D2D distributed resource allocation method for maximizing generalized energy efficiency of system
A technology of distributed resources and allocation method, applied in the field of terminal direct-to-distributed resource allocation, can solve the problem that user pairing and power allocation cannot be satisfied at the same time, and achieve the effect of improving system performance
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Embodiment 1
[0108] Such as figure 2 As shown, a D2D distributed resource allocation method that maximizes the generalized energy efficiency of the system includes the following steps:
[0109] S1: Build a communication system model and define the generalized energy efficiency of the system;
[0110] S2: Optimizing the communication system model and constructing an optimization problem that maximizes the generalized energy efficiency of the system;
[0111] S3: Use the method of step-by-step processing to solve the problem of maximizing the generalized energy efficiency of the system, including the user matching solution stage and the power allocation solution stage, and complete the distributed allocation of D2D resources.
[0112] In the specific implementation process, a system generalized energy efficiency measure is proposed to describe the benefit and pay ratio of all D2D user pairs in the whole system, and based on this, the problems of user pairing and power allocation are respec...
Embodiment 2
[0192] In order to more fully set forth the beneficial effects that the present invention has, below in conjunction with specific embodiment and relevant simulation result and analysis, further the effectiveness and advancement of the present invention are described.
[0193] Some typical parameter values are selected for system simulation, as shown in Table 1. Unless otherwise specified below, all simulation results use the parameters in Table 1.
[0194] Table 1: System simulation parameter settings
[0195]
[0196] Without loss of generality, the present invention assumes that the HUE and VUE in the system are randomly and evenly distributed in a square area with a side length of 100 meters, such as Figure 5 shown. The content attributes of each HUE and VUE are simulated using CRAWDAD upb / hyccups (v.2016-10-17) [15] real social network experimental data. The database contains a total of 5 content attribute factors. The iterative convergence precision is set to ε=...
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