Enabling distributed throughput maximization in wireless mesh networks

Abstract

This paper considers the interaction between channel assignment and distributed scheduling in multi-channel multiradio Wireless Mesh Networks (WMNs). Recently, a number of distributed scheduling algorithms for wireless networks have emerged. Due to their distributed operation, these algorithms can achieve only a fraction of the maximum possible throughput. As an alternative to increasing the throughput fraction by designing new algorithms, in this paper we present a novel approach that takes advantage of the inherent multi-radio capability of WMNs. We show that this capability can enable partitioning of the network into subnetworks in which simple distributed scheduling algorithms can achieve 100% throughput. The partitioning is based on the recently introduced notion of Local Pooling. Using this notion, we characterize topologies in which 100% throughput can be achieved distributedly. These topologies are used in order to develop a number of channel assignment algorithms that are based on a matroid intersection algorithm. These algorithms partition a network in a manner that not only expands the capacity regions of the subnetworks but also allows distributed algorithms to achieve these capacity regions. Finally, we evaluate the performance of the algorithms via simulation and show that they significantly increase the distributedly achievable capacity region.

Joint logical topology design, interface assignment, channel allocation, and routing for multi-channel wireless mesh networks

“…The centralized schemes (e.g., [10], [13]) require a network controller to collect the topology information and assign the channels. In the distributed schemes (e.g., [14], [15]), some of the mesh routers are responsible for channel assignment for a subset of interfaces. The channel allocation algorithms in MC-WMNs can also be classified as static and dynamic.…”

“…Also let |N | and |E| denote the cardinality of sets N and E, respectively. The linear mixed-integer problem (14) has |E|C(1 + W ) integer variables and C(2|E| + |N |) + 1 real variables. It also has 1.5|E|C + |N |W equality and |E|(5C + W ) + |N |(C + 1) + W inequality constraints.…”

“…Most of them use the branch-and-cut algorithm [34]. Problem (14) can easily be solved in practice for small-scale MC-WMNs. However, finding the optimal solutions are not trivial for large-scale networks.…”

“…In this section, we compare the solutions for problem (14) obtained from the ILS algorithm with those obtained from the optimal branch-and-cut solver. Let δ * min denote the optimal value.…”

“…Therefore, the modified problem only has a few integer variables and can be solved easily. Given the sub-optimal topology formation, interface assignment and channel allocation solutions, the routing path from source s to destination d is assigned by traversing the logical topology from source s to destination d, and by choosing the next hop based on the maximum observed value for routing variable a (lines [10][11][12][13][14][15][16][17][18]. The intuitive justification is that if the relaxed a sd mn,i is close to 1, it indicates that it is better to forward the packets from source s to destination d on the logical link (m, n) over channel i.…”