The distributed coordination function (DCF) for 802.11 medium access control (MAC) leads to poor spatial spectral reuse and results in low user throughput in dense user settings. In this paper, we consider simple modifications to the carrier sense mechanism that substantially improve the bits/sec/Hz/area, without increasing transceiver complexity. For 802.11g and 802.11ac, we augment the CTS with channel gain information and modify the virtual and physical carrier sense for the duration of NAV in the CTS. In addition, we consider a roughly synchronized network with alternate control and data transmission phases. During the control phase, a carrier sense multiple access with collision avoidance (CSMA-CA) MAC is used to exchange a few RTS-CTS messages in a local network neighborhood to determine the transmission scheme in the ensuing data phase. Our methods enable the application of signal to interference and noise (SINR) based scheduling algorithms to WiFi networks resulting in tremendous increase in throughput and QoS/fairness.

Interference is a central feature of the wireless channel. However, in many cases, the interferer's activity can be bursty and it is overly pessimistic to assume that interference is always present. In this paper, we use a degraded message set formulation of the two-user interference channel to study the statistical gains that can be harnessed from bursty interference. We consider a linear deterministic model of the Gaussian interference channel and characterize the degraded message set capacity region.

Interference is a central feature of wireless communication. In many scenarios, interference is bursty: interfering wireless links come and go. Designing the system assuming interference to be always present is very conservative. In this paper, we take a fundamental information theoretic stand point and address the issue of statistical gain associated with bursty interference in the context of a pair of unicast interfering wireless links. Modeling the problem as a ldquodegraded message setrdquo two user Gaussian interference channel, we approximately characterize the symmetric capacity region. Our results demonstrate the fundamental existence of three regimes: one where treating interference as always there is without loss of optimality, another where one can harness as well as if interference was never there and a third where the performance is in between these two regimes.

We consider a scenario in which a wireless sensor network is formed by randomly deploying n sensors to measure some spatial function over a field, with the objective of computing a function of the measurements and communicating it to an operator station. We restrict ourselves to the class of type-threshold functions (as defined in the work of Giridhar and Kumar, 2005), of which max, min, and indicator functions are important examples: our discussions are couched in terms of the max function. We view the problem as one of message-passing distributed computation over a geometric random graph. The network is assumed to be synchronous, and the sensors synchronously measure values and then collaborate to compute and deliver the function computed with these values to the operator station. Computation algorithms differ in (1) the communication topology assumed and (2) the messages that the nodes need to exchange in order to carry out the computation. The focus of our paper is to establish (in probability) scaling laws for the time and energy complexity of the distributed function computation over random wireless networks, under the assumption of centralized contention-free scheduling of packet transmissions. First, without any constraint on the computation algorithm, we establish scaling laws for the computation time and energy expenditure for one-time maximum computation. We show that for an optimal algorithm, the computation time and energy expenditure scale, respectively, as Theta(radicn/log n) and Theta(n) asymptotically as the number of sensors n rarr infin. Second, we analyze the performance of three specific computation algorithms that may be used in specific practical situations, namely, the tree algorithm, multihop transmission, and the Ripple algorithm (a type of gossip algorithm), and obtain scaling laws for the computation time and energy expenditure as n rarr infin. In particular, we show that the computation time for these algorithms scales as Theta(radicn/log n), Theta(n), and Theta(radicn log n), respectively, whereas the energy expended scales as , Theta(n), Theta(radicn/log n), and Theta(radicn log n), respectively. Finally, simulation results are provided to show that our analysis indeed captures the correct scaling. The simulations also yield estimates of the constant multipliers in the scaling laws. Our analyses throughout assume a centralized optimal scheduler, and hence, our results can be viewed as providing bounds for the performance with practical distributed schedulers.

We consider a scenario where a wireless sensor network is formed by randomly deploying n sensors to measure some spatial function over a field, with the objective of computing the maximum value of the measurements and communicating it to an operator station. We view the problem as one of message passing distributed computation over a geometric random graph. The network is assumed to be synchronous; at each sampling instant each sensor measures a value, and then the sensors collaboratively compute and deliver the maximum of these values to the operator station. Computation algorithms differ in the messages they need to exchange, and our formulation focuses on the problem of scheduling of the message exchanges. We do not exploit techniques such as source compression, or block coding of the computations. For this problem, we study the computation time and energy expenditure for one time maximum computation, and also the pipeline throughput. We show that, for an optimal algorithm, the computation time, energy expenditure and the achievable rate of computation scale as /spl Theta/(/spl radic/ n/log n), /spl Theta/(n) and /spl Theta/(1/log n) asymptotically (in probability) as the number of sensors n/spl rarr//spl infin/. We also analyze the performance of three specific computational algorithms, namely, the tree algorithm, multihop transmission, and the ripple algorithm, and obtain scaling laws for the computation time and energy expenditure as n/spl rarr//spl infin/. Simulation results are provided to show that our analysis indeed captures the correct scaling; the simulations also yield estimates of the constant multipliers in the scaling laws. Our analyses throughout assume a centralized scheduler and hence our results can be viewed as providing bounds for the performance with a distributed scheduler.