Fractional programming based resource distribution method of multi-relay cooperative communication system
A technology for cooperative communication and system resources, which is applied in the field of resource allocation of multi-relay cooperative communication systems based on fractional planning, and can solve the problems of not fully considering the improvement of the performance of retransmission signals of base stations in the second time slot.
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Embodiment 1
[0062] A resource allocation method for a multi-relay cooperative communication system based on fractional planning, including
[0063] Step 1: build system model;
[0064] The present invention is aimed at a special application scene, comes from practical application, and the scene setting is meticulous and reasonable, and has practical guiding significance. Such as figure 1 As shown, this aspect considers a two-hop multi-relay multi-user OFDM system. There is a source node S in the system 1 , N relay nodes, K user nodes, the transmission bandwidth is divided into M subcarriers, and each subcarrier shares the system bandwidth equally and experiences independent Rayleigh fading. All relay nodes apply the half-duplex DF relay mode, and can obtain instantaneous channel information under different subcarriers. The communication process of the system is divided into two time slots. In the first time slot, all relay nodes and user nodes receive 1 Broadcast the transmitted sign...
Embodiment 2
[0098] In order to further improve and improve the computational efficiency of the algorithm, on the basis of the Lagrangian multiplier algorithm, we can use the subgradient method in the process of each loop iteration, and select the progressive step length to make the optimization more accurate . Specifically, the Lagrangian factor β in the Lagrangian form of the optimization problem P1 S ,β R,m ,β φ,n The iterative update method of the subgradient algorithm adopts the subgradient algorithm, which has lower complexity and is more efficient. The iterative update equation of the subgradient algorithm is
[0099] β 0 ( τ + 1 ) = [ β 0 ( τ ) - δ 0 ...
Embodiment 3
[0105] In order to reduce the complexity of the algorithm for practical application, the present invention proposes a simplified embodiment, specifically:
[0106] The solution of the step 3 optimization problem includes simplifying the objective function;
[0107] The optimization objective function in P1 becomes a linear continuous function, it is advisable to relax the constraints first, that is, define the decision matrix to replace t={t i,j,n,k},in Redefine:
[0108] P ~ i , j , n , k s 1 = t ~ i , j , n , k P i , ...
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