Research On Joint Power Control And Channel Assignment For Wireless Mesh Network

As a low-cost solution to providing high-speed Internet services,Wireless Mesh Network(WMN)can provide network access for mobile users in its coverage area which has important application values in the next generation broadband wireless communication system.A mesh router needs to forward data traffic from mesh clients and other mesh routers,and it can transmit concurrently with different transmit powers and transmission channels according to communication requirements,which brings the problem that how to reduce network interference through power control and channel assignment.The network interference is the main constraint factor to prevent the improvement of WMN,which is determined by power control and channel assignment.When the transmit powers and transmission channels of mesh routers are configured appropriately,the link collision can be effectively avoided and a higher data transmission rate can be obtained.Existing joint power control and channel assignment researches in WMN haven't taken fully consideration of the interaction between them.Meanwhile,the measurement of network load and interference can't accurately capture the actual network status,which may cause bottleneck links and network congestion.Therefore,designing an effective joint power control and channel assignment optimization model and optimization algorithm can improve the overall performance of WMN.To solve the problem that the distribution of load in WMN is imbalanced,a gateway node is preferentially selected to balance the gateway load.By defining the link weights,a joint resource allocation model is constructed to maximize the network weighted throughput.In order to solve the joint resource allocation model,a Q learning and differential evolution based joint power control and channel assignment algorithm(QDJPCA)is proposed.The QDJPCA algorithm obtains the feedback result of power control as input parameter,uses the multiple mutations and adaptive crossover rate based differential evolution algorithm to perform channel assignment.In each iteration,the state clustering and state correction based Q learning algorithm is utilized on the channel assignment result to achieve power control.With the help of loop nesting and feedback mechanism,information is exchanged between the power control and channel assignment subproblem to obtain the joint resource allocation results.The network weighted throughput maximized oriented joint resource allocation model considers only the optimization of the overall network throughput performance,and ignores the impact of bottleneck links and network congestion on network performance.In order to accurately capture the distribution of network load and interference to solve the problem of network congestion,a joint resource allocation model is constructed to minimize the network congestion avoidance factor.On the basis of QDJPCA algorithm,a congestion control oriented joint power control and channel assignment algorithm(CCJPCA)is proposed for the joint resource allocation model.In order to accurately capture the network interference,the load model and interference model are used to measure the interference strength of interference links to the same channel.The network congestion avoidance factor is defined to reflect the degree of network congestion,so as to effectively protect the bottleneck link.Under the hybrid coding strategy,the CCJPCA algorithm achieves the co-evolution of the power and channel variable.The reward mechanism of Q learning algorithm is used to realize the adaptive selection of mutation strategy,thus ensure the reasonable allocation of network resource.The performances of QDJPCA and CCJPCA algorithms are verified through NS-3 simulation platform.The simulation results indicate that the proposed joint resource allocation optimization models and optimization algorithms can improve the overall network performance by giving priority to allocating network resource for bottleneck link.