In interfering WLANs, clients are usually non-uniformly distributed. Therefore, their throughput can be improved by association control. WLANs adopt DCF (Distributed Coordinating Function) for media access control. The transmit probability of an AP is governed by its CW (Contention Window). Random Access control is to determine the CW size and hence the transmit probability of each AP. Client association control and random access control are coupled. APs associated with more clients require higher channel access opportunities. On the other hand, APs with higher transmit probability should serve more clients. We study the novel problem of maximizing the proportional fairness of client throughput by joint association control and random access control. As the joint problem is non-convex, we first consider that client association is given and propose an optimal policy for random access control. Given the CWs of APs, we propose a factor-2 approximation algorithm, Greedy-Asso to address the client association problem. Inspired by the optimal access control policy and Greedy-Asso, we further propose a distributed algorithm, termed CARA to tackle the joint optimization problem. Extensive simulation and experimental studies show that it outperforms comparison schemes by a wide margin in terms of throughput and fairness.

Interference Alignment (IA) has emerged as a promising interference coordination approach for cooperative MIMO systems. Due to heavy CSI feedback overhead, APs (Access Points) need to be partitioned into cooperation groups no larger than a certain size where only APs in the same group are able to cooperate with IA. We consider a general MIMO network using a hybrid interference coordination approach, i.e. intra-group interference is managed with IA, while inter-group interference is overcome with traditional orthogonal multiple access techniques. Users are usually non-uniformly distributed. Their throughput can be improved by association optimization. We study the novel problem of minimizing AP load by joint AP grouping and user association. The problem is shown to be NP-hard. Based on alternating direction optimization, we propose DAGA (Distributed Joint AP Grouping and User Association) to tackle the problem. DAGA is distributed and uses only long-term CSI. Based on current AP grouping, it produces an approximated user association solution which is at most elogm (m is the number of APs) times of the optimum. Based on current user association, it adjusts AP grouping with local search. Extensive simulation results show that it substantially outperforms other comparison schemes.

In wireless local area networks (WLANs), association of the access points (APs) is often not uniform due to joining and leaving of wireless users. Such imbalance leads to unsatisfactory user throughput due to congestion at some APs and channel under-utilization at others. As users may be covered by multiple APs in WLAN deployment, their throughput can be improved by AP re-association, i.e., migrating some users from the congested APs to the less loaded neighboring APs. Such reassociation is expected to come with some cost (due to overhead in handshaking, authentication and data flow management). In this paper, we study the novel problem of optimizing AP re-association by maximizing the minimum user throughput (i.e., max-min fairness), subject to a certain total user migration cost constraint. We show that the problem is NP-hard. We then propose an efficient approximation algorithm called CACA (Cost-constrained Association Control Algorithm). CACA has provable performance, achieving an approximation factor of (4+ϵ), for any ϵ > 0. It is simple and implementable. Our extensive simulation results based on NS3 show that it substantially outperforms other comparison schemes with close-to-optimal performance (the case without cost constraint). Our testbed experiments further confirm the effectiveness of CACA.

A wireless mesh employing directional antenna, termed DMesh in this paper, can greatly extend coverage and improve spatial reuse of wireless channels. As beaming direction of the antennas changes network topology which in turns affects routing and channel decisions, we address in this work how to jointly optimize topology (in terms of beaming directions of antennas), routing and channel assignment so as to maximize network throughput. Specifically, we use a model based on SINR which captures much more realistically network interference than the traditional conflict graph approach. Using the model, we then formulate the NP-hard optimization problem for a general DMesh with multiple gateways, possibly heterogeneous number of antennas in routers, and routers generating traffic to any of the gateways or routers. As the problem is NP-hard, we propose a simple and implementable joint optimization heuristic called TORCA (topology control, routing and channel assignment). TORCA is based on iterative LP rounding guaranteed to converge. Extensive simulation based on NS3 shows that TORCA is closely optimal and highly efficient, performing significantly better than recent approaches by wide margins in terms of loss rate, delay, fairness and throughput.

The performance of multi-channel (MRMC) wireless mesh networks (WMNs) is largely affected by interference, which depends on router placement, traffic routing and channel assignment. This work seeks to jointly optimize the placement, routing and channel assignment for mesh deployment given flow requirements. The problem is complex because router placement changes network topology which in turns impacts routing and channel decisions, while routing and channel assignment algorithms affect router placement decision. We present a novel scheme PRACA (Placement, Routing And Channel Assignment), which seeks to minimize the network-wide interference by considering the three inter-dependent factors. Through extensive simulation, we show that PRACA significantly outperforms other schemes in terms of loss rate, delay, fairness and throughput.

Due to unpredictable peer churns (joins, leaves and failures), it is challenging to offer video continuity in peer-to-peer (P2P) live streaming. In this paper, we study a push-based P2P network formed by unreliable nodes (i.e., nodes which may churn at any time). To achieve high stream continuity, the video is encoded into k MDC (Multiple-Description Coded) streams and t FEC (Forward Error Correction) streams. To achieve low delay and reduce error correlation between streams, the k + t streams are pushed to the nodes in parent-disjoint spanning trees. The issue is how to construct these trees minimizing the worst-case node delay. We address the optimization of the spanning trees through problem analysis and algorithmic design. After presenting a model capturing important system parameters and delay components, we formulate the problem and prove that it is NP-hard. We then propose SUN (Streaming with Unreliable Nodes), a simple, adaptive and distributed algorithm which continuously reduces delay through overlay adaptation. Through extensive simulation on real Internet and Internet-like topologies, we show that stream continuity can be achieved with push-based P2P streaming. SUN is effective, achieving low delay and high continuity in the presence of node churns for P2P live streaming.

Using directional antennas in wireless mesh networks (termed DMesh in this paper) is attractive due to its longer coverage range and spatial separation between transmissions of the same channel. However, the connectivity in a DMesh is much lower than its omni-directional counterpart. This makes topology control (through beaming) a critical problem in DMesh. Because topology control coupled with routing decision, their joint optimization is critical to achieve the best performance. In this paper, we consider a multimedia DMesh with a certain traffic demand at each mesh router. We first formulate the joint topology control and routing assignment as an optimization problem and show that it is NP-hard. To address the problem, we propose a novel and efficient heuristic called TORA (Joint Topology Control and Routing Assignment), an ant-colony optimization algorithm which seeks to jointly optimize topology and routing for DMesh. Simulation results based on NS3 show that TORA performs substantially better in terms of loss rate, delay and throughput as compared with a recent scheme.