Interference mitigation in wireless mesh networks through radio co-location aware conflict graphs

Abstract

Abstract-Wireless Mesh Networks (WMNs) have evolved into a wireless communication technology of immense interest. But technological advancements in WMNs have inadvertently spawned a plethora of network performance bottlenecks, caused primarily by the rise in prevalent interference. Conflict Graphs are indispensable tools used to theoretically represent and estimate the interference in wireless networks. We propose a generic algorithm to generate conflict graphs which is independent of the underlying interference model. Further, we propose the notion of radio co-location interference, which is caused and experienced by spatially co-located radios in multi-radio multichannel (MRMC) WMNs. We experimentally validate the concept, and propose a new all-encompassing algorithm to create a radio co-location aware conflict graph. Our novel conflict graph generation algorithm is demonstrated to be significantly superior and more efficient than the conventional approach, through theoretical interference estimates and comprehensive experiments. The results of an extensive set of ns-3 simulations run on the IEEE 802.11g platform strongly indicate that the radio co-location aware conflict graphs are a marked improvement over their conventional counterparts. We also question the use of total interference degree as a reliable metric to predict the performance of a Channel Assignment scheme in a given WMN deployment.

Predicting performance of channel assignments in Wireless Mesh Networks through statistical interference estimation

“…For example, in interferenceaware CA schemes, the guiding idea is to lower the TID to reduce the intensity of prevalent interference and make the CA more efficient [4]. However, in a recent study [5], we have demonstrated that although TID does give a measure of the impact of interference, it is inconsistent and unreliable as a CA performance prediction, or interference estimation metric. Thus, we can not, with high confidence, compare two CAs or select the most efficient CA for a given WMN from a set of CA schemes by employing their TID estimates as the sole criterion for performance prediction.…”

“…Other CA schemes that we implement are the breadth first traversal approach (BFS) [13], a static maximum clique based algorithm (CLQ) [14] and a maximum independent set based scheme (MIS) [10]. We employ two multi-radio multi-channel conflict graph models (MMCGs) viz., the conventional MMCG (C-MMCG) and the enhanced MMCG (E-MMCG) [5] to implement each of the above CA schemes, except GSCA. C-MMCG represents link conflicts in a traditional fashion and does not take into consideration the impact of radio co-location interference (RCI) in a wireless network.…”

Inter-flow and intra-flow interference mitigation routing in wireless mesh networks

“…The algorithm suggested in [31,33] is used to generate conflict graphs independently of the underlying interference model. Authors used the notion of radio co-location interference, which is caused and experienced by spatially co-located radios in multiradio multichannel (MRMC) WMNs.…”

Evaluation of channel assignment performance prediction techniques in random wireless mesh networks

“…Throughput is computed by first recording the aggregate throughputs for the RWMN for a variety of test cases, and then computing their average to obtain a measure of network capacity. Disrupted flows are those which fail to deliver the complete datafile at its intended destination owing to failures in routing mechanisms caused by high interference in the WMN [13]. Thus, DFC is a measure of the intensity of interference in the network.…”

“…Thus, we implement a set of graph theoretic CA schemes making use of the enhanced multi-radio multi-channel conflict graph (EMMCG) proposed in [13]. The CA set is diverse in terms of network performance, including CAs of different performance levels which is ideal [4], to radio co-location aware Elevated Interference Zone Mitigation CA (EIZM) which exhibits high network capacity [14].…”