Time modulation array space division multiple access optimization method based on limited character input

Abstract

The invention discloses a time modulation array space division multiple access optimization method based on limited character input, belongs to the technical field of space division multiple access in a wireless communication system, and constructs a space division multiple access uplink transmission model based on a time modulation array, which comprises a base station end and a user end, the base station end is provided with a uniform linear time modulation array TMA composed of N array elements, and the time modulation array TMA is in a receiving state when working and performs space division multiple access communication with K mobile users equipped with single antennas of the user end; a base station end of the time modulation array receives a signal with K user data and carries out time modulation at a radio frequency front end, and a time modulation module adopts a single-pole single-throw SPST radio frequency switch and is uniformly controlled by an FPGA (Field Programmable Gate Array).

Description

Technical Field

[0001] The present invention belongs to the technical field of space division multiple access in wireless communication systems, and in particular relates to a time modulation array space division multiple access optimization method based on limited character input. Background Art

[0002] Spatial Division Multiple Access (SDMA) technology leverages the spatial diversity of wireless channels to allow multiple users to share the same frequency band for parallel transmission, thereby improving spectrum efficiency and system capacity. However, as 6G / B5G networks evolve towards ultra-dense deployment, the number of terminal devices will grow exponentially, increasing the complexity of communication system design and scale. Therefore, it is necessary to adopt more advanced and efficient methods to maintain performance and reliability. However, traditional MIMO-based SDMA communication systems often require multiple RF chains to implement, resulting in a sharp increase in hardware complexity, cost, and power consumption as the number of devices increases. This characteristic severely restricts its ability to be deployed on a large scale in ultra-dense scenarios.

[0003] A time-modulated array (TMA) dynamically controls the operating mode of antenna elements by periodically modulating the state of RF switches, generating fundamental and harmonic components with varying radiation patterns. This provides a low-complexity solution for SDMA. Its core advantage lies in the introduction of the time dimension as an additional design parameter, allowing a beam containing both fundamental and harmonic components to be generated with a single RF link. By optimizing switch timing parameters (such as the on-duty cycle and the on-midpoint position), TMAs can be flexibly adapted to multi-user channel environments. Existing research has validated the feasibility of TMAs in SDMA. For example, a novel multi-user communication system based on a time-modulated circular array (TMCA) has been proposed and experimentally verified. The prior art also introduces a TMA-based SDMA system, further enhancing it through comprehensive spectrum analysis and harmonic selection. Furthermore, the prior art studies the fundamental principles and performance of TMA-based MIMO transceivers in multi-user multipath propagation scenarios.

[0004] However, in the existing time modulation array-based spatial division multiple access (TMA-SDMA) system, the technical solutions usually rely on the Gaussian input signal assumption to achieve the theoretical capacity upper limit, but the actual communication system widely adopts the modulation method of limited character input (such as QAM, PSK). Given the distribution difference between Gaussian signals and limited character signals, there will be a large performance gap between the communication system designed under the Gaussian input assumption and the communication system designed under the limited character input assumption, which will lead to a disconnect between theory and actual scenarios. The traditional method optimizes the RF switching timing of the TMA based on the Gaussian input assumption to maximize the theoretical rate. However, the discrete distribution characteristics of the limited character input signal cause a significant rate gap between the actual system and the theoretical capacity upper limit. In addition, for the TMA-SDMA system with limited character input, its achievable rate expression is difficult to optimize directly because it is not in a closed form. The existing methods need to rely on high-dimensional numerical search or heuristic algorithms, resulting in huge computational overhead and no convergence guarantee. At the same time, existing optimization algorithms (such as traditional convex optimization or genetic algorithms) are inefficient in solving non-convex coupling problems, making it difficult to meet the demand for fast beamforming in real-time communication scenarios, further exacerbating the contradiction between system performance and complexity. These issues collectively limit the application potential of TMA-SDMA technology in practical digital modulation communication scenarios. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a time modulation array space division multiple access optimization method based on limited character input, taking the system achievable rate under the limited character input constraint as the optimization target, jointly optimizing the modulation timing of TMA, and further improving the transmission rate of the system.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A spatial division multiple access optimization method for time modulation array based on limited character input,

[0008] A time modulation array-based spatial division multiple access (SDMA) uplink transmission model is constructed, which includes a base station and a user end. The base station is equipped with a uniform linear time modulation array (TMA) consisting of N array elements. The TMA is in a receiving state and simultaneously performs SDMA communication with K mobile users equipped with single antennas on the user end.

[0009] The base station end of the time modulation array receives a signal with K user data and performs time modulation at the RF front end. The time modulation module uses a single-pole single-throw SPST RF switch and is uniformly controlled by an FPGA.

[0010] As a further preferred embodiment of the present invention, it is assumed that the base station TMA-BS of the time modulation array has the channel state information of all users, and the channel from the kth user to the nth element of the time modulation array TMA is represented by h n,k Indicates that the kth user has a center frequency f c Send complex baseband signal s k (t); Therefore, the received signal on the nth array element is expressed as

[0011]

[0012] The front end of the TMA antenna element is provided with an SPST switch, and each SPST switch performs square wave modulation through timing control. p In (for integers ), the switch is at t n,on +mT p Always open, at t n,off +mT p Therefore, the nth array element is closed at a period of T p The time switching function is

[0013]

[0014] Since the time switching function is a periodic function, the n (t) Perform Fourier series expansion

[0015]

[0016] in, represents the time modulation frequency, Indicates the order of the harmonic frequency component; β n,q It represents the qth order Fourier series coefficient on the nth array element, and its expression is

[0017]

[0018] in, represents the normalized on-time, It is expressed as the normalized middle conduction moment;

[0019] After passing through the SPST switch, the received signal will be combined into an RF chain signal by the power divider. Therefore, the modulated single-channel signal in the TMA-based RF front end is expressed as

[0020]

[0021] The modulated single-channel signal is down-converted to complex baseband by the receiver, and then further down-converted to zero frequency by the mixer and sampled by the analog-to-digital converter ADC;

[0022] Increase the sampling rate f s , so that the receiving end obtains the entire signal, and then performs Fourier transform, the receiving end separates the digital harmonic signals from -Q to Q order. By using vector symbols to represent the digital harmonic signals with orders from -Q to Q order, the receiving signal model is

[0023] y=BHs+n (6)

[0024] Where s represents the signal vector transmitted by K single-antenna users, which can be expressed as s = [s1, s2, ..., s K ] T , the matrix H represents the channel matrix from the user to the TMA-BS, which is represented by H = [h1,h2,…,h K ] is given. Among them, h k =[h 1,k ,h 2,k ,…,h N,k ] T It is represented as the channel from the kth single antenna user to the TMA-BS. In addition, the matrix B represents the harmonic characteristic matrix, which is expressed as

[0025]

[0026] n is defined as the additive white Gaussian noise vector from -Q to Q order harmonics introduced in the process, expressed as

[0027] n=[n -Q ,n -Q+1 ,…,n Q ] T (8)

[0028] Among them, n~CN(0,R out ), R out It represents the covariance of the aliased noise after time modulation, and its expression is

[0029]

[0030] As a further preference of the present invention, the number of selected harmonics satisfies 2Q+1≥N.

[0031] As a further preferred embodiment of the present invention, the achievable rate R based on limited character input is calculated:

[0032] The input signal elements are selected from a set of equally probable constellations with a cardinality of M and unit covariance. The expression of the achievable rate R of the model is

[0033]

[0034] in, It can be seen that the variables satisfy n′~CN(0,R out ), and c ij =Hs i -Hs j ;

[0035] Then, through Jensen's inequality, we can get the approximate value of the achievable rate R as

[0036]

[0037] As a further preferred embodiment of the present invention, an approximate expression for maximizing the achievable rate by adjusting the modulation sequence of the time modulation array at the base station is:

[0038]

[0039] Where, τ={τ n │n∈N} and

[0040] As a further preferred embodiment of the present invention, when calculating the approximate expression of the modulation sequence maximizing the achievable rate, the block coordinate descent (BCD) method is used to decompose the original problem, process τ and For each sub-problem, due to its objective function Since it is non-convex, the problem is convexified using the continuous convex approximation (SCA) algorithm.

[0041] As a further preferred embodiment of the present invention, the design of the normalized on-duty cycle τ: the optimization of the normalized on-duty cycle τ is regarded as a power allocation problem among the array elements; therefore, the optimization sub-problem is expressed as

[0042]

[0043] The SCA-based optimization algorithm is used to solve the problem. For the objective function (16), when updating the variable τ at the kth (k>1) iteration, Use its first-order Taylor expansion to approximate:

[0044]

[0045] Among them, τ k-1 It represents the on-duty ratio after the k-1th optimization, α is a positive constant, express The partial derivative with respect to the normalized on-duty cycle is expressed as in, express Partial derivative with respect to the normalized on-duty cycle at the nth element;

[0046] Therefore, according to formula (17), for this sub-problem, the SCA-based proxy problem is expressed as

[0047]

[0048] Since problem (22) is a strictly concave function associated with convex constraints, the solution of τ is obtained using the CVX tool or the Lagrange multiplier method.

[0049] As a further preferred embodiment of the present invention, the normalized conduction intermediate time Design: Bring the optimized τ into the optimization target, and calculate the normalized conduction intermediate time of the TMA element Optimize, the optimization problem at this time is as follows

[0050]

[0051] For the objective function (23), update the variable at the kth iteration (k>1) hour, Use its first-order Taylor expansion to approximate

[0052]

[0053] in, represents the middle conduction moment after the k-1th optimization, express The partial derivative relative to the normalized conduction intermediate time is expressed as in, express The partial derivative with respect to the normalized conduction intermediate moment on the nth element,

[0054] Therefore, according to formula (24), for the normalized conduction intermediate time design, the proxy problem based on SCA is expressed as

[0055]

[0056] Problem (29) is a strictly concave function associated with a convex constraint, so The solution is obtained using the CVX tool or the Lagrange multiplier method.

[0057] The beneficial effects of the present invention are:

[0058] This invention provides a time modulation array spatial division multiple access (SDMA) optimization method based on limited character input. This method uses the model achievable rate under the constraints of limited character input as the optimization target and jointly optimizes the TMA modulation timing, further improving the system's transmission rate. Compared with existing technologies, this achievable rate optimization scheme under the constraints of limited character input significantly outperforms traditional capacity optimization methods. Simulations show significant performance gains, demonstrating that optimizing the TMA modulation sequence design can effectively improve the achievable rate of SDMA. This scheme achieves a lower symbol error rate for both users while minimizing the performance gap between users compared to the comparative scheme.

[0059] Other advantages, objectives and features of the present invention will be described in the following description and will be apparent to those skilled in the art to some extent, or those skilled in the art can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to make the purpose, technical solutions and beneficial effects of the present invention more clear, the present invention provides the following drawings for illustration:

[0061] Figure 1 This is a system model diagram of space division multiple access uplink communication based on time modulation array;

[0062] Figure 2 The block diagram of a receiver based on a time modulation array is shown;

[0063] Figure 3 The achievable rates, corresponding approximations, and capacity graphs obtained from Monte Carlo simulations;

[0064] Figure 4 The relationship between the system achievable rate and user transmit power under different optimization schemes is shown in the figure;

[0065] Figure 5 The relationship between the system achievable rate and user transmit power under different transmission schemes is shown in the figure;

[0066] Figure 6 This is a graph showing the relationship between user symbol error rate and user transmit power under different transmission schemes. DETAILED DESCRIPTION

[0067] like Figures 1 to 6 As shown, the present invention proposes a time modulation timing optimization method under the constraint of limited character input, and its direct application scenario is a multi-user wireless communication system using digital modulation technology (such as QAM, PSK).

[0068] The present invention establishes a model of a space-division multiple access uplink transmission system based on a time modulation array. Based on this model, the system's achievable rate is analyzed from the perspective of limited character input. However, since the rate expression is not closed, the optimization calculation is very difficult. The present invention uses Jensen's inequality to obtain an approximate value of the achievable rate, thereby simplifying the parameter optimization process. In addition, to improve the system's achievable rate performance, a time modulation timing optimization method based on limited character input is proposed. Compared with traditional optimization methods based on the Gaussian input assumption, this method can further improve the system's achievable rate performance. To achieve the above objectives, the present invention provides the following solutions:

[0069] 1. Such as Figure 1 As shown in Figure 1, a time-modulated array-based spatial division multiple access (SDMA) uplink scenario is considered. A uniform linear TMA consisting of N array elements is configured at the base station. The TMA operates in a receiving state and simultaneously communicates with K mobile users equipped with single antennas through SDMA. The block diagram of the TMA-based receiver in the proposed SDMA uplink scenario is shown in Figure 1. Figure 2 As shown in Figure 2, the TMA-BS receives a signal carrying K user data and performs time modulation in the RF front end. The time modulation module is a single-pole, single-throw (SPST) RF switch, which is controlled by an FPGA.

[0070] 2. Space Division Multiple Access Receiver Signal Model Based on Time Modulation Array:

[0071] Assuming that the TMA-BS has the channel state information of all users, the channel from the kth user to the nth element of the TMA can be expressed as h n,k The kth user has a center frequency f c Send complex baseband signal s k (t).

[0072] Therefore, the received signal on the nth array element can be expressed as

[0073]

[0074] The front end of the TMA antenna element is equipped with an SPST switch, and each SPST switch realizes square wave modulation through special timing control. p In (for integers ), the switch is at t n,on +mT p Always open, at t n,off +mT p Therefore, the nth array element is closed at a period of T p The time switching function is

[0075]

[0076] Since the time switching function is a periodic function, U n (t) can be expanded by Fourier series as

[0077]

[0078] in, represents the time modulation frequency, Indicates the order of the harmonic frequency component. β n,q It represents the qth order Fourier series coefficient on the nth array element, and its expression is

[0079]

[0080] in, represents the normalized on-time, Expressed as the normalized middle turn-on time.

[0081] After passing through the SPST switch, the received signal will be combined into an RF chain signal by the power divider. Therefore, the modulated single-channel signal in the RF front end based on TMA can be expressed as

[0082]

[0083] The modulated single-channel signal is down-converted to complex baseband by the receiver, and then further down-converted to zero frequency by the mixer and sampled by the analog-to-digital converter (ADC). s If the signal is high enough, the receiving end can obtain the entire signal, and then perform Fourier transform, the receiving end can separate the digital harmonic signals from -Q to Q. By using vector symbols to represent the digital harmonic signals from -Q to Q, the receiving signal model can be written as

[0084] y=BHs+n (6)

[0085] Where s represents the signal vector transmitted by K single-antenna users, which can be expressed as s = [s1, s2, ..., s K ] T The matrix H represents the channel matrix from the user to the TMA-BS, which is represented by H = [h1,h2,…,h K ] is given. Among them, h k =[h 1,k ,h 2,k ,…,h N,k ] T It is represented as the channel from the kth single antenna user to the TMA-BS. In addition, the matrix B represents the harmonic characteristic matrix, which can be expressed as

[0086]

[0087] n is defined as the additive white Gaussian noise vector from -Q to Q order harmonics introduced in the process. It is statistically independent of the incident signal and can be expressed as

[0088] n=[n -Q ,n -Q+1 ,…,n Q ] T (8)

[0089] Among them, n~CN(0,R out ),R out It represents the covariance of the aliased noise after time modulation, and its expression is

[0090]

[0091] From (6), we can see that BH can be regarded as the equivalent channel between the user and the TMA-BS. In order to avoid the rank deficiency of the equivalent channel and ensure that the main beam pattern of the selected harmonic covers all angles to receive signals from all directions, the number of selected harmonics should satisfy 2Q+1≥N.

[0092] 3. Derivation of the achievable rate based on limited character input

[0093] The input signal elements are selected from a set of equally probable constellations of cardinality M and unit covariance, such as PSK and QAM. In this context, the achievable rate R of the proposed system is given by the mutual information between the transmitted signal s and the received signal y, which is expressed as

[0094] R=I(y;s)=h(y)-h(n) (10)

[0095] The condition for this expression to be valid is that the received noise n is independent of the received signal y. Among them, the differential entropy h(n) of the received noise n is

[0096]

[0097] The differential entropy h(y) of the received signal y can be expressed as

[0098]

[0099] in, It can be seen that the variables satisfy n′~CN(0,R out Substituting formulas (11) and (12) into formula (10), we can get the expression of the achievable rate R of the TMA-based SDMA system:

[0100]

[0101] Among them, c ij =Hs i-Hs j However, since the system's achievable rate R involves the mathematical expectation operator, formula (13) is difficult to obtain through a finite number of elementary calculations, which makes parameter optimization complicated. Then, through the Jensen inequality, the approximate value of the achievable rate R is obtained as

[0102]

[0103] 4. Optimization objective: Maximize the achievable rate by adjusting the modulation sequence of the TMA on the BS. Since the achievable rate has a non-closed form and involves an intractable logarithmic expectation value, in order to reduce the computational complexity, the present invention uses an approximate value of the achievable rate (14) to optimize the variables. Specifically, the mathematical expression of this problem is

[0104]

[0105] Where, τ={τ n |n∈N} and The constraints indicate the switching time limits of TMA time modulation. The problem proposed is a non-convex optimization problem, and its non-convexity is due to In addition, considering τ and The coupling between them makes the optimization problem more challenging.

[0106] 5. Modulation Timing Joint Optimization Algorithm

[0107] Problem (15) is a modulation timing joint design problem, that is, to find the normalized on-duty cycle τ and the normalized on-interval time In order to deal with τ and The present invention uses the BCD method to decompose the original problem. For each sub-problem, due to its objective function Since it is non-convex, the SCA algorithm is used to convexify the problem.

[0108] 1) Design of normalized on-duty cycle: Since this sub-problem is a non-convex function related to the normalized on-duty cycle, a solution based on SCA is proposed. Due to the specific setting of the normalized on-center moment, the optimization of the normalized on-duty cycle τ can be regarded as a power allocation problem among the array elements. Therefore, the optimization sub-problem can be expressed as

[0109]

[0110] st0≤τ≤1

[0111] Since the original problem is non-convex, it can be solved using an SCA-based optimization algorithm. For the objective function (16), when updating the variable τ at the kth (k>1) iteration, It can be approximated using its first-order Taylor expansion:

[0112]

[0113] Among them, τ k-1 It represents the on-duty cycle after the k-1th optimization, and α is a positive constant. express The partial derivative with respect to the normalized on-duty cycle can be expressed as in, express The partial derivative of the normalized on-duty cycle with respect to the nth element is expressed as follows:

[0114]

[0115] in, It represents the partial derivative of the harmonic characteristic matrix with respect to the normalized conduction duty cycle on the nth element, and its expression is

[0116]

[0117] matrix represents the partial derivative of the noise covariance matrix with respect to the normalized on-duty cycle on the nth element, which can be expressed as

[0118]

[0119] in, express Relative to τ n The gradient of the i,jth item is expressed as

[0120]

[0121] Therefore, according to formula (17), for this sub-problem, the SCA-based proxy problem can be expressed as

[0122]

[0123] st0≤τ≤1

[0124] Since problem (22) is a strictly concave function associated with convex constraints, the solution of τ can be easily obtained using CVX tools or the Lagrange multiplier method.

[0125] 2) Design of normalized conduction intermediate time: Bring the optimized τ into the optimization target and optimize the normalized conduction intermediate time of the TMA element. The optimization problem at this time can be written as

[0126]

[0127] Similar to the previous optimization problem, this subproblem is a non-convex function related to the normalized conduction intermediate time of the TMA element. The SCA method is still used, and the specific details of the cost function and the proxy problem are as follows. For the objective function (23), the variable is updated at the kth iteration (k>1). hour, It can be approximated using its first-order Taylor expansion

[0128]

[0129] in, represents the middle conduction moment after the k-1th optimization. express The partial derivative relative to the normalized conduction intermediate time can be expressed as in, express The partial derivative of the normalized conduction intermediate moment relative to the nth element is expressed as follows

[0130]

[0131] in, It represents the partial derivative of the harmonic characteristic matrix with respect to the normalized conduction intermediate moment on the nth element, and its expression is

[0132] matrix It represents the partial derivative of the noise covariance matrix with respect to the normalized conduction intermediate moment on the nth element, which can be expressed as

[0133] in, express Relative to The gradient of the i,jth item. n ∈(0,1), The expression is

[0134]

[0135] When τ n =0 or 1, Therefore, according to formula (24), for the normalized conduction intermediate time design, the proxy problem based on SCA can be expressed as

[0136]

[0137] Similar to the previous optimization problem, Problem (29) is a strictly concave function associated with a convex constraint, so The solution can be easily obtained using CVX tools or the Lagrange multiplier method.

[0138] The present invention takes the system achievable rate under the constraint of limited character input as the optimization goal, jointly optimizes the modulation timing of TMA, and further improves the transmission rate of the system.

[0139] To verify the accuracy of the achievable rate approximation, Figure 3 Three transmission cases (QPSK, BPSK and Gaussian input) are considered. In which, the conduction time of each array element is the same. And adopt one-by-one conduction sequence. Figure 3 As shown, for both QPSK and BPSK inputs, the approximate achievable rates closely match the actual achievable rates simulated by the Monte Carlo method. Clearly, the computational task of deriving the approximate achievable rates is not as complex as the actual achievable rates simulated by the Monte Carlo method. Furthermore, there is a performance gap between the system capacity under Gaussian input and the achievable rate under the finite character input constraint. This gap widens as user transmit power increases.

[0140] In order to evaluate the achievable rate performance under the constraint of limited character input, Figure 4 The variation trend of the achievable rate of user transmitted BPSK signal with increasing transmit power is compared under different input assumptions to optimize TMA timing. Figure 4 It can be seen that for the system and rate, the achievable rate optimization scheme under the finite character input constraint significantly outperforms the traditional capacity optimization method. This is attributed to the explicit introduction of BPSK modulation constraints in the optimization process, which strictly matches the reception strategy with the finite constellation signal structure. For different user rates, the achievable rate scheme achieves a more balanced user rate distribution within the power range of -2 to 14dBm. However, the capacity optimization scheme is completely unable to activate the user with poor channel conditions (user 1). This is attributed to the fact that in actual BPSK signal transmission, the achievable rate scheme allocates additional power to weaker channels that are far from saturation. The capacity optimization scheme is essentially oriented towards the optimal configuration of Gaussian input signals and lacks the ability to perceive finite constellation signals.

[0141] Figure 5The performance comparison of different transmission schemes with respect to the variation of user achievable rates with transmission power under BPSK signal input is demonstrated. All schemes use a single RF link to achieve multi-user communication. Simulation results show that as the transmission power increases, the achievable rates of each scheme show a monotonically increasing trend. It is worth noting that when the transmission power increases, the scheme using single-RF NOMA communication and this scheme both approach the theoretical limit, while the scheme using single-RF TDMA communication only converges to log2M. This difference is due to the time slot exclusive nature of the TDMA framework - each time slot is only allowed to serve a single user. In particular, compared with the TMA-SDMA scheme proposed in the prior art, this scheme shows a large performance gain, proving that by optimizing the TMA modulation sequence design, the achievable rate of the SDMA system can be effectively improved.

[0142] To evaluate the bit error rate performance under the constraint of limited character input, Figure 6 The impact of different transmission schemes on user symbol error rate (SER) is shown. Figure 6 As shown in the figure, when BPSK modulation is used as the user input signal, this scheme significantly outperforms the TMA-SDMA benchmark scheme and the single-radio NOMA communication scheme in the existing technology within the transmission power range shown. It is worth noting that while this scheme achieves a lower symbol error rate for dual users, the performance gap between users is smaller than that of the comparison scheme. This performance advantage highlights the effectiveness of time domain modulation optimization in suppressing multi-user interference. Although the TMA-SDMA benchmark in the existing technology adopts multi-antenna spatial division multiplexing technology, the SER of the user with better channel conditions (user 2) in this scheme is higher than that of the user with poor channel conditions, reflecting that the potential of spatial division multiplexing has not been effectively tapped. In contrast, although the single-radio NOMA communication scheme enables user 2 with better channel conditions to obtain a steeper SER decline curve, the performance improvement for user 1 in the weak channel is limited, exposing the inherent asymmetry defect of power domain multiplexing.

[0143] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A method for optimizing time modulation array space division multiple access based on limited character input, characterized by: A time modulation array-based spatial division multiple access (SDMA) uplink transmission model is constructed, which includes a base station and a user end. The base station is equipped with a uniform linear time modulation array (TMA) consisting of N array elements. The TMA is in a receiving state and simultaneously performs SDMA communication with K mobile users equipped with single antennas on the user end. The base station end of the time modulation array receives a signal with K user data and performs time modulation at the RF front end. The time modulation module uses a single-pole single-throw SPST RF switch and is uniformly controlled by an FPGA.

2. The method for optimizing time modulation array space division multiple access based on limited character input according to claim 1, characterized in that: Assume that the base station TMA-BS of the time modulation array has the channel state information of all users, and the channel from the kth user to the nth element of the time modulation array TMA is represented by h n,k Indicates that the kth user has a center frequency f c Send complex baseband signal s k (t); Therefore, the received signal on the nth array element is expressed as The front end of the TMA antenna element is provided with an SPST switch, each SPST switch is controlled by timing to perform square wave modulation, and in each modulation period T p For integers Switch at t n,on +mT p Always open, at t n,off +mT p It is closed at the same time, otherwise it remains disconnected. Therefore, the nth array element is closed at a period of T p The time switching function is Since the time switching function is a periodic function, the n (t) Perform Fourier series expansion in, represents the time modulation frequency, Indicates the order of the harmonic frequency component; β n,q It represents the qth order Fourier series coefficient on the nth array element, and its expression is in, represents the normalized on-time, It is expressed as the normalized middle conduction moment; After passing through the SPST switch, the received signal will be combined into an RF chain signal by the power divider. Therefore, the modulated single-channel signal in the TMA-based RF front end is expressed as The modulated single-channel signal is down-converted to complex baseband by the receiver, and then further down-converted to zero frequency by the mixer and sampled by the analog-to-digital converter ADC; Increase the sampling rate f s , so that the receiving end obtains the entire signal, and then performs Fourier transform, the receiving end separates the digital harmonic signals from -Q to Q order. By using vector symbols to represent the digital harmonic signals with orders from -Q to Q order, the receiving signal model is y=BHs+n (6) Where s represents the signal vector transmitted by K single-antenna users, which can be expressed as s = [s1, s2, ..., s K ] T , the matrix H represents the channel matrix from the user to the TMA-BS, which is represented by H = [h1,h2,…,h K ] is given, where h k =[h 1,k ,h 2,k ,…,h N,k ] T It is represented as the channel from the kth single-antenna user to the TMA-BS. In addition, the matrix B represents the harmonic characteristic matrix, which is expressed as n is defined as the additive white Gaussian noise vector introduced from -Q to Q order harmonics in the process, expressed as n=[n -Q ,n -Q+1 ,…,n Q ] T (8) Among them, n~CN(0,R out ), R out It represents the covariance of the aliased noise after time modulation, and its expression is 3. The method for optimizing time modulation array space division multiple access based on limited character input according to claim 2, characterized in that: The number of selected harmonics satisfies 2Q+1≥N.

4. The method for optimizing time modulation array space division multiple access based on limited character input according to claim 3, characterized in that: Calculate the achievable rate R based on a limited character input: The input signal elements are selected from a set of equally probable constellations with a cardinality of M and unit covariance. The expression of the achievable rate R of the model is in, It can be seen that the variables satisfy n′~CN(0,R out ), and c ij =Hs i -Hs j ; Then, through Jensen's inequality, we can get the approximate value of the achievable rate R as 5. The method for optimizing time modulation array space division multiple access based on limited character input according to claim 4, characterized in that: The approximate expression for maximizing the achievable rate by adjusting the modulation sequence of the base station time modulation array is: Where τ={τ n │n∈N} and 6. The method for optimizing time modulation array space division multiple access based on limited character input according to claim 5, characterized in that: When calculating the approximate expression of the modulation sequence to maximize the achievable rate, the block coordinate descent (BCD) method is used to decompose the original problem and process τ and For each sub-problem, due to its objective function Since it is non-convex, the problem is convexified using the continuous convex approximation (SCA) algorithm.

7. The method for optimizing time modulation array space division multiple access based on limited character input according to claim 5, characterized in that: Design of normalized on-duty cycle τ: The optimization of normalized on-duty cycle τ is considered as a power allocation problem among array elements; therefore, the optimization sub-problem is expressed as The SCA-based optimization algorithm is used to solve the problem. For the objective function (16), when updating the variable τ at the kth (k>1) iteration, Use its first-order Taylor expansion to approximate: Among them, τ k-1 It represents the on-duty ratio after the k-1th optimization, α is a positive constant, express The partial derivative with respect to the normalized on-duty cycle is expressed as in, express Partial derivative with respect to the normalized on-duty cycle at the nth element; Therefore, according to formula (17), for this sub-problem, the SCA-based proxy problem is expressed as Since problem (22) is a strictly concave function associated with convex constraints, the solution of τ is obtained using the CVX tool or the Lagrange multiplier method.

8. The method for optimizing time modulation array space division multiple access based on limited character input according to claim 7, characterized in that: Normalized conduction intermediate time Design: Bring the optimized τ into the optimization target, and calculate the normalized conduction intermediate time of the TMA element Optimize, the optimization problem at this time is as follows For the objective function (23), update the variable at the kth iteration (k>1) hour, Use its first-order Taylor expansion to approximate in, represents the middle conduction moment after the k-1th optimization, express The partial derivative relative to the normalized conduction intermediate time is expressed as in, express The partial derivative with respect to the normalized conduction intermediate moment on the nth element, Therefore, according to formula (24), for the normalized conduction intermediate time design, the proxy problem based on SCA is expressed as Problem (29) is a strictly concave function associated with a convex constraint, so The solution is obtained using the CVX tool or the Lagrange multiplier method.

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