An adaptive interference-aware and traffic-aware channel assignment strategy for backhaul networks

The transformation of traditional networks is being done by incorporating billions of daily life devices to provide service centric facilities. With such transformation, the major traffic load will be shifted toward the backhaul networks, for which guaranteed bandwidth and low latency are the basic requirements. In order to meet varying and dynamic requirements of each service, the development of a traffic aware network is unavoidable. For achieving last mile connectivity, wireless mesh is considered among the best of the backhaul networks. Additionally, classical single radio mesh routers restrict the whole network on a single channel and hence the full potential of available multiple channels is not achieved. Mesh routers plugged with multiple radios allow parallel transmissions and increase the capacity of the whole network. To utilize network resources more efficiently, the key issue of channel assignment for wireless mesh networks is explored by incorporating the concept of time-based traffic in a distributed environment. This paper discusses the problem of assigning a limited number of channels to a large number of radios while keeping in view the restrictions involved in maintaining a minimal level of interference and preservation of network topology. Bayesian estimation approach is used to gather knowledge from surroundings to determine the high-interfered region and hence a distributed solution is proposed where mesh routers can find a more suitable alternative channel for respective region. The proposed algorithm is evaluated through traces on multiple flows, collected from simulations. Results show that the proposed algorithm performed better than existing ones in the presence of interference.