CN104993859A - Distributed beam forming method applied under time asynchronous environment - Google Patents
Distributed beam forming method applied under time asynchronous environment Download PDFInfo
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- H—ELECTRICITY
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- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
- H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
- H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
- H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
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- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
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Abstract
The invention provides a distributed beam forming method applied under a time asynchronous environment. The distributed beam forming method is mainly used for solving the problem of influence to system performance of same frequency interference and inter-symbol interference brought by the condition that the time of receiving a signal by the user is asynchronous. The method uses approximation to piecewise linear function, a convex optimization theory, user acceptance control and a weight factor iteration mechanism to form quasi-optimal distributed transmitting beams. The method of the invention is applied to a distributed antenna system, and could realize reliable united multi-user transmitting beam forming design under the time asynchronous condition of the received signal. Simultaneously, the method of the invention effectively ensures QoS requirement of the user and reduces the return link load of the system.
Description
Technical Field
The invention belongs to the technical field of communication, and relates to a distributed joint multi-user transmission beam forming method under a time asynchronous environment of received signals, which can be used for transmission beam forming design in a distributed antenna system.
Background
The distributed antenna system is characterized in that each antenna unit is deployed at different geographical positions in a cellular network cell according to a certain rule or randomly and is connected to a central processing unit through a special medium (such as optical fiber, cable or microwave), each distributed antenna unit only has a signal transceiving function, and all signal processing functions are completed by the central processing unit. In a distributed antenna system, multiple antenna units simultaneously serve users within a cell. Due to the different distances from the antenna units, the time asynchronization phenomenon occurs when the user receives the signals from the antennas. The asynchronous time of receiving signals can increase the same frequency interference among multiple users, and simultaneously, the users can generate strong intersymbol interference, thereby seriously influencing the user experience and the system performance. In addition, since user data needs to be transmitted between the antenna unit and the central processing unit through the backhaul link, group sparsity of transmission beam vectors needs to be ensured as much as possible to reduce backhaul link overhead of the system. Therefore, in a distributed antenna system with a time asynchronous characteristic, in order to guarantee the QoS requirements of users and reduce the backhaul link load of the system, it is very important to reasonably design a multi-user transmit beamforming vector in a joint manner.
Currently, some documents research the design method of transmit beamforming vector, such as m.y.hong in IEEE j.sel.areas commun., 2013, 31 (2): 226-240 "Joint base station timing and beam former design for partitioned transmission in heterogeneous network" researches the problems of Joint base station clustering and beam forming during the cooperative transmission of heterogeneous networks, and adopts a utility function with a beam vector structure penalty value to ensure that the designed user beam vectors have group sparsity. However, this method seeks to maximize the sum rate of all users in the system and does not take into account the QoS requirements of each user. Zhao in IEEE trans. wire.commu., 2013, 12 (6): 2762-. But the method assumes that the system can meet the QoS requirements of all users.
In addition, the existing distributed beam forming method does not consider that the time asynchronization phenomenon of the received signals is caused by the distance difference between the user and each antenna unit, which results in poor practicability of the method. The invention provides a transmit beam forming design method aiming at the problem of asynchronous time of received signals in a distributed antenna system, which can effectively ensure the QoS requirement of a user and simultaneously reduce the load of a return link of the system.
Disclosure of Invention
The invention aims to provide a distributed beam forming method suitable for a time asynchronous environment, which can ensure the QoS requirement of a user and reduce the load of a return link of a system by combining the design of a beam forming vector.
The technical key point for realizing the invention is that the lower bound of the SINR received by the user is calculated by utilizing a piecewise linear approximation method; then, loosening and solving the optimization problem by using a convex optimization theory, and gradually removing the infeasible users in the system; and finally, constructing a transmitting beam vector with group sparsity by using a weight factor iteration method. A distributed beam forming method suitable for time asynchronous environment includes the following steps:
(1) constructing a time asynchronous receiving signal of each user in a current distributed antenna system, obtaining an approximate time asynchronous receiving signal of each user by adopting a piecewise linear method for all the time asynchronous receiving signals, and calculating an SINR lower bound of the approximate time asynchronous receiving signal of each user according to the obtained approximate time asynchronous receiving signal; constructing a target function with minimized load of a return link of the current distributed antenna system, and performing convex relaxation on the target function to obtain a convex target function;
(2) obtaining QoS constraint of each user according to SINR lower bound of each user approximate time asynchronous receiving signal, respectively introducing auxiliary variables to the QoS constraint of each user, and introducing penalty terms corresponding to the auxiliary variables to the convex objective function;
(3) with QoS constraint of each user and antenna transmitting power after introducing auxiliary variables as constraint conditions, solving a convex objective function after introducing a penalty term by adopting a convex optimization theory to obtain a beam vector matrix after introducing the penalty term;
(4) calculating an SINR lower bound value of each user approximate time asynchronous receiving signal according to the beam vector matrix after the penalty term is introduced, and calculating a satisfaction value of each user according to the SINR lower bound value;
(5) judging whether all users in the current distributed antenna system are feasible according to the satisfaction value of each user, and executing the step (6) if all users in the current distributed antenna system are feasible; otherwise, removing the user with the minimum satisfaction value in the system, updating the distributed antenna system, and turning to the step (1);
(6) introducing a weight factor into the convex objective function to obtain an objective function with minimized weighted power;
(7) the QoS constraint and the antenna transmitting power of each user are taken as constraint conditions, the objective function value of the weighted power minimization and the beam vector matrix after power weighting are solved by adopting a convex optimization theory, and the relative value of the objective function of the weighted power minimization is obtained by calculation according to the objective function value of the weighted power minimization; calculating a beam forming vector of each user by the distributed antenna unit according to the beam vector matrix after the power weighting;
(8) comparing the relative value of the weighted power minimization target function with the size of a convergence threshold, if the relative value of the weighted power minimization target function is larger than the convergence threshold, updating a weight factor according to the beam forming vector of each user by the distributed antenna unit, and turning to the step (6); otherwise, the beamforming vector of the distributed antenna unit for each user is used as the final beamforming vector of the distributed antenna unit.
Wherein, in the step (1), the time asynchronous receiving signal of each user is constructed in the current distributed antenna system, after the approximate time asynchronous receiving signal of each user is obtained by adopting a piecewise linear method for all the time asynchronous receiving signals, the SINR lower bound of the approximate time asynchronous receiving signal of each user is calculated according to the obtained approximate time asynchronous receiving signal, which specifically comprises the following steps:
(101) constructing a time asynchronous receiving signal of each user in the current distributed antenna system;
(102) approximating the impulse response function of the raised cosine pulse shaping filter by using a piecewise linear function to obtain a piecewise linear approximate expression of the impulse response function;
(103) and deducing the approximate time asynchronous receiving signal of each user by using the obtained piecewise linear approximate expression, and calculating the SINR lower bound of the approximate time asynchronous receiving signal of each user.
Wherein the step (103) specifically comprises:
(201) calculating an expression of approximate time asynchronous receiving signals of each user according to a piecewise linear approximate expression of the impulse response function;
wherein, yk(m) Indicating that the kth user receives the approximate time asynchronous signal, s, corresponding to the mth symbolk,mAn mth useful symbol representing a kth user, N representing the number of distributed antenna units in the distributed antenna system, K representing the number of users in the distributed antenna system,denotes the conjugate transpose of the channel fading vector from the nth distributed antenna element to the kth user, wk,nRepresenting the beam vector, L, of the nth distributed antenna element to the kth user0Indicating the number of side lobes, ξ, of the impulse response function that are considered to cause intersymbol interferencek,nRepresenting the relative unit time delay, T, experienced by the electromagnetic signal transmitted from the nth distributed antenna element to the kth usersSymbol period, g ', representing useful signals of users in distributed antenna system'n(lTs) Indicating the corresponding relative unit time delay at the ith zero point of the impulse response function as xik,nIs piecewise linearly approximated to the slope value of the line segment, zk(m) represents channel additive white gaussian noise when the kth user receives the mth symbol;
(202) calculating an SINR expression of the approximate receiving time asynchronous signal of each user according to the expression of the approximate receiving time asynchronous signal of each user;
wherein, γkIndicating the SINR of the kth user's approximately time-asynchronous received signal,representing the channel fading vector from the nth antenna element to the kth user due to time asynchronism at the center of the impulse response function main lobe，ISIkIndicating intersymbol interference, IUI, of user k itselfk,jIndicates the co-channel interference, σ, caused by user j to user k2A variance representing white gaussian noise of the wireless channel;
(203) solving the minimum value of the numerator of the SINR expression, and solving the maximum value of the denominator to obtain an SINR lower bound expression;
wherein,SINR lower bound, W, representing the approximate time-asynchronous received signal for the kth userkRepresenting a matrix consisting of beam vectors of all distributed antenna elements for the k-th user, hkRepresenting a vector consisting of the channel fading vectors of all distributed antenna elements to the k-th user, Tr (-) representing the traces of the extraction matrix, ρk,lRepresents the upper bound of the moduli of the channel fading vectors of all distributed antenna elements to the kth user at the ith null of the impulse response function due to time asynchronization.
Wherein, the expression of the objective function in the step (1) is as follows:
wherein,represents the minimum transmission rate required by the K-th user, K represents the number of users in the distributed antenna system, akA set of distributed antenna elements represented as kth subscriber service, | · | represents the cardinality of the set, wk,nRepresenting the beam vector of the nth distributed antenna element to the kth user.
In the step (1), performing convex relaxation on the objective function to obtain a convex objective function, specifically: and (4) relaxing the objective function by adopting a 1-norm convex hull form to obtain a convex objective function.
Wherein the expression of the convex objective functionComprises the following steps:
wherein,indicating the minimum transmission rate, w, required by the k-th userk,nRepresenting the beam vector of the nth distributed antenna element to the kth user,the conjugate transpose of the beam vector from the nth distributed antenna unit to the kth user is represented, N represents the number of distributed antenna units in the distributed antenna system, and K represents the number of users in the distributed antenna system.
Wherein, the QoS constraint expression in the step (2) established according to the SINR lower bound of the asynchronous received signal of each user approximate time is as follows:
wherein,the lower bound of SINR for the approximate time-asynchronous received signal for the kth user,the required receive SINR threshold for user k.
Wherein, the expression of each user QoS constraint after introducing the auxiliary variable in the step (2) is as follows: <math>
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wherein,lower bound, β, on the SINR of the approximately time-asynchronous received signal for the kth userkIs a non-negative auxiliary variable and is,the required receive SINR threshold for user k.
Wherein, the expression of the convex objective function after the penalty term is introduced in the step (2) is as follows:
wherein,denotes the minimum transmission rate required by the kth user, Tr (-) denotes the trace of the acquisition matrix, AnA diagonal matrix for specifying the beam vector of the nth distributed antenna element, "omicron" denotes the Hadamard product, WkRepresenting a matrix consisting of beam vectors for all distributed antenna elements for the kth user,for the penalty term, Φ is a normal number, N represents the number of distributed antenna units in the distributed antenna system, and K represents the number of users in the distributed antenna system.
Compared with the background technology, the invention has the following advantages:
1. the invention adopts the method of sending raised cosine pulse shaping filter impulse response function by piecewise linear function approximation, can obtain approximate time asynchronous receiving signal, and is convenient for further calculating the SINR lower bound of the receiving signal of the user.
2. The invention adopts the admission control method to gradually remove the infeasible users in the distributed antenna system, and can ensure the QoS requirements of the rest users in the system;
3. the invention provides a sending beam forming method which is suitable for a distributed antenna system with time asynchronization of received signals, and the load of a return link of the system can be reduced through group sparse design of beam vectors.
Drawings
FIG. 1 is a diagram of an application scenario of the present invention;
FIG. 2 is a flow chart of an implementation of the present invention;
FIG. 3 is a schematic diagram of an impulse response function of a piecewise linear function approximation transmit raised cosine pulse shaping filter employed in the present invention;
FIG. 4 is a diagram comparing the performance of the present invention with the prior art beamforming method for satisfying the user ratio in the system under the condition of varying the maximum transmitting power of different antenna units;
fig. 5 is a diagram comparing the system backhaul link load performance under the condition of the maximum transmit power variation of different antenna units according to the present invention and the existing beamforming method;
fig. 6 is a diagram comparing the total transmission power consumption performance of the system under the condition of the maximum transmission power variation of different antenna units according to the present invention and the existing beamforming method.
Detailed Description
The principles and technical solutions of the present invention are further described below.
Referring to fig. 2, the implementation process of the present invention includes the following steps:
step 1, constructing a time asynchronous receiving signal of each user in a current distributed antenna system, obtaining an approximate time asynchronous receiving signal of each user by adopting a piecewise linear method for all the time asynchronous receiving signals, and calculating an SINR lower bound of the approximate time asynchronous receiving signal of each user according to the obtained approximate time asynchronous receiving signal; constructing a target function with minimized load of a return link of the current distributed antenna system, and performing convex relaxation on the target function to obtain a convex target function;
the method specifically comprises the following steps:
(1.1) constructing a time asynchronous received signal of each user in the current distributed antenna system:
wherein, tauk,nG (-) is an impulse response function of a sending raised cosine pulse shaping filter, wherein g (-) is the transmission time delay of an electromagnetic signal from an nth antenna unit to a kth user;
(1.2) approximating the impulse response function of the raised cosine pulse shaping filter by using a piecewise linear function to obtain a piecewise linear approximate expression of the impulse response function;
using piecewise linear functionsApproximating the impulse response function of a raised cosine pulse shaping filter, where o · represents the high order infinity, the slope of each segment using g (t) at t＝lTs±ξmaxTsAndvalue of time, where ξmaxTsIs the maximum relative transmission delay, L, of the system0Representing the number of side lobes of the impulse response function considering causing intersymbol interference;
example (b): in the invention, the number L of side lobes is taken03, as shown in fig. 3, 12 line segments are required to achieve an approximation to the g t function;
(1.3) deducing an approximate time asynchronous receiving signal of each user by using the obtained piecewise linear approximate expression, and calculating the SINR lower bound of the approximate time asynchronous receiving signal of each user;
wherein the step (1.3) comprises:
(1.3.1) calculating an expression of the approximate time-asynchronous received signal for each user from a piecewise linear approximation expression of the impulse response function:
wherein, yk(m) represents that the kth user receives the approximate time asynchronous signal corresponding to the mth symbol, sk,mThe mth useful symbol representing the kth user,denotes the conjugate transpose of the channel fading vector from the nth distributed antenna element to the kth user, wk,nRepresents the beamforming vector, z, of the nth distributed antenna unit to the kth userk(m) represents channel additive white gaussian noise when the kth user receives the mth symbol;
(1.3.2) calculating an SINR expression of the approximate time asynchronous received signal of each user according to the expression of the approximate time asynchronous received signal of each user:
wherein ISIkFor inter-symbol interference, IUI, of the users themselvesk,jFor co-channel interference between users, σ2A variance representing white gaussian noise of the wireless channel;
(1.3.3) solving the minimum value of the numerator of the SINR expression, and solving the maximum value of the denominator to obtain the SINR lower bound expression:
(1.4) constructing an objective function of the distributed antenna system with minimized load of a return link, and performing convex relaxation on the objective function to obtain a convex objective function; the method specifically comprises the following steps:
(1.4.1) constructing an objective function of minimizing the load of the backhaul link of the system:wherein A iskIs a set of distributed antenna elements serving the kth user, | · | represents the cardinality of the set;
(1.4.2) carrying out convex relaxation on the objective function by adopting a 1-norm convex hull form to obtain a convex objective function: <math>
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step 2, after QoS constraint of each user is obtained by using SINR lower bound of each user approximate time asynchronous receiving signal in the system and an auxiliary variable is introduced to the QoS constraint, a penalty term corresponding to the auxiliary variable is introduced to the convex objective function; the method specifically comprises the following steps:
(2.1) establishing a QoS constraint with a lower bound on SINR for approximately time-asynchronous received signals per user:whereinA receive SINR threshold required for user k;
(2.2) introduction of non-negative auxiliary variable beta to QoS constraints per userkGet each after introducing the auxiliary variableExpression of individual user QoS constraints:
(2.3) introducing penalty terms corresponding to all auxiliary variables into the convex objective functionWherein phi is a normal number with a large value, and a convex target function expression after a penalty term is introduced is obtained:
step 3, taking QoS constraint of each user and antenna transmitting power after introducing auxiliary variables as constraint conditions, and solving a convex objective function after introducing penalty terms by adopting a convex optimization theory; the method specifically comprises the following steps:
(3.1) establishing transmission power constraint of each distributed antenna unit:whereinThe maximum transmitting power of the nth distributed antenna unit;
(3.2) adopting a convex optimization tool box to solve the following optimization problem to obtain the beam vector matrix with introduced penalty term
Step 4, calculating the SINR lower bound obtained by each user according to the solving result, and calculating the satisfaction value of each user; the method specifically comprises the following steps:
(4.1) according to the obtained beam vector moment after the penalty term is introducedMatrix ofCalculating the SINR lower bound of the asynchronous received signal of each user approximate time
(4.2) calculating a satisfaction value for each user:
step 5, if all users in the distributed antenna system are feasible, executing step 6; otherwise, removing the user with the minimum satisfaction value in the system, and turning to the step 1; the method specifically comprises the following steps:
(5.1) selecting the user with the minimum satisfaction value in the distributed communication system <math>
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(5.2) ifThen user k will be*Removing the system, and transferring to the step 1, otherwise, executing the step 6;
step 6, introducing a weight factor into the convex objective function to obtain an objective function with minimized weighted power; the method specifically comprises the following steps:
(6.1) initializing the weight factor, i equals 0, and setting the initial value of the weight factor to be <math>
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(6.2) constructing an objective function for weighted power minimization:
step 7, taking QoS constraint of each user and antenna transmitting power as constraint conditions, solving an objective function value of weighted power minimization and a beam vector matrix after power weighting by adopting a convex optimization theory, and calculating a relative value of the objective function of weighted power minimization according to the objective function value of weighted power minimization; calculating a beam forming vector of each user by the distributed antenna unit according to the beam vector matrix after the power weighting; the method specifically comprises the following steps:
(7.1) taking QoS constraint of each user and antenna transmitting power as constraint conditions, adopting a convex optimization theory to solve a weighted power minimization optimization problem, and obtaining a beam vector matrix after power weightingCalculating a weighted power minimization objective function valueWeighted power minimization optimization problem:
wherein, UremIs the set of remaining users in the system;
(7.2) calculating the relative value of the weighted power minimization objective function
(7.3) adopting a rank 1 decomposition method to calculate the wave beam forming vector of each distributed antenna unit to each user
Step 8, if the relative value of the weighted power minimization target function is larger than the convergence threshold, updating the weight factor according to the beam forming vector of the distributed antenna unit, and turning to step 6; otherwise, the beam forming vector of the distributed antenna unit is used as the final beam forming vector of the distributed antenna unit.
Minimizing the relative value of the objective function if the weighted power is minimizedWherein k is the convergence threshold value, based onCalculating a new weight factor value which is a very small positive number, and then turning to step 6; otherwise, the beam vector of the distributed antenna unit is used as the final beam forming vector of the distributed antenna unit.
The technical effects of the present invention are described in detail by a simulation experiment as follows:
1) simulated system parameters
The simulated scenario is as shown in fig. 1, and considers that 7 users are randomly distributed in a cell, and each user only has 1 receiving antenna. Meanwhile, 5 antenna units are randomly distributed in the cell, and each antenna unit is provided with 3 transmitting antennas. The system bandwidth is 5MHz, the transmission rate requirement of each user is 1Mbps, and the maximum relative time delay of signals is 0.1 symbol period.
2) Simulation content and results
Comparing the performances of the ideal method (DA) of the known signal delay, the full cooperation method (FC) of the antenna unit, the method (PS) proposed by the present invention, the asynchronous method (NDA) without considering the delay and the maximum ratio transmission Method (MRT) on the satisfactory user ratio, the system backhaul link load and the total transmission power consumption under the condition that the maximum transmission power of the antenna unit varies, the results as shown in fig. 4, fig. 5 and fig. 6 are obtained.
As can be seen from fig. 4, since the DA method knows the signal delay, an accurate SINR expression can be obtained, and therefore the highest satisfactory user ratio is obtained. Both the FC method and the PS method use the SINR lower bound instead of the true SINR value, so the performance is consistent and very close to the optimal performance. Neither the NDA nor the MRT methods consider the influence of intersymbol interference and same frequency interference caused by time asynchronism, so that only few users can be ensured to meet the QoS requirements of the users.
As can be seen from fig. 5, since all antenna elements serve users simultaneously in the FC method, the system backhaul link is most heavily loaded. In the MRT method, each user is connected to the antenna unit closest to the user, so the load of the backhaul link of the system remains unchanged. The PS method of the invention adopts the QoS constraint of the SINR lower bound, and needs relatively more antenna units to meet the QoS requirement of users, so the load of a return link is slightly larger than that of an ideal DA method. With the increase of the transmitting power of the antenna unit, the PS method can further reduce the number of cooperative antenna units by using redundant power resources, so that the load of the backhaul link of the system gradually decreases to a steady state after increasing.
As can be seen from fig. 6, since each antenna unit directly transmits signals with the maximum power in the MRT method, the total power consumption of the system increases linearly. The FC method employs cooperation of all antenna elements, and can fully utilize space diversity, so that power consumption is lowest. Because the SINR lower bound is adopted to ensure the QoS requirement of the user, the total power consumption consumed by the PS method is slightly higher than that of the ideal DA method. In addition, since the number of users in the system and the antenna set used by each user are gradually determined, the total power consumption of the system finally tends to be stable gradually.
Claims (9)
1. A distributed beam forming method suitable for time asynchronous environment includes the following steps:
(1) constructing a time asynchronous receiving signal of each user in a current distributed antenna system, obtaining an approximate time asynchronous receiving signal of each user by adopting a piecewise linear method for all the time asynchronous receiving signals, and calculating an SINR lower bound of the approximate time asynchronous receiving signal of each user according to the obtained approximate time asynchronous receiving signal; constructing a target function with minimized load of a return link of the current distributed antenna system, and performing convex relaxation on the target function to obtain a convex target function;
(2) obtaining QoS constraint of each user according to SINR lower bound of each user approximate time asynchronous receiving signal, respectively introducing auxiliary variables to the QoS constraint of each user, and introducing penalty terms corresponding to the auxiliary variables to the convex objective function;
(3) with QoS constraint of each user and antenna transmitting power after introducing auxiliary variables as constraint conditions, solving a convex objective function after introducing a penalty term by adopting a convex optimization theory to obtain a beam vector matrix after introducing the penalty term;
(4) calculating an SINR lower bound value of each user approximate time asynchronous receiving signal according to the beam vector matrix after the penalty term is introduced, and calculating a satisfaction value of each user according to the SINR lower bound value;
(5) judging whether all users in the current distributed antenna system are feasible according to the satisfaction value of each user, and executing the step (6) if all users in the current distributed antenna system are feasible; otherwise, removing the user with the minimum satisfaction value in the system, updating the distributed antenna system, and turning to the step (1);
(6) introducing a weight factor into the convex objective function to obtain an objective function with minimized weighted power;
(7) the QoS constraint and the antenna transmitting power of each user are taken as constraint conditions, the objective function value of the weighted power minimization and the beam vector matrix after power weighting are solved by adopting a convex optimization theory, and the relative value of the objective function of the weighted power minimization is obtained by calculation according to the objective function value of the weighted power minimization; calculating a beam forming vector of each user by the distributed antenna unit according to the beam vector matrix after the power weighting;
(8) comparing the relative value of the weighted power minimization target function with the size of a convergence threshold, if the relative value of the weighted power minimization target function is larger than the convergence threshold, updating a weight factor according to the beam forming vector of each user by the distributed antenna unit, and turning to the step (6); otherwise, the beamforming vector of the distributed antenna unit for each user is used as the final beamforming vector of the distributed antenna unit.
2. The distributed beamforming method under time asynchronous environment as claimed in claim 1, wherein in step (1), a time asynchronous received signal of each user is constructed in a current distributed antenna system, and after an approximate time asynchronous received signal of each user is obtained by a piecewise linear method for all the time asynchronous received signals, a lower SINR bound of the approximate time asynchronous received signal of each user is calculated according to the obtained approximate time asynchronous received signal, which specifically includes the steps of:
(101) constructing a time asynchronous receiving signal of each user in the current distributed antenna system;
(102) approximating the impulse response function of the raised cosine pulse shaping filter by using a piecewise linear function to obtain a piecewise linear approximate expression of the impulse response function;
(103) and deducing the approximate time asynchronous receiving signal of each user by using the obtained piecewise linear approximate expression, and calculating the SINR lower bound of the approximate time asynchronous receiving signal of each user.
3. The method of claim 2, wherein the step (103) specifically comprises:
(201) calculating an expression of approximate time asynchronous receiving signals of each user according to a piecewise linear approximate expression of the impulse response function;
wherein, yk(m) represents that the kth user receives the approximate time asynchronous signal corresponding to the mth symbol, sk,mAn mth useful symbol representing a kth user, N representing the number of distributed antenna units in the distributed antenna system, K representing the number of users in the distributed antenna system,denotes the conjugate transpose of the channel fading vector from the nth distributed antenna element to the kth user, wk,nTo representBeam vector, L, for the nth distributed antenna element to the kth user0Indicating the number of side lobes, ξ, of the impulse response function that are considered to cause intersymbol interferencek,nRepresenting the relative unit time delay, T, experienced by the electromagnetic signal transmitted from the nth distributed antenna element to the kth usersSymbol period, g ', representing useful signals of users in distributed antenna system'n(lTs) Indicating the corresponding relative unit time delay at the ith zero point of the impulse response function as xik,nIs piecewise linearly approximated to the slope value of the line segment, zk(m) represents channel additive white gaussian noise when the kth user receives the mth symbol;
(202) calculating an SINR expression of the approximate receiving time asynchronous signal of each user according to the expression of the approximate receiving time asynchronous signal of each user;
wherein, γkIndicating the SINR of the kth user's approximately time-asynchronous received signal,representing the channel fading vector, ISI, from the nth antenna element to the kth user due to time asynchrony at the center of the impulse response function main lobekIndicating intersymbol interference, IUI, of user k itselfk,jIndicates the co-channel interference, σ, caused by user j to user k2A variance representing white gaussian noise of the wireless channel;
(203) solving the minimum value of the numerator of the SINR expression, and solving the maximum value of the denominator to obtain an SINR lower bound expression;
wherein,SINR lower bound, W, representing the approximate time-asynchronous received signal for the kth userkThe representation is distributed by allMatrix formed by wave beam vectors of antenna units to kth user, hkRepresenting a vector consisting of the channel fading vectors of all distributed antenna elements to the k-th user, Tr (-) representing the traces of the extraction matrix, ρk,lRepresents the upper bound of the moduli of the channel fading vectors of all distributed antenna elements to the kth user at the ith null of the impulse response function due to time asynchronization.
4. The method as claimed in claim 1, wherein the expression of the objective function in step (1) is:
wherein,represents the minimum transmission rate required by the K-th user, K represents the number of users in the distributed antenna system, akA set of distributed antenna elements represented as kth subscriber service, | · | represents the cardinality of the set, wk,nRepresenting the beam vector of the nth distributed antenna element to the kth user.
5. The distributed beamforming method in a time asynchronous environment as claimed in claim 1, wherein the convex relaxation of the objective function in step (1) is performed to obtain a convex objective function, specifically: and (4) relaxing the objective function by adopting a 1-norm convex hull form to obtain a convex objective function.
6. The method as claimed in claim 5, wherein the expression of the convex objective function is:
wherein,indicating the minimum transmission rate, w, required by the k-th userk,nRepresenting the beam vector of the nth distributed antenna element to the kth user,the conjugate transpose of the beam vector from the nth distributed antenna unit to the kth user is represented, N represents the number of distributed antenna units in the distributed antenna system, and K represents the number of users in the distributed antenna system.
7. The distributed beamforming method in time asynchronous environment as claimed in claim 1, wherein the QoS constraint expression in step (2) is established according to the SINR lower bound of the approximate time asynchronous received signal of each user as follows:
wherein,the lower bound of SINR for the approximate time-asynchronous received signal for the kth user,the required receive SINR threshold for user k.
8. The distributed beamforming method in time asynchronous environment as claimed in claim 1, wherein the expression of the QoS constraint of each user after introducing the auxiliary variable in step (2) is: <math>
<mrow>
<msubsup>
<mi>γ</mi>
<mi>k</mi>
<mrow>
<mi>l</mi>
<mi>o</mi>
<mi>w</mi>
</mrow>
</msubsup>
<mo>+</mo>
<msub>
<mi>β</mi>
<mi>k</mi>
</msub>
<mo>≥</mo>
<msubsup>
<mi>γ</mi>
<mi>k</mi>
<mn>0</mn>
</msubsup>
<mo>,</mo>
<mo>∀</mo>
<mi>k</mi>
<mo>;</mo>
</mrow>
</math>
wherein,lower bound, β, on the SINR of the approximately time-asynchronous received signal for the kth userkIs a non-negative auxiliary variable and is,the required receive SINR threshold for user k.
9. The distributed beamforming method in time asynchronous environment according to claim 1, wherein the expression of the convex objective function after introducing the penalty term in step (2) is:
wherein,denotes the minimum transmission rate required by the kth user, Tr (-) denotes the trace of the acquisition matrix, AnA diagonal matrix for specifying a beam vector of the nth distributed antenna element, ". "denotes the Hadamard product, WkRepresenting a matrix consisting of beam vectors for all distributed antenna elements for the kth user,for the penalty term, Φ is a normal number, N represents the number of distributed antenna units in the distributed antenna system, and K represents the number of users in the distributed antenna system.
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