CN104901731A - Method for detecting uplink signal in multi-cell MIMO system for interacting part of soft information - Google Patents

Abstract

The invention discloses a multi-cell MIMO system uplink signal detection method for exchanging partial soft information. Arrange the posterior probability values from large to small, select the posterior probability data before selection, and send the posterior probability data of the selected constellation points to the corresponding adjacent base stations participating in the cooperative detection; each base station receives the data sent by other base stations After the soft information of local user symbols, calculate the posterior probability data that other base stations have not selected to send; each base station softly merges the processed complete probability data to calculate the posterior probability of symbols sent by users in this cell, and select the symbol with the highest probability as the final test result. The method of the invention can effectively reduce the complexity of uplink baseband signal detection in a multi-cell MIMO system.

Description

A method for detecting uplink signals in multi-cell MIMO systems with interactive partial soft information

technical field

The invention belongs to the mobile communication technology, and in particular relates to a method for detecting uplink signals of a multi-cell MIMO system for exchanging partial soft information.

Background technique

In a multi-user multi-cell MIMO system, multiple base stations cooperate to jointly detect the uplink signals of multiple users to improve the accuracy of signal detection, but this method is complex and requires a large amount of calculation. As the number increases, the amount of computation increases exponentially.

After searching the existing technology, Shaoshi Yang and TiejunLv found in "DistributedProbabilistic-Data-Association-Based SoftReception Employing BaseStation Cooperation inMIMO-AidedMultiuserMulticell Systems" IEEE Trans.Vehicular Technology.vol.60, no.7, pp.3532-3538 ,Sept.2011. (i.e. base station distributed cooperative detection based on probabilistic data association technology in multi-user multi-cell MIMO system) and Yin Yufang inventor [invention patent application publication number: CN 103002480 A] for multi-user multi-cell MIMO system using probability The data cooperative detection method is used to overcome this problem of large amount of calculation. The probabilistic data collaborative detection methods of the two researchers are as follows: one is based on the assumption of uninformative prior probability distribution, and the base station performs parallel detection And exchange soft information and then perform simple soft merger; the other is to use the soft information obtained after the interaction as prior information on the basis of the first method and then perform iterative detection. However, for a multi-user system using high-order modulation, as the number of base stations and users increases, the amount of soft information to be transmitted is very large. Therefore, the present invention aims to explore a distributed symbol detection method with low communication complexity.

Contents of the invention

The purpose of the present invention is to provide a multi-cell MIMO system uplink signal detection method for exchanging partial soft information to address the deficiencies in the prior art.

The purpose of the present invention is achieved through the following technical solutions: a method for detecting uplink signals of a multi-cell MIMO system that exchanges partial soft information, comprising the following steps:

(1) Each base station performs parallel detection, which specifically includes the following sub-steps:

(1.1) Each base station first determines the initial prior probability information of its received signal based on the uniform distribution of symbol probability;

(1.2) each base station detects all signals according to the initial prior probability information that step 1.1 obtains, and described all signals not only comprise the user signal of this cell, also comprise the user signal of surrounding cell co-channel;

(1.3) Each base station updates the posterior probability through iterative calculation in the probability data cooperative detection method until the predetermined condition is reached, and finally the maximum posterior probability is obtained. The predetermined condition is the posterior probability of the current update and the posterior probability of the previous update. The difference between the test probabilities is less than a predetermined threshold, or the number of repeated executions reaches a predetermined value;

(2) Each base station arranges the maximum posterior probability values of all symbols obtained in step 1.3 from large to small, and selects the posterior probability data of the first X%, wherein, 0<X≤25;

(3) Send the posterior probability data of the symbol selected in step 2 to the corresponding adjacent base stations participating in the cooperative detection;

(4) After receiving the soft information of local user symbols sent by other base stations, each base station calculates the posterior probability values that are not selected and sent by other base stations as (1-m)/(M-n), where n is the selected The number of points in the constellation diagram, m is the sum of the posterior probabilities of the received local user symbols sent by other base stations, and M is the modulation order;

(5) Each base station soft-combines the posterior probabilities of other base stations that are not selected and transmitted by other base stations calculated in step 4, and calculates the posterior probability of symbols sent by local users, and selects the symbol with the largest posterior probability as the final test result.

The beneficial effects of the present invention are: by using the method provided in the present invention, each base station arranges the obtained maximum posterior probability values of all symbols from large to small, and selects the prior X% posterior probability data for interaction, wherein, 0<X≤25; it can significantly reduce the amount of information exchange required between base stations and reduce the amount of calculation without significantly losing the performance of symbol detection. communication complexity.

Description of drawings

Fig. 1 is a flow chart of the method of the present invention;

FIG. 2 is a diagram of a networked signal transmission mode in a scenario of coordinated detection of four base stations in an embodiment of the present invention;

Fig. 3 is the influence of base station cooperative detection performance on base station cooperative detection performance under the scenario of four base station cooperative detection in which base station 1 exchanges a constant amount of information and exchanges all soft information between base stations; (a) modulation order is 16, (b) The modulation order is 64;

Fig. 4 is the relationship between the amount of information exchanged between base stations and the communication environment in which the base stations are located in a four-base station cooperative detection scenario according to an embodiment of the present invention. In the case of ensuring the cooperative detection performance of the base stations, the modulation order is 64.

Detailed ways

The purpose and effects of the present invention will become more apparent by describing the present invention in detail below in conjunction with the accompanying drawings and specific embodiments.

Probabilistic data association (PDA) detection is based on the assumption of uninformative prior distribution, and all soft information is exchanged between the base stations of cooperative detection. Each base station arranges the maximum posterior probability values of all symbols obtained in step 1.3 from large to small, and selects the top X% posterior probability data, where 0<X≤25, which greatly reduces the complexity of interaction between base stations.

In the multi-user multi-cell MIMO system of the present invention, the virtual signal model received by base station k can be constructed as the following virtual MIMO system y ^k :

${\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&NotEqual;</span>\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; h</span>}}_{{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}^{\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}^{{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; N</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

where x ^k represents the sign vector of the target user (local user) in base station k, Indicates the symbols received by co-channel users from adjacent cells, C ₀ indicates the number of co-channel users, Indicates the channel matrix of N _r ×N _t between user α _i and base station k, ^nk represents the Gaussian noise signal with zero mean and covariance σ _n ² IN in base station k, where _IN represents _{N r} ×N _t identity matrix, N _t is the number of transmitting antennas, N _r is the number of receiving antennas,

Formula (1) can be expressed as:

${\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; G</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; S</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

in, $G^k=[h_k^k,h_{{\mathrm{\&alpha;}}_1}^k,...,h_{{\mathrm{\&alpha;}}_{c_0}}^k],S^k=[{\left(x^k\right)}^T,{\left(x^{{\mathrm{\&alpha;}}_1}\right)}^T,...,{\left(x^{{\mathrm{\&alpha;}}_{c_0}}\right)}^T]$

The posterior probability information of x ^k is calculated according to Bayes' theorem to perform soft decision.

The elements of the vector S ^k are To represent, where q=1,2,3...( _NT N _m ), N _m is the number of base stations sending symbols. For convenience of description, the superscript k is omitted. Then formula (2) can be written as the following formula:

$\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; l</span>}<span\; class="notranslate">\; \&NotEqual;</span>\mathrm{<span\; class="notranslate">\; t</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; no</span>}\stackrel{\mathrm{<span\; class="notranslate">\; \&Delta;</span>}}{<span\; class="notranslate">\; =</span>}{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

in $\mathrm{the\; y}={\left(G^{h}G\right)}^{-1}{G}^h\widetilde{\mathrm{the\; y}},v_t\stackrel{\mathrm{\&Delta;}}{=}{\mathrm{\&Sigma;}}_{l\&NotEqual;t}{g}_l{\mathrm{the\; s}}_l+\widetilde{\mathrm{no}},$ A Gaussian noise signal with a mean of zero and a covariance of N ₀ (G ^H G ) ^-1 , g _l is a column vector whose lth element is 1 and the rest of the elements are 0.

For each symbol s _t , it corresponds to a probability vector P(t), whose mth element P _m (st _t |y) represents the estimated value of the posterior probability of s _t = a _m , m=1,2,. ..M, a _m is the mth element in the constellation diagram. The basic idea of the PDA algorithm is to approximate the non-Gaussian random variable v _t to a Gaussian random variable, V{v _t } is the covariance matrix, and U{v _t } is the pseudo-covariance matrix.

${\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; the\; s</span>}}}_{\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

$\mathrm{<span\; class="notranslate">\; V</span>}<span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}}<span\; class="notranslate">\; )</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}}<span\; class="notranslate">\; )</span>}^{<span\; class="notranslate">\; *</span>}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

$\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}}<span\; class="notranslate">\; )</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; T</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$

${\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span>\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; the\; s</span>}}}_{\mathrm{<span\; class="notranslate">\; l</span>}}{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$

in ${\mathrm{\&Lambda;}}_t\stackrel{\mathrm{\&Delta;}}{=}\left(\begin{array}{cc}R(V\left(v_t\right)+u\left(v_t\right))& -I(V\left(v_t\right)-u\left(v_t\right))\\ I(V\left(v_t\right)+u\left(v_t\right))& -R(V\left(v_t\right)-u\left(v_t\right))\end{array}\right)---\left(9\right)$

Among them, "*" stands for conjugation, "Τ" stands for transposing the matrix, R(*) stands for taking the real part, and I(*) stands for taking the imaginary part. the

In the PDA algorithm, first initialize P _m (st _t |y) to a uniform distribution, and then according to The current value of P _m (st _t |y) is calculated and updated through formulas (4)-(10) until the algorithm exit condition is met.

As shown in Figure 1, a method for detecting an uplink signal of a multi-cell MIMO system for exchanging partial soft information of the present invention comprises the following steps:

(1) Each base station performs parallel detection, which specifically includes the following sub-steps:

(1.1) Each base station first determines the initial prior probability information of its received signal based on the uniform distribution of symbol probability;

(1.2) each base station detects all signals according to the initial prior probability information that step 1.1 obtains, and described all signals not only comprise the user signal of this cell, also comprise the user signal of surrounding cell co-channel;

(1.3) Each base station updates the posterior probability through iterative calculation in the probability data cooperative detection method until the predetermined condition is reached, and finally the maximum posterior probability is obtained. The predetermined condition is the posterior probability of the current update and the posterior probability of the previous update. The difference between the test probabilities is less than the predetermined threshold, or the number of repeated executions reaches the predetermined value; the predetermined threshold can be appropriately set according to the required calculation accuracy, and the predetermined value can be set to 5 but not limited to 5. When the predetermined value is 5, it is enough to achieve sufficient calculation accuracy and avoid excessive calculation;

(2) Each base station arranges the maximum posterior probability values of all symbols obtained in step 1.3 from large to small, and selects the posterior probability data of the first X%, wherein, 0<X≤25; wherein, different amounts of mutual information are selected , can achieve different levels of communication performance, for example, when exchanging 25% probability data, the communication performance will reach more than 90% of the performance obtained by exchanging all soft information. Therefore, in the process of selecting the amount of interaction, it is necessary to consider the actual situation. Communication performance requirements.

(3) Send the posterior probability data of the symbol selected in step 2 to the corresponding adjacent base stations participating in the cooperative detection;

(4) After receiving the soft information of local user symbols sent by other base stations, each base station calculates the posterior probability values that are not selected and sent by other base stations as (1-m)/(M-n), where n is the selected The number of points in the constellation diagram, m is the sum of the posterior probabilities of the received local user symbols sent by other base stations, and M is the modulation order;

(5) Each base station soft-combines the posterior probabilities of other base stations that are not selected and transmitted by other base stations calculated in step 4, and calculates the posterior probability of symbols sent by local users, and selects the symbol with the largest posterior probability as the final test result.

Example

As shown in Figure 2, four base stations are selected for cooperative detection. Base stations BS1, BS2, BS3, and BS4 are adjacent base stations to each other, and soft information is exchanged between the four base stations. During the interaction process, each base station uses the posterior probability The data is sent to all neighboring base stations participating in cooperative detection.

Fig. 3 shows that under the cooperative detection scenario of four base stations in one embodiment of the present invention, Fig. 3 (a) modulation order M is 16, Fig. 3 (b) modulation order M is 64, base station n (n can be 1 , one of 2, 3, and 4) select 25% of the amount of information before the interaction and interact all the soft information with the base station. At the same time, regardless of the signal-to-noise ratio, if 25% of the information is constantly exchanged, the detection effect achieved can basically reach more than 80% of the performance obtained when all information is exchanged. Fig. 4 shows a scene of cooperative detection of four base stations according to an embodiment of the present invention. When the modulation order M=64, the base station is guaranteed to achieve 90% or more of the performance obtained by exchanging all information when the cooperative detection performance of the base stations is guaranteed. The relationship between the amount of exchanged information and the communication environment where the base station is located. As shown in Figure 4(a), as the SNR varies from 0-20, the proportion of mutual information is shown in the following table:

SNR 0 5 10 15 20 interaction ratio 15/64 14/64 12/64 9/64 5/64

From Figure 4(a) and 4(b), it can be seen that the selection of the amount of interactive information can also be adjusted appropriately according to the specific communication environment, which can also effectively guarantee the communication performance; in this embodiment, it is guaranteed 90% of the cooperative detection performance The above, in actual situations, is not limited to this ratio, and can be adjusted appropriately.

The present invention is not limited to the above-mentioned specific embodiments, and those skilled in the art can adopt various other specific embodiments to implement the present invention according to the content disclosed in the present invention. Therefore, any design that adopts the design structure and ideas of the present invention and makes some simple changes or changes falls within the scope of protection of the present invention.

Claims (1)

1. A multi-cell MIMO system uplink signal detection method of exchanging part soft information, is characterized in that, comprises the following steps: (1) Each base station performs parallel detection, which specifically includes the following sub-steps: (1.1) Each base station first determines the initial prior probability information of its received signal based on the uniform distribution of symbol probability; (1.2) each base station detects all signals according to the initial prior probability information that step 1.1 obtains, and described all signals not only comprise the user signal of this cell, also comprise the user signal of surrounding cell co-channel; (1.3) Each base station updates the posterior probability through iterative calculation in the probability data cooperative detection method until the predetermined condition is reached, and finally the maximum posterior probability is obtained. The predetermined condition is the posterior probability of the current update and the posterior probability of the previous update. The difference between the test probabilities is less than a predetermined threshold, or the number of repeated executions reaches a predetermined value; (2) Each base station arranges the maximum posterior probability values of all symbols obtained in step 1.3 from large to small, and selects the posterior probability data of the first X%, wherein, 0<X≤25; (3) Send the posterior probability data of the symbol selected in step 2 to the corresponding adjacent base stations participating in the cooperative detection; (4) After receiving the soft information of local user symbols sent by other base stations, each base station calculates the posterior probability values that are not selected and sent by other base stations as (1-m)/(M-n), where n is the selected The number of points in the constellation diagram, m is the sum of the posterior probabilities of the received local user symbols sent by other base stations, and M is the modulation order; (5) Each base station soft-combines the posterior probabilities of other base stations that are not selected and transmitted by other base stations calculated in step 4, and calculates the posterior probability of symbols sent by local users, and selects the symbol with the largest posterior probability as the final test result.