- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
To make the most efficient use of scarce bandwidth, channel assignment methods for wireless mesh networks (WMNs) should try to minimize the number of frequency channels used while achieving maximum network throughput. Beamforming is a well-known technique that improves spatial reuse in wireless networks. However, there are no channel assignment methods for WMNs that use beamforming to reduce the number of frequency channels. We develop the first channel assignment method for dynamic WMNs that incorporates beamforming in the conflict graph and matrix. This reduces co-channel interference significantly, thereby reducing the number of frequency channels required (NCR) to ensure interference-free communication among the mesh nodes while achieving maximum network throughput. Our novel Linear Array Beamforming-based Channel Assignment (LAB-CA) method significantly increases the spectrum utilization efficiency of WMNs at the expense of increased hardware complexity. It outperforms classical omni-directional antenna pattern-based channel assignment (OAP-CA) in terms of NCR. In a heterogeneous WMN where mesh nodes have differing numbers of radio interfaces, LAB-CA also outperforms OAP-CA in terms of NCR in both sparse and dense scenarios. A further significant reduction in NCR is achieved when the number of antennas in the linear antenna arrays of mesh nodes is increased.
We develop a new and effective channel assignment method that improves the frequency channel utilization of MRMC WMNs at the expense of increased hardware complexity by incorporating beamforming directly into the conflict graph and matrix during interference modeling. LAB-CA significantly reduces the number of frequency channels required to ensure interference-free communication among the mesh nodes for achieving maximum network throughput. The experimental results show that LAB-CA signifi- cantly outperforms classical OAP-CA in terms of NCR. We extend our channel assignment framework to incorporate heterogeneous mesh nodes in order to model a more realistic WMN architecture. The extended channel assignment method LABCA_HT significantly outperforms OAP-CA_HT in terms of NCR in sparse as well as dense mesh networks. Compared to sparse mesh networks, the throughput per node is lower and the solution times of the routing stage are higher in dense mesh networks. The throttling of the throughput per node in dense mesh networks can be alleviated by adding more gateways. Also, when using multiple gateways, mesh nodes in a dense mesh network can be divided nto groups, one group per gateway. This will divide the computational effort among the multiple gateways, which will reduce the solution times. As part of our future work, we plan to incorporate multiple gateways in our channel assignment framework.