용하여 데이터 전송을 위한 관측값

를 이용하지 못한다. 프레임내의

와

가 동일한 희소구조를 가지고 있다고 가정하면 아래와 같은 수식을 이용하여 활성 사용자 검출이 가능해진다. The details of the active user selection technique for step 220 are described below. Equation (7) can be used to detect active users. Conventional greed algorithms can detect using the inner product of the residual vector and the system matrix. However, in the prior art, pilot observations

Observation for data transmission using only

Cannot be used. In a frame

Wow

Assuming that has the same sparse structure, active users can be detected using the following equation.

(12)

(12)

이 증가하는 경우 연산량이 지수적으로 증가하게 된다. 데이터 심볼들이 일정한 크기를 갖는다고 가정하면, 식 (12)는 다음과 같이 표현이 가능하다.However, the number of data symbols

If this increases, the amount of computation increases exponentially. Assuming that the data symbols have a constant size, equation (12) can be expressed as follows.

(13)

(13)

Using equation (13), we can estimate the active user without dependence on the data symbols in.

를 최대화하는

와

를 찾기 위해서 닫힌 형식으로 구해내기 어렵지만, 이러한 문제를 해결하기 위해 BCD(Block Coordinate Descent) 기법은 효율적으로 공동 최적화 문제를 해결할 수 있다. BCD기법은 다른 변수그룹을 고정시키고 각 변수 그룹을 하나씩 최적화하며, 본 문제에서 BCD는

을 고정시키고

에 대한 최적화와

을 고정시키고

에 대해 최적화하여 보정하는 두 과정을 반복적으로 수행하게 된다. Next, in step 230 the objective function

To maximize

Wow

In order to solve this problem, it is difficult to obtain a closed form, but the Block Coordinate Descent (BCD) technique can solve the co-optimization problem efficiently. The BCD technique fixes different groups of variables and optimizes each group of variables one by one.

Fixed

With optimization for

Fixed

The two steps of optimizing and calibrating are repeated.

을 고정하여

를 최대화하는 최적 솔루션

는 아래와 같이 나타낼 수 있다.first

By fixing

Solution to maximize

Can be expressed as

(14)

(14)

(15)

(15)

는

에 해당하는 성분을 포함하는

의 하위 벡터를 나타내며

는

에 해당하는 열로 구성된

의 하위 행렬을 나타내며,

From here

Is

Contains ingredients corresponding to

Represents a subvector of

Is

Consists of columns corresponding to

Represents a sub-matrix of,

(16)

(16)

를 최대화하는 해

는 식 (6)에서

에 대해 최소화함으로써 얻어지며 아래와 같이 표현 가능하다.

Solution to maximize

In equation (6)

It is obtained by minimizing for and can be expressed as

(17)

(17)

의 변수

와

이 볼록하기 때문에 BCD가 최적의 추정 값을 찾을 수 있게 된다. 하지만, 데이터심볼은 이산화되어 있는 변수이기 때문에 BCD추정값의 근사하여 표현하게 된다. When the objective function is convex for all groups of variables, the BCD proved to converge to the optimal solution.

Variable

Wow

This convexity allows the BCD to find the best estimate. However, since data symbols are discretized variables, they are expressed as approximations of BCD estimates.

3 is a graph illustrating active user detection performance according to an embodiment of the present invention.

Referring to Figure 3, a graph showing the active user detection performance is shown, compared with the conventional OMP, BSASP. Since the method proposed in the present invention can use both pilot and data measurement, it is possible to simultaneously detect or support 75% more active users than the conventional method as shown in FIG. 3.

4 is a graph illustrating channel estimation performance according to an embodiment of the present invention.

Referring to FIG. 4, a graph showing channel estimation performance is shown in comparison with conventional OMP and BSASP. As can be seen in FIG. 4, the proposed method achieves much better channel estimation performance than OMP and BSASP. Thus, the present invention allows a significant amount of performance improvement to be achieved with less computation time compared to existing approaches.

5 is a diagram illustrating a configuration of an active user detection and channel estimation apparatus according to an embodiment of the present invention.

The proposed active user detection and channel estimation apparatus 500 includes an initialization unit 510, a detection and estimation unit 520, and a determination unit 530. The initialization unit 510, the detection and estimation unit 520, and the determination unit 530 may be configured to perform the steps 210 to 250 of FIG. 2.

The initialization unit 510 initializes the user's active vector, the observed value of the pilot signal, the observed value of the data symbol, and the number of repetitions in order to perform the greed algorithm. Perform data reception and Fourier transform, and perform resource demapping.

After the initialization, the detection and estimator 520 estimates the active user, updates the index detected from the active vector corresponding to the active user, estimates the channel initial value and the data symbol using the active vector, and estimates the active user. Update the residual vector to estimate.

The detection and estimator 520 assumes that the observed value of the pilot signal and the observed value of the data symbol in the frame have the same sparse structure, and assumes that the data symbols have a constant size, thereby providing an active user without dependency on the data symbol. Estimate.

In order to find the channel and data symbols that maximize the objective function, we estimate the channel initial values and data symbols using the Block Coordinate Descent (BCD) technique, and we use the BCD technique to fix the data symbols, optimize the channel and fix the channel. Two processes of correcting through optimization of data symbols are repeated.

According to an embodiment of the present invention, since the discretized data symbols are approximated and the channel and data symbols which are variables of the objective function are convex, the BCD technique converges to an optimal solution when the objective function is convex for all variables. Find the best estimate.

The determination unit 530 determines whether all users have been identified and repeats from the step of detecting the active user until all users have been identified.

TABLE 1

Table 1 shows the conventional approach and the average computational complexity of the present invention and is shown in comparison with conventional OMP and BSASP. The simultaneous detection and estimation technique of active user, channel and data proposed by the present invention has a calculation time that is 4.15 times faster than the conventional method, as shown in Table 1.

The apparatus described above may be implemented as a hardware component, a software component, and / or a combination of hardware components and software components. For example, the devices and components described in the embodiments may be, for example, processors, controllers, arithmetic logic units (ALUs), digital signal processors, microcomputers, field programmable arrays (FPAs), It may be implemented using one or more general purpose or special purpose computers, such as a programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing device may be described as one used, but those skilled in the art will appreciate that the processing device includes a plurality of processing elements and / or a plurality of types of processing elements. It can be seen that it may include. For example, the processing device may include a plurality of processors or one processor and one controller. In addition, other processing configurations are possible, such as parallel processors.

The software may include a computer program, code, instructions, or a combination of one or more of the above, and configure the processing device to operate as desired, or process it independently or collectively. You can command the device. Software and / or data may be any type of machine, component, physical device, virtual equipment, computer storage medium or device in order to be interpreted by or to provide instructions or data to the processing device. It can be embodied in. The software may be distributed over networked computer systems so that they may be stored or executed in a distributed manner. Software and data may be stored on one or more computer readable recording media.

Method according to the embodiment is implemented in the form of program instructions that can be executed by various computer means may be recorded on a computer readable medium. The computer readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions recorded on the media may be those specially designed and constructed for the purposes of the embodiments, or they may be of the kind well-known and available to those having skill in the computer software arts. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tape, optical media such as CD-ROMs, DVDs, and magnetic disks, such as floppy disks. Magneto-optical media, and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine code, such as produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like.

Although the embodiments have been described by the limited embodiments and the drawings as described above, various modifications and variations are possible to those skilled in the art from the above description. For example, the described techniques may be performed in a different order than the described method, and / or components of the described systems, structures, devices, circuits, etc. may be combined or combined in a different manner than the described method, or other components. Or even if replaced or replaced by equivalents, an appropriate result can be achieved.

Therefore, other implementations, other embodiments, and equivalents to the claims are within the scope of the claims which follow.

Claims (10)

Initializing, by the initialization unit, the active vector of the user, the observed value of the pilot signal, the observed value of the data symbol, and the number of repetitions to perform the greed algorithm;
Detecting and estimating by the detection and estimating unit based on the greed algorithm after the initialization, and updating the index detected in the active vector corresponding to the active user;
A detection and estimating unit estimating a channel initial value and a data symbol using the activity vector;
The detection and estimating unit updating the residual vector for estimating the active user; And
The determination unit determines whether all users are identified, and repeats the steps of estimating active users until all users are identified.
Including,
After the initialization, the detecting and estimating unit estimates the active user based on the greed algorithm and updates the index detected in the active vector corresponding to the active user.
The detection and estimator assumes that the observed value of the pilot signal and the data symbol in the frame have the same sparse structure, and assumes that the data symbols have a constant size to estimate the active user without dependency on the data symbol.
Active user detection and channel estimation method. delete The method of claim 1,
Estimating a channel initial value and a data symbol using the active vector,
Detecting and estimating unit estimates channel initial value and data symbol using Block Coordinate Descent (BCD) technique to find channel and data symbol that maximizes objective function.
Active user detection and channel estimation method. The method of claim 3,
The BCD technique repeats two processes of fixing a data symbol, optimizing the channel and correcting the channel by fixing the channel and optimizing the data symbol.
Active user detection and channel estimation method. The method of claim 4, wherein
Estimating a channel initial value and a data symbol using the active vector,
The detection and estimation unit approximates the discretized data symbols, and the channel and data symbols, which are variables of the objective function, are convex, so that the optimal estimation value is achieved through the BCD technique that converges to the optimal solution when the objective function is convex for all the variables. Finding
Active user detection and channel estimation method. An initialization unit for initializing an active vector of a user, an observation value of a pilot signal, an observation value of a data symbol, and a repetition frequency to perform a greed algorithm;
After the initialization, the active user is estimated based on the greed algorithm, the index detected from the active vector corresponding to the active user is updated, the channel initial value and the data symbol are estimated using the active vector, and the active user is estimated. A detector for estimating the residual vector; And
Determination unit for determining whether all users have been identified and repeating the step of estimating active users until all users have been identified
Including,
The detection and estimation unit
It is assumed that the observed value of the pilot signal and the observed value of the data symbol in the frame have the same sparse structure, and that the active symbols are estimated without dependence on the data symbol by assuming that the data symbols have a constant size.
Active user detection and channel estimation device. delete The method of claim 6,
The detection and estimation unit
Estimating channel initial values and data symbols using Block Coordinate Descent (BCD) techniques to find channel and data symbols that maximize the objective function.
Active user detection and channel estimation device. The method of claim 8,
The detection and estimation unit
The BCD technique repeats two processes of fixing data symbols, optimizing the channel and correcting the channel by optimizing the data symbol.
Active user detection and channel estimation device. The method of claim 9,
The detection and estimation unit
Approximates discretized data symbols, and since the channel and data symbols, which are variables of the objective function, are convex, the optimal solution is found through the BCD technique that converges to the optimal solution when the objective function is convex for all variables.
Active user detection and channel estimation device.

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