Current wireless network design is built on the ethos of avoiding interference. In this paper we question this long-held design principle. We show that with appropriate design, successful concurrent transmissions can be enabled and exploited on both the uplink and downlink. We show that this counter-intuitive approach of encouraging interference can be exploited to increase network capacity significantly and simplify network design. We design and implement name, a novel MAC and PHY protocol that exploits recently proposed rateless coding techniques to provide such concurrency. We show via a prototype implementation and experimental evaluation that name can provide a 60% increase in network capacity on the uplink compared to traditional Wifi that does omniscient rate adaptation and a $35\%$ median throughput gain on the downlink PHY layer as compared to an omniscient scheme that picks the best conventional bitrate.

Current generation wireless systems employ high frequency reuse and shrinking cell sizes. Thus, there has been significant recent attention on the analysis of interference limited systems). We consider single input single output (SISO) broadcast (BC) with intercell interference. We allow the received powers from each source of interference to be different For example, for the two interferer case, a user receives average power c1> 0 from interferer 1 and c2> 0 from interferer 2 where c1and c2need not be equal. We characterize the cumulative distribution function of the resulting signal to interference ratio (SIR) which is a ratio of weighted exponential random variables. It is thus a generalization of the F-distribution which is a scaled ratio of equally weighted exponential random variables. Surprisingly, there is no simple closed form expression for the resulting cdf in the literature. We present a nontrivial calculation that yields a simple, closed form expression for the cumulative distribution function (cdf) of the SIR. We show that this function is a Schur-concave function of the vector of average received powers from the various interferers. Furthermore, we derive a simple closed form expression for the cdf of the SINR. Again, we find that this function is Schur-concave in the average received powers. As a result we conclude that for any average transmit, receive, and thermal noise powers, the probability of achieving any SINR is highest when all interference power originates from a single interferer and lowest when the power is divided equally among the interferers. Opportunistic scheduling (OS) can be an effective tool to mitigate interference. For high signal and interference transmit power, the SINR is well approximated by the SIR. We analyze the scaling of the SIR using OS as the number of users grows for an arbitrary number of interferers. For J received interference powers {cj}j=1j, the SIR is asymptotically inversely proportional to the geometric mean, (Pij=1jcj)1/j.

Subclasses of finite alphabet denoising and filtering (causal denoising) schemes are compared. Performance is measured by the normalized cumulative loss (a.k.a. distortion), as measured by a single-letter loss function. We aim to minimize the probability that the normalized cumulative loss exceeds a given threshold. We call this quantity the probability of excess loss. Specifically, we consider a scheme to be optimal if it attains the maximal exponential decay rate of the probability of excess loss. This provides another way of comparing schemes that complements and contrasts previous work which considered the expected value of the normalized cumulative loss. In particular, the question of whether the optimal denoiser is symbol-by-symbol for an independent and identically distributed (i.i.d.) source and a discrete memoryless channel (DMC) is investigated. For Hamming loss, the optimal denoiser is proven to be symbol-by-symbol. Perhaps somewhat counterintuitively, for a general single letter loss function, the optimal scheme need not be symbol-by-symbol. The optimal denoiser requires unbounded delay and unbounded look-ahead while symbol-by-symbol schemes mandate zero delay and look-ahead. It is natural to wonder about the effect of limited delay and limited look-ahead. Consequently, finite sliding-window denoisers and finite block denoisers are defined. They are shown to perform no better than symbol-by-symbol denoisers. Finally, the effect of causality is investigated. While it is difficult to characterize the performance of filters with unbounded memory explicitly, it is shown that finite memory filters perform no better than symbol-by-symbol filters