Radio Co-location Aware Channel Assignments for Interference Mitigation in Wireless Mesh Networks

Designing high performance channel assignment schemes to harness the potential of multi-radio multi-channel deployments in wireless mesh networks (WMNs) is an active research domain. A pragmatic channel assignment approach strives to maximize network capacity by restraining the endemic interference and mitigating its adverse impact on network performance. Interference prevalent in WMNs is multi-faceted, radio co-location interference (RCI) being a crucial aspect that is seldom addressed in research endeavors. In this effort, we propose a set of intelligent channel assignment algorithms, which focus primarily on alleviating the RCI. These graph theoretic schemes are structurally inspired by the spatio-statistical characteristics of interference. We present the theoretical design foundations for each of the proposed algorithms, and demonstrate their potential to significantly enhance network capacity in comparison to some well-known existing schemes. We also demonstrate the adverse impact of radio co- location interference on the network, and the efficacy of the proposed schemes in successfully mitigating it. The experimental results to validate the proposed theoretical notions were obtained by running an exhaustive set of ns-3 simulations in IEEE 802.11g/n environments.Comment: Accepted @ ICACCI-201

Joint QoS multicast routing and channel assignment in multiradio multichannel wireless mesh networks using intelligent computational methods

Copyright @ 2010 Elsevier B.V. All rights reserved.In this paper, the quality of service multicast routing and channel assignment (QoS-MRCA) problem is investigated. It is proved to be a NP-hard problem. Previous work separates the multicast tree construction from the channel assignment. Therefore they bear severe drawback, that is, channel assignment cannot work well with the determined multicast tree. In this paper, we integrate them together and solve it by intelligent computational methods. First, we develop a unified framework which consists of the problem formulation, the solution representation, the fitness function, and the channel assignment algorithm. Then, we propose three separate algorithms based on three representative intelligent computational methods (i.e., genetic algorithm, simulated annealing, and tabu search). These three algorithms aim to search minimum-interference multicast trees which also satisfy the end-to-end delay constraint and optimize the usage of the scarce radio network resource in wireless mesh networks. To achieve this goal, the optimization techniques based on state of the art genetic algorithm and the techniques to control the annealing process and the tabu search procedure are well developed separately. Simulation results show that the proposed three intelligent computational methods based multicast algorithms all achieve better performance in terms of both the total channel conflict and the tree cost than those comparative references.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1

State-of-the-art of distributed channel assignment

Channel assignment for Wireless Mesh Networks (WMNs) attempts to increase the network performance by decreasing the interference of simultaneous transmissions. The reduction of interference is achieved by exploiting the availability of fully or partially non-overlapping channels. Although it is still a young research area, many different approaches have already been developed. These approaches can be distinguished into centralized and distributed. Centralized algorithms rely on a central entity, usually called Channel Assignment Server (CAS), which calculates the channel assignment and sends the result to the mesh routers. In distributed approaches, each mesh router calculates its channel assignment decision based on local information. Distributed approaches can react faster to topology changes due to node failures or mobility and usually introduce less protocol overhead since communication with the CAS is not necessary. As a result, distributed approaches are more suitable once the network is operational and running. Distributed approaches can further be classified into static and dynamic, in regard to the modus of channel switching. In dynamic approaches, channels can be switched on a per-packet basis, whereas in static approaches radios stay on a specific channel for a longer period of time. Static assignments have been more in focus, since the channel switching time for current Institute of Electrical and Electronics Engineers (IEEE) 802.11 hardware is in the order of milliseconds which is two orders higher than the packet transmission time. Recently, surveys of channel assignment algorithms have been presented which cover certain aspects of the research field. The survey in [1] introduces the problem and presents a couple of distributed algorithms and [2] gives a broad introduction to centralized and distributed approaches. The survey herein is focused on distributed approaches for peer- to-peer network architectures. This report describes the problem formulation for channel assignment in WMNs and the fundamental concepts and challenges of this research area. We present different distributed channel assignment algorithms and characterize them according to a set of classification keys. Since channel assignment algorithms may change the connectivity and therefore the network topology, they may have a high impact on routing. Therefore, we present routing metrics that consider channel diversity and adapt better to the multi- radio multi-channel scenario than traditional routing metrics designed for single channel networks. The presented algorithms are discussed and compared focusing on practical evaluations in testbed and network environments. The implementation for real networks is a hard and labor-intensive task because the researcher has to deal with the complexity of the hardware, operating system, and wireless network interface drivers. As a result, frameworks emerged in order to simplify the implementation process. We describe these frameworks and the mechanisms used to help researchers implementing their algorithms and show their limitations and restrictions

A Socio-inspired CALM Approach to Channel Assignment Performance Prediction and WMN Capacity Estimation

A significant amount of research literature is dedicated to interference mitigation in Wireless Mesh Networks (WMNs), with a special emphasis on designing channel allocation (CA) schemes which alleviate the impact of interference on WMN performance. But having countless CA schemes at one's disposal makes the task of choosing a suitable CA for a given WMN extremely tedious and time consuming. In this work, we propose a new interference estimation and CA performance prediction algorithm called CALM, which is inspired by social theory. We borrow the sociological idea of a "sui generis" social reality, and apply it to WMNs with significant success. To achieve this, we devise a novel Sociological Idea Borrowing Mechanism that facilitates easy operationalization of sociological concepts in other domains. Further, we formulate a heuristic Mixed Integer Programming (MIP) model called NETCAP which makes use of link quality estimates generated by CALM to offer a reliable framework for network capacity prediction. We demonstrate the efficacy of CALM by evaluating its theoretical estimates against experimental data obtained through exhaustive simulations on ns-3 802.11g environment, for a comprehensive CA test-set of forty CA schemes. We compare CALM with three existing interference estimation metrics, and demonstrate that it is consistently more reliable. CALM boasts of accuracy of over 90% in performance testing, and in stress testing too it achieves an accuracy of 88%, while the accuracy of other metrics drops to under 75%. It reduces errors in CA performance prediction by as much as 75% when compared to other metrics. Finally, we validate the expected network capacity estimates generated by NETCAP, and show that they are quite accurate, deviating by as low as 6.4% on an average when compared to experimentally recorded results in performance testing