On The Use Of Game Theory And Immune Clonal Algorithm For The Optimal Allocation Of The Resources In Cognitive Radio Networks

In wireless networks, the introduction and application of the cognitive radio technology have improved the system communication performance greatly, and reduced the resources waste greatly, which is in the traditional static spectrum assignment scheme. Based on that, optimizing the communication performance with reasonable strategies becomes a challenge topic in recent years. The resources in cognitive radio networks mainly include the spectrums, the transmission power, the modulation methods, the spectrum sense methods and the sense time, and all these resources’ allocation affects the whole system channel capacity and the systems’ Qo S(Quality of Service), so the appropriate allocation of these resources is the key to improve the whole system performance. The cognitive OFDM network applies the OFDM(Orthogonal frequency-division multiplexing) technology into the cognitive radio networks, which is a kind of multi-subcarrier modulation technologies that reduces the interference between different channels and improves the transmission signals’ quality. The resources in OFDM networks mainly include subcarriers and transmission power, the appropriate allocation of which influences the system communication quality greatly. Therefore, optimizing all these resources’ allocation becomes a popular topic in recent years. In this thesis, we mainly study the spectrums’ assignment under SS(Spectrum sharing), and the allocation of the subcarriers and transmission power in cognitive OFDM networks.The main works in this thesis are as follows.1. On the use of game theory, we mode the spectrum assignment problem based on the SS mode in cognitive radio networks, and then find the optimal solution through the ICSA(Immune clonal select algorithm). First, we mode the problem using the potential game, initialize the spectrum assignment scheme based on the handshake process, and get to NE(Nash equilibrium) through limit games. Then we optimize this point by ICSA to find the optimal solution through the operations of clonal, mutation and selection. Finally, we get better channel assignment performance with higher SIRs of all users and larger system channel capacity.2. In this thesis, we mode the problem of subcarrier and transmission power allocation in cognitive OFDM networks based on the complete information dynamic game. First, we regard each subcarrier as a game player, all the secondary users as the strategy of a player. And taking the system throughput, the power constraint and proportional fairness rate constraint into considerate, we set the evaluate function with reason. Then, we get the optimal solution with higher system transmit rates through limit games. Finally, the proposed algorithm improves the system throughput of the whole cognitive OFDM network.3. We study the subcarriers and power assignment problem in the downlink cognitive OFDM networks, and propose an improved algorithm based on ICSA. We execute the process in two steps:(1) assign the subcarriers to the secondary users;(2) allocate the transmission power to different subcarriers. First, we get the initial population by a near greedy strategy with high proportional fairness rates. Then, we use punish function, which combines the system throughput, the power constraint and the proportional fairness rate constraint, as the evaluate function in ICSA to search the optimal allocation scheme. And finally we get better communication performance of the whole system and the users’ throughput proportionality which is closest to the ideal values.