In this paper, we propose Opportunistic Carrier Prediction (OCP) that jointly addresses exposed terminal and hidden terminal problems in wireless networks. OCP is based on the rationale that past interference information can be a good indicator for the outcome of future packet delivery. Each OCP sender maintains a summary of past interference information and opportunistically accesses the channel when it is confident that the packet transmission will be successful and cause no collision to other flows. To realize OCP, we propose (1) a novel data structure for each sender to summarize the interference information and (2) physical layer preemptive decoding scheme for each sender to collect the identities of the interferers. We show that OCP improves the system throughput by up to 170%, packet delivery success ratio by up to 400% in random topologies, while almost eliminating starvation.

In this paper, we propose greedy selection rounding (GSR), an efficient and near-optimal algorithm to design a wireless mesh network topology that maximizes the coverage of the users while ensuring that the network is resilient to node failures and and the deployment cost is under a given budget. In the case that GSR fails to find a solution satisfying the budget constraint, the incurred cost does not exceed the budget by a constant factor. Through extensive evaluation, we show that in all our test cases GSR always generates a topology above 95% of the optimal in terms of the number of covered users while never exceeding the budget by more than 15%.

The limited number of orthogonal channels and autonomous installations of hot spots and home wireless networks often leave neighboring 802.11 basic service sets (BSSs) operating on the same or overlapping channels, therefore interfering with each other. However, the 802.11 medium access control (MAC) does not work well in resolving inter-BSS interference due to the well-known hidden/exposed-receiver problem, which has been haunting the research community for more than a decade. In this paper, we propose SELECT, an effective and efficient self-learning collision avoidance strategy to address the hidden/exposed-receiver problem in 802.11 wireless networks. SELECT is based on the observation that carrier sense with received signal strength (RSS) measurements at the sender and the receiver can be strongly correlated. A SELECT-enabled sender exploits such correlation using an automated online learning algorithm and makes an informed judgment of the channel availability at the intended receiver. SELECT achieves collision avoidance at packet-level time granularity, involves zero communication overhead, and easily integrates with the 802.11 distributed coordination function (DCF). Our evaluation in analysis, simulations, and prototype experiments show that SELECT addresses the hidden/exposed-receiver problem well. In typical hidden/exposed-receiver scenarios, SELECT improves the throughput by up to 140 percent and the channel access success ratio by up to 302 percent while almost completely eliminating contention-induced data packet drops.

The majority of existing wireless rate controls are based on the implicit assumption that frames are corrupted due to the random, arbitrary environmental and thermal noises. They generally reduce the channel rate on frame losses, trading lower efficiency in frequency band utilization for more robust modulation so that the current noise level may be tolerable. In highly interfered wireless networks where frames are lost mainly due to interference from other wireless transceivers, simply reducing the channel rate prolongs the frame transmission time and therefore aggravates frame loss ratio. This positive feedback in the rate control loop quickly diverges the interfered transceivers into a suboptimal channel rate and drives the network into a state with high interference. In the worst case, interfered transceivers can be starved. In this paper we present RAF, the rate-adaptive framing that jointly controls the channel rate and frame size according to the observed interference patterns and noise level at the receiver. Based on the inputs from physical layer carrier sense, the receiver derives the optimal channel rate and frame size that maximize throughput, and informs the transmitter of such optimal configuration in a few bits in the per-frame acknowledgement. Through intensive simulations we show that RAF consistently outperforms ARF, RBAR, and OAR in all simulated scenarios.

The limited number of orthogonal channels and the autonomous installations of hotspots and home wireless networks often leave neighboring 802.11 basic service sets (BSS's) operating on the same or overlapping channels, therefore interfering with each other. However, the 802.11 MAC does not work well in resolving inter-BSS interferences due to the well-known hidden/exposed receiver problem, which has been haunting in the research community for more than a decade. In this paper we propose SELECT, an effective and efficient self-learning collision avoidance strategy to address the open hidden/exposed receiver problem in wireless networks. SELECT is based on the observation that carrier sense with received signal strength (RSS) measurements at the sender and the receiver are strongly correlated. A SELECT-enabled sender exploits such correlation using automated on-line learning algorithm, and makes informed judgment of the channel availability at the intended receiver. SELECT achieves collision avoidance at packetlevel time granularity, involves zero communication overhead, requires no hardware support beyond what is available in offthe -shelf 802.11 devices, and easily integrates with the 802.11 DCF. Our evaluation in both analysis and simulations show that SELECT addresses the hidden/exposed receiver problem well. In typical hidden/exposed receiver scenarios SELECT improves the throughput by up to 140% and channel access success ratio by up to 302%, while almost completely eliminating contention-induced data packet drops.