A low complexity MIMO precoding design method for quality of service guarantee

Abstract

The application discloses a low-complexity MIMO precoding design method for guaranteeing service quality, which comprises the following steps: firstly, determining a performance index of a communication system, and constructing a precoding design problem model with a maximum index as an objective function and service quality and base station transmitting power as constraint conditions; secondly, constructing a feasible signal-to-interference-and-noise ratio region, and converting the proposed precoding design problem into a signal-to-interference-and-noise ratio allocation problem for maximizing the performance index in the feasible signal-to-interference-and-noise ratio region; and finally, solving the signal-to-interference-and-noise ratio allocation problem and mapping the obtained optimal user signal-to-interference-and-noise ratio into a corresponding precoding matrix. The application converts a high-dimension precoding matrix design problem into a low-dimension signal-to-interference-and-noise ratio allocation problem, approximates the best system performance with low calculation complexity, and is widely applicable to various communication system performance indexes.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a low-complexity MIMO precoding design method that ensures quality of service. Background Technology

[0002] Multiple-in-multiple-out (MIMO) systems are the cornerstone of modern wireless communication systems and an extremely important technology in mobile communication. They can effectively combat channel fading and achieve high-speed data transmission, while precoding technology can provide rich spatial diversity and multiplexing gains for MIMO communication systems.

[0003] Existing precoding designs typically involve high computational complexity. Generally, current methods focus on directly optimizing the precoding matrix or optimizing user power given a precoding vector. However, the high dimensionality of variables leads to significant computational overhead for directly optimizing the precoding matrix, while methods for optimizing power given a precoding vector fail to fully utilize the spatial gains of MIMO and incur substantial performance losses. Therefore, low-complexity precoding design methods for MIMO systems are crucial for improving system performance and reducing implementation complexity.

[0004] Furthermore, existing low-complexity precoding design methods are often only applicable to specific system performance metrics and usually cannot guarantee service quality, which has significant limitations. Summary of the Invention

[0005] Purpose of the invention: This invention provides a low-complexity MIMO precoding design method to ensure quality of service. By transforming the high-dimensional precoding matrix design problem into a low-dimensional signal-to-interference-plus-noise ratio (SINR) allocation problem, it approximates the optimal system performance with lower computational complexity and is widely applicable to various communication system performance indicators.

[0006] Technical solution: The low-complexity MIMO precoding design method for ensuring quality of service as described in this invention includes the following steps:

[0007] Step 1: Determine the performance index of the communication system and construct a precoding design problem model with maximizing the index as the objective function and service quality and base station transmit power as constraints.

[0008] Step 2: Construct a feasible signal-to-interference-plus-noise ratio (SIR) region, transforming the precoding design problem in Step 1 into an SIR allocation problem that maximizes performance within the feasible SIR region;

[0009] Step 3: Solve the user signal-to-interference-plus-noise ratio (SINR) allocation problem proposed in Step 2, and map the obtained optimal SINR to the corresponding precoding matrix.

[0010] Furthermore, in step 1, the objective function for the communication system performance index is a function of the user signal-to-interference-plus-noise ratio (SINR), expressed as f(γ1,γ2,…,γ). K ), where γ k It is the signal-to-interference-plus-noise ratio (SIR) for user k, defined as the ratio of the average power of the effective signal to the average power of the interference signal plus noise.

[0011] Furthermore, in step 1, the quality of service constraint means that the signal-to-interference-plus-noise ratio of any receiving user is not lower than a given threshold; the base station transmit power constraint means that the base station transmit power is not higher than a given power.

[0012] Furthermore, in step 1, the precoded design problem model additionally includes any constraint g with the following form. j (γ1,γ2,…,γ K )≤0, where g j (γ1,γ2,…,γ K ) is a function of the user's signal-to-interference-plus-noise ratio.

[0013] Furthermore, in step 2, the constructed feasible signal-to-interference-plus-noise ratio (SIR) region is a set of SIRs that simultaneously satisfy both the quality of service (QoS) constraints and the base station's transmit power constraints, specifically:

[0014]

[0015] in The channel vector corresponding to user k. Let γ be the precoding vector corresponding to user k. th =(γ 1,th ,…,γ K,th ) T The user's signal-to-interference-plus-noise ratio threshold. Let Psum be the variance of the noise at user k, Psum be the given power of the base station, and N and K represent the number of base station antennas and the number of users, respectively.

[0016] Furthermore, in step 2, the resulting signal-to-interference-plus-noise ratio (SIR) allocation problem is an optimization problem within the feasible SIR region, with the objective function being to maximize the system performance index from step 1. Specifically:

[0017]

[0018] Furthermore, in step 3, the optimal user signal-to-interference-plus-noise ratio is denoted as... Optionally, the obtained optimal user signal-to-interference-plus-noise ratio can be mapped to the corresponding precoding matrix, including direct mapping and mapping based on the optimal structure.

[0019] Furthermore, in direct mapping, let's denote... The optimal solution to the following problem:

[0020]

[0021] The precoding matrix after mapping is

[0022] Furthermore, in the mapping based on the optimal structure, the optimal precoding matrix must satisfy the following structure:

[0023]

[0024] where Λ=Diag(λ1,λ2,…,λ K H = [h1,…,h] K ],

[0025] The precoding matrix after mapping is

[0026] Beneficial effects: This invention transforms the high-dimensional precoding matrix design problem into a low-dimensional signal-to-interference-plus-noise ratio (SINR) allocation problem, and greatly reduces the computational complexity of precoding matrix design by replacing matrix inversion with scalar operations. This invention can effectively protect the quality of service for all users and is widely applicable to the performance indicators of various communication systems, enhancing versatility and flexibility, and can effectively approximate optimal system performance. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0028] Figure 2 This is a schematic diagram illustrating the system rate performance of the precoding design method for ensuring quality of service proposed in this invention.

[0029] Figure 3 This is a schematic diagram of the worst signal-to-interference-plus-noise ratio (SIR) of the precoding design method for ensuring service quality proposed in this invention.

[0030] Figure 4 This is a schematic diagram illustrating the complexity and performance of the precoding design method for ensuring service quality proposed in this invention.

[0031] Figure 5 This is a schematic diagram illustrating the symbol error rate performance of the precoding design method for ensuring service quality proposed in this invention. Detailed Implementation

[0032] like Figure 1 As shown, a low-complexity MIMO precoding design method for ensuring quality of service includes the following steps:

[0033] Step 1: Determine the performance index of the communication system and construct a precoding design problem model with maximizing the index as the objective function and service quality and base station transmit power as constraints.

[0034] Step 2: Construct a feasible signal-to-interference-plus-noise ratio (SIR) region, transforming the precoding design problem in Step 1 into an SIR allocation problem that maximizes performance within the feasible SIR region;

[0035] Step 3: Solve the user signal-to-interference-plus-noise ratio (SINR) allocation problem proposed in Step 2, and map the obtained optimal SINR to the corresponding precoding matrix.

[0036] In step 1, one possible expression for the optimization problem is:

[0037]

[0038] Where the objective function is f(γ1,γ2,…,γ) K Let g be a function of the user's signal-to-interference-plus-noise ratio (SIR). j (γ1,γ2,…,γ K ) is a convex function with respect to the user's signal-to-interference-plus-noise ratio.

[0039] In step 2, all feasible signal-to-interference-plus-noise ratio (SINNR) regions corresponding to the precoding matrix are as follows:

[0040]

[0041] Several alternative approximation sets for the above set include, but are not limited to, the following sets:

[0042]

[0043] Where α k ,β kj Defined respectively

[0044]

[0045] Therefore, the transformed signal-to-interference-plus-noise ratio (SIR) allocation problem is as follows:

[0046]

[0047] In step 3, let the optimal solution to the problem after the above transformation be denoted as . Mapping schemes from the optimal signal-to-interference-plus-noise ratio (SINR) to the corresponding precoding matrix, including but not limited to direct mapping and mapping schemes based on the optimal structure:

[0048] Direct mapping: The optimal solution to the following problem:

[0049]

[0050] Mapping based on optimal structure: The optimal precoding matrix must satisfy the following structure:

[0051]

[0052] where Λ=Diag(λ1,λ2,…,λ K ),

[0053]

[0054] The precoding matrix after mapping is

[0055] The paper uses specific examples to demonstrate the application process and performance advantages of the proposed design framework in maximizing system and rate performance, maximizing system and rate performance based on the zero-forcing criterion, and minimizing symbol error rate performance, fully demonstrating the flexibility of the proposed precoding design framework.

[0056] (1) Maximize system and rate

[0057] In this example, in the simulation example provided by the present invention, the number of base station antennas N = 128, the number of users K = N / 2 = 64, and the user signal-to-interference-plus-noise ratio threshold is set to... Maximum transmit power P = 10 dBW.

[0058] The system performance function f(γ1,γ2,…,γ) in step 1 K Select as The corresponding optimization problem is:

[0059]

[0060] Feasible signal-to-interference-plus-noise ratio region selection is

[0061]

[0062] Where α k ,β kj Defined respectively

[0063]

[0064] Therefore, the signal-to-interference-plus-noise ratio (SIR) allocation problem is as follows:

[0065]

[0066] This problem can be effectively solved using methods such as the gradient method and the interior point method. Here, we use a quadratic transformation and water-filling algorithm to solve this problem. First, we introduce an auxiliary variable for the quadratic transformation. The approximate problem is equivalent to the following problem

[0067]

[0068] Where A k (Y),B k (Y),C k (Y) is defined as:

[0069]

[0070] Next, the iterative process of the above approximation problem based on the alternating optimization method is as follows:

[0071]

[0072] Among them, in updating γ (n+1) The following water-filling algorithm can be used to solve the subproblem composed of the original variables. Specifically, we first construct an auxiliary variable μ and define a function. The relevant parameters are defined as follows:

[0073] as well as Analysis shows that the optimal solution to the subproblem satisfies Where μ ＊ satisfy

[0074]

[0075] The above problem can be effectively solved using the bisection method, and then the subproblems can be solved. The optimal signal-to-interference-plus-noise ratio (SIR) can then be calculated. Then, the corresponding precoding matrix is ​​obtained based on the optimal structure mapping.

[0076] Figure 2 Curves showing the achievable rate versus maximum power for several precoding schemes are presented. Baseline schemes 1 and 2 are based on the weighted least mean square error algorithm, while baseline scheme 3 is a zero-forcing precoding scheme based on maximizing the sum and rate. It can be seen that the proposed scheme achieves the same performance as baseline schemes 1 and 2, but with a 10% performance gain compared to baseline scheme 3.

[0077] Figure 3 The minimum signal-to-interference-plus-noise ratio (SNR) versus maximum power curves for various precoding schemes are presented. It can be seen that baseline scheme 1 fails to meet the user's quality of service (QoS) constraints at low transmit power, while the scheme of this invention can guarantee the QoS constraints for the worst-case user under all circumstances.

[0078] Figure 4Curves showing the average central processor runtime of various precoding schemes versus the number of base station antennas are presented. It can be seen that the scheme of this invention has the lowest average runtime, with time gains of over 10dB and 20dB compared to baseline schemes 1 and 2, respectively.

[0079] (2) Maximizing system sum and rate based on the zero-forcing criterion

[0080] Zero-forcing precoding, or zero-forcing precoding, is a commonly used linear precoding method. It simplifies the precoding design by forcing inter-user interference to zero. In the simulation example provided in this invention, the number of base station antennas N = 128, the number of users K = N / 2 = 64, and the user signal-to-interference-plus-noise ratio (SINNR) threshold is set to... Maximum transmit power P sum =10dBW.

[0081] In this example, the communication system performance index f(γ1,γ2,…,γ) in step 1 K Select as The corresponding optimization problem is:

[0082]

[0083] The feasible signal-to-interference-plus-noise ratio (SIR) region in step 2 is

[0084]

[0085] in The corresponding signal-to-interference-plus-noise ratio (SIR) allocation problem is:

[0086]

[0087] This problem can be efficiently solved using the water-filling algorithm, and its optimal solution is denoted as . The corresponding precoding matrix is:

[0088]

[0089] (3) Minimize the system and the symbol error rate

[0090] In the simulation example provided by this invention, the number of base station antennas N = 24, the number of users K = 18, and the user signal-to-interference-plus-noise ratio threshold is set to... With a maximum transmit power P = 10 dBW and QPSK modulation selected, the user's symbol error rate can be expressed as:

[0091]

[0092] in The performance function of the communication system, f(γ1,γ2,…,γ), is to minimize the system and the symbol error rate. KThe following can be selected:

[0093]

[0094] In this example, the optimization problem corresponding to step 1 is:

[0095]

[0096] The feasible signal-to-interference-plus-noise ratio (SIR) region in step 2 is selected as follows:

[0097]

[0098] Where α k ,β kj Defined respectively

[0099]

[0100] Therefore, the signal-to-interference-plus-noise ratio (SIR) allocation problem is as follows:

[0101]

[0102] This problem can also be effectively solved using gradient method, block coordinate descent, and other methods. After calculating the optimal signal-to-interference-plus-noise ratio, the corresponding precoding matrix can be obtained.

[0103] In the simulation example provided in this invention, the number of base station antennas N = 24, the number of users K = 18, and the user signal-to-interference-plus-noise ratio threshold is set to... Baseline schemes 4 and 5 are based on maximizing sum and rate design, while baseline scheme 6 is based on zero-forcing precoding that minimizes bit error rate.

[0104] Figure 5 The curves of the average symbol error rate versus maximum power for various precoding schemes are given, and it can be seen that the scheme of the present invention has the best symbol error rate performance.

Claims

1. A low-complexity MIMO precoding design method to ensure quality of service, characterized in that, Includes the following steps: Step 1: Determine the performance index of the communication system and construct a precoding design problem model with maximizing the index as the objective function and service quality and base station transmit power as constraints. The objective function for the performance indicators of a communication system is a function of the user signal-to-interference-plus-noise ratio (SINR), expressed as f(γ1,γ2,…,γ). K ), where γ k The signal-to-interference-plus-noise ratio (SINR) for user k is defined as the ratio of the average power of the effective signal to the average power of the interference signal plus noise. Quality of service constraint means that the signal-to-interference-plus-noise ratio (SIR) of any receiving user is not lower than a given threshold; base station transmit power constraint means that the base station transmit power is not higher than a given power. The optimization problem expression is: function g j (γ1,γ2,…,γ K () is a convex function with respect to the user's signal-to-interference-plus-noise ratio; Step 2: Construct a feasible SIR (signal-to-noise ratio) region. The precoding design problem in Step 1 is transformed into an SIR allocation problem that maximizes performance metrics within the feasible SIR region. The constructed feasible SIR region is a set of SIRs that simultaneously satisfy both quality of service constraints and base station transmit power constraints, specifically: Where h k Let w be the channel vector corresponding to user k. k Let γ be the precoding vector corresponding to user k. th =(γ 1,th ,…,γ K,th ) T The user's signal-to-interference-plus-noise ratio threshold. Let P be the variance of the noise at user k. sum Given the base station's power, K represents the number of users; The resulting signal-to-interference-plus-noise ratio (SIR) allocation problem is an optimization problem within the feasible SIR region, with the objective function being to maximize the system performance index from step 1. Specifically: Step 3: Solve the user signal-to-interference-plus-noise ratio (SINR) allocation problem proposed in Step 2, and map the obtained optimal SINR to the corresponding precoding matrix; denote the obtained optimal SINR as... Mapping the obtained optimal user signal-to-interference-plus-noise ratio (SINR) to the corresponding precoding matrix is ​​a mapping based on the optimal structure. In the mapping based on the optimal structure, the optimal precoding matrix satisfies the following structure: where Λ=Diag(λ1,λ2,…,λ K H = [h1,…,h] K ], Where α k ,β kj Defined respectively The precoding matrix after mapping is

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