Joint optimization method for energy efficiency and bit error rate in MIMO (Multiple Input Multiple Output) system

Abstract

The invention discloses a joint optimization method for energy efficiency and bit error rate in an MIMO (Multiple Input Multiple Output) system. The joint optimization method comprises the following steps: establishing an MIMO system network model; respectively calculating the effective energy efficiency and the bit error rate of the MIMO system network model; based on the effective energy efficiency and the bit error rate, constructing a joint revenue function of the effective energy efficiency and the bit error rate; and solving the joint revenue function maximization problem by using an improved hiking optimization algorithm to obtain the optimal power and the optimal revenue. According to the method, the problem that the efficiency and the reliability of the system are ensured at the same time under the QoS constraint condition is solved, bandwidth resources are effectively saved, and valuable reference bases are provided for construction and deployment of large-scale MIMO at the same time.

Description

Technical Field

[0001] The present invention belongs to the technical field of communication technology application, and in particular relates to a method for jointly optimizing energy efficiency and bit error rate in a MIMO system. Background Art

[0002] In the development of 6G networks, massive MIMO technology has played an important role as one of the key technologies to improve the performance of communication systems. The MIMO system configures multiple antennas at the data receiving and transmitting ends of the wireless communication system to establish multiple parallel channels in the channel space, effectively utilizing space resources and better promoting the communication services to meet the requirements of high reliability and low latency.

[0003] Existing technologies often focus on optimizing energy efficiency or bit error rate alone, without fully considering the trade-off between the two. As a result, in practical applications, the system may face a high bit error rate while being energy efficient, or consume too much energy at a low bit error rate. It is also crucial to ensure stable communication performance of the system while reducing the bit error rate in a complex and changing communication environment.

[0004] The rapid development and widespread application of wireless communication technology have led to increasing requirements for communication QoS. Latency is one of the key indicators for measuring the performance of communication systems. However, existing MIMO systems still have shortcomings in terms of efficient guarantee of latency QoS, especially in high-load and complex network environments, which requires more attention.

[0005] In view of the above problems, the present invention provides a method for jointly optimizing energy efficiency and bit error rate in a MIMO system, which improves energy efficiency, reduces bit error rate and effectively guarantees latency, thereby meeting the requirements of future communication services for high reliability and efficiency. Summary of the invention

[0006] In order to solve the above technical problems, the present invention proposes a method for jointly optimizing energy efficiency and bit error rate in a MIMO system, which can further optimize the service efficiency of the system while ensuring the service quality.

[0007] The present invention provides a method for jointly optimizing energy efficiency and bit error rate in a MIMO system, comprising:

[0008] Establish MIMO system network model;

[0009] Calculate the effective energy efficiency and bit error rate of the MIMO system network model respectively;

[0010] Based on the effective energy efficiency and the bit error rate, constructing a joint benefit function of the effective energy efficiency and the bit error rate;

[0011] The improved walking optimization algorithm is used to solve the problem of maximizing the joint benefit function to obtain the optimal power and the best benefit.

[0012] Optionally, calculating the effective energy efficiency of the MIMO system network model includes:

[0013] Calculating the channel capacity of the network model under zero-forcing precoding;

[0014] Based on the channel capacity, calculating the effective capacity under the Rayleigh fading channel;

[0015] Based on the effective capacity, the effective energy efficiency is obtained.

[0016] Optionally, calculating the channel capacity of the network model under zero-forcing precoding includes:

[0017] Get the initial channel capacity based on zero-forcing precoding:

[0018]

[0019] Among them, N T is the number of transmitting antennas, N R is the number of receiving antennas, I NR is an N R The unit matrix of order, H is N R ×N T The complex channel matrix, P is the transmitted signal power, N0 is the noise power spectrum density, (·) H is the conjugate transpose, F is the precoding matrix, R XX is the autocorrelation matrix of the transmitted signal vector;

[0020] Perform singular value decomposition on the initial channel capacity to obtain the channel capacity:

[0021]

[0022] Among them, |h i | 2 and ||f i || 2 They are the matrices Λ D and Λ F The diagonal elements of , |·|, ||·|| are the absolute value and Euclidean norm.

[0023] Optionally, the method for calculating the effective capacity of the network model under the Rayleigh fading channel is:

[0024]

[0025] Among them, E C(θ,P) is the effective capacity under Rayleigh fading channel, θ is the QoS index, η is the mean square value of channel gain, and f H (|h|) is the probability density function of the Rayleigh fading channel.

[0026] Optionally, based on the effective capacity, obtaining the effective energy efficiency includes:

[0027] Get the total power consumed by the MIMO system network model;

[0028] According to the effective capacity, the effective energy efficiency is obtained:

[0029]

[0030] Among them, P t is the total power consumption of the system, P c is the power consumption from the circuit elements in the link, and α is the efficiency of the power amplifier at the transmitting end.

[0031] Optionally, a method for calculating the bit error rate of the MIMO system network model is:

[0032]

[0033] Among them, N e To accumulate the number of bits decoded incorrectly in each data block, l r is the total number of bits transmitted, N b is the length of each data block, N r is the number of antennas, N n is the number of data blocks transferred.

[0034] Optionally, the joint profit function is:

[0035]

[0036] Among them, U is the joint profit function.

[0037] Optionally, solving the problem of maximizing the joint benefit function using an improved walking optimization algorithm to obtain the optimal power and the optimal benefit includes:

[0038] S1, perform Circle chaos mapping to initialize the individual positions in the population, that is, initialize the power;

[0039] S2, speed update based on the walking function;

[0040] S3, introduce Cauchy mutation, combine with speed update, update the individual positions in the population, and obtain the current optimal power;

[0041] S4, iterate the loop S1-S3 until the maximum number of iterations is reached to obtain the optimal power and the best benefit.

[0042] Compared with the prior art, the present invention has the following advantages and technical effects:

[0043] The present invention studies the joint optimization of energy efficiency and bit error rate of MIMO systems, solves the problem of ensuring both high efficiency and reliability of the system under QoS constraints, effectively saves bandwidth resources, and provides a valuable reference for the construction and deployment of large-scale MIMO. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0045] Figure 1 It is a flow chart of a method for jointly optimizing energy efficiency and bit error rate in a MIMO system in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0047] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0048] The present invention proposes a method for jointly optimizing energy efficiency and bit error rate in a MIMO system. Figure 1 As shown, the specific steps include:

[0049] Establish MIMO system network model;

[0050] Calculate the effective energy efficiency and bit error rate of the MIMO system network model respectively;

[0051] Based on the effective energy efficiency and bit error rate, a joint benefit function of effective energy efficiency and bit error rate is constructed;

[0052] The improved walking optimization algorithm is used to solve the joint benefit function maximization problem to obtain the optimal power and the best benefit.

[0053] Specifically, the method includes the following steps: first, by establishing a network queue system model under the MIMO service mechanism, according to the effective capacity (EffectiveCapacity, E C ) theory, the effective capacity expression under Rayleigh fading channel and zero forcing (ZF) precoding scheme is derived, and on this basis, the effective energy efficiency (EE) performance indicator is evaluated. Then, the bit error rate (BER) of the system under different detection and modulation methods is calculated, and the joint benefit function of EE and BER is defined. Finally, an optimization problem of maximizing the benefit function with the network quality of service (QoS) as the constraint is constructed, and the improved hiking optimization algorithm (I-HOA) is used using the Circle chaos initialization and Cauchy mutation hybrid strategy to solve the joint optimization problem and obtain the optimal power requirement.

[0054] More specifically, step (1) is for the MIMO system downlink. By establishing a complex network queue system under the MIMO service mechanism, the base station transmitter has N T There are N transmitting antennas and N receiving terminals. R Considering different types of data streams, the data arrives at the base station transmitter after precoding and is transmitted to the receiving end through MIMO diversity multiplexing technology.

[0055] Step (1.1) constructs a MIMO system service model.

[0056] H is N R ×N T The complex channel matrix can be expressed as:

[0057]

[0058] Among them, h i,j represents the transmission coefficient of the channel between the i-th receiving antenna and the j-th transmitting antenna, and any two transmission coefficients are independent of each other.

[0059] For N T independent symbols x1, x2, ┈, x NT The transmitted symbol vector x∈C NT×1 , received signal y∈C NR×1 It can be expressed as:

[0060]

[0061] in, is the noise vector, zi represents the additive white Gaussian noise on the i-th receiving antenna, whose power spectral density is N0, and P is the transmitted signal power.

[0062] Step (1.2) is to model different traffic characteristics.

[0063] Modeling is performed for different types of service arrivals, and two typical arrival processes, Poisson process and Markov Modulated On Off (MMOO) process, are taken as examples for analysis. However, the research method of the present invention is not limited to the above arrival processes.

[0064] Furthermore, calculating the effective energy efficiency of the MIMO system network model includes:

[0065] Calculate the channel capacity of the network model under zero-forcing precoding;

[0066] Based on the channel capacity, calculate the effective capacity under Rayleigh fading channel;

[0067] Based on the effective capacity, obtain the effective energy efficiency.

[0068] Specifically, step (2) derives the effective capacity based on ZF precoding under Rayleigh fading of the MIMO system.

[0069] Step (2.1) calculates the channel capacity based on ZF precoding.

[0070] The capacity of a deterministic MIMO channel can be expressed as follows:

[0071]

[0072] in, is an N R The unit matrix of order N0 is the additive noise The power spectral density of , det(·) represents the determinant.

[0073] The present invention adopts ZF linear precoding technology, and the ZF precoding matrix F can be expressed as:

[0074] F=H H (H H H) -1

[0075] After ZF precoding, the MIMO signal model is:

[0076]

[0077] At this time, the MIMO channel capacity can be rewritten as:

[0078]

[0079] Step (2.2) performs singular value decomposition on the channel matrix.

[0080] Perform singular value decomposition on the channel matrix, that is, H = UDV H , where U and V are both unitary matrices, that is D is a diagonal matrix consisting of the singular values ​​of H. The above formula can be rewritten as:

[0081]

[0082] Define the matrix Λ D , Λ F As follows: D =D H D, Λ F =F H F. According to the properties of the unitary matrix UU H =U H U=I, and the identity det(I+AB)=det(I+BA), the above equation can be simplified to:

[0083]

[0084] Because tr(V H Λ F V)=tr(Λ F ), the above formula can be expressed as:

[0085]

[0086] where |h i | 2 and ||f i || 2 They are the matrices Λ D and Λ F The diagonal elements of , the symbols |·|, ||·|| represent the absolute value and Euclidean norm.

[0087] Step (2.3) derives the effective capacity under Rayleigh fading.

[0088] The probability density function of the Rayleigh fading channel gain |h| is:

[0089]

[0090] Where η is the mean square value of the channel gain, that is, η = E(|h| 2 ).

[0091] The expression of effective capacity is shown below.

[0092]

[0093] θ is the QoS index, since |h i | is an independent and identically Rayleigh-distributed random variable. In order to more conveniently calculate the integral of the channel gain amplitude, |h| is used instead of |h i |. The above formula can be used to derive the effective capacity expression of the MIMO system based on ZF precoding under Rayleigh fading:

[0094]

[0095] Step (3) constructs a joint optimization benefit function for the effective energy efficiency and bit error rate of the MIMO system.

[0096] Step (3.1) calculates the effective energy efficiency of the MIMO system.

[0097] The total power consumption of the system can be expressed as:

[0098]

[0099] Among them, P c is the power consumption from the circuit elements in the link, P is the total transmit power at the base station, and α is the efficiency of the power amplifier at the transmitter.

[0100] The effective energy efficiency expression of the MIMO system with Rayleigh fading based on ZF precoding under QoS constraints is calculated as follows:

[0101]

[0102] Step (3.2) calculates the bit error rate of the MIMO system.

[0103] The bit error rate performance expression of the MIMO system is as follows:

[0104]

[0105] Among them, N e Indicates the number of bits with accumulated decoding errors in each data block, l r Indicates the total number of bits transmitted, using the number of antennas N r The number of data blocks N transmitted n and the length N of each data block b The product of .

[0106] Step (3.3) defines the joint optimization profit function.

[0107] In order to ensure that the two metrics are at the same order of magnitude in the objective function, the bit error rate exponential with base e is taken as the reliability-related indicator.

[0108] The profit function is defined as follows:

[0109]

[0110] A profit function maximization optimization problem is constructed, which shows that under limited power conditions, the system can achieve better effective energy efficiency and lower bit error rate.

[0111]

[0112] oeLh C ≥E B

[0113] P>0

[0114] Step (3.4) analyzes the arrival process of the system.

[0115] The arrival process is not limited to a certain arrival effective bandwidth. In practice, the Poisson process, MMOO process, etc. are also applicable to this system.

[0116] Take the Poisson arrival process as an example. According to the effective bandwidth theory, the bandwidth requirement for Poisson arrival is as follows:

[0117] where λ represents the strength of the Poisson arrival.

[0118] Taking the MMOO arrival process as an example, according to the effective bandwidth theory, the bandwidth requirement when MMOO arrives is as follows:

[0119]

[0120] Among them, p o represents the probability of transitioning from the off state to the on state, q o represents the probability of transitioning from the on state to the off state, and R represents the rate at which data packets arrive when in the on state.

[0121] To meet the QoS requirement of statistical delay, the effective bandwidth and effective capacity of the system need to satisfy the following formula: C (θ)≥E B (θ), and the total transmission power at the base station should be greater than 0, which is set as a constraint condition.

[0122] Step (4) designs an improved hiking optimization algorithm (I-HOA) that is initialized by Circle chaos and introduces a Cauchy mutation hybrid strategy to solve the optimization problem.

[0123] The following are the specific implementation steps of I-HOA.

[0124] Step (4.1) initializes the Circle chaotic map, which can be expressed as:

[0125]

[0126] a=0.5andb=0.2

[0127] Among them, a and b are control parameters, commonly used values ​​are 0.5 and 0.2, and mod is the remainder function.

[0128] Step (4.2) is the search phase. The mathematical model of HOA is based on the famous Tobler's Hiking Function (THF). The Tobler Hiking Function is expressed as follows:

[0129]

[0130] Among them, W i,t is the speed of hiker i at iteration t; S i,t is the slope of the path or terrain, and S i,t The calculation formula is as follows:

[0131]

[0132] where dh and dx represent the altitude and horizontal distance traveled by the hiker, respectively, and θ i,t is the inclination angle of the terrain, and its value range is [0,50°].

[0133] The formula for updating the current speed of a hiker in HOA is:

[0134] W i,t =W i,t-1 +γ i,t (β best -α i,t β i,t )

[0135] Among them, γ i,t is a number uniformly distributed in the range [0,1]; W i,t and W i,t-1 denote the current speed and initial speed of hiker i respectively; β best is the leader's position; i,t is the position of hiker i at iteration t; α i,t is the sweep factor (SF) of hiker i, and SF is in [1,3].

[0136] Based on the hiker's velocity, the position update formula is calculated as follows:

[0137] β i,t+1 =β i,t +W i,t

[0138] Step (4.3) incorporates the Cauchy mutation. The Cauchy mutation search update formula is as follows:

[0139] β newbest =β best +β best ×Cauchy(0,1)

[0140] Where Cauchy(0,1) is the standard Cauchy distribution function, and the one-dimensional Cauchy variogram centered at the origin is as follows:

[0141]

[0142] Step (4.4) determines whether the population has reached the maximum number of iterations. If so, output the optimal solution benefit function. Otherwise, return to step (4.2) to continue optimizing.

[0143] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for jointly optimizing energy efficiency and bit error rate in a MIMO system, characterized in that: include: Establish MIMO system network model; Calculate the effective energy efficiency and bit error rate of the MIMO system network model respectively; Based on the effective energy efficiency and the bit error rate, constructing a joint benefit function of the effective energy efficiency and the bit error rate; The improved walking optimization algorithm is used to solve the problem of maximizing the joint benefit function to obtain the optimal power and the best benefit.

2. The method for jointly optimizing energy efficiency and bit error rate in a MIMO system according to claim 1, characterized in that: Calculating the effective energy efficiency of the MIMO system network model involves: Calculating the channel capacity of the network model under zero-forcing precoding; Based on the channel capacity, calculating the effective capacity under the Rayleigh fading channel; Based on the effective capacity, the effective energy efficiency is obtained.

3. The method for jointly optimizing energy efficiency and bit error rate in a MIMO system according to claim 2, characterized in that: Calculating the channel capacity of the network model under zero-forcing precoding includes: Get the initial channel capacity based on zero-forcing precoding: Among them, N T is the number of transmitting antennas, N R is the number of receiving antennas, is an N R The unit matrix of order, H is N R ×N T The complex channel matrix, P is the transmitted signal power, N0 is the noise power spectral density, (·) H is the conjugate transpose, F is the precoding matrix, R XX is the autocorrelation matrix of the transmitted signal vector; Perform singular value decomposition on the initial channel capacity to obtain the channel capacity: Among them, |h i | 2 and ||f i || 2 They are the matrices Λ D and Λ F The diagonal elements of , |·|, ||·|| are the absolute value and Euclidean norm.

4. The method for jointly optimizing energy efficiency and bit error rate in a MIMO system according to claim 3, characterized in that: The method for calculating the effective capacity of the network model under the Rayleigh fading channel is: Among them, E C (θ,P) is the effective capacity under Rayleigh fading channel, θ is the QoS index, η is the mean square value of channel gain, and f H (h|) is the probability density function of the Rayleigh fading channel.

5. The method for joint optimization of energy efficiency and bit error rate in a MIMO system according to claim 4, characterized in that: Based on the effective capacity, obtaining the effective energy efficiency includes: Get the total power consumed by the MIMO system network model; According to the effective capacity, the effective energy efficiency is obtained: Among them, P t is the total power consumption of the system, P c is the power consumption from the circuit elements in the link, and α is the efficiency of the power amplifier at the transmitting end.

6. The method for joint optimization of energy efficiency and bit error rate in a MIMO system according to claim 5, characterized in that: The method for calculating the bit error rate of the MIMO system network model is: Among them, N e To accumulate the number of bits decoded incorrectly in each data block, l r is the total number of bits transmitted, N b is the length of each data block, N r is the number of antennas, N n The number of data blocks transferred.

7. The method for joint optimization of energy efficiency and bit error rate in a MIMO system according to claim 6, characterized in that: The joint profit function is: Where U is the joint profit function.

8. The method for joint optimization of energy efficiency and bit error rate in a MIMO system according to claim 1, characterized in that: The improved walking optimization algorithm is used to solve the problem of maximizing the joint benefit function, and the optimal power and the best benefit are obtained, including: S1, perform Circle chaos mapping to initialize the individual positions in the population, that is, initialize the power; S2, speed update based on the walking function; S3, introduce Cauchy mutation, combine with speed update, update the individual positions in the population, and obtain the current optimal power; S4, iterate the loop S1-S3 until the maximum number of iterations is reached to obtain the optimal power and the best benefit.

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