Research On Power Allocation In Cooperative Wireless Communication

Cooperative wireless communication technology, which is a key technique in next generation wireless communication system, is attracting more and more attentions for its ability to combat channel fading and improve the system transmission performance. This thesis introduces the genetic algorithm to the cooperative wireless communication system to solve the complicated power allocation so as to improve system performance. The main works are as follows.(1) Focused on the cooperative communication system with relays, an effective relay selection strategy is proposed, which computes the equivalent SNR in the different communication links and the equivalent SNR in the destination node and selects the relay with the best wireless channel environment as the optimized relay according to the standard of maximizing the equivalent SNR in the destination node. Then the system outage probability based on the relay selection result is deduced. Simulation results show that the proposed relay selection strategy is effective. Moreover, the conclusion is also found that more candidate relays will decrease system outage probability even the relays do not actively transmit.(2) Focused on the cooperative communication system with relays, an effective power allocation solution is proposed. The solution expands the system to the multiple relays and destination nodes system which is based on the three nodes and two-hops model. The genetic algorithm is introduced to optimize the system break probability with the selected optimized relay. Simulation results show that the proposed optimal power allocation significantly enhances system performance.(3) Focused on the multiple users OFDM cooperative communication system, the sub-channel and power allocation based on genetic algorithm is proposed to maximize the overall system capacity with the proportional rate constraints. The proposed algorithm first performs sub-channel allocation with a rough rate constraint in the premise of equal power allocation among the sub-channels. Then the power allocation implements the proportional fairness and maintains the maximization of the system capacity based on sub-channel allocation scheme. Due to the separation of sub-channel and power allocation, the computational complexity can be reduced from exponential to linear. Additionally, both the sub-channel allocation and power allocation are based on genetic algorithm, so the computational complexity of proposed resource allocation algorithm can be further reduced. Simulation results show the proposed algorithm can achieve about95%of the maximum capacity and the data rate ratio among users can be set freely.