In classical channel assignment (CA) in Multi-Radio Multi-Channel (MRMC) Wireless Mesh Networks (WMNs), the number of available frequency channels is assumed to be fixed. Two links that are within the interference range of each other could be assigned the same frequency channel, causing co-channel interference that degrades the network throughput. The objective of this research is to develop a realistic CA method that finds the smallest number of frequency channels required for interference-free communication among the mesh nodes (MNs) in order to achieve the maximum network throughput while maintaining fairness among the multiple network flows in a dynamic MRMC WMN.
As a first step towards achieving this objective, a novel CA method is developed, which ensures interference-free communication among the MNs based on the protocol interference model, and determines a small number of frequency channels required to achieve the maximum network throughput while maintaining fairness among the multiple network flows. Secondly, in order to develop a CA method using a realistic interference model, a novel and computationally simple method of building the conflict graph based on signal-to-interference ratio model with shadowing is developed.
Computationally simple and effective new heuristics are developed to find channel assignments from the conflict graph for the extended coloring problem with cumulative interference constraints. The heuristics are orders of magnitude faster than the exact solution method while consistently returning near-optimum results.
As a final step, the problem of co-channel interference in a dynamic WMN environment is addressed by using beamforming. The novel Linear Array Beamforming-based Channel Assignment (LAB-CA) method reduces the number of frequency channels required (NCR) and significantly outperforms the classical omni-directional antenna pattern-based channel assignment (OAP-CA) method in terms of NCR.
The beamforming-based CA framework is extended to incorporate heterogeneous MNs (i.e. nodes having differing numbers of radio interfaces). The LAB-CA method for heterogeneous MNs outperforms OAP-CA for heterogeneous MNs in terms of NCR in both sparse and dense mesh networks. It also provides a further significant reduction in NCR when the number of antennas in the linear antenna arrays of MNs is increased.