Q-Learning and MADMM Optimization Algorithm Based Interference Aware Channel Assignment Strategy for Load Balancing in WMNS

Wireless Mesh Networks (WMNs) have been considered one of the main technologies for configuring wireless machines since they appeared. In a WMN, wireless routers provide multi-hop wireless connectivity between hosts on the network and allow access to the internet through the gateway routers. These wireless routers are normally equipped with the multiple radios in the wireless mesh network that operate on multiple channels with the multiple interference, which is caused to reduce the network performance and end-to-end delay. In this paper, we proposed an efficient optimization algorithm to solve the channel assignment problem which cause due to the multichannel multiradios in WMN’s. The main objective of our paper is to minimize the channel interference among networked devices. So, initially we construct a multicast tree with minimum interference by using Q-Learning algorithm, which is helps to minimize the end-to-end delay of packet delivery. From the constructed multicast tree, we intend to develop a channel assignment strategy with the minimum interference by using Modified version of Alternative Direction Method of Multipliers (MADMM) optimization algorithm, which is helps to increase the network throughput and packet delivery ratio. The proposed strategy was implemented by using NS-2 (Network Simulator-2) and the experimental result show that the performance of the proposed method is very high compared to the other method and the performance was calculated by using the feature metrics such as average throughput, packet delivery ratio, end-toend delay and total cost, which is compared with the other existing channel assignment strategies such as Learning Automata and Genetic Algorithm (LA-GA), GA-based approach, link-channel selection and rate-allocation (LCR) and learning automata based multicast routing (LAMR) channel assignment methods.