CN110012489A - The communication processing method of full duplex MIMO cellular system under non-ideal communication channel - Google Patents
The communication processing method of full duplex MIMO cellular system under non-ideal communication channel Download PDFInfo
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Abstract
The invention discloses a kind of communication processing methods of full duplex MIMO cellular system under non-ideal communication channel.Full duplex MIMO cellular system includes several upstream plants, several downstream plants and base station.Upstream plant works in semiduplex mode, and uplink and downlink device is through uplink and base station connection communication；Base station operation receives the information that upstream plant is sent in full-duplex mode, by multiple antennas, while sending information to downstream plant in same frequency；Establish joint optimization of resources problem, and it is divided into uplink and downlink device pairing subproblem and power distribution subproblem and processing solution respectively, the Mean Speed for obtaining optimal uplink and downlink device pairing and uplink and downlink device pairing, distributes the correspondence between setting base station and uplink and downlink device with this.Resource optimal distribution method proposed by the present invention effectively increases system velocity, can fight channel estimation errors, self-interference and cochannel interference bring system performance decline, have the advantage for carrying out efficient communication under non-ideal condition.
Description
Technical Field
The invention relates to the technical field of wireless communication, and aims to optimize transmission power resources in a cellular network under the condition of non-ideal channel estimation. The invention maximizes the system and the rate by combining the uplink and downlink equipment pairing and the power optimization allocation method. And finally, solving the optimization problem obtained by modeling by using a decomposition and gradient projection algorithm to obtain optimal power distribution and achieve the effect of maximizing the system speed.
Background
The Full-duplex (FD) technology can realize simultaneous bidirectional transmission of data on the same frequency band, and compared with the conventional Half-duplex (HD) technology, the Full-duplex (FD) technology can significantly increase the spectrum efficiency, thereby having a wider development prospect. Since the transmission and reception are performed simultaneously at the same frequency, the full-duplex technology faces a serious Self-Interference (SI) problem. A great deal of work is put forward various self-interference cancellation techniques from theoretical research level and hardware experimental level, and these self-interference cancellation techniques have enough capability to suppress the self-interference to a lower level to meet the communication requirement.
However, early studies were mostly based on ideal conditions, i.e., the receiver knows the Channel State Information (CSI) or does not consider the effects of Co-channel-Interference (CCI), Residual-Self-Interference (RSI), etc. However, in practical situations, due to instantaneous channel changes and limitations of the wireless device itself, complete CSI is difficult to obtain, and it is also very difficult to completely eliminate SI and CCI. Full duplex system designs under non-ideal conditions have attracted some attention from scholars. Document 1(d.kim, h.ju, s.park and d.hong, Effect of channel estimation error on full-duplex two-way networks, IEEE Tran Vehicular tech., vol.62, pp.4666-4672; i.e., d.kim, h.ju, s.p arkand d.hong, influence of channel estimation error on full-duplex two-way networks, IEEE internet of things technology, vol.62, pp.4666-4672) studies the traversal capacity of a two-way full-duplex system by using maximum ratio combining and optimum combining, and observes the influence caused by channel estimation error. Document 2(a.c. cirik, y.rong, and y.hua, Achievable rates of full-duplex MIMO radios in fast fading channels with interference channels, IEEE trans.signal process, vol.62, No.15, pp.3874-3886, aug.2014; i.e. a.c. cirik, y.rong, and y.hua, realizable rate of full-duplex MIMO radios in fast fading channels under incomplete channel estimation, IEEE signal processing, vol.62, No.15, pp.3874-3886, aug.2014.) it is assumed that the Achievable rate of a bidirectional full-duplex MIMO system is studied under perfect self-interference cancellation and in the presence of channel estimation errors. The transmit beamforming problem of a full-duplex point-to-point MIMO system under non-ideal channel state information conditions is developed in document 3(j.zhang, o.taghizadeh, and m.haardt, Robust transmit beamforming design for full-duplex point-to-point MIMO Systems, in proc.tent International Symposium on wireless communication Systems 2013, pp.346-350,2013; i.e., j.zhang, o.taghizadeh, and m.haardt, Robust transmit beamforming design of a full-duplex point-to-point MIMO system, International seminar 2013, pp.346-350,2013 of the tenth wireless communication system). The above researches only consider the situation of single-user MIMO bidirectional channels, and the influence of non-ideal channel state information on FD MIMO multi-user systems in cellular environment is not seen so far.
Disclosure of Invention
In order to solve the problems existing in the background technology, aiming at the problems that the accurate channel state information is difficult to obtain in the actual communication environment, the self-interference of a full-duplex MIMO cellular system and the co-channel interference of the cellular system are difficult to obtain, the invention provides a communication processing method of the full-duplex MIMO cellular system under the non-ideal channel, the communication resource is optimized by combining the pairing of uplink and downlink equipment and the power allocation, the technical problem of performance reduction caused by the conditions of resisting channel estimation error, residual self-interference, co-channel interference and the like to the full-duplex MIMO cellular multi-user system is solved,
the technical scheme of the invention comprises the following steps:
s1, system model establishing:
the full-duplex MIMO cellular system comprises a plurality of uplink devices, a plurality of downlink devices and a base station.
The uplink equipment works in a half-duplex mode, is connected with the base station for communication through an uplink and sends information to the base station through a plurality of antennas of the uplink equipment;
the base station works in a full duplex mode, receives information sent by uplink equipment through multiple antennas, and sends the information to downlink equipment at the same frequency;
the downlink equipment is provided with multiple antennas, is in connection communication with the base station through a downlink and receives information sent by the base station;
the uplink equipment and the downlink equipment are both provided with multiple antennas, and the uplink equipment and the downlink equipment are respectively transmitted with the base station through channels corresponding to the multiple antennas.
S2, modeling a joint resource optimization problem: establishing a joint resource optimization problem of joint uplink and downlink equipment pairing and power distribution for maximizing the system rate by taking the total rate of an uplink and a downlink as the system rate;
s3, decomposing a joint resource optimization problem: decomposing the joint resource optimization problem into two sub-problems, namely an uplink and downlink equipment pairing sub-problem and a power distribution sub-problem;
s4, solving a joint resource optimization problem: and solving the power distribution subproblem by adopting a gradient projection algorithm, solving the uplink and downlink device pairing subproblem by adopting a Hungarian algorithm, and obtaining the optimal average rate of the uplink and downlink device pairing and the uplink and downlink device pairing (i, j) so as to distribute and set the communication relationship and the communication resources between the base station and the uplink and downlink devices.
The S1 system model establishing step specifically comprises the following steps:
one base station communicates with K uplink devices and J downlink devices simultaneously, the uplink devices and the downlink devices serve as nodes, and the uplink devices and the downlink devices are both communication devices such as mobile phones. Both the uplink device and the downlink device are equipped with N antennas, as shown in fig. 2;
the full-duplex MIMO cellular system has the following interference: the base station simultaneously receives and transmits information with the same frequency to generate residual self-interference (RSI), co-channel interference (CCI) generated by the same frequency shared by an uplink and a downlink and channel estimation error.
Estimating the channel by using Minimum Mean Square Error (MMSE), and expressing the estimated value of the channel state information asThe corresponding channel estimation error is denoted as △ H, so the actual channel is denoted asWhereinAnd △ H are uncorrelated channel estimationElements in the error △ H are mean 0 and varianceThe cyclic symmetric complex gaussian variable of (1). The channel matrices are respectively expressed as The four channel matrixes respectively correspond to a base station self-interference channel, an uplink channel from the ith uplink device to the base station, a downlink channel from the base station to the jth downlink device, and an interference channel of the ith uplink device to the jth downlink device, namely H0Represents a self-interference channel matrix generated by the base station simultaneously transceiving data,represents an uplink channel matrix from the ith uplink device to the base station,representing the downlink channel matrix from the base station to the jth downlink device, HijIs the interference channel matrix of the uplink device i to the downlink device j; respectively representing four channel matrices H0、HijRespective channel state information estimates, △ H0、△HijRespectively representing four channel matrices H0、HijThe respective channel estimation error.
The signal received by the base station and the signal received by the j downlink device are represented as follows:
wherein, y0Representing signals received by the base station, yjIndicating a signal received by the j downlink device;andfor independent and equally distributed data transmitted by the uplink and downlink and for unit power,anda transmit beamforming filter function for the uplink and downlink;andrespectively representing an uplink channel matrix from the ith uplink equipment to the base station and a downlink channel matrix from the base station to the jth downlink equipment, and satisfying that i is more than or equal to 1 and less than or equal to K, J is more than or equal to 1 and less than or equal to J, o represents the base station, i represents the ordinal number of the uplink equipment, J represents the ordinal number of the downlink equipment, K represents the total number of the uplink equipment, and J represents the total number of the downlink equipment;andrespectively representing uplink channel matrixes from the ith uplink equipment to the base stationThe estimated value of the channel state information and the channel estimation error,and △ H0Self-interference channel matrix H respectively representing base stations0The estimated value of the channel state information and the channel estimation error,andrespectively representing downlink channel matrixes from the base station to the jth downlink deviceThe estimated value of the channel state information and the channel estimation error,and △ HijRespectively representing interference channel matrixes H of uplink equipment i to downlink equipment jijThe estimated value of the channel state information and the channel estimation error; rhojAnd gammaiPower coefficient, n, representing residual self-interference and co-channel interference, respectively0And njWhite Gaussian Noise (AWGN) received by the base station and the j-th downlink device respectively,n0and njAre subject to a mean of 0 and a variance of IN(ii) a gaussian distribution of;
the noise interference covariance matrix calculation processing is as follows:
wherein, CiAnd CjAre the noise interference covariance matrices of the ith upstream device and the jth downstream device respectively,andis a channel matrixH0,HijThe estimated error power of (2);andmultiple antenna signal amplification power matrix, I, for the ith uplink device and the jth downlink device, respectivelyNIs an NxN unit matrix, tr {. is equal to the trace of the matrix, and H is the matrix conjugate transpose;
amplifying power matrix for multi-antenna signalsAndand (3) carrying out characteristic value decomposition:
wherein, UiAnd UjIs a unitary matrix of eigenvectors of the ith uplink device and the jth downlink device,andis the diagonal power allocation matrix of the ith uplink device and the jth downlink device.
The step of modeling the joint resource optimization problem in the step S2 is specifically as follows:
the noise interference covariance follows Gaussian distribution, and the average rate of the uplink and downlink equipment pairing (i, j) is obtained as follows:
wherein,for calculating the expectation, the probability distribution calculation of the variable multiplied by the variable is obtained;
establishing the following joint resource optimization objective function for maximizing the system rate:
wherein, ai,jRepresenting the pairing parameters of the uplink and downlink devices, if the ith uplink device and the jth downlink device are paired, i.e. share the same channel resource, then ai,j1, otherwise ai,j＝0；Andis the signaling transmit power constraint for the uplink and downlink.
The step S3 is a joint resource optimization problem decomposition step, which is specifically divided into two sub-problems as follows:
the joint resource optimization problem is NP difficult problem, and needs to search all possible pairs a exhaustivelyijAnd when the number of the uplink and downlink equipment is large, huge calculation amount is generated, and the invention particularly decomposes the original optimization problem into two sub-problems and can better solve the technical problem.
1) Sub-problem of power distribution
Under the condition that the pairing of uplink and downlink equipment is determined, the following power distribution objective function is constructed:
wherein,is a randomly generated uplink and downlink device pairing parameter,represents the average rate of the uplink and downlink device pairs (i, j),andrespectively representing diagonal power distribution matrixes of the ith uplink device and the jth downlink device;
2) uplink and downlink equipment allocation sub-problem
On the basis of obtaining the optimal power distribution, the following uplink and downlink equipment distribution objective functions are constructed:
wherein, ai,jIs the pairing parameter of the uplink and downlink equipment,indicating functionAverage rate of each uplink and downlink device pair (i, j) after rate assignment optimization.
The step S4 of solving the joint resource optimization problem specifically includes the following steps:
s41, initializing, randomly generating a group of uplink and downlink device pairs (i, j) and uplink and downlink device pair parameters ai,jRandomly generating power distribution of uplink and downlink devices, namely diagonal power distribution matrix of ith uplink device and jth downlink deviceAnd
s42, solving a power distribution objective function by adopting a gradient projection algorithm for iterative optimization according to the data randomly generated in the step S41, and obtaining the optimal power of the uplink and downlink equipment, namely the diagonal power distribution matrix of the ith uplink equipment and the jth downlink equipmentAndso as to obtain the maximum average rate of the uplink and downlink device pairs (i, j);
and S43, repeating the steps S41-S42, solving a power distribution objective function of the uplink and downlink devices by using different randomly generated uplink and downlink device pairs, and obtaining an optimal uplink and downlink device pair by adopting a Hungarian algorithm.
In the specific implementation, in each iteration process of the Hungarian algorithm, the speed of the ith uplink device and the speed of the jth downlink device are recorded, K multiplied by J speeds are generated through one iteration, the Hungarian algorithm is used for searching, the system speed when different uplink and downlink device combinations are paired is calculated, the maximum system speed is output, and the corresponding uplink and downlink device combination is used as the optimal uplink and downlink device pairing mode.
The invention avoids the problem that the accurate channel state information is difficult to obtain in the actual communication environment, establishes an optimization problem combining uplink and downlink equipment pairing and power distribution to maximize the system speed under the condition of not obtaining the accurate channel state information, provides a progressive algorithm based on decomposition and gradient projection to solve the problem, and verifies that the channel estimation error has the greatest influence on the performance in different types of interference.
Compared with the prior art, the invention has the advantages that:
the base station and the user in the system model established by the invention both use the multiple antennas to send and receive information, thereby effectively improving the frequency spectrum utilization rate and being more in line with the actual communication process.
Secondly, the cellular network of the invention further improves the spectrum efficiency because the user equipment is limited to work in a half-duplex mode and the base station works in a full-duplex mode.
Finally, compared with the existing resource optimization allocation method, the communication resource optimization processing scheme combining uplink and downlink equipment pairing and power allocation considers the influence of channel estimation errors and various interferences in the system, can resist the system performance reduction caused by the channel estimation errors, self-interference and co-channel interference, and has the advantage of effective communication under non-ideal conditions.
In addition, the invention also provides reference for other related problems in the same field, can be expanded and extended on the basis of the reference, is applied to technical schemes of other algorithms in the same field, and has very wide application prospect.
In general, the communication resource optimization processing method provided by the invention effectively improves the system rate, can resist the system performance reduction caused by channel estimation error, self-interference and co-channel interference, has the advantage of effective communication under non-ideal conditions, has excellent use effect and has very high use and popularization values.
Drawings
Fig. 1 is a flowchart of a communication processing method of a full-duplex MIMO cellular system under a non-ideal channel.
Fig. 2 is a schematic diagram of a full-duplex MIMO cellular system based on non-ideal channel estimation conditions.
Fig. 3 is a schematic diagram of the convergence performance result of the algorithm proposed by the present invention.
Fig. 4 is a diagram illustrating the effect of different types of interference on system performance.
Fig. 5 is a diagram comparing uplink and downlink device pairing modes under different channel estimation errors.
Detailed Description
The following detailed description of the embodiments of the present invention is provided in connection with the accompanying drawings for the purpose of facilitating understanding and understanding of the technical solutions of the present invention.
The technical solution of the invention is further illustrated below in connection with an embodiment of the invention carried out according to a complete method and the accompanying drawings thereof:
fig. 1 is a flow chart of a communication processing method algorithm of a full duplex MIMO cellular system under a non-ideal channel, which is initialized first, including average user power allocation and random pairing of uplink and downlink devices; setting a random power distribution matrix at the beginning of the 0 th iterationAndduring the k iteration, performing gradient calculation to perform gradient projection, and then updating the power distribution matrix; until the algorithmStopping iteration during convergence to obtain an optimal power distribution matrix; finally, obtaining an optimal uplink and downlink device pairing mode by using a Hungarian algorithm; at this point, the overall system and rate are at a maximum and the algorithm ends.
Fig. 2 is a communication processing method of a full-duplex MIMO cellular system under a non-ideal channel, and it is considered that there are K uplink devices and J downlink devices in the system, and each node is equipped with N antennas. The base station operates in full duplex mode, and the user operates in half duplex mode due to equipment limitation. The channel estimation is non-ideal in actual environment, and channel estimation errors exist. Meanwhile, there is residual self-interference (RSI) at the base station and co-channel interference (CCI) in the downlink.
Fig. 3 is a diagram of the convergence performance of the algorithm proposed by the present invention. As shown, under different residual self-interference, the joint optimization algorithm achieves convergence through only a few iterations, and the system can achieve higher rate under lower residual self-interference. The algorithm of the invention has low complexity and wide application prospect.
Fig. 4 illustrates the effect of different types of interference on a full duplex MIMO cellular system. The solid line indicates that the uplink and the downlink have the same transmission power, and the dotted line indicates that the transmission power of the downlink is ten times that of the uplink. As shown, when the uplink transmission power is equal to the downlink transmission power, the effect of the RSI and the CCI on the system rate is the same. At this time, the CSI has the greatest influence on the system rate because the RSI exists only in the uplink, the CCI exists only in the downlink, and the CSI exists in both the uplink and downlink. The effect of RSI on the overall system rate becomes more pronounced when the downlink transmission power is much higher than the uplink, since the CCI is relatively low due to the low uplink transmission power, and the RSI is relatively high due to the high downlink transmission power. In addition, the CSI exists in both uplink and downlink, and still has the most serious influence on the system rate.
Figure 5 tests the importance of uplink and downlink device pairing in the full duplex MIMO cellular system optimization problem. The figure shows the inventionCompared with the random uplink and downlink equipment pairing scheme, the uplink and downlink equipment pairing scheme has high CSI errorLower-level CSI error still appears to be lower than that of random uplink and downlink device pairingThe performance is more excellent. Meanwhile, experiments show that the total system rate is reduced along with the increase of the channel estimation error and the RSI.
In summary, the present invention establishes an optimization problem combining uplink and downlink device pairing and power allocation to maximize the system rate, and provides a progressive algorithm based on decomposition and gradient projection to solve the problem, which verifies that the channel estimation error has the greatest impact on performance in different types of interference, for the problems that accurate channel state information is difficult to obtain in the actual communication environment, and that the self-interference of a full-duplex system and the multi-user interference of a cellular system are difficult to obtain. The resource optimization allocation method provided by the invention can effectively improve the system rate, can resist the system performance reduction caused by channel estimation error, self-interference and co-channel interference, and has the advantage of effective communication under non-ideal conditions.
In addition, the invention also provides reference for other related problems in the same field, can be expanded and extended on the basis of the reference, is applied to technical schemes of other algorithms in the same field, and has very wide application prospect.
The communication processing method of the full-duplex MIMO cellular system under the non-ideal channel provided by the invention has excellent use effect and very high use and popularization values.
Equivalent structural changes made by those skilled in the art according to the contents of the specification and the drawings are included in the scope of the present invention.
Claims (6)
1. A communication processing method of a full duplex MIMO cellular system under a non-ideal channel is characterized by comprising the following steps:
s1, system model establishing:
the uplink equipment works in a half-duplex mode, is connected with the base station for communication through an uplink and sends information to the base station through a plurality of antennas of the uplink equipment; the base station works in a full duplex mode, receives information sent by uplink equipment through multiple antennas, and sends the information to downlink equipment at the same frequency; the downlink equipment is provided with multiple antennas, is in connection communication with the base station through a downlink and receives information sent by the base station;
s2, modeling a joint resource optimization problem: establishing a joint resource optimization problem of joint uplink and downlink equipment pairing and power distribution for maximizing the system rate by taking the total rate of an uplink and a downlink as the system rate;
s3, decomposing a joint resource optimization problem: decomposing the joint resource optimization problem into two sub-problems, namely an uplink and downlink equipment pairing sub-problem and a power distribution sub-problem;
s4, solving a joint resource optimization problem: and solving the power distribution subproblem by adopting a gradient projection algorithm, solving the uplink and downlink device pairing subproblem by adopting a Hungarian algorithm, obtaining the optimal average rate of the uplink and downlink device pairing and the uplink and downlink device pairing (i, j), and setting the communication relationship between the base station and the uplink and downlink devices by means of distribution.
2. The communication processing method of the full-duplex MIMO cellular system under the non-ideal channel as claimed in claim 1, wherein: the S1 system model establishing step specifically comprises the following steps:
a base station is simultaneously communicated with K uplink devices and J downlink devices, and the uplink devices and the downlink devices are both provided with N antennas; the signal received by the base station and the signal received by the j downlink device are represented as follows:
wherein, y0Representing signals received by the base station, yjIndicating a signal received by the j downlink device;andfor independent and equally distributed data transmitted by the uplink and downlink and for unit power,anda transmit beamforming filter function for the uplink and downlink;andrespectively representing an uplink channel matrix from the ith uplink equipment to the base station and a downlink channel matrix from the base station to the jth downlink equipment, and satisfying that i is more than or equal to 1 and less than or equal to K, J is more than or equal to 1 and less than or equal to J, o represents the base station, i represents the ordinal number of the uplink equipment, J represents the ordinal number of the downlink equipment, K represents the total number of the uplink equipment, and J represents the total number of the downlink equipment;andrespectively representing uplink channel matrixes from the ith uplink equipment to the base stationThe estimated value of the channel state information and the channel estimation error,and △ H0Self-interference channel matrix H respectively representing base stations0The estimated value of the channel state information and the channel estimation error,andrespectively representing downlink channel matrixes from the base station to the jth downlink deviceThe estimated value of the channel state information and the channel estimation error,and △ HijRespectively representing interference channel matrixes H of uplink equipment i to downlink equipment jijThe estimated value of the channel state information and the channel estimation error; rhojAnd gammaiPower coefficient, n, representing residual self-interference and co-channel interference, respectively0And njWhite Gaussian Noise (AWGN) received by the base station and the jth downlink device respectively;
the noise interference covariance matrix calculation processing is as follows:
wherein, CiAnd CjAre the noise interference covariance matrices of the ith upstream device and the jth downstream device respectively,andis a channel matrixH0,HijThe estimated error power of (2);andmultiple antenna signal amplification power matrix, I, for the ith uplink device and the jth downlink device, respectivelyNIs an NxN unit matrix, tr {. is equal to the trace of the matrix, and H is the matrix conjugate transpose;
amplifying power matrix for multi-antenna signalsAndand (3) carrying out characteristic value decomposition:
wherein, UiAnd UjIs a unitary matrix of eigenvectors of the ith uplink device and the jth downlink device,andis the diagonal power allocation matrix of the ith uplink device and the jth downlink device.
3. The communication processing method of the full-duplex MIMO cellular system under the non-ideal channel as claimed in claim 1, wherein: the step of modeling the joint resource optimization problem in the step S2 is specifically as follows:
the noise interference covariance follows Gaussian distribution, and the average rate of the uplink and downlink equipment pairing (i, j) is obtained as follows:
wherein,to calculate the expectation;
establishing the following joint resource optimization objective function for maximizing the system rate:
wherein, ai,jRepresenting the pairing parameters of the uplink and downlink devices, if the ith uplink device and the jth downlink device are paired, i.e. share the same channel resource, then ai,j1, otherwise ai,j＝0；Andis the signaling transmit power constraint for the uplink and downlink.
4. The communication processing method of the full-duplex MIMO cellular system under the non-ideal channel as claimed in claim 1, wherein: the step S3 is a joint resource optimization problem decomposition step, which is specifically divided into two sub-problems as follows:
1) sub-problem of power distribution
The following power allocation objective function is constructed:
wherein,is a randomly generated uplink and downlink device pairing parameter,represents the average rate of the uplink and downlink device pairs (i, j),andrespectively representing diagonal power distribution matrixes of the ith uplink device and the jth downlink device;
2) uplink and downlink equipment allocation sub-problem
The following uplink and downlink equipment distribution objective functions are constructed:
wherein, ai,jIs the pairing parameter of the uplink and downlink equipment,represents the average rate of each uplink and downlink device pair (i, j) after power allocation optimization.
5. The communication processing method of the full-duplex MIMO cellular system under the non-ideal channel as claimed in claim 1, wherein: the step S4 of solving the joint resource optimization problem specifically includes the following steps:
s41, initializing, randomly generating a group of uplink and downlink device pairs (i, j) and uplink and downlink device pair parameters ai,jRandomly generating power distribution of uplink and downlink devices, namely diagonal power distribution matrix of ith uplink device and jth downlink deviceAnd
s42, solving a power distribution objective function by adopting a gradient projection algorithm for iterative optimization according to the data randomly generated in the step S41, and obtaining the optimal power of the uplink and downlink equipment, namely the diagonal power distribution matrix of the ith uplink equipment and the jth downlink equipmentAndmaximizing upstream and downstream device pairing(ii) an average rate of (i, j);
and S43, repeating the steps S41-S42, solving a power distribution objective function of the uplink and downlink devices by using different randomly generated uplink and downlink device pairs, and obtaining an optimal uplink and downlink device pair by adopting a Hungarian algorithm.
6. The communication processing method of the full-duplex MIMO cellular system under the non-ideal channel as claimed in claim 1, wherein: the full-duplex MIMO cellular system has the following interference: the base station simultaneously receives and transmits information with the same frequency to generate residual self-interference (RSI), co-channel interference (CCI) generated by the same frequency shared by an uplink and a downlink and channel estimation error.
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| CN116318288A (en) * | 2023-05-23 | 2023-06-23 | 江苏集萃移动通信技术研究所有限公司 | MIMO full duplex power distribution method based on intelligent reflecting surface |
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| CN116318288B (en) * | 2023-05-23 | 2023-08-04 | 江苏集萃移动通信技术研究所有限公司 | MIMO full duplex power distribution method based on intelligent reflecting surface |
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