CN101616427A

CN101616427A - Multi-user MIMO test method between a kind of base station

Info

Publication number: CN101616427A
Application number: CN200910090140A
Authority: CN
Inventors: 龚萍; 吕旌阳; 林雪红; 房亮; 赵振山; 雷旭; 胡炜
Original assignee: Potevio Institute of Technology Co Ltd; Beijing University of Posts and Telecommunications
Current assignee: Potevio Institute of Technology Co Ltd; Beijing University of Posts and Telecommunications
Priority date: 2009-07-29
Filing date: 2009-07-29
Publication date: 2009-12-30
Anticipated expiration: 2029-07-29
Also published as: CN101616427B

Abstract

The invention discloses the multi-user MIMO test method between a kind of base station, comprising:, determine in this sub-district and the neighbor cell same frequency user with the identical running time-frequency resource of described arbitrary CU for arbitrary user of each sub-district, place, base station; Between the base station of the base station at described arbitrary user place and this arbitrary user's sub-district, same frequency user place, share with user's identification information frequently, be used for that this cell base station estimates other sub-districts described with user frequently in the characteristic of channel of this base station; Each base station is carried out joint-detection with the user of sub-district, place with this user's same frequency user, obtains this user and with user's testing result frequently.Use the present invention, can realize the multiuser MIMO joint-detection of a plurality of sub-districts, effectively suppress co-channel interference, improve systematic function.

Description

Multi-user MIMO detection method between base stations

Technical Field

The present invention relates to Multiple Input Multiple Output (MIMO) signal detection technology, and more particularly, to a multi-user MIMO detection method between base stations.

Background

For the MIMO communication system, the optimal detection method in the sense that the joint error probability is the smallest is the Maximum Likelihood (ML) method or the maximum a posteriori probability (MAP) method, but both detection methods have high complexity and are difficult to apply in an actual system. The demands of practical applications make the research of MIMO detection algorithms focus mainly on the search for high performance detectors with low complexity.

At present, the low-complexity high-performance detection method mainly comprises the following steps: a linear Zero Forcing (ZF) algorithm, a ZF-OSIC algorithm combined with sequential Successive Interference Cancellation (OSIC), a linear Minimum Mean Square Error (MMSE), an MMSE-OSIC algorithm, a QR decomposition (QRD) algorithm, a sorting QRD algorithm, a Sphere Decoding (SD) algorithm, a QRD-M algorithm, a semi-deterministic relaxation detection (SDR) algorithm, a Probabilistic Data Association (PDA) algorithm, and the like. Where the SD algorithm is the suboptimal detection algorithm that has performed closest to the maximum likelihood algorithm to date. Under the ideal condition that the search does not fail, the theoretical performance is the same as that of the maximum likelihood algorithm. Although the average computational complexity of the SD algorithm is polynomial complexity in most application environments of medium-scale wireless communication systems, the average computational complexity is still exponential complexity in the worst case.

The SDR algorithm models the MIMO detection problem into a semi-definite programming problem, then relaxes into a convex optimization problem, solves the convex optimization problem by using an interior point algorithm of numerical calculation, can achieve the detection performance close to the maximum likelihood algorithm, and only pays out polynomial calculation complexity. The worst-case complexity of the SDR algorithm is still polynomial complexity compared to the worst-case exponential complexity of the SD algorithm, but the disadvantage is that the SDR algorithm is an approximation algorithm that can only approach the maximum likelihood algorithm in performance and the order of its polynomial complexity is still high. In addition, for different modulation modes, the specific forms of SDR algorithms are different, and no general algorithm form exists, so that the complexity of practical application is higher.

The PDA algorithm is another less-than-optimal detection algorithm that is important. The algorithm uses gaussian approximation to estimate the a posteriori probability of each transmit antenna data symbol, which can be said to be an approximate form of MAP algorithm. It is more suitable for larger scale communication systems, while for smaller scale systems, the performance is reduced, but better than MMSE-OSIC algorithm and worse than sphere decoding algorithm and SDR algorithm. The worst-case complexity of the PDA algorithm is also polynomial complexity from a complexity perspective and is lower than the worst-case complexity of the SDR algorithm. Since the PDA algorithm is iterative in estimating the a posteriori probability of each symbol, there are some cases where the convergence speed of the a posteriori probability of an individual symbol is slow, but given a reasonable maximum number of iterations, the problem has little effect on the detection performance.

Besides the above suboptimal detection algorithm, an algorithm for iterative detection using an iterative signal processing idea is also a very important research direction. The algorithm utilizes the properties of SISO (soft input soft output) to provide excellent detection performance with low complexity, for example, a PDA algorithm can be regarded as a SISO iterative detection algorithm. It should be noted that the iterative processing of the signal at the receiving end may be a simple iterative detection (for example, MIMO detection using only PDA algorithm), or an iterative joint detection decoding (for example, Turbo iterative joint detection decoding algorithm). The latter is more complex but provides better performance, especially for systems employing Turbo, convolutional, LDPC codes. Considering the natural applicability of SISO algorithms like PDA in iterative received signal processing modules, the basic MIMO detection algorithm referred to in this patent is the PDA algorithm.

The various MIMO detection algorithms introduced above are typically applied in single-user MIMO. At present, due to the limitation of the volume and processing capability of the user terminal, the number of antennas thereof is usually limited, and in order to better utilize the MIMO technology to improve the transmission performance, MIMO is applied among a plurality of users to form multi-user MIMO.

In a multi-user MIMO system, if the detection method under single-user MIMO is directly applied to signal detection, for a user to be detected, only other users with the same frequency as the user in other adjacent cells outside the cell where the user is located can be used as interference, and independent signal detection is performed for the user, so that signal information of other users with the same frequency cannot be reasonably utilized in the detection process, and the accuracy of a final detection result is not high.

Disclosure of Invention

In view of this, the present invention provides a multi-user MIMO detection method between base stations, which can implement multi-user MIMO joint detection of multiple cells, effectively suppress co-channel interference, and improve system performance.

In order to achieve the purpose, the invention adopts the following technical scheme:

a multi-user MIMO detection method between base stations comprises the following steps:

for any user of a cell in which each base station is located, determining co-frequency users occupying the same time-frequency resources with the user in the cell and an adjacent cell;

sharing identification information of the same-frequency users between the base station where the any user is located and the base station of the cell where the same-frequency users of the any user are located, wherein the identification information is used for the base station of the cell to estimate the channel characteristics of the same-frequency users of other cells in the base station; each base station jointly detects the user of the cell and the same-frequency user of the user to obtain the detection result of the user and the same-frequency user.

Preferably, after obtaining the detection results of the user and the co-frequency users thereof, the method further comprises:

determining probability information corresponding to detection results of the users and the same-frequency users thereof;

sending the detected detection result of the same-frequency users of the adjacent cells and the probability information corresponding to the detection result to the base station of the corresponding adjacent cell;

each base station combines the probability information corresponding to the signal detection result of the user detected by the base station of the cell and the probability information corresponding to the detection result of the user signal sent by other adjacent cells to any user of the cell, and determines the final detection result of the user signal.

Preferably, the probability information corresponding to the signal detection result of the user detected by the base station of the cell and the probability information corresponding to the signal detection result of the user sent by other adjacent cells are combined by the maximum ratio.

Preferably, jointly detecting a user and a co-frequency user of the user comprises: carrying out joint detection on the user and the same-frequency user thereof by using iterative detection;

in each iterative detection, sequentially detecting signals on the user and each transmitting antenna of the same-frequency user; when the signal on each transmitting antenna is detected, the signal on other transmitting antennas except the transmitting antenna to be detected is used as interference, and the latest information symbol probability on the transmitting antenna to be detected is determined;

and using the latest information symbol probability on each transmitting antenna obtained by each iterative detection for the next iterative detection until the information symbol probabilities on all the transmitting antennas are converged or a preset iteration upper limit is reached, ending the iterative detection, determining user signal detection results on each transmitting antenna by using the latest information symbol probability on each transmitting antenna, and taking the user signal detection results on all the transmitting antennas of each user as the detection results of corresponding user signals.

Preferably, the method for detecting a signal on a transmitting antenna to be detected includes:

constructing the user and the channel matrix between the same frequency user and the base station of the cell where the user is located

Wherein H_i，sThe channel characteristics between the ith user and the s base station in the users with the same frequency are obtained;

calculating using received signals at each of the transmit antennas

\tilde{y} = {(H^{H} H)}^{- 1} H^{H} y_{s},

Wherein, (.)^HRepresenting taking conjugate transpose;

according to the latest information symbol probability P on other transmitting antennas k except the transmitting antenna j to be detected_m(x_kY), determining the probability of the latest information symbol on the transmitting antenna to be detected

Wherein,

<math> <mrow> <mi>Q</mi> <mo>=</mo> <munder> <mi>Σ</mi> <mrow> <mi>k</mi> <mo>&NotEqual;</mo> <mi>j</mi> </mrow> </munder> <mrow> <mo>(</mo> <msub> <mi>ω</mi> <mi>k</mi> </msub> <mo>+</mo> <mi>Re</mi> <mrow> <mo>(</mo> <msub> <mi>ξ</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <msub> <mi>e</mi> <mi>k</mi> </msub> <msubsup> <mi>e</mi> <mi>k</mi> <mi>T</mi> </msubsup> <mo>+</mo> <munder> <mi>Σ</mi> <mrow> <mi>k</mi> <mo>&NotEqual;</mo> <mi>j</mi> </mrow> </munder> <mrow> <mo>(</mo> <msub> <mi>ω</mi> <mi>k</mi> </msub> <mo>-</mo> <mi>Re</mi> <mrow> <mo>(</mo> <msub> <mi>ξ</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <msub> <mi>e</mi> <mrow> <mi>k</mi> <mo>+</mo> <msub> <mi>N</mi> <mi>T</mi> </msub> </mrow> </msub> <msubsup> <mi>e</mi> <mrow> <mi>k</mi> <mo>+</mo> <msub> <mi>N</mi> <mi>T</mi> </msub> </mrow> <mi>T</mi> </msubsup> <mo>+</mo> <munder> <mi>Σ</mi> <mrow> <mi>k</mi> <mo>&NotEqual;</mo> <mi>j</mi> </mrow> </munder> <mi>Im</mi> <mrow> <mo>(</mo> <msub> <mi>ξ</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <msub> <mi>e</mi> <mi>k</mi> </msub> <msubsup> <mi>e</mi> <mrow> <mi>k</mi> <mo>+</mo> <msub> <mi>N</mi> <mi>T</mi> </msub> </mrow> <mi>T</mi> </msubsup> <mo>+</mo> <munder> <mi>Σ</mi> <mrow> <mi>k</mi> <mo>&NotEqual;</mo> <mi>j</mi> </mrow> </munder> <mi>Im</mi> <mrow> <mo>(</mo> <msub> <mi>ξ</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <msub> <mi>e</mi> <mrow> <mi>k</mi> <mo>+</mo> <msub> <mi>N</mi> <mi>T</mi> </msub> </mrow> </msub> <msubsup> <mi>e</mi> <mi>k</mi> <mi>T</mi> </msubsup> <mo>,</mo> </mrow> </math>

e_kis a unit vector with the k-th element being 1 and the other elements being zero,

<math> <mrow> <mi>w</mi> <mo>=</mo> <mover> <mi>y</mi> <mo>~</mo> </mover> <mo>-</mo> <msub> <mi>x</mi> <mi>j</mi> </msub> <msub> <mi>e</mi> <mi>j</mi> </msub> <mo>-</mo> <munder> <mi>Σ</mi> <mrow> <mi>k</mi> <mo>&NotEqual;</mo> <mi>j</mi> </mrow> </munder> <msub> <mover> <mi>x</mi> <mo>~</mo> </mover> <mi>k</mi> </msub> <msub> <mi>e</mi> <mi>k</mi> </msub> <mo>,</mo> </mrow> </math>

re (-) denotes the real part, Im (-) denotes the imaginary part, σ²As the noise power, a_mThe value of the mth constellation point of the information symbol is obtained, and M is the number of constellation points of the information symbol on the sending antenna k.

Preferably, at the first iteration, the a posteriori probabilities for each of the transmit antennas are initialized to a uniform distribution over a set of constellation points.

According to the technical scheme, for any user of the cell where each base station is located, the same-frequency users occupying the same time-frequency resources with the user in the cell and the adjacent cells are determined; sharing identification information of the same-frequency users between the base station of any user and the base station of the cell of the same-frequency users of any user; each base station jointly detects the user of the cell and the same-frequency user of the user to obtain the detection result of the user and the same-frequency user. By the method, the mutual interference among the same-frequency users can be converted into useful signals, and the joint detection of each same-frequency user is carried out, so that the same-frequency interference is effectively inhibited, and the system performance is improved.

Drawings

Fig. 1 is a network model of seven cells.

Fig. 2 is a network interference model for nineteen cells.

Fig. 3 is a schematic diagram of simulation results of three MIMO detection methods.

Detailed Description

For the purpose of making the objects, technical means and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.

Users occupying the same time frequency resource are called as same frequency users, the same frequency users occupy the same time frequency resource, so that mutual interference is large, and for different frequency users occupying different time frequency resources, the mutual interference is small, so that the users can be ignored in detection, and a signal detection result cannot be influenced. Therefore, when the multi-user MIMO signal detection is carried out, the mutual interference among the same-frequency users is considered, the interference among the same-frequency users is utilized, and the joint detection is carried out on the same-frequency users.

Further, multiple users participating in multi-user MIMO may be located in different cells, and channel characteristics of the users and the cell base stations in which the users are located are different from each other, so that if joint detection is performed on co-frequency users among multiple base stations, user identification information needs to be shared among different cells in which the co-frequency users are located, and the user identification information is used for calculating channel information between the cell base station and the co-frequency users in other cells.

Based on the above analysis, the basic idea of the present invention is: the identification information of the same-frequency users is shared between the base station and the base stations of the adjacent cells, and one user in any base station and the same-frequency users of the user in the cell and the adjacent cells are jointly detected.

Meanwhile, after the joint detection is carried out on the same-frequency users, the detection results of the same-frequency users can be obtained at any base station side, and the detection results of the same-frequency users can be further shared among all base stations where the same-frequency users are located, so that any base station can receive the detection results of other base stations for the user besides the detection result of the user in the cell detected by the base station, and the detection results of the same user by all base stations can be combined, thereby further improving the accuracy of the detection results. As described above, by sharing the identification information and the user detection result of the co-frequency user among a plurality of base stations, the interference signal of the co-frequency user among cells is converted into useful information, the joint signal detection of multi-user MIMO is realized through cooperation of the plurality of base stations, and the detection performance can be greatly improved.

The method for performing multi-user MIMO detection through cooperation between multiple base stations according to the present application is described in detail below.

Consider the VBLAST model. Assume that there is N_TA transmitting antenna, N_RA receiving antenna, N_T≤N_R. The original bit stream is first converted from serial to parallel into N_TAnd the parallel sub bit streams are respectively encoded, modulated and mapped into data symbols for each path of sub bit stream, and sent to respective transmitting antennas to be transmitted. The sub-bit streams on each transmitting antenna are assumed to adopt the same modulation mode, and the number of constellation points is M. The data symbols on each antenna are burst-transmitted in units of burst frames. Each burst frame consists of L data symbols. For convenience of analysis, assuming that L is 1 and the modulation scheme is QPSK, the signal vector received by the receiver at a certain time can be represented as

y＝Hx+n (1)

Wherein

Is N_RThe dimension receives a signal vector. H is N_R×N_TDimension channel matrix, its element h_ijRepresenting the channel fading coefficients from transmit antenna j to receive antenna i. X is dimension N_TX 1 multi-user multi-antenna transmit signal vector.

Transmitting a vector of data symbols assuming only one transmit antenna per user

Can be equivalent to N_TA transmission signal vector of each user; transmitting data symbol vectors assuming multiple transmit antennas per user

Can be equivalent to N_UTransmission signal vector of individual users, N in total_TAnd a transmit symbol.

Assuming that the channel is a quasi-static flat rayleigh fading channel, where quasi-static means that the channel fading coefficient is not changed in one burst frame, and the channel fading coefficient is randomly changed between burst frames, the channel of the current general communication system can satisfy the above conditions. In addition, it is assumed that the receiving end has accurate channel estimation and synchronization, and thus the CSI is accurately known. In a rich scattering channel environment, h_ijAnd (4) obtaining a complex Gaussian distribution with independent same distribution and zero mean value.

Is N_RDimensional AWGN noise vector, each component of which has a mean of zero and a variance of 2 σ²Are independently distributed on random variables. The channels of the current general communication system can satisfy the above conditions. This provides convenience for multi-cell collaboration because the PDA algorithm can detect multiple users simultaneously. Assuming that each cell uses the PDA algorithm, the mathematical model for multi-user detection is the same as equation (1) for a single cell.

The base station cooperation MIMO system model is a further extension of the formula (1). It is assumed that orthogonal multiple access is adopted among users in a cell, so that mutual interference among the users in the cell can be considered to be absent; only adjacent cells generate mutual interference. Taking a simple cell alignment model as an example, the received signal at the s-th base station can be represented as:

wherein y is_sIs N_RVector of dimension, X_iTransmitting for users of ith cellSignal vector, dimension N_Ti×1，N_TiTransmitting antenna for ith cell user (assuming that only one user in each cell forms co-channel interference with adjacent cell users at the same time), H_isIs the channel response vector between the co-frequency users of the ith cell and the s-th base station. N base stations are arranged in a straight line of the network. For each base station, X may be mapped to X using various detectors_iRespectively detected, for example PDA algorithms etc. When considering a more practical network model, the number of interfering signals in (2) will be larger, depending on the specific network architecture.

In the following embodiments, the specific implementation of the multi-user MIMO detection method in the present invention is described by taking the PDA algorithm as an example for signal detection.

As shown in fig. 1, the location of the network element is represented in polar coordinates with the center of the central cell as the origin and the horizontal right direction as an angle of 0 degrees. If the base station of the upper right cell is to be represented, its coordinates can be represented by eNB1(R, π/6). A group of co-frequency users in fig. 1 is shaded, e.g., UE0, UE1, UE2, UE3, UE04, UE5, and UE6 are co-frequency users, which may be determined by a network frequency plan. Assume that a certain frequency is allocated in the lower right sector within a cell.

The cell interference model is shown in fig. 2, where only co-channel interference is considered. If the base station eNB0 of the Cell0 is to process the information of the user UE0, then the eNB0 receives the interference signals simultaneously, and these interference signals are transmitted from the same-frequency users contained in the circle with the center of the eNB0 and the radius of R, that is, the signals x transmitted from the users UE2, UE3 and UE4₂、x₃And x₄. So signal y received by base station eNB0₀Is composed of

y₀＝H₀₀x₀+H₂₀x₂+H₃₀x₃+H₄₀x₄+n₀ (3)

Assuming that the base station eNB0 performs multi-user MIMO detection, the specific method includes:

step 1, determining that a base station eNB0 covers the same-frequency users of users UE0 in a cell.

As previously described, co-channel users of user UE0 are UE2, UE3 and UE4 of neighboring cells.

And 2, sharing identification information of the same-frequency users between the base station eNB0 and the eNB2, the eNB3 and the eNB 4.

The identification information of the users with the same frequency (namely UE0, UE2, UE3 and UE4) shared among a plurality of base stations (namely the base station eNB0, the eNB2, the eNB3 and the eNB4) is used for each base station to estimate the channel characteristics from other cell users to the base station.

And step 3, the base station eNB0 performs joint detection on the UE0, the UE2, the UE3 and the UE4 by using a PDA algorithm.

Firstly, deducing a mode of determining information symbol probability information of each user in each iteration by a PDA algorithm under multi-user MIMO. Specifically, when the PDA algorithm is used to perform joint detection on multi-user MIMO signals, signals on each transmitting antenna in each user are sequentially detected, and the detection of a signal on one transmitting antenna is taken as an example for description, and the detection methods of signals on other transmitting antennas are the same.

Expanding the system model of the formula (2), wherein the received signal of the s-th base station is y_sDimension N_R×1，N_UThe total number of the same-frequency users detected by the base station at the same time. Order to

H_i，sIs the channel characteristic between the ith co-channel user and the s base station, N_TFor the total number of transmitted symbols detected simultaneously by the base station, equation (2) can be extended to:

y_{s} = H_{1 s} x_{1} + H_{2 s} x_{2} + . . . + H_{N_{U} s} x_{N_{U}} + n_{s} = Hx + n_{s}

for the above formula, both sides are multiplied by (H)^HH)^-1H^HTo obtain the following formula:

<math> <mrow> <mover> <mi>y</mi> <mo>~</mo> </mover> <mo>=</mo> <mi>x</mi> <mo>+</mo> <mover> <mi>n</mi> <mo>~</mo> </mover> <mo>=</mo> <msub> <mi>x</mi> <mi>j</mi> </msub> <msub> <mi>e</mi> <mi>j</mi> </msub> <mo>+</mo> <munder> <mi>Σ</mi> <mrow> <mi>k</mi> <mo>&NotEqual;</mo> <mi>j</mi> </mrow> </munder> <msub> <mi>x</mi> <mi>k</mi> </msub> <msub> <mi>e</mi> <mi>k</mi> </msub> <mo>+</mo> <mover> <mi>n</mi> <mo>~</mo> </mover> <mover> <mo>=</mo> <mi>Δ</mi> </mover> <msub> <mi>x</mi> <mi>j</mi> </msub> <msub> <mi>e</mi> <mi>j</mi> </msub> <mo>+</mo> <msub> <mover> <mi>N</mi> <mo>~</mo> </mover> <mi>j</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein,

\tilde{y} = {(H^{H} H)}^{- 1} H^{H} y_{s},

j，k∈{1，2，...，N_T}。is a mean of zero and a covariance of

Complex Gaussian noise of (a)²As the noise power, e_kIs a unit vector with the k-th element being 1 and the other elements being zero.

The key of PDA algorithm is to make interference and noise terms

(referred to as pseudo noise) the part that is not Gaussian noise in the strict sense of the word is mandatory to be regarded asGaussian noise and then estimate a in the constellation in each iteration₁，a₂，...，a_m，...，a_MEach data symbol a_mA posteriori probability P (x)_j＝a_m|y)＝P_m(x_jY) based on which a decision is made for each received signal. Specifically, in the multi-user MIMO detection of the present invention, that is, when detecting a data symbol on a certain transmitting antenna j, other transmitting antennas of the user and all transmitting antennas k (k ═ 1, 2., N.) of the co-frequency user of the user are used_TAnd k ≠ j) as interference or noise.

Specific pseudo noise

By a mean value of

The covariance matrix is

<math> <mrow> <msub> <mi>Ω</mi> <mi>j</mi> </msub> <mover> <mo>=</mo> <mi>Δ</mi> </mover> <munder> <mi>Σ</mi> <mrow> <mi>k</mi> <mo>&NotEqual;</mo> <mi>j</mi> </mrow> </munder> <msub> <mi>ω</mi> <mi>k</mi> </msub> <msub> <mi>e</mi> <mi>k</mi> </msub> <msubsup> <mi>e</mi> <mi>k</mi> <mi>T</mi> </msubsup> <mo>+</mo> <mi>Cov</mi> <mrow> <mo>(</mo> <mover> <mi>n</mi> <mo>~</mo> </mover> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </math>

The pseudo covariance matrix is

<math> <mrow> <msub> <mi>Ξ</mi> <mi>j</mi> </msub> <mover> <mo>=</mo> <mi>Δ</mi> </mover> <munder> <mi>Σ</mi> <mrow> <mi>k</mi> <mo>&NotEqual;</mo> <mi>j</mi> </mrow> </munder> <msub> <mi>ξ</mi> <mi>k</mi> </msub> <msub> <mi>e</mi> <mi>k</mi> </msub> <msubsup> <mi>e</mi> <mi>k</mi> <mi>T</mi> </msubsup> </mrow> </math>

Is approximated by gaussian noise. Wherein

Order to

And is

<math> <mrow> <mi>Q</mi> <mover> <mo>=</mo> <mi>Δ</mi> </mover> <munder> <mi>Σ</mi> <mrow> <mi>k</mi> <mo>&NotEqual;</mo> <mi>j</mi> </mrow> </munder> <mrow> <mo>(</mo> <msub> <mi>ω</mi> <mi>k</mi> </msub> <mo>+</mo> <mi>Re</mi> <mrow> <mo>(</mo> <msub> <mi>ξ</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <msub> <mi>e</mi> <mi>k</mi> </msub> <msubsup> <mi>e</mi> <mi>k</mi> <mi>T</mi> </msubsup> <mo>+</mo> <munder> <mi>Σ</mi> <mrow> <mi>k</mi> <mo>&NotEqual;</mo> <mi>j</mi> </mrow> </munder> <mrow> <mo>(</mo> <msub> <mi>ω</mi> <mi>k</mi> </msub> <mo>-</mo> <mi>Re</mi> <mrow> <mo>(</mo> <msub> <mi>ξ</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <msub> <mi>e</mi> <mrow> <mi>k</mi> <mo>+</mo> <msub> <mi>N</mi> <mi>T</mi> </msub> </mrow> </msub> <msubsup> <mi>e</mi> <mrow> <mi>k</mi> <mo>+</mo> <msub> <mi>N</mi> <mi>T</mi> </msub> </mrow> <mi>T</mi> </msubsup> <mo>+</mo> <munder> <mi>Σ</mi> <mrow> <mi>k</mi> <mo>&NotEqual;</mo> <mi>j</mi> </mrow> </munder> <mi>Im</mi> <mrow> <mo>(</mo> <msub> <mi>ξ</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <msub> <mi>e</mi> <mi>k</mi> </msub> <msubsup> <mi>e</mi> <mrow> <mi>k</mi> <mo>+</mo> <msub> <mi>N</mi> <mi>T</mi> </msub> </mrow> <mi>T</mi> </msubsup> <mo>+</mo> <munder> <mi>Σ</mi> <mrow> <mi>k</mi> <mo>&NotEqual;</mo> <mi>j</mi> </mrow> </munder> <mi>Im</mi> <mrow> <mo>(</mo> <msub> <mi>ξ</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <msub> <mi>e</mi> <mrow> <mi>k</mi> <mo>+</mo> <msub> <mi>N</mi> <mi>T</mi> </msub> </mrow> </msub> <msubsup> <mi>e</mi> <mi>k</mi> <mi>T</mi> </msubsup> </mrow> </math>

Wherein Re (. cndot.) represents a real part, Im (. cndot.) represents an imaginary part, and a_mThe value of the mth constellation point of the information symbol. Since the transmitted symbols are assumed to be a priori equal, the a posteriori probability of each symbol is

As can be seen from the above, the posterior probability of the information symbol on the transmission antenna j can be calculated by equations (5) to (10) based on the posterior probabilities of the transmission symbols on the transmission antennas k other than the transmission antenna j.

Based on the above detection of signals on any transmit antenna, the whole process of performing joint detection on each user signal in multi-user MIMO includes:

1) initialization: constructing the user and the channel matrix between the same frequency user and the base station of the cell where the user is located

And calculate

Will P_m(x_jY) is initialized to a uniform distribution over the set of constellation points, j 1, 2, …, N_TM is 1, 2, …, M, and the iteration number counter z is 1;

2) and sequentially detecting signals on the user and each transmitting antenna of the user with the same frequency, and when detecting the signal on each transmitting antenna, taking the signals on other transmitting antennas except the transmitting antenna to be detected as interference to determine the latest information symbol probability on the transmitting antenna to be detected.

In particular, for

，j∈{1，2，...，N_TBased on { P (k) }_k≠jP is calculated by (5) to (10)_m(x_jY) and updates the original P (j) with the result, where each P (k) { P (j) }_m(x_kY) is a probability vector consisting of M probability values, M being the number of elements of the constellation set;

3) if for

P (j) has converged or has reached the upper limit of the given number of iterations, go to 4), otherwise let z be z +1, return 2);

4) for x_jGiving out the detection result

Namely, it is

{\hat{x}}_{j} = a_{i},

Then

Through the above steps, we can detect the signals of the users with the same frequency in multiple cells at the same time.

So far, the most basic multi-user MIMO joint detection process in the present invention is finished. In the basic process, the interference signals of the same-frequency users are converted into useful information for signal detection of the users. In order to further improve the detection performance, the method can further comprise the following steps:

and step 4, the base station eNB0 determines probability information corresponding to the detection results of the user in the cell and each same-frequency user thereof, and sends the detection results of the same-frequency users in the adjacent cells and the probability information corresponding to the same-frequency users to the corresponding base stations in the adjacent cells.

Each base station can determine the information of the users in the cell and the users in the same frequency of the adjacent cells thereof through step 3, including the detection result of the users (namely, the detection result of the users is obtained)

) And corresponding probability information

Therefore, the information detected by each base station not only includes the same-frequency user information of the cell, but also includes the same-frequency user information of the adjacent cell, at this time, the base station transmits the detected same-frequency user information of the adjacent cell to the base station of the adjacent cell, and simultaneously receives the information about the same-frequency user of the cell detected by the base station of the adjacent cell. In this embodiment, the eNB0 will obtain 4 pieces of information of the UE0

And

from the own eNB0 and other three neighbouring cell base stations eNB1, eNB5 and eNB6, respectively.

And 5, each base station performs soft combination processing on the probability information corresponding to the detection result of the user in the cell of the base station and the probability information corresponding to the detection result of the user sent by other adjacent cells to determine the final detection result of the user in the cell of the base station.

After the information transmission between the base stations is finished, the detected probability information about the user data comprises the local probability informationAnd finally, carrying out total soft processing combination on the probability information detected by the cell base station and the probability information detected by the adjacent cell base station. Specifically, all probability information of users may be combined by a maximum ratio, e.g., for a UE0 under eNB0, x may be obtained₀Final decision information of (1):

{\hat{x}}_{0} = f ({\hat{x}}_{01}, {\hat{x}}_{02}, {\hat{x}}_{03}, {\hat{x}}_{04}) .

in order to illustrate the performance of the multi-user MIMO detection method of the present invention, the detection of three modes is simulated, the specific simulation conditions refer to table 1, and the simulation results refer to fig. 3.

TABLE 1 simulation conditions

Parameter(s)	Numerical values and modes
Parameter(s)	Numerical values and modes	Modulation system	QPSK
Number of antennas per user	2	Modulation system	QPSK
Number of antennas per user	2	Number of receiving antennas	8
Same frequency interference user in adjacent cell and local cell	0.5	Number of receiving antennas	8

Power ratio p of users of the same frequency
Power ratio p of users of the same frequency		Channel type	Flat rayleigh fading channel

In fig. 3, a curve 301 represents a corresponding relationship curve of the snr and the ber when single-cell single-user independent detection is adopted; curve 302 represents the corresponding relationship curve of the snr and the ber when multi-cell cooperative multi-user MIMO joint detection is adopted and the detection results are not combined; curve 301 represents the corresponding relationship curve between the snr and the ber when multi-cell cooperative multi-user MIMO joint detection is used and the detection results are combined. As can be seen from the three curves, the system detection performance is very poor when a single cell and a single user perform independent detection; the multi-cell cooperative iterative detection is carried out, but when information combination is not carried out, the detection performance is improved greatly compared with the performance of single-cell single-user independent detection; when multi-cell cooperative iterative detection is carried out and information is combined, the system detection performance is further improved, and the error rate is 10^-4A performance gain of about 3dB is obtained.

Therefore, by the multi-user MIMO joint detection method, on one hand, the thought of single-user respective detection is abandoned, and multi-user joint detection is simultaneously carried out on the same-frequency users in a plurality of cells in one base station; meanwhile, the idea of simple linear detection is abandoned, soft information iteration is carried out in the detection, and the property of a soft-in soft-out algorithm and the gain brought by the iteration are fully utilized; furthermore, the method breaks through the idea of single-cell independent detection, can effectively inhibit the co-channel interference between cells under the condition that the identification information and the user data information of co-channel users are shared between adjacent cells, fully utilizes the space diversity gain brought by the MIMO model, and greatly improves the system performance particularly when the interference is serious.

In addition, because the cooperative MIMO multi-user iterative detection algorithm between the base stations only needs to carry out the interactive process of soft information transmission between the base stations when soft information combination is carried out in the last step, the additional information load is very small, the algorithm complexity is moderate, the data volume transmitted between adjacent cells is moderate, and the practicability is strong.

The method for determining the same-frequency users in the embodiment can be suitable for the hexagonal cellular network model commonly used in the industry at present, and has great practical pertinence.

The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-user MIMO detection method between base stations is characterized by comprising the following steps:

2. The method of claim 1, after obtaining the detection results of the user and its co-frequency users, the method further comprises:

3. The method according to claim 2, wherein the probability information corresponding to the signal detection result of the user detected by the base station of the cell and the probability information corresponding to the signal detection result of the user transmitted by other neighboring cells are combined by a maximum ratio.

4. A method according to any one of claims 1 to 3 wherein jointly detecting a user with co-frequency users of that user comprises: carrying out joint detection on the user and the same-frequency user thereof by using iterative detection;

5. The method of claim 4, wherein detecting a signal on a transmit antenna to be detected comprises:

calculating using received signals at each of the transmit antennas

\tilde{y} = {(H^{H} H)}^{- 1} H^{H} y_{s},

Wherein, (. H) represents taking a conjugate transpose;

Wherein,

6. The method of claim 5, wherein the a posteriori probabilities for each of the transmit antennas are initialized to a uniform distribution over the set of constellation points at the first iteration.