CN111083084B - Uplink transmission method, computer-readable storage medium, and distributed multi-antenna system - Google Patents

Uplink transmission method, computer-readable storage medium, and distributed multi-antenna system Download PDF

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CN111083084B
CN111083084B CN201911419491.2A CN201911419491A CN111083084B CN 111083084 B CN111083084 B CN 111083084B CN 201911419491 A CN201911419491 A CN 201911419491A CN 111083084 B CN111083084 B CN 111083084B
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CN111083084A (en
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吴涛
张昱
徐锡强
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Sunwave Communications Co Ltd
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
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Abstract

The application relates to an uplink transmission method for resisting inter-carrier interference, a computer readable storage medium, a distributed multi-antenna system and a joint optimization method for matrix coefficients of an inter-carrier interference self-elimination coding matrix and degree distribution coefficients of rateless codes. The joint optimization method is applied to a distributed multi-antenna system adopting an orthogonal frequency division multiplexing technology under a block fading channel, and comprises the following steps: determining an optimization problem of jointly optimizing matrix coefficients and degree distribution coefficients by taking the maximum system transmission rate as a target according to statistical information of channel states and external information transmission analysis in a decoding process; and solving the optimization problem to obtain the optimized matrix coefficient and degree distribution coefficient. By the method and the device, the problem that the matrix coefficient of the ICI self-elimination coding matrix and the degree distribution coefficient of the non-rate coding are independently optimized to cause difficulty in considering both the rate of wireless transmission and the ICI elimination effect is solved, and the ICI elimination effect is improved on the basis of ensuring the rate of wireless transmission.

Description

Uplink transmission method, computer-readable storage medium, and distributed multi-antenna system
Technical Field
The present invention relates to the field of communications, and in particular, to an uplink transmission method for countering inter-carrier interference, a joint optimization method for matrix coefficients of an inter-carrier interference self-cancellation coding matrix and degree distribution coefficients of rateless codes, a distributed multi-antenna system, and a computer-readable storage medium.
Background
Distributed Antenna Systems (DAS) can effectively solve the technical problems of the next generation wireless mobile communication system in both spectral efficiency and transmission power. And it is a network architecture that can be integrated with many existing systems to improve coverage and system performance. However, the network status and channel status of the distributed multi-antenna system is more complex and variable than the conventional cellular network. Noise, interference and channel fading have a great influence on the quality of electromagnetic wave signals and transmission reliability, and severe noise, interference and channel fading may even cause interruption of a communication process. In order to counter these unstable factors of the wireless channel to ensure reliable transmission of information, error control techniques are often used to protect the message to be transmitted during actual transmission. Among them, channel coding is an effective error control technique.
Conventional fixed rate channel coding requires acquisition of user channel information and use of Hybrid Automatic Repeat reQuest (HARQ) when decoding fails, which increases the overhead of the digital forward link. In the distributed multi-antenna system using the rateless code for channel coding, the sending node only needs to feed back an Acknowledgement Character (ACK) signal to the receiver to indicate successful decoding, so that signaling overhead can be effectively reduced. The research of the rateless code in the related technology mainly comprises degree distribution design, decoding method design and the like, wherein a degree distribution function is directly related to the performance of the rateless code, and the decoding success rate, the decoding expense, the decoding complexity and the like are determined.
In addition, Orthogonal Frequency Division Multiplexing (OFDM) technology is suitable for use in DAS systems due to its advantages such as excellent interference rejection and high spectrum utilization. However, DAS systems using OFDM technology are susceptible to frequency offset, which results in Inter-Carrier Interference (ICI). The ICI self-cancellation method can effectively combat ICI and is widely used due to its low complexity of implementation. In the related art, the ICI self-cancellation method is generally applied to a point-to-point transmission system, and research on the ICI self-cancellation method mainly includes design of a coding matrix, a method for receiving end demodulation, and the like, where the design of the ICI self-cancellation coding matrix determines the ICI cancellation effect of the system.
In the related art, an ICI self-cancellation method has not been applied to a DAS system using an OFDM technique in a fading channel, and only a matrix coefficient of an ICI self-cancellation coding matrix or a degree distribution coefficient of a non-rate code is optimized individually in the related art, which results in that a rate of wireless transmission and an ICI cancellation effect cannot be considered at the same time.
Disclosure of Invention
Based on this, it is necessary to provide an uplink transmission method for resisting inter-carrier interference, a joint optimization method for matrix coefficients of an inter-carrier interference self-cancellation coding matrix and frequency distribution coefficients of no-rate coding, a distributed multi-antenna system, and a computer-readable storage medium, for the problem that it is difficult to achieve both the rate of wireless transmission and the ICI cancellation effect in a distributed multi-antenna OFDM system under a block fading channel due to separate optimization of the matrix coefficients of the ICI self-cancellation coding matrix and the frequency distribution coefficients of no-rate coding in the related art.
In a first aspect, an embodiment of the present application provides a method for jointly optimizing a matrix coefficient of an inter-carrier interference self-cancellation coding matrix and a degree distribution coefficient of a no-rate code, which is applied to a distributed multi-antenna system that adopts an orthogonal frequency division multiplexing technique under a block fading channel, and includes:
determining an optimization problem of jointly optimizing the matrix coefficient and the degree distribution coefficient by taking the maximum system transmission rate as a target according to the statistical information of the channel state and the external information transmission analysis in the decoding process;
and solving the optimization problem to obtain the optimized matrix coefficient and the optimized degree distribution coefficient.
In some of these embodiments, the optimization problem is listed as:
Figure BDA0002351970430000021
the constraints of the optimization problem include:
(1) constraint condition C1 of degree distribution of edges of output node of LT code pattern:
Figure BDA0002351970430000031
(2) receiving-end decoding start condition C2:
ω1>θ
(3) receiving end decoding convergence condition C3:
Figure BDA0002351970430000032
for all Hr,q,r＝1,2,…,L,q＝1,2,…,Q
(4) Constraint of output signal power on matrix coefficients of ICI self-cancelling coding matrix C4:
|c1|2+|c2|2≤1,2|c3|2≤1
wherein epsilonmaxMaximum tolerable frequency offset for the link from the user to the RRH;
Figure BDA0002351970430000033
is the maximum average code length; c. C1,c2,c3Matrix coefficients of a self-cancelling coding matrix for ICI; { omega [ [ omega ] ]dExpressing the degree distribution coefficient of the edge of the output node of the LT code graph without the rate code; omegajDegree distribution coefficient of the edge of the output node with degree j in LT code diagram; dcThe maximum number of degrees of edges that are output nodes of the LT code graph; t is an ICI self-elimination coding matrix; theta is a preset value larger than zero; x is the number ofuThe information is external information; gamma ray1,γ2,γ3,γ4Respectively is the proportion of variable nodes with the degrees of 1,2,3 or 4 in the LDPC code graph;
Figure BDA0002351970430000034
is a constant independent of the instantaneous gain of the channel;
Figure BDA0002351970430000035
a minimum threshold for correctly decoded extrinsic information; hr,qRepresenting the channel gain of the link of the r RRH in the q channel state; r is 1,2, …, L; l is the total number of RRHs; q is the total number of channel states.
In some of these embodiments, solving the optimization problem to obtain the optimized matrix coefficients and degree distribution coefficients comprises:
exhausting the frequency offset value within a preset range;
randomly generating a plurality of groups of matrix coefficients meeting the constraint condition of output signal power on matrix coefficients of an ICI self-elimination coding matrix under each exhaustive frequency offset value; calculating the minimum value of the channel coding code length corresponding to the multiple groups of matrix coefficients and the degree distribution coefficient of the edge of the output node of the LT code graph under the degree distribution sum constraint condition of the edge of the output node of the LT code graph, the decoding starting condition of a receiving end and the decoding convergence condition of the receiving end; selecting matrix coefficients from the multiple groups of matrix coefficients by adopting a genetic algorithm according to the minimum value of the channel coding code length and the degree distribution coefficient of the edge of the output node of the LT code pattern;
calculating a maximum tolerable frequency offset value according to the selected matrix coefficient and the corresponding degree distribution coefficient of the edge of the output node of the LT code graph;
and taking the matrix coefficient corresponding to the maximum value in the maximum tolerable frequency deviation values and the degree distribution coefficient of the edge of the output node of the corresponding LT code graph as the optimal solution of the optimization problem.
In some embodiments, selecting matrix coefficients from the plurality of sets of matrix coefficients using a genetic algorithm according to the minimum value of the channel coding code length and degree distribution coefficients of edges of the LT code pattern comprises:
calculating the probability that the matrix coefficient corresponding to the minimum value of the channel coding code length is inherited to the next generation according to the minimum value of the channel coding code length;
calculating the cumulative probability of each group of matrix coefficients according to the probability of the matrix coefficients being inherited to the next generation;
and randomly extracting a plurality of groups of matrix coefficients according to the cumulative probability.
In some of these embodiments, randomly extracting sets of matrix coefficients according to the cumulative probability comprises:
in [0,1 ]]Generating a uniformly distributed pseudo-random number s within the interval if s<q1Then the 1 st set of matrix coefficients is selected, otherwise the k-th set is selected such that q isk-1<s≤qkIf true; repeating the steps for M times to obtain M groups of matrix coefficients;
wherein M is the multiple groupsThe number of sets of matrix coefficients; q. q.skIs the cumulative probability of the kth set of matrix coefficients.
In some of these embodiments, after randomly drawing sets of matrix coefficients according to the cumulative probability, the method further comprises:
pairwise matching a plurality of groups of matrix coefficients obtained by random extraction by adopting a genetic algorithm, and exchanging binary codes of each pair of matrix coefficients by using a first preset probability to obtain a plurality of groups of matrix coefficients updated for the first time;
and replacing some random bit encoding values in binary encoding of the obtained groups of matrix coefficients updated for the first time by second preset probability to obtain a plurality of groups of matrix coefficients updated for the second time, and taking the plurality of groups of matrix coefficients updated for the second time as the matrix coefficients for calculating the maximum tolerable frequency offset value.
In a second aspect, an embodiment of the present application provides an uplink transmission method for countering inter-carrier interference, which is applied to a distributed multi-antenna system that adopts an orthogonal frequency division multiplexing technique under a block fading channel, and includes:
the distributed multi-antenna system receives uplink transmission signals from a plurality of radio remote heads to obtain a plurality of uplink transmission signals; the uplink transmission signal is obtained by modulating user information after no-rate coding, obtaining an uplink modulation signal and then transforming according to an ICI self-elimination coding matrix; the matrix coefficients of the ICI self-canceling coding matrix are determined by the joint optimization method of the first aspect;
the distributed multi-antenna system respectively performs preprocessing and quantization processing on the plurality of uplink transmission signals to obtain a plurality of quantized signals;
and the distributed multi-antenna system respectively performs soft demodulation on the quantized signals according to the matrix coefficients, and then performs joint decoding by using a belief propagation algorithm to obtain the user information.
In some embodiments, the performing, by the distributed multi-antenna system, soft demodulation on the quantized signals according to the matrix coefficients, and then performing joint decoding by using a belief propagation algorithm to obtain the user information includes:
the distributed multi-antenna system respectively calculates the log-likelihood ratio of each coding bit of the rateless codes according to the quantized signals, and then combines the log-likelihood ratios of the same coding bit to obtain a combined log-likelihood ratio;
and the distributed multi-antenna system performs joint decoding by using a belief propagation algorithm according to the combined log-likelihood ratio to obtain the user information.
In a third aspect, embodiments of the present application provide a distributed multi-antenna system, which is applied in a block fading channel and employs an orthogonal frequency division multiplexing technique, and includes multiple remote radio heads and a baseband processing unit pool, wherein,
the radio remote head is used for receiving an uplink transmission signal, preprocessing and quantizing the uplink transmission signal and then sending the uplink transmission signal to the baseband processing unit pool; the uplink transmission signal is obtained by modulating user information after no-rate coding, obtaining an uplink modulation signal and then transforming according to an ICI self-elimination coding matrix; the matrix coefficients of the ICI self-cancellation coding matrix are determined by jointly optimizing the matrix coefficients and the degree distribution coefficients of the no-rate coding;
and the baseband processing unit pool is used for respectively carrying out soft demodulation on the quantized signals according to the matrix coefficients and then carrying out joint decoding by using a belief propagation algorithm to obtain the user information.
In a fourth aspect, the present application provides a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method for jointly optimizing the matrix coefficients of the inter-carrier interference self-cancellation coding matrix and the degree distribution coefficients of the no-rate coding according to the second aspect is implemented.
Compared with the prior art, the uplink transmission method for resisting inter-carrier interference, the joint optimization method for the matrix coefficient of the inter-carrier interference self-elimination coding matrix and the degree distribution coefficient of the no-rate coding, the distributed multi-antenna system and the computer readable storage medium provided by the embodiment of the application solve the problem that the matrix coefficient of the ICI self-elimination coding matrix and the degree distribution coefficient of the no-rate coding are independently optimized to cause difficulty in considering both the rate of wireless transmission and the ICI elimination effect by applying the ICI self-elimination method to the distributed multi-antenna system adopting the orthogonal frequency division multiplexing technology under a block fading channel and combining and optimizing the matrix coefficient of the ICI self-elimination coding matrix and the degree distribution coefficient of the no-rate coding, and improve the ICI elimination effect on the basis of ensuring the rate of wireless transmission.
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In order to more clearly illustrate the embodiments of the present application or technical solutions in related arts, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a flow diagram of a joint optimization method according to an embodiment of the present application;
FIG. 2 is a flow chart of a joint optimization method according to a preferred embodiment of the present application;
fig. 3 is a schematic structural diagram of a distributed multi-antenna system according to an embodiment of the present application;
fig. 4 is a flowchart of an ICI-countering uplink transmission method according to an embodiment of the present application;
FIG. 5 is a graph of rate-free coding according to the preferred embodiment of the present application;
fig. 6 is a diagram illustrating simulation results according to a preferred embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other examples, which can be obtained by a person skilled in the art without making any inventive step based on the examples in this application, are within the scope of protection of this application.
It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Unless otherwise defined, technical or scientific terms used herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. The use of "first," "second," and similar terms in the description and claims of this patent application do not denote any order, quantity, or importance, but rather the terms are used to distinguish one element from another. The terms "a," "an," "the," and the like, do not denote a limitation of quantity, and may denote the singular or plural.
The word "comprise" or "comprises", and the like, means that the element or item listed before "comprises" or "comprising" covers the element or item listed after "comprising" or "comprises" and its equivalent, and does not exclude other elements or items. "connected" or "coupled" and similar terms are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect.
The various techniques described herein may be used in various Mobile communication systems, such as 2G, 3G, 4G, 5G Mobile communication systems and next generation Mobile communication systems, such as Global System for Mobile communications (GSM), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Wideband Code Division Multiple Access (OFDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), FDMA (SC-FDMA), General Packet Radio Service (Radio Frequency-Division Multiple Access, GPRS) systems, abbreviated NR) systems and other such communication systems. The various techniques described herein may also be used for various other Wireless communication systems, such as Wireless Local Area Network (WLAN) and WiMAX systems.
The method for jointly optimizing the matrix coefficient of the ICI self-eliminating coding matrix and the degree distribution coefficient of the non-rate coding is particularly suitable for a distributed multi-antenna system adopting the OFDM technology under a block fading channel. In the block fading channel, the channel gain is kept unchanged in one round of transmission, but changes after each round of transmission, so that the uplink transmission scheme adopting the joint optimization method can ensure the wireless transmission rate even if the user does not know the channel state.
The user in the embodiment of the present application refers to a node device that transmits user information, and the node device may be an intelligent terminal, or may be other node devices that need to transmit user information, such as a relay device.
The embodiment provides a method for jointly optimizing the matrix coefficient of an inter-carrier interference self-cancellation coding matrix and the degree distribution coefficient of non-rate coding. Fig. 1 is a flowchart of a joint optimization method according to an embodiment of the present application, and as shown in fig. 1, the flowchart includes the following steps:
and step S101, determining an optimization problem of jointly optimizing matrix coefficients and degree distribution coefficients by taking the maximum system transmission rate as a target according to statistical information of channel states and external information transmission analysis in a decoding process.
And S102, solving an optimization problem to obtain an optimized matrix coefficient and an optimized degree distribution coefficient.
Through the steps, the matrix coefficient of the ICI self-elimination coding matrix and the degree distribution coefficient of the no-rate coding are optimized in a combined mode by taking the maximum system transmission rate as a target, so that the problem that the matrix coefficient of the ICI self-elimination coding matrix and the degree distribution coefficient of the no-rate coding are optimized independently, the rate of wireless transmission and the ICI elimination effect are difficult to take into account in a distributed multi-antenna OFDM system under a block fading channel is solved, and the ICI elimination effect is improved on the basis of guaranteeing the rate of wireless transmission.
In some of these embodiments, the optimization problem is listed as:
Figure BDA0002351970430000091
the constraints of the optimization problem include:
(1) constraint condition C1 of degree distribution of edges of output node of LT code pattern:
Figure BDA0002351970430000101
(2) receiving-end decoding start condition C2:
ω1>θ
(3) receiving end decoding convergence condition C3:
Figure BDA0002351970430000102
for all Hr,q,r＝1,2,…,L,q＝1,2,…,Q
(4) Constraint of output signal power on matrix coefficients of ICI self-cancelling coding matrix C4:
|c1|2+|c2|2≤1,2|c3|2≤1
wherein epsilonmaxMaximum tolerable frequency offset for the link from the user to the RRH;
Figure BDA0002351970430000103
is the maximum average code length; c. C1,c2,c3Matrix coefficients of a self-cancelling coding matrix for ICI; { omega [ [ omega ] ]dExpressing the degree distribution coefficient of the edge of the output node of the LT code graph without the rate code; omegajDegree distribution coefficient of the edge of the output node with degree j in LT code diagram; dcThe maximum number of degrees of edges that are output nodes of the LT code graph; t is an ICI self-elimination coding matrix; theta is a preset value larger than zero; x is the number ofuThe information is external information; gamma ray1,γ2,γ3,γ4Respectively is the proportion of variable nodes with the degrees of 1,2,3 or 4 in the LDPC code graph;
Figure BDA0002351970430000104
is a constant independent of the instantaneous gain of the channel;
Figure BDA0002351970430000105
a minimum threshold for correctly decoded extrinsic information; hr,qRepresenting the channel gain of the link of the r RRH in the q channel state; r is 1,2, …, L; l is the total number of RRHs; q is the total number of channel states.
The constraint condition C4 ensures that the output signal power of the user is not increased after the uplink modulation signal is transformed according to the ICI self-cancellation coding matrix.
FIG. 2 is a flow chart of a joint optimization method according to the preferred embodiment of the present application, as shown in FIG. 2, in some embodiments, step S102 includes:
step S102-1, exhausting the frequency offset value within a preset range;
step S102-2, under each exhaustive frequency offset value, randomly generating a plurality of groups of matrix coefficients meeting the constraint condition of output signal power to the matrix coefficients of the ICI self-elimination coding matrix; calculating the minimum value of the channel coding code length corresponding to the multiple groups of matrix coefficients and the degree distribution coefficient of the edge of the output node of the LT code graph under the degree distribution sum constraint condition of the edge of the output node of the LT code graph, the decoding starting condition of the receiving end and the decoding convergence condition of the receiving end; selecting matrix coefficients from the multiple groups of matrix coefficients by adopting a genetic algorithm according to the minimum value of the channel coding code length and the degree distribution coefficient of the edge of the output node of the LT code pattern;
step S102-3, calculating a maximum tolerable frequency offset value according to the selected matrix coefficient and the corresponding degree distribution coefficient of the edge of the output node of the LT code pattern;
and step S102-4, taking the matrix coefficient corresponding to the maximum value in the maximum tolerable frequency deviation values and the corresponding degree distribution coefficient of the edge of the output node of the LT code graph as the optimal solution of the optimization problem.
With continued reference to fig. 2, in some embodiments, in step S102-2, selecting matrix coefficients from the plurality of sets of matrix coefficients using a genetic algorithm according to the minimum value of the channel coding code length and degree distribution coefficients of edges of the LT code pattern comprises:
step S102-2-1, according to the minimum value of the channel coding code length, calculating the probability that the matrix coefficient corresponding to the minimum value of the channel coding code length is inherited to the next generation;
step S102-2-2, calculating the cumulative probability of each group of matrix coefficients according to the probability of the matrix coefficients being inherited to the next generation;
and S102-2-3, randomly extracting a plurality of groups of matrix coefficients according to the cumulative probability.
In some of these embodiments, step S102-2-3 includes: in [0,1 ]]Generating a uniformly distributed pseudo-random number s within the interval if s<q1Then the 1 st set of matrix coefficients is selected, otherwise the k-th set is selected such that q isk-1<s≤qkIf true; repeating the steps for M times to obtain M groups of matrix coefficients; wherein M is the number of groups of the multiple groups of matrix coefficients; q. q.skIs the cumulative probability of the kth set of matrix coefficients.
With continued reference to FIG. 2, in some embodiments, after step S102-2-3, the method further comprises:
s102-2-4, pairwise matching a plurality of groups of matrix coefficients obtained by random extraction by adopting a genetic algorithm, and exchanging binary codes of each pair of matrix coefficients by using a first preset probability to obtain a plurality of groups of matrix coefficients updated for the first time;
and S102-2-5, replacing some random bit encoding values in binary encoding of the obtained groups of matrix coefficients updated for the first time by second preset probability to obtain a plurality of groups of matrix coefficients updated for the second time, and taking the plurality of groups of matrix coefficients updated for the second time as the matrix coefficients for calculating the maximum tolerable frequency offset value.
The embodiment also provides a distributed multi-antenna system. The distributed multi-antenna system is applied to a block fading channel and adopts an orthogonal frequency division multiplexing modulation technology. Fig. 3 is a schematic structural diagram of a distributed multi-antenna system according to an embodiment of the present application, and as shown in fig. 3, the system includes a Remote Radio Head (RRH) and a baseband processing unit (BBU) pool.
Before the system is used for uplink transmission, the degree distribution of the rateless codes and the matrix coefficients of the ICI self-cancellation coding matrix are optimized by using the statistical information of the fading channels according to the joint optimization method provided in the embodiment of the present application.
Fig. 4 is a flowchart of an uplink transmission method for combating ICI according to an embodiment of the present application, and as shown in fig. 4, in the above-mentioned distributed multi-antenna system, the uplink transmission method for combating ICI includes the following steps:
step S401, determining a matrix coefficient of an ICI self-elimination coding matrix and a degree distribution coefficient of no-rate coding through a combined optimization matrix coefficient and the degree distribution coefficient of no-rate coding;
step S402, after the user carries out no-rate coding on the user information according to the degree distribution coefficient, the code word is modulated to obtain an uplink modulation signal, then the uplink modulation signal is transformed according to the ICI self-elimination coding matrix corresponding to the matrix coefficient to obtain an uplink transmission signal, and the uplink transmission signal is sent to each radio remote head of the distributed multi-antenna system covering the user;
step S403, each remote radio head of the distributed multi-antenna system receives the uplink transmission signal, and the uplink transmission signals are respectively preprocessed by the remote radio heads to become baseband signals, and then the signals are quantized and sent to a baseband processing unit pool of the distributed multi-antenna system;
and step S404, respectively performing soft demodulation on the received quantized signals by a baseband processing unit pool of the distributed multi-antenna system according to matrix coefficients of the ICI self-elimination coding matrix, and then performing joint decoding by using a belief propagation algorithm to obtain user information.
The ICI self-elimination method in the above steps modulates the code word of the non-rate coding to a group of adjacent sub-carriers at the user terminal, each sub-carrier has its own matrix coefficient, and the ICI reaches the minimum value on the basis of ensuring the wireless transmission rate by jointly optimizing the degree distribution coefficient of the non-rate coding and the matrix coefficient of the ICI self-elimination coding matrix; and at a receiving end of the distributed multi-antenna system, the matrix coefficients are used for carrying out linear combination on the sub-carriers, so that the ICI (inter-carrier interference) residual in the received uplink transmission signals is further reduced. Compared with the related art that the ICI elimination effect cannot be guaranteed by independently optimizing the power distribution coefficient of the rateless code and the wireless transmission rate cannot be guaranteed by independently optimizing the matrix coefficient of the ICI self-elimination code matrix, the wireless transmission rate and the ICI elimination effect can be considered in the steps.
In some embodiments, step S404 includes the following steps: the baseband processing unit pool of the distributed multi-antenna system respectively calculates the log-likelihood ratio of each coding bit of the rateless code according to a plurality of quantized signals, and then combines the log-likelihood ratios of the same coding bit to obtain a combined log-likelihood ratio; and the baseband processing unit pool of the distributed multi-antenna system performs joint decoding by using a belief propagation algorithm according to the combined log-likelihood ratio to obtain user information.
The uplink transmission method of the present application is described and illustrated by the preferred embodiments below.
The uplink transmission process provided by the preferred embodiment includes the following steps:
step 1, a distributed multi-antenna system based on an ICI self-elimination coding matrix adopts an OFDM modulation technology, a single user carries out uplink communication with a BBU through L distributed multi-antennas, and the number of OFDM subcarriers is N. The user first encoding the length K by using no rateThe original information m is coded into a codeword c of length N, here with a code rate RPThe LDPC code of (1) is pre-coded as a rate-free code, and then is subjected to Luby Transform codes (LT) coding for outputting a degree distribution of Ω (x).
Step 2, modulating the rateless code c into
Figure BDA0002351970430000141
Wherein x (k), k is 0,1, …, W-1, and multiplied by ICI self-eliminating coding matrix T to obtain
Figure BDA0002351970430000142
Wherein
Figure BDA0002351970430000143
The order of T is NxW, and the coding efficiency is
Figure BDA0002351970430000144
T is as follows:
Figure BDA0002351970430000145
in matrix c1,c2,c3Are matrix weight coefficients.
Step 3, the signal is sent to each RRH covering the user through the antennarAnd r is 1,2, …, L, the preprocessor of RRH preprocesses the received signal to obtain a baseband signal:
Figure BDA0002351970430000146
wherein y isr(7i + j) and nr(7i + j) represents the result in RRHrThe received signal on the (7i + j) th subcarrier and gaussian noise,
Figure BDA0002351970430000149
j ═ 0,1, …,6.x (4a + b) represents the (4a + b) th transmission signal,
Figure BDA0002351970430000147
b is 0,1,2, 3. In the formula
Figure BDA0002351970430000148
Figure BDA0002351970430000151
Is shown in RRHrInter-carrier interference between m and k sub-carriers m 1,2, …, N, k 1,2, …, N, epsilonrRepresenting a user to an RRHrThe link at (a) normalizes the frequency offset. Hr(k) K is 1,2, …, and N is represented in RRHrThe channel gain factor on the k-th sub-carrier, in a block fading channel, remains constant during one transmission round but varies from round to round.
RRH quantizes the signal, and the number of quantization levels satisfies 2M 2bWhere b is the quantization bit, the quantization interval is Δ, and the quantization threshold is
Figure BDA0002351970430000152
The quantized signal is
Figure BDA0002351970430000153
The quantization rule is as follows:
Figure BDA0002351970430000154
wherein the content of the first and second substances,
Figure BDA0002351970430000155
is a quantized value.
Step 4, RRHrSending the obtained quantized signal to the BBU through a high-speed link; applying differential reception to RRHrIs converted into a demodulated signal x'r：
Figure BDA0002351970430000156
Figure BDA0002351970430000157
Figure BDA0002351970430000158
Figure BDA0002351970430000159
4i code bit c of user no-rate code4iTaking 0 and 1 with equal probability, the quantized signal x' r (4i) ═ q uploaded by the r-th RRH to BBUk. The log-likelihood ratio (LLR) corresponding to the 4 th bit output by the soft demodulator of the baseband processing unit pool can be expressed as:
Figure BDA0002351970430000161
where Pr (-) denotes that when the output bit is 0 or 1, the received quantized signal is qkThe probability of (c). After combining the LLRs corresponding to the RRH uploading signals, the LLR of the 4i th bit is as follows:
Figure BDA0002351970430000162
similarly, LLRs corresponding to (4i +1), (4i +2) and (4i +3) th bits can be obtained.
And 5, carrying out iterative decoding by the baseband processing unit pool joint decoder. Fig. 5 is a decoding diagram of the non-rate coding according to the preferred embodiment of the present application, and as shown in fig. 5, the iterative decoding performed by the baseband processing unit pool joint decoder includes two steps: in the first step, iterative decoding is performed on the entire decoding graph until the mean LLR value of the input nodes exceeds a certain threshold xp(ii) a And secondly, iteratively decoding the LDPC decoding graph to eliminate residual errors.
The specific procedure of the first step is as follows: the 0 th iteration decoding, the initial LLR of the input node i in the decoding graph is
Figure BDA0002351970430000163
Initial LLR of the output node isL (i) the I-th iteration, the message transmitted from the input node i to the check node c is updated as follows:
Figure BDA0002351970430000164
where o is the output node connected to the input node. The message sent back by the check node c to the input node i is updated as follows:
Figure BDA0002351970430000165
wherein i' is an input node connected with the check node c except the input node i in the decoding graph. The message passed by the input node i to the output node o is updated as:
Figure BDA0002351970430000166
where o' represents an output node other than o. The message sent back to the input node i by the output node o is updated as follows:
Figure BDA0002351970430000167
where i' represents an input node other than i,
Figure BDA0002351970430000168
is the message sent by the output node o to the input node i in the first iteration;
Figure BDA0002351970430000171
is the message sent by the input node i to the output node o in the first iteration; z0The LLR calculated by the output node according to the quantization value of the corresponding codeword bit is obtained in step 2.4. The LLR of the input node i of the current round is:
Figure BDA0002351970430000172
when the mean LLR value of the input nodes of the round exceeds the threshold xpAnd then performing iterative decoding on the LDPC code graph independently.
The second step of iterative decoding is as follows: the 0 th iteration decoding of the LDPC subgraph, and the message transmitted from the variable node v to the check node c is updated as follows:
Figure BDA0002351970430000173
in the formula mvThe LLR of the input node in the last previous iteration. In the first iteration, the message transmitted from the variable node v to the check node c is updated as follows:
Figure BDA0002351970430000174
wherein C' represents a check node other than C, CvRepresenting a set of check nodes adjacent to variable node v,
Figure BDA0002351970430000175
representing the message passed by check node c' to the variable node in the previous round. The message passing from the check node c to the variable node v is updated as follows:
Figure BDA0002351970430000176
where v' represents a variable node other than v connected to the check node c.
Log likelihood ratio information of decision bit s
Figure BDA0002351970430000177
If LLR(s)>And judging the information bit s as 0 if the decoding number is 0, otherwise judging the information bit s as 1, outputting a result according to the judgment, continuing iteration if the decoding is incorrect, and ending the decoding if the decoding is correct or the maximum iteration number t is reached.
And finishing the uplink transmission process through the steps 1 to 5.
In the uplink transmission method, the joint optimization method of the power distribution coefficient of the non-rate coding and the matrix coefficient of the ICI self-cancellation coding matrix includes the following steps:
and step 1, carrying out external information transmission analysis in the decoding process.
First, an out-of-decoding information update function of the baseband processing unit pool is defined as follows. The LT input node transmits LLR information to the LDPC code graph check node, and the carried external information is as follows:
Figure BDA0002351970430000181
in the formula
Figure BDA0002351970430000182
Is the average extrinsic information, ζ, passed from the LT output node to the input node for the l-1 st iterationiFor the ratio of input nodes in the LT decoding diagram with degree i, dvAnd J is an extrinsic information function carried by the message which satisfies the symmetric Gaussian distribution. The extrinsic information returned by the LDPC check node to the LT input node is:
Figure BDA0002351970430000183
in the formula of gammaiIs the variable node proportion with degree i in the LDPC code graph,
Figure BDA0002351970430000184
the ratio of edges in the LDPC code graph connected to the degree j check node, dvIs LDPC code graph variable node maximum degree, d'cThe maximum degree of the node is checked for the LDPC code graph. The external information that the LT input node transmits a message to the output node is:
Figure BDA0002351970430000185
in the formula
Figure BDA0002351970430000186
Is the proportion of edges connected to degree i input nodes, dvThe maximum degree of the input node. Finally, the external information returned by the output node corresponding to the first coded bit to the LT input node is respectively:
Figure BDA0002351970430000187
similarly, the external information returned by the output nodes corresponding to the second, third and fourth groups of coded bits to the LT input node is respectively:
Figure BDA0002351970430000188
Figure BDA0002351970430000189
Figure BDA00023519704300001810
in the formula f0(γi)＝J(4γi) And i is 1,2,3,4, which represents the signal-to-noise ratio γiThe average amount of information carried by the ith group of output bits. The average extrinsic information carried by the message passing from these four sets of output bits to the input node is:
Figure BDA0002351970430000191
substituting the equations (18), (19), (20), (21) and (22) into (23) yields each round
Figure BDA0002351970430000192
Is updated to
Figure BDA0002351970430000193
In the formula
Figure BDA0002351970430000194
Average degree of input nodes, { omega, { for LT code graphdIs the coefficient of the degree distribution of the edges of the LT output node.
And 2, determining an optimization problem of jointly optimizing matrix coefficients and degree distribution coefficients by taking the maximum system transmission rate as a target according to the statistical information of the channel state.
Then, the channel gains of the L links are defined as a vector
Figure BDA0002351970430000195
I.e., { H }rR is 1,2, …, L, the frequency offset on each link is epsilonrAnd r is 1,2, …, L, which all possible values of the vector form a continuous channel gain vector space, discretized into Q states. Wherein { H }r＝(Hr,1,Hr,2,…,Hr,Q) Representing Q different channel states. Code length
Figure BDA0002351970430000196
Figure BDA0002351970430000197
Pr(Hr,q,εr) Representing the probability of each state being taken. The channel gain is { H }rAnd the frequency offset is epsilonrAverage degree of input nodes of LT code graph in time
Figure BDA0002351970430000198
Wherein
Figure BDA0002351970430000199
Is a constant independent of the instantaneous gain of the channel,
Figure BDA00023519704300001910
in order to achieve the theoretical maximum achievable rate,
Figure BDA00023519704300001911
Figure BDA00023519704300001912
representing the capacity of a binary input additive white gaussian noise channel with a signal-to-noise ratio of gamma.
Based on the above definitions, the optimization problem of the joint optimization problem of the embodiments of the present application is listed as follows:
Figure BDA00023519704300001913
the constraint conditions are respectively as follows:
C1:
Figure BDA00023519704300001914
C2:ω1>θ
C3:
Figure BDA00023519704300001915
for all Hr,q,r＝1,2,…,L,q＝1,2,…,Q
C4:|c1|2+|c2|2≤1,2|c3|2≤1
In the formula ofmaxIs the maximum tolerable frequency offset value of the link from the user to the RRH, which is defined as: (ii) for arbitrary frequency offset | β $<εmaxThe average code length of the system is less than
Figure BDA0002351970430000201
Figure BDA0002351970430000202
Is limited by the maximum average code length. Theta is a small amount greater than zero,
Figure BDA0002351970430000203
minimum threshold for correctly decoded extrinsic information.
And 3, solving the optimization problem to obtain the optimized matrix coefficient and degree distribution coefficient.
In step 3, the method comprises the following steps:
step 31: setting the maximum iteration times and at any fixed frequency deviation epsilonrNext, M sets of matrix coefficients c are randomly generated1,c2,c3And binary-coded and initialized at the same time
Figure BDA0002351970430000204
Step 32: for each set of matrix coefficients c1,c2,c3Under the condition of C1, C2 and C3, firstly in
Figure BDA0002351970430000205
Finding out corresponding optimal [ omega ] by a linear programming method under fixationdOn the basis of which they are exhaustive
Figure BDA0002351970430000206
Find the corresponding code length (i.e. find
Figure BDA0002351970430000207
) Minimum and corresponding degree distribution { omegad}；
Step 33:
(1) the code length value calculated from each set of matrix coefficients in step 32
Figure BDA0002351970430000208
Calculating the probability of each group of coefficients being inherited to the next generation
Figure BDA0002351970430000209
(2) Obtaining the cumulative probability of each group
Figure BDA00023519704300002010
(3) In [0,1 ]]Generating a uniformly distributed pseudo-random number s within the interval if s<q1Then the 1 st set of matrix coefficients is selected, otherwise the kth set is selected,so that q isk-1<s≤qkIf true;
(4) repeating the step (3) for M times;
step 34: for arbitrary fixed frequency offsets epsilonrThe matrix coefficients selected above are paired pairwise using a genetic algorithm, and their binary codes are exchanged with each other with a probability of Pr1 (between 0 and 1, e.g., 0.97), thereby forming two new sets of coefficients. Next, for each group of newly generated coefficients, replacing a random certain bit code value in the original code string by the probability (between 0 and 1, for example, 0.1) of Pr2, thereby completing the updating again;
step 35: repeating the steps 32 to 34, and optimizing to obtain the corresponding matrix coefficient c with the minimum code length value1,c2,c3And degree distribution { omegad}；
Step 36: within a certain range of exhaustion of epsilonrFor any epsilonrThe matrix coefficients c obtained in step 351,c2,c3And degree distribution { omegadCalculating the corresponding maximum tolerable frequency deviation
Figure BDA0002351970430000211
Selecting ε having the largest valuerCorresponding c1,c2,c3And degree distribution { omegadAs a solution to the problem.
The effects of the above embodiments of the present application are verified by computer simulation, and the matrix coefficient and the degree distribution obtained by optimization are compared with Binary Erasure Channel (BEC) degree distribution, randomly generated matrix coefficient and BEC degree distribution, traditional OFDM system and BEC degree distribution, and theoretically reachable rate. Fig. 6 is a schematic diagram of a simulation result according to a preferred embodiment of the present application, and as shown in fig. 6, compared with a standard OFDM system and BEC frequency distribution in the related art, or a randomly generated matrix coefficient and BEC frequency distribution scheme, a transmission rate based on the joint optimization method provided by the present application is closer to a theoretical achievable rate, and an ICI cancellation effect is improved.
Also provided in this embodiment is a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the joint optimization method described above.
In the embodiments provided in the present application, it should be understood that the disclosed system, apparatus or method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual or direct or communication connection may be an indirect or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. The processor may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application. The storage media described above may be used for mass storage of data or instructions. By way of example, and not limitation, memory may include a Hard Disk Drive (Hard Disk Drive, abbreviated as HDD), a floppy Disk Drive, flash memory, an optical Disk, a magneto-optical Disk, magnetic tape, or a Universal Serial Bus (USB) Drive or a combination of two or more of these. The memory may include removable or non-removable (or fixed) media, where appropriate. The memory may be internal or external to the data processing apparatus, where appropriate. In a particular embodiment, the memory is non-volatile solid-state memory. In a particular embodiment, the memory includes Read Only Memory (ROM). Where appropriate, the ROM may be mask-programmed ROM, Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory or a combination of two or more of these.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (9)

1. A method for jointly optimizing a matrix coefficient of an inter-carrier interference self-elimination coding matrix and a degree distribution coefficient of a no-rate code is applied to a distributed multi-antenna system adopting an orthogonal frequency division multiplexing technology under a block fading channel, and is characterized by comprising the following steps:
determining an optimization problem of jointly optimizing the matrix coefficient and the degree distribution coefficient by taking the maximum system transmission rate as a target according to the statistical information of the channel state and the external information transmission analysis in the decoding process;
solving the optimization problem to obtain the optimized matrix coefficients and the degree distribution coefficients, wherein solving the optimization problem to obtain the optimized matrix coefficients and the degree distribution coefficients comprises:
exhausting the frequency offset value within a preset range;
randomly generating a plurality of groups of matrix coefficients meeting the constraint condition of output signal power on matrix coefficients of an ICI self-elimination coding matrix under each exhaustive frequency offset value; calculating the minimum value of the channel coding code length corresponding to the multiple groups of matrix coefficients and the degree distribution coefficient of the edge of the output node of the LT code graph under the degree distribution sum constraint condition of the edge of the output node of the LT code graph, the decoding starting condition of a receiving end and the decoding convergence condition of the receiving end; selecting matrix coefficients from the multiple groups of matrix coefficients by adopting a genetic algorithm according to the minimum value of the channel coding code length and the degree distribution coefficient of the edge of the output node of the LT code pattern;
calculating a maximum tolerable frequency offset value according to the selected matrix coefficient and the corresponding degree distribution coefficient of the edge of the output node of the LT code graph;
and taking the matrix coefficient corresponding to the maximum value in the maximum tolerable frequency deviation values and the degree distribution coefficient of the edge of the output node of the corresponding LT code graph as the optimal solution of the optimization problem.
2. The method of claim 1, wherein the optimization problem is listed as:
Figure FDA0003146931230000011
the constraints of the optimization problem include:
(1) constraint condition C1 of degree distribution of edges of output node of LT code pattern:
Figure FDA0003146931230000021
(2) receiving-end decoding start condition C2:
ω1＞θ
(3) receiving end decoding convergence condition C3:
Figure FDA0003146931230000022
for all Hr,q,r＝1,2,…,L,q＝1,2,…,Q
(4) Constraint of output signal power on matrix coefficients of ICI self-cancelling coding matrix C4:
|c1|2+|c2|2≤1,2|c3|2≤1
wherein epsilonmaxMaximum tolerable frequency offset for the link from the user to the RRH;
Figure FDA0003146931230000023
is the maximum average code length; c. C1,c2,c3Matrix coefficients of a self-cancelling coding matrix for ICI; { omega [ [ omega ] ]dExpressing the degree distribution coefficient of the edge of the output node of the LT code graph without the rate code; omegajDegree distribution coefficient of the edge of the output node with degree j in LT code diagram; dcThe maximum number of degrees of edges that are output nodes of the LT code graph; t is an ICI self-elimination coding matrix; theta is a preset value larger than zero; x is the number ofuThe information is external information; gamma ray1,γ2,γ3,γ4Respectively is the proportion of variable nodes with the degrees of 1,2,3 or 4 in the LDPC code graph;
Figure FDA0003146931230000024
is a constant independent of the instantaneous gain of the channel;
Figure FDA0003146931230000025
a minimum threshold for correctly decoded extrinsic information; hr,qRepresenting the channel gain of the link of the r RRH in the q channel state; r is 1,2, …, L; l is the total number of RRHs; q is the total number of channel states.
3. The method of claim 1, wherein selecting matrix coefficients from the plurality of sets of matrix coefficients using a genetic algorithm based on the minimum value of the channel coding code length and degree distribution coefficients of edges of the LT code pattern comprises:
calculating the probability that the matrix coefficient corresponding to the minimum value of the channel coding code length is inherited to the next generation according to the minimum value of the channel coding code length;
calculating the cumulative probability of each group of matrix coefficients according to the probability of the matrix coefficients being inherited to the next generation;
and randomly extracting a plurality of groups of matrix coefficients according to the cumulative probability.
4. The method of claim 3, wherein randomly extracting sets of matrix coefficients according to the cumulative probability comprises:
in [0,1 ]]Generating a uniformly distributed pseudo-random number s within the interval if s<q1Then the 1 st set of matrix coefficients is selected, otherwise the k-th set is selected such that q isk-1<s≤qkIf true; repeating the steps for M times to obtain M groups of matrix coefficients;
wherein M is the number of the multiple groups of matrix coefficients; q. q.skIs the cumulative probability of the kth set of matrix coefficients.
5. The method of claim 3, wherein after randomly drawing sets of matrix coefficients based on the cumulative probabilities, the method further comprises:
pairwise matching a plurality of groups of matrix coefficients obtained by random extraction by adopting a genetic algorithm, and exchanging binary codes of each pair of matrix coefficients by using a first preset probability to obtain a plurality of groups of matrix coefficients updated for the first time;
and replacing some random bit encoding values in binary encoding of the obtained groups of matrix coefficients updated for the first time by second preset probability to obtain a plurality of groups of matrix coefficients updated for the second time, and taking the plurality of groups of matrix coefficients updated for the second time as the matrix coefficients for calculating the maximum tolerable frequency offset value.
6. An uplink transmission method for resisting inter-carrier interference is applied to a distributed multi-antenna system adopting an orthogonal frequency division multiplexing technology under a block fading channel, and is characterized by comprising the following steps:
the distributed multi-antenna system receives uplink transmission signals from a plurality of radio remote heads to obtain a plurality of uplink transmission signals; the uplink transmission signal is obtained by modulating user information after no-rate coding, obtaining an uplink modulation signal and then transforming according to an ICI self-elimination coding matrix; the matrix coefficients of the ICI self-canceling coding matrix are determined by the joint optimization method of the matrix coefficients of the ICI self-canceling coding matrix and the degree distribution coefficients of the rateless codes according to any of claims 1 to 5;
the distributed multi-antenna system respectively performs preprocessing and quantization processing on the plurality of uplink transmission signals to obtain a plurality of quantized signals;
and the distributed multi-antenna system respectively performs soft demodulation on the quantized signals according to the matrix coefficients, and then performs joint decoding by using a belief propagation algorithm to obtain the user information.
7. The method of claim 6, wherein the obtaining the user information by the distributed multi-antenna system performing soft demodulation on the quantized signals according to the matrix coefficients and then performing joint decoding by using a belief propagation algorithm comprises:
the distributed multi-antenna system respectively calculates the log-likelihood ratio of each coding bit of the rateless codes according to the quantized signals, and then combines the log-likelihood ratios of the same coding bit to obtain a combined log-likelihood ratio;
and the distributed multi-antenna system performs joint decoding by using a belief propagation algorithm according to the combined log-likelihood ratio to obtain the user information.
8. A distributed multi-antenna system for use in block fading channels and employing OFDM techniques, comprising a plurality of RRUs and a pool of baseband processing units, wherein,
the radio remote head is used for receiving an uplink transmission signal, preprocessing and quantizing the uplink transmission signal and then sending the uplink transmission signal to the baseband processing unit pool; the uplink transmission signal is obtained by modulating user information after no-rate coding, obtaining an uplink modulation signal and then transforming according to an ICI self-elimination coding matrix; the matrix coefficients of the ICI self-cancellation coding matrix are determined by jointly optimizing the matrix coefficients and the degree distribution coefficients of the no-rate coding;
and the baseband processing unit pool is used for respectively carrying out soft demodulation on the quantized signals according to the matrix coefficients and then carrying out joint decoding by using a belief propagation algorithm to obtain the user information.
9. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method for joint optimization of matrix coefficients of an inter-carrier interference self-cancelling coding matrix and degree distribution coefficients of a rateless code according to any one of claims 1 to 5.
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