Multi-relay selection algorithm based on distribution estimation
A technology of selection algorithm and distribution estimation, applied in the field of wireless communication, can solve problems such as inability to communicate in real time online, high energy consumption and system overhead, and high algorithm complexity
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Embodiment 1
[0067] Step1: System modeling, a typical multi-relay selection scenario is as follows: a source workstation is recorded as S, a destination workstation is recorded as D, N R relay workstations, denoted as R 1 , R 2 ,...,R NR , from N R Among the relay workstations, select K relay workstations to participate in the transmission work, and the remaining N R -K relay stations sleep, where k≤N R , the problem boils down to, the optimization goal is to maximize the equivalent receiving signal-to-noise ratio, which is faster and less complex than the ordinary signal-to-noise ratio function calculation, and is easy to calculate and make decisions in real time.
[0068] The objective function is:
[0069] max SNR e f f = B R i ...
Embodiment 2
[0103] Studies have shown that our system overhead for population sorting accounts for more than 60%, and among them, the calculation of the objective function (ie, fitness function) is the most time-consuming. Therefore, we propose a setp1 in The simplified calculation formula of the objective function:
[0104] max SNR e f f = B R i D E SR ...
Embodiment 3
[0112] For the multi-relay selection algorithm described in Embodiment 1 and Embodiment 2, the algorithm in step 2 is initialized to generate the 0th generation individual set Δ 0 The probability distribution function of can satisfy the (0-1) distribution, and the probability density function is
[0113] p ( θ 1 , θ 2 , ... , θ n ) = Π i = 1 n p i ( θ i ) ,
[0114] ∀ i = 1 , 2 , ... , ...
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