Power allocation method for HTC and MTC coexistence in CF-mMIMO scenario

Abstract

The present invention proposes a power allocation method for the coexistence of HTC and MTC in a CF-mMIMO scenario. The method includes initializing parameters, iterating, and recording the power allocation scheme to complete the power allocation. The method of the present invention can maximize the spectrum efficiency of HTC devices while maximizing the energy efficiency of MTC. The method of the present invention can balance the relationship between spectrum efficiency and energy efficiency by changing λ. The power allocation method proposed in the present invention is superior to other power control methods in terms of spectrum efficiency and energy efficiency.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular to a power allocation method for coexistence of HTC and MTC in a CF-mMIMO scenario. Background Art

[0002] With the development of communication technology, the number of wireless access devices and the consumption of data traffic will show explosive growth. Massive MIMO is a promising 5G wireless access technology. Base stations with multiple antennas can serve many users simultaneously in the same time-frequency resources. This technology can provide high throughput, reliability and energy efficiency through simple signal processing. In recent years, Cell-Free Massive MIMO has been proposed as a new type of distributed massive MIMO network architecture, which is equipped with a large number of distributed access points (APs) to serve a smaller number of distributed users. For CF-mMIMO, all access points are connected to the central processing unit through fronthaul links, and the concept of cell boundaries no longer exists. Compared with the existing centralized Massive MIMO, CF-mMIMO not only inherits the good characteristics of channel hardening to a certain extent, but also has higher energy efficiency and system deployment flexibility. The distributed network topology enables CF-mMIMO to benefit from macro-diversity, mitigate the edge effects of centralized Massive MIMO, and ensure better system coverage, user service fairness and network throughput. Obviously, CF-mMIMO network is expected to become one of the potential architectures in the future.

[0003] A lot of research has focused on the potential of CF-mMIMO systems to support eMBB. Various papers have studied the user power control problem with the goal of maximizing downlink and uplink sum rates. Recently, a lot of research has also begun to focus on IoT scenarios based on Cell-Free Massive MIMO, but no research has focused on the coexistence of the two types of communication in Cell-Free Massive MIMO scenarios. The coexistence of mMTC and HTC in the same network is very common, which will also make the network design very complicated. Summary of the invention

[0004] The purpose of the present invention is to solve the problems in the prior art and propose a power allocation method for the coexistence of HTC and MTC in a CF-mMIMO scenario.

[0005] The present invention is implemented by the following technical solution. The present invention proposes a power allocation method for HTC and MTC coexistence in a CF-mMIMO scenario. Assume Equipped AP with antennas and Single-antenna devices are randomly distributed in a certain area; among these devices, HTC devices, MTC devices, ; HTC device group is represented by , the MTC device group is represented as , represents all users; the maximum power of HTC devices is expressed as , the maximum power of the MTC device is expressed as ;

[0006] The method specifically comprises:

[0007] Step 1. Initialize parameters: , maximum number of iterations ,error ; Weight coefficient ;

[0008] Step 2. If , then stop the iteration, otherwise execute step 3;

[0009] Step 3. Setup , initial point ;

[0010] Step 4. Iteration: Solve with GUROBI or MOSEK ,get ;

[0011] Step 5. If ,or Then go to step 7, otherwise go to step 6;

[0012] Step 6. , , return to step 4;

[0013] Step 7. Record the power allocation plan , record the time and ; , return to step 2.

[0014] Furthermore, in uplink transmission, all HTC users and all active MTC devices transmit data simultaneously; The closed expression of the signal-to-interference-to-noise ratio of a user can be expressed as:

[0015]

[0016] in, , , , , , represents the large-scale fading coefficient, represents the MMSE estimate of the channel state information, Indicates The power control coefficient of each user is , represents the noise power, Indicates A set of users that share the same pilot.

[0017] Furthermore, the HTC device rate and can be expressed as:

[0018] .

[0019] Furthermore, define The power consumption of an MTC device is expressed as ,in represents the power required to run the MTC device, then the total energy efficiency of MTC can be expressed as:

[0020] .

[0021] Furthermore, the HTC spectrum efficiency and MTC energy efficiency are optimized simultaneously, and a multi-objective optimization problem is used to balance the two optimization objectives, specifically:

[0022] Multi-objective optimization problem:

[0023]

[0024] in Indicates the minimum signal-to-interference-noise ratio threshold for HTC users. Indicates the minimum signal to interference and noise ratio threshold of the MTC device;

[0025] In order to obtain the Pareto optimal set, the weighted Chebyshev method is used to solve the multi-objective optimization problem. The multi-objective optimization problem can be re-converted into the following form:

[0026]

[0027] in, and is the optimal target value, is the weight coefficient, , by changing An optimal set of Pareto resource allocation strategies can be obtained; the re-transformed problem is a non-convex problem. By introducing slack variables and applying SCA to find an approximate solution, the problem is converted to:

[0028]

[0029] in, yes At the point The first-order Taylor expansion at yes At the point The first-order Taylor expansion at ; and The specific expression is as follows:

[0030] .

[0031] The beneficial effects of the present invention are:

[0032] The method of the present invention proposes a resource allocation method when MTC devices and HTC devices coexist on CF-mMIMO. The method of the present invention can maximize the spectrum efficiency of HTC devices while maximizing the energy efficiency of MTC. The relationship between spectrum efficiency and energy efficiency can be balanced. The power allocation method proposed in the present invention is superior to other power control methods in terms of spectrum efficiency and energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a schematic diagram of the Pareto frontier of the multi-objective optimization problem obtained by changing the value of lamma; the number of HTCs is 20 and the number of MTCs is 40;

[0034] Figure 2 is a normalized Pareto front diagram; where the number of HTCs is 20 and the number of MTCs is 40;

[0035] Figure 3 This is a schematic diagram of the energy efficiency of each solution when the number of HTCs is 20 and the number of MTCs is 40;

[0036] Figure 4 This is a schematic diagram of the spectrum efficiency of each solution when the number of HTCs is 20 and the number of MTCs is 40. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] SCA Algorithm

[0039] The SCA algorithm describes a general scheme for solving non-convex optimization problems, where at each iteration the non-convex feasible set is approximated by an inner convex approximation. The inner convexity is defined using an upper bound on the non-convex constraint function. Under appropriate conditions, monotonic convergence to the KKT point is established.

[0040] Consider the following general optimization problem:

[0041]

[0042] in , Both A continuously differentiable function on . In addition, suppose the function and finally (for ) Constraint function exist Therefore, the “non-convex part” of the problem is due to the previous Constraint Function It is caused by the non-convexity of . Assume There exists a convex upper estimate function, assuming that , and continuous functions So that for all , Both , for a fixed , is a convex function and is continuously differentiable. The vector plays the role of a parameter vector, and accordingly The basic idea of ​​the SCA method is to convert each non-convex function into Replace it with the appropriate parameter vector The convex approximation function of Therefore, in the steps of the method , we need to solve a convex problem of the following form:

[0043]

[0044] vector is a fixed parameter vector that depends on the solution to the problem .

[0045] Multi-objective optimization

[0046] The concept of multi-objective optimization is that when multiple goals need to be achieved in a certain scenario, due to the inherent conflicts between the goals, the optimization of one goal is at the expense of other goals. Therefore, it is difficult to find a unique optimal solution. Instead, coordination and compromise are made among them to achieve the best overall goal as much as possible. Generally, multi-objective optimization can be written as the following model:

[0047]

[0048] The multi-objective optimization problem can be decomposed into a set of optimization sub-problems by the Chebyshev method, and their mathematical description is shown in the following formula:

[0049]

[0050] in, is the weight vector of the subproblem, and , is the ideal target vector. The optimization result is a set of optimal solutions on the Pareto front, which is obtained by taking different sub-problem weight vectors.

[0051] Combination Figure 1-Figure 4 The present invention proposes a power allocation method for HTC and MTC coexistence in a CF-mMIMO scenario. Considering a CF-mMIMO system in which HTC devices and MTC devices coexist, it is assumed that Equipped AP with antennas and Single-antenna devices are randomly distributed in a certain area; among these devices, HTC devices, MTC devices, ; HTC device group is represented by , the MTC device group is represented as , represents all users; the maximum power of HTC devices is expressed as , the maximum power of the MTC device is expressed as ;

[0052] The method specifically comprises:

[0053] Step 1. Initialize parameters: , maximum number of iterations ,error ; Weight coefficient ;

[0054] Step 2. If , then stop the iteration, otherwise execute step 3;

[0055] Step 3. Setup , initial point ;

[0056] Step 4. Iteration: Solve with GUROBI or MOSEK ,get ;

[0057] Step 5. If ,or Then go to step 7, otherwise go to step 6;

[0058] Step 6. , , return to step 4;

[0059] Step 7. Record the power allocation plan , record the time and ; , return to step 2.

[0060] In the uplink transmission, all HTC users and all active MTC devices transmit data simultaneously; The closed expression of the signal-to-interference-to-noise ratio of a user can be expressed as:

[0061]

[0062] in, , , , , , represents the large-scale fading coefficient, represents the MMSE estimate of the channel state information, Indicates The power control coefficient of each user is , represents the noise power, Indicates A set of users that share the same pilot;

[0063] In the mMTC system, a large number of devices attempt to communicate autonomously in the uplink. Since these devices are usually battery-powered, they require energy-efficient communication. HTC users usually require high-speed data transmission. The sum of the HTC device rates can be expressed as:

[0064] .

[0065] Definition The power consumption of an MTC device is expressed as ,in represents the power required to run the MTC device, then the total energy efficiency of MTC can be expressed as:

[0066] .

[0067] The main purpose of this invention is to study the simultaneous optimization of HTC spectrum efficiency and MTC energy efficiency. Obviously, there is a contradiction between these two optimization objectives. In order to clearly discuss the trade-off between the two optimization objectives, this invention uses multi-objective optimization theory to construct a multi-objective optimization problem. HTC spectrum efficiency and MTC energy efficiency are optimized simultaneously, and the two optimization objectives are weighed using a multi-objective optimization problem, specifically:

[0068] Multi-objective optimization problem:

[0069]

[0070] in Indicates the minimum signal-to-interference-noise ratio threshold for HTC users. Indicates the minimum signal to interference and noise ratio threshold of the MTC device;

[0071] In order to obtain the Pareto optimal set, the weighted Chebyshev method is used to solve the multi-objective optimization problem, because it not only provides a complete Pareto optimal set but also has a lower computational complexity. The multi-objective optimization problem can be re-converted into the following form:

[0072]

[0073] in, and is the optimal target value, is the weight coefficient, , by changing An optimal Pareto resource allocation strategy set can be obtained; the re-transformed problem is a non-convex problem and cannot be solved directly by calculation, so it is necessary to find an approximate solution by introducing slack variables and applying SCA. The problem is converted to:

[0074]

[0075] in, yes At the point The first-order Taylor expansion at yes At the point The first-order Taylor expansion at ; and The specific expression is as follows:

[0076] .

[0077] The present invention uses Monte Carlo simulation to verify the effects of different pilot allocation schemes. The simulation range is square area, assuming that randomly placed APs and The present invention adopts a three-segment path loss model to model large-scale fading.

[0078]

[0079] in Indicates user and base station The distance between ,and Indicates user and base station The path loss between . represents shadow fading, and The specific parameters are shown in the table below, and the simulation parameters of the system are shown in Table 1;

[0080] Table 1 Simulation parameters

[0081]

[0082]

[0083] Figure 1 is the Pareto frontier of the multi-objective optimization problem obtained by changing the value of lamma, Figure 2 is the normalized Pareto front. If there is no special preference, the knee point (KP) is usually chosen as the optimal solution. When applying the weighted Chebyshev method, KP is usually around lamma=0.5. Figure 3 and Figure 4It is the HTC spectrum efficiency and MTC energy efficiency of different power control schemes under different MTC maximum transmit powers. It can be seen that the multi-objective optimization scheme is better than other schemes in terms of frequency efficiency, and is second only to the energy efficiency maximization scheme in terms of energy efficiency. Although the energy efficiency maximization scheme is better than other schemes in terms of energy efficiency, it is the second worst in terms of spectrum efficiency. Taking all factors into consideration, the multi-objective optimization scheme is the best scheme in terms of HTC spectrum efficiency and MTC energy efficiency. In addition, it can be seen that as the maximum transmit power of MTC increases, both frequency efficiency and energy efficiency increase. The gradual increase in HTC spectrum efficiency is because when the maximum transmit power of MTC is small, in order to meet the signal-to-interference-to-noise ratio requirements of MTC devices, HTC devices will suppress the transmit power. As the maximum transmit power of MTC increases, the transmit power of HTC devices can gradually approach its maximum transmit power, and the spectrum efficiency also increases.

[0084] The power allocation method for coexistence of HTC and MTC in the CF-mMIMO scenario proposed in the present invention is introduced in detail. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A power allocation method for coexistence of HTC and MTC in a CF-mMIMO scenario, characterized in that: Equipped AP with antennas and Single-antenna devices are randomly distributed in a certain area; among these devices, HTC devices, MTC devices, ; HTC device group is represented by , the MTC device group is represented as , represents all users; the maximum power of HTC devices is expressed as , the maximum power of the MTC device is expressed as ; The method specifically comprises: Step 1. Initialize parameters: , maximum number of iterations ,error ; Weight coefficient ; Step 2. If , then stop the iteration, otherwise execute step 3; Step 3. Setup , initial point ; Step 4. Iteration: Solve with GUROBI or MOSEK ,get ; Step 5. If ,or Then execute step 7, otherwise execute step 6; Step 6. , , return to step 4; Step 7. Record the power allocation plan , record the time and ; , return to step 2; In the uplink transmission, all HTC users and all active MTC devices transmit data simultaneously; The closed expression of the signal-to-interference-to-noise ratio of a user can be expressed as: ； in, , , , , , represents the large-scale fading coefficient, represents the MMSE estimate of the channel state information, Indicates The power control coefficient of each user is , represents the noise power, Indicates A set of users that share the same pilot; The HTC device rate and can be expressed as: ； Definition The power consumption of an MTC device is expressed as ,in represents the power required to run the MTC device, then the total energy efficiency of MTC can be expressed as: ； The HTC spectrum efficiency and MTC energy efficiency are optimized simultaneously, and a multi-objective optimization problem is used to balance the two optimization objectives, specifically: Multi-objective optimization problem: ； in Indicates the minimum signal-to-interference-noise ratio threshold for HTC users. Indicates the minimum signal to interference and noise ratio threshold of the MTC device; In order to obtain the Pareto optimal set, the weighted Chebyshev method is used to solve the multi-objective optimization problem. The multi-objective optimization problem can be transformed into the following form: ； in, and is the optimal target value, is the weight coefficient, , by changing An optimal set of Pareto resource allocation strategies can be obtained; the re-transformed problem is a non-convex problem. By introducing slack variables and applying SCA to find an approximate solution, the problem is converted to: ； in, yes At the point The first-order Taylor expansion at yes At the point The first-order Taylor expansion at ; and The specific expression is as follows: ； 。

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