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

Method for detecting uplink signal in multi-cell MIMO system for interacting part of soft information Download PDF

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CN104901731A
CN104901731A CN201510161260.1A CN201510161260A CN104901731A CN 104901731 A CN104901731 A CN 104901731A CN 201510161260 A CN201510161260 A CN 201510161260A CN 104901731 A CN104901731 A CN 104901731A
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posterior probability
base station
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CN104901731B (en
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史清江
樊晓琦
俞佳敏
彭成
徐伟强
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Zhejiang Sci Tech University ZSTU
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B7/00Radio transmission systems, i.e. using radiation field
    • H04B7/02Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413MIMO systems
    • H04B7/0452Multi-user MIMO systems

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Abstract

The invention discloses a method for detecting an uplink signal in a multi-cell MIMO system for interacting part of soft information. The method comprises the following steps: detecting all signals with each base station, obtaining posterior probabilities of each base station to all symbols, ranking the maximum posterior probability of all symbols from the large to small with each base station, selecting front posterior probability data, transmitting the posterior probability data of the selected constellation points to adjacent base stations who participate in a cooperative detection; calculating posterior probability data which is not selected and transmitted by other base stations after each base station receives soft information of local user symbol transmitted by other base stations; performing soft combination to the processed integrating probability data with the each base station, calculating the posterior probability of the symbol transmitted by the user in local cell, and selecting symbol whose probability is the largest as a final detecting result. The method of the invention could effectively reduce complexity of detecting an uplink baseband signal in the multi-cell MIMO system.

Description

Multi-cell MIMO system uplink signal detection method for interacting partial soft information
Technical Field
The invention belongs to the mobile communication technology, and particularly relates to a multi-cell MIMO system uplink signal detection method for interacting partial soft information.
Background
In a multi-user multi-cell MIMO system, multiple base stations cooperate to perform joint detection on uplink signals of multiple users, so that the accuracy of signal detection can be improved.
Through the search of the prior art, the results of Shaoshi Yang and TiejunLv are found in "distributed Probabolic-Data-Association-Based software deployment Based on coordination of multiple antennas MIMO-air interface multiple users multiple cell Systems" IEEE trans. Vehicular technology. vol.60, No.7, pp.3532-3538, Sept.2011. (i.e. base station distributed cooperative detection Based on probability Data Association technology in a multiuser multiple cell MIMO system) and astronauts inventor [ invention patent application publication No.: CN 103002480A ] aiming at a multi-user multi-cell MIMO system, a probability data cooperative detection method is adopted to overcome the problem of large operation amount, the probability data cooperative detection methods of the two researchers are respectively the following two situations, one is based on the assumption of non-information prior probability distribution, and a base station executes parallel detection and interacts soft information and then carries out simple soft combining; and the other method is that soft information obtained after interaction is used as prior information to carry out iterative detection on the basis of the first method. However, when a multi-user system using high-order modulation is used, the amount of soft information to be transmitted is very large as the number of base stations and the number of users increase. The present invention is therefore directed to a distributed symbol detection method with low communication complexity.
Disclosure of Invention
The invention aims to provide a multi-cell MIMO system uplink signal detection method for interacting partial soft information aiming at the defects of the prior art.
The purpose of the invention is realized by the following technical scheme: a multi-cell MIMO system uplink signal detection method for interacting partial soft information comprises the following steps:
(1) each base station executes parallel detection, and the method specifically comprises the following substeps:
(1.1) each base station firstly determines initial prior probability information of a received signal based on symbol probability uniform distribution;
(1.2) each base station detects all signals according to the initial prior probability information obtained in the step 1.1, wherein all signals not only comprise user signals of the cell, but also comprise user signals of the same channel of the surrounding cells;
(1.3) each base station updates the posterior probability through iterative calculation in the probability data cooperative detection method until a preset condition is reached, and finally obtains the maximum posterior probability, wherein the preset condition is that the difference value between the current updated posterior probability and the posterior probability updated at the previous time is smaller than a preset threshold value, or the repeated execution times reaches a preset value;
(2) all base stations sort the maximum posterior probability values of all the symbols obtained in the step 1.3 from large to small, and the posterior probability data of the front X% is selected, wherein X is more than 0 and less than or equal to 25;
(3) sending the posterior probability data of the symbol selected in the step 2 to the corresponding adjacent base station participating in the cooperative detection;
(4) after receiving soft information of local user symbols sent by other base stations, each base station respectively calculates (1-M)/(M-n) posterior probability values which are not selected and sent by other base stations, wherein n is the number of selected constellation points, M is the sum of the received posterior probabilities of the local user symbols sent by other base stations, and M is a modulation order;
(5) and each base station soft-combines the posterior probability which is obtained by calculation in the step 4 and is not selected by other base stations to be sent with the posterior probability sent by other base stations, calculates the posterior probability of the symbol sent by the local user, and selects the symbol with the maximum posterior probability as the final detection result.
The invention has the beneficial effects that: by using the method provided by the invention, all base stations arrange the maximum posterior probability values of all the obtained symbols from large to small, and the posterior probability data of the first X% is selected for interaction, wherein X is more than 0 and less than or equal to 25; the method can obviously reduce the information interaction quantity required among the base stations and the operation quantity on the premise of not obviously losing the symbol detection performance, and can also reduce the communication complexity among the base stations to different degrees according to different requirements on the communication performance.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a diagram of a mesh signal transfer mode in a scenario of cooperative detection of four base stations according to an embodiment of the present invention;
fig. 3 is a diagram illustrating an influence of constant information amount exchanged by a base station 1 and total soft information exchanged between the base stations on cooperative detection performance of the base stations in a scenario of cooperative detection of four base stations according to an embodiment of the present invention; (a) the modulation order is 16, (b) the modulation order is 64;
fig. 4 shows a relationship between an amount of mutual information between base stations and a communication environment of the base stations, where a modulation order is 64 and the cooperative detection performance of the base stations is guaranteed in a scenario of cooperative detection of four base stations according to an embodiment of the present invention.
Detailed Description
The objects and effects of the present invention will become more apparent from the following detailed description of the present invention when taken in conjunction with the accompanying drawings and specific embodiments.
Probability data cooperation (PDA) detection is based on the assumption of no-information prior distribution, and all soft information is interacted between base stations in cooperation detection. And (3) arranging the maximum posterior probability values of all the symbols obtained in the step (1.3) from large to small by each base station, and selecting posterior probability data of the front X%, wherein X is more than 0 and less than or equal to 25, so that the complexity of interaction among the base stations is greatly reduced.
In the multi-user multi-cell MIMO system, a virtual signal model received by a base station k can be constructed as followsVirtual MIMO system yk
<math><mrow> <msup> <mover> <mi>y</mi> <mo>~</mo> </mover> <mi>k</mi> </msup> <mo>=</mo> <msubsup> <mi>H</mi> <mi>k</mi> <mi>k</mi> </msubsup> <msup> <mi>x</mi> <mi>k</mi> </msup> <mo>+</mo> <msub> <mi>&Sigma;</mi> <mrow> <msub> <mi>&alpha;</mi> <mi>i</mi> </msub> <mo>&NotEqual;</mo> <mi>k</mi> </mrow> </msub> <msubsup> <mi>H</mi> <msub> <mi>&alpha;</mi> <mi>i</mi> </msub> <mi>k</mi> </msubsup> <msup> <mi>x</mi> <msub> <mi>&alpha;</mi> <mi>i</mi> </msub> </msup> <mo>+</mo> <msup> <mi>n</mi> <mi>k</mi> </msup> <mo>=</mo> <msubsup> <mi>H</mi> <mi>k</mi> <mi>k</mi> </msubsup> <msup> <mi>x</mi> <mi>k</mi> </msup> <mo>+</mo> <msup> <mi>N</mi> <mi>k</mi> </msup> <mo>+</mo> <msup> <mi>n</mi> <mi>k</mi> </msup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow></math>
Wherein xkA symbol vector representing the target user (local user) in base station k,symbols representing co-channel users received from neighbouring cells, C0Indicating the number of users on the same channel,indicating a connected user alphaiAnd N between base station kr×NtOf the channel matrix nkMeans zero mean and covariance in base station kσn 2INOf Gaussian noise signal of (1), wherein INRepresents Nr×NtIdentity matrix of NtNumber of transmitting antennas, NrIn order to receive the number of antennas,
equation (1) can be expressed as:
y ~ k = G k S k + n k - - - ( 2 )
wherein, <math><mrow> <msup> <mi>G</mi> <mi>k</mi> </msup> <mo>=</mo> <mo>[</mo> <msubsup> <mi>H</mi> <mi>k</mi> <mi>k</mi> </msubsup> <mo>,</mo> <msubsup> <mi>H</mi> <msub> <mi>&alpha;</mi> <mn>1</mn> </msub> <mi>k</mi> </msubsup> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <msubsup> <mi>H</mi> <msub> <mi>&alpha;</mi> <msub> <mi>c</mi> <mn>0</mn> </msub> </msub> <mi>k</mi> </msubsup> <mo>]</mo> <mo>,</mo> <msup> <mi>S</mi> <mi>k</mi> </msup> <mo>=</mo> <mo>[</mo> <msup> <mrow> <mo>(</mo> <msup> <mi>x</mi> <mi>k</mi> </msup> <mo>)</mo> </mrow> <mi>T</mi> </msup> <mo>,</mo> <msup> <mrow> <mo>(</mo> <msup> <mi>x</mi> <msub> <mi>&alpha;</mi> <mn>1</mn> </msub> </msup> <mo>)</mo> </mrow> <mi>T</mi> </msup> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <msup> <mrow> <mo>(</mo> <msup> <mi>x</mi> <msub> <mi>&alpha;</mi> <msub> <mi>c</mi> <mn>0</mn> </msub> </msub> </msup> <mo>)</mo> </mrow> <mi>T</mi> </msup> <mo>]</mo> </mrow></math>
computing x according to Bayes' theoremkTo perform soft decisions.
Vector SkFor elements ofWherein q is 1,2,3. (N)TNm),NmIs the number of base stations transmitting the symbol. For convenience of description, the superscript k is omitted. Then equation (2) can be written as:
<math><mrow> <mi>y</mi> <mo>=</mo> <msub> <mi>g</mi> <mi>t</mi> </msub> <msub> <mi>s</mi> <mi>t</mi> </msub> <mo>+</mo> <munder> <mi>&Sigma;</mi> <mrow> <mi>l</mi> <mo>&NotEqual;</mo> <mi>t</mi> </mrow> </munder> <msub> <mi>g</mi> <mi>l</mi> </msub> <msub> <mi>s</mi> <mi>l</mi> </msub> <mo>+</mo> <mi>n</mi> <mover> <mo>=</mo> <mi>&Delta;</mi> </mover> <msub> <mi>g</mi> <mi>t</mi> </msub> <msub> <mi>s</mi> <mi>t</mi> </msub> <mo>+</mo> <msub> <mi>v</mi> <mi>t</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow></math>
wherein <math><mrow> <mi>y</mi> <mo>=</mo> <msup> <mrow> <mo>(</mo> <msup> <mi>G</mi> <mi>H</mi> </msup> <mi>G</mi> <mo>)</mo> </mrow> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> <msup> <mi>G</mi> <mi>H</mi> </msup> <mover> <mi>y</mi> <mo>~</mo> </mover> <mo>,</mo> <msub> <mi>v</mi> <mi>t</mi> </msub> <mover> <mo>=</mo> <mi>&Delta;</mi> </mover> <msub> <mi>&Sigma;</mi> <mrow> <mi>l</mi> <mo>&NotEqual;</mo> <mi>t</mi> </mrow> </msub> <msub> <mi>g</mi> <mi>l</mi> </msub> <msub> <mi>s</mi> <mi>l</mi> </msub> <mo>+</mo> <mover> <mi>n</mi> <mo>~</mo> </mover> <mo>,</mo> </mrow></math> Mean zero and covariance N0(GHG)-1Gaussian noise signal of glIs a column vector with the l-th element being 1 and the remaining elements being 0.
For each symbol stCorresponding to a probability vector P (t) whose m-th element Pm(stY) represents st=amM1, 2.. M, amIs the mth element in the constellation. The basic idea of the PDA algorithm is to use a non-Gaussian random variable vtThe approximation is a Gaussian-shaped random variable, V { V }tIs the covariance matrix, U { v }tIs the pseudo covariance matrix.
<math><mrow> <msub> <mover> <mi>s</mi> <mo>&OverBar;</mo> </mover> <mi>l</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>m</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <msub> <mi>a</mi> <mi>m</mi> </msub> <msub> <mi>P</mi> <mi>m</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>s</mi> <mi>l</mi> </msub> <mo>|</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow></math>
<math><mrow> <mi>V</mi> <mo>{</mo> <msub> <mi>s</mi> <mi>l</mi> </msub> <mo>}</mo> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>m</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <mrow> <mo>(</mo> <msub> <mi>a</mi> <mi>m</mi> </msub> <mo>-</mo> <mover> <msub> <mi>s</mi> <mi>l</mi> </msub> <mo>&OverBar;</mo> </mover> <mo>)</mo> </mrow> <msup> <mrow> <mo>(</mo> <msub> <mi>a</mi> <mi>m</mi> </msub> <mo>-</mo> <mover> <msub> <mi>s</mi> <mi>l</mi> </msub> <mo>&OverBar;</mo> </mover> <mo>)</mo> </mrow> <mo>*</mo> </msup> <msub> <mi>P</mi> <mi>m</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>s</mi> <mi>l</mi> </msub> <mo>|</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow></math>
<math><mrow> <mi>U</mi> <mo>{</mo> <msub> <mi>s</mi> <mi>l</mi> </msub> <mo>}</mo> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>m</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <mrow> <mo>(</mo> <msub> <mi>a</mi> <mi>m</mi> </msub> <mo>-</mo> <mover> <msub> <mi>s</mi> <mi>l</mi> </msub> <mo>&OverBar;</mo> </mover> <mo>)</mo> </mrow> <msup> <mrow> <mo>(</mo> <msub> <mi>a</mi> <mi>m</mi> </msub> <mo>-</mo> <mover> <msub> <mi>s</mi> <mi>l</mi> </msub> <mo>&OverBar;</mo> </mover> <mo>)</mo> </mrow> <mi>T</mi> </msup> <msub> <mi>P</mi> <mi>m</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>s</mi> <mi>l</mi> </msub> <mo>|</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow></math>
<math><mrow> <msubsup> <mi>w</mi> <mi>m</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <mover> <mi>y</mi> <mo>~</mo> </mover> <mo>-</mo> <msubsup> <mi>a</mi> <mi>m</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </msubsup> <msub> <mi>g</mi> <mi>t</mi> </msub> <mo>-</mo> <mi>&Sigma;</mi> <msub> <mover> <mi>s</mi> <mo>&OverBar;</mo> </mover> <mi>l</mi> </msub> <msub> <mi>g</mi> <mi>l</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>7</mn> <mo>)</mo> </mrow> </mrow></math>
Wherein <math><mrow> <msub> <mi>&Lambda;</mi> <mi>t</mi> </msub> <mover> <mo>=</mo> <mi>&Delta;</mi> </mover> <mfenced open='(' close=')'> <mtable> <mtr> <mtd> <mi>R</mi> <mrow> <mo>(</mo> <mi>V</mi> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>t</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <mi>U</mi> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>t</mi> </msub> <mo>)</mo> </mrow> <mo>)</mo> </mrow> </mtd> <mtd> <mo>-</mo> <mi>I</mi> <mrow> <mo>(</mo> <mi>V</mi> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>t</mi> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mi>U</mi> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>t</mi> </msub> <mo>)</mo> </mrow> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mi>I</mi> <mrow> <mo>(</mo> <mi>V</mi> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>t</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <mi>U</mi> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>t</mi> </msub> <mo>)</mo> </mrow> <mo>)</mo> </mrow> </mtd> <mtd> <mo>-</mo> <mi>R</mi> <mrow> <mo>(</mo> <mi>V</mi> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>t</mi> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mi>U</mi> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>t</mi> </msub> <mo>)</mo> </mrow> <mo>)</mo> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>9</mn> <mo>)</mo> </mrow> </mrow></math>
Where "#" represents a conjugate, "t" represents a transpose of the matrix, R (#) represents taking the real part, and I (#) represents taking the imaginary part.
In the PDA algorithm, P is first putm(stY) is initialized to a uniform distribution and then based onIs given byFormula (4) — (10) calculates the update Pm(stY) until the algorithm exit condition is satisfied.
As shown in fig. 1, the method for detecting uplink signals of a multi-cell MIMO system interacting with partial soft information according to the present invention includes the following steps:
(1) each base station executes parallel detection, and the method specifically comprises the following substeps:
(1.1) each base station firstly determines initial prior probability information of a received signal based on symbol probability uniform distribution;
(1.2) each base station detects all signals according to the initial prior probability information obtained in the step 1.1, wherein all signals not only comprise user signals of the cell, but also comprise user signals of the same channel of the surrounding cells;
(1.3) each base station updates the posterior probability through iterative calculation in the probability data cooperative detection method until a preset condition is reached, and finally obtains the maximum posterior probability, wherein the preset condition is that the difference value between the current updated posterior probability and the posterior probability updated at the previous time is smaller than a preset threshold value, or the repeated execution times reaches a preset value; the predetermined threshold may be set appropriately according to the required calculation accuracy, and the predetermined value may be set to 5 but is not limited to 5, and when the predetermined value is 5, it is sufficient to achieve sufficient calculation accuracy and avoid an excessive calculation amount;
(2) all base stations sort the maximum posterior probability values of all the symbols obtained in the step 1.3 from large to small, and the posterior probability data of the front X% is selected, wherein X is more than 0 and less than or equal to 25; the selection of different amounts of interaction information can achieve different degrees of communication performance, for example, probability data of 25% interaction, and the communication performance can achieve more than 90% of performance obtained by interacting all soft information, so in the process of selecting the interaction amount, the requirement of the communication technology on the communication performance under the actual condition needs to be considered.
(3) Sending the posterior probability data of the symbol selected in the step 2 to the corresponding adjacent base station participating in the cooperative detection;
(4) after receiving soft information of local user symbols sent by other base stations, each base station respectively calculates (1-M)/(M-n) posterior probability values which are not selected and sent by other base stations, wherein n is the number of selected constellation points, M is the sum of the received posterior probabilities of the local user symbols sent by other base stations, and M is a modulation order;
(5) and each base station soft-combines the posterior probability which is obtained by calculation in the step 4 and is not selected by other base stations to be sent with the posterior probability sent by other base stations, calculates the posterior probability of the symbol sent by the local user, and selects the symbol with the maximum posterior probability as the final detection result.
Examples
As shown in fig. 2, four base stations are selected for cooperative detection, the base stations BS1, BS2, BS3 and BS4 are adjacent base stations, soft information interaction is performed among the four base stations, and in the interaction process, each base station sends posterior probability data to all adjacent base stations participating in cooperative detection.
Fig. 3 shows that in a scenario of cooperative detection of four base stations in an embodiment of the present invention, the modulation order M in fig. 3(a) is 16, the modulation order M in fig. 3(b) is 64, and a base station n (n may be one of 1,2,3, and 4) selects an amount of information before interaction of 25% to interact with all soft information of the base station, which affects performance of cooperative detection of the base station, as shown in fig. 3, performance of independent detection of the base station is obviously inferior to that of cooperative detection, and therefore, advantages of cooperative detection are seen, and meanwhile, no matter a signal-to-noise ratio is present, information of 25% is constantly interacted, and a detection effect achieved can basically achieve more than 80% of performance obtained when all information is interacted. Fig. 4 shows a relationship between an amount of information exchanged between base stations and a communication environment where the base stations are located when a modulation order M is 64 in a four-base-station cooperative detection scenario according to an embodiment of the present invention and when it is ensured that cooperative detection performance of the base stations reaches 90% or more of performance obtained by exchanging all information. As shown in fig. 4(a), as the signal-to-noise ratio varies from 0 to 20, the ratio of the mutual information is as shown in the following table:
signal to noise ratio 0 5 10 15 20
Interaction ratio 15/64 14/64 12/64 9/64 5/64
As can be seen from fig. 4(a) and 4(b), the selection of the amount of the interactive information can be properly adjusted according to the specific communication environment, and the communication performance can be effectively ensured as well; in this embodiment, the cooperative detection performance is ensured to be 90% or more, and in practice, the ratio is not limited to this ratio and may be appropriately adjusted.
The present invention is not limited to the above-described embodiments, and those skilled in the art can implement the present invention in other various embodiments based on the disclosure of the present invention. Therefore, the design of the invention is within the scope of protection, with simple changes or modifications, based on the design structure and thought of the invention.

Claims (1)

1. A multi-cell MIMO system uplink signal detection method for interacting part of soft information is characterized by comprising the following steps:
(1) each base station executes parallel detection, and the method specifically comprises the following substeps:
(1.1) each base station firstly determines initial prior probability information of a received signal based on symbol probability uniform distribution;
(1.2) each base station detects all signals according to the initial prior probability information obtained in the step 1.1, wherein all signals not only comprise user signals of the cell, but also comprise user signals of the same channel of the surrounding cells;
(1.3) each base station updates the posterior probability through iterative calculation in the probability data cooperative detection method until a preset condition is reached, and finally obtains the maximum posterior probability, wherein the preset condition is that the difference value between the current updated posterior probability and the posterior probability updated at the previous time is smaller than a preset threshold value, or the repeated execution times reaches a preset value;
(2) all base stations sort the maximum posterior probability values of all the symbols obtained in the step 1.3 from large to small, and the posterior probability data of the front X% is selected, wherein X is more than 0 and less than or equal to 25;
(3) sending the posterior probability data of the symbol selected in the step 2 to the corresponding adjacent base station participating in the cooperative detection;
(4) after receiving soft information of local user symbols sent by other base stations, each base station respectively calculates (1-M)/(M-n) posterior probability values which are not selected and sent by other base stations, wherein n is the number of selected constellation points, M is the sum of the received posterior probabilities of the local user symbols sent by other base stations, and M is a modulation order;
(5) and each base station soft-combines the posterior probability which is obtained by calculation in the step 4 and is not selected by other base stations to be sent with the posterior probability sent by other base stations, calculates the posterior probability of the symbol sent by the local user, and selects the symbol with the maximum posterior probability as the final detection result.
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