In this paper, we propose a method for user selection and channel estimation using compressed sensing. In particular, we consider a multiple-input multiple-output (MIMO) downlink broadcast scenario. We establish that full channel state information (and not just channel quality) for each self-selecting user can be obtained at the base-station via compressed sensing with no increase in overhead for the uplink feedback channel. We demonstrate the new method as a medium access technique for MIMO downlink broadcast with transmitter precoding and linear receiver processing.

We consider the power optimization problem of maximizing the sum rate of a symmetric network of interfering links in Gaussian noise. All transmitters have an average transmit power constraint, the same for all transmitters. This problem has application to DSL, as well as wireless networks. We solve this nonconvex problem by indentifying some underlying convex structure. In particular, we characterize the maximum sum rate of the network, and show that there are essentially two possible states at the optimal solution depending on the cross-gain (√ε) between the links, and/or the average power constraint: the first is a wideband (WB) state, in which all links interfere with each other, and the second is a frequency division multiplexing (FDM) state, in which all links operate in orthogonal frequency bands. The FDM state is optimal if the cross-gain between the links is above 1/√2. If √ε <; 1/√2, then FDM is still optimal provided the SNR of the links is sufficiently high. With √ε <; 1/√2, the WB state occurs when the SNR is low, but as we increase the SNR from low to high, there is a smooth transition from the WB state to the FDM state: For intermediate SNR values, the optimal configuration is a mixture, with some fraction of the bandwidth in the WB state, and the other fraction in the FDM state. We also consider an alternative formulation in which the power is mandated to be frequency flat. In this formulation, the optimal configuration is either all links at full power, or just one link at full power. In this setting, there is an abrupt phase transition between these two states.

We consider the power optimization problem of maximizing the sum rate of a symmetric network of interfering links in Gaussian noise. All transmitters have an average transmit power constraint, the same for all transmitters. We solve this nonconvex problem by indentifying some underlying convex structure. In particular, we characterize the maximum sum rate of the network, and show that there are essentially two possible states at the optimal solution depending on the cross-gain (radicisin) between the links, and/or the average power constraint: the first is a wideband (WB) state , in which all links interfere with each other, and the second is a frequency division multiplexing (FDM) state, in which all links operate in orthogonal frequency bands. The FDM state is optimal if the cross-gain between the links is above 1/radic2. If isin < 1/2, then FDM is still optimal provided the average power of the links is sufficiently high. With e < 1/2, the WB state occurs when the average power level is low (relative to the noise and the cross-gain factor), but as we increase the average power level from low to high, there is a smooth transition from the WB state to the FDM state: For intermediate average power levels, the optimal configuration is a mixture, with some fraction of the bandwidth in the WB state, and the other fraction in the FDM state. This work has applications to DSL, as well as to wireless networks.

We analyze the asymptotic performance of sparse signal recovery from noisy measurements. In particular, we generalize some of the existing results for the Gaussian case to sub-Gaussian and other ensembles. An achievable result is presented for the linear sparsity regime. A converse on the number of required measurements in the sub-linear regime is also presented, which cover many of the widely used measurement ensembles. Our converse idea makes use of a correspondence between compressed sensing ideas and compound channels in information theory.

We propose a novel access technique for cellular downlink resource sharing. In particular, a distributed self-selection procedure is combined with the technique of compressed sensing to identify a set of users who are getting simultaneous access to the downlink broadcast channel. The performance of the proposed method is analyzed, and its suitability as an alternate access mechanism is argued.

A communication strategy proposed by Cover and El Gamal was until recently the best known achievable scheme over a discrete memoryless relay channel ([1], Theorem 7). This strategy combines cooperation and facilitation between the transmitter and relay. A possible improvement to this achievable region recently appeared in literature [2].We present an alternate achievability proof of the region in [2] using the so called irregular encoding and foward decoding strategy.

The relay broadcast channel (RBC) models a system where a transmitter sends information to multiple receivers, aided by the presence of a dedicated relay node. In the Gaussian case with two receivers, under the assumption that both receivers are degraded with respect to the relay, it is shown that the capacity region is achieved by a superposition strategy. This strategy is also shown to be optimal in the case where there is feedback from the receivers to the relay and one receiver assumed to be degraded with respect to the other in an appropriate sense. We also present an achievable region for a more general Gaussian RBC (GRBC) model.

For a Gaussian broadcast channel, it is known that: the capacity region in general is enlarged in the presence of feedback from all the receivers; and in the physically degraded case, feedback does not enlarge the capacity region. It is shown that the capacity region of a two-user Gaussian broadcast channel (GBC) is enlarged even when the feedback is available from only one of the receivers. Furthermore, in the degraded case with full feedback, we demonstrate an explicit capacity achieving coding strategy, with the decay of error doubly exponential in the number of transmissions specific to any given message.

The availability of noiseless feedback in an additive white Gaussian noise channel can considerably improve the rate of decay of error-probability with block length. A doubly exponential error decay can be attained even in the presence of an additive interference term non-causally known at the transmitter alone. Adapting this result, we show that the capacity region of a Gaussian broadcast channel (GBC) can be enlarged even when the feedback is available from only one of the receivers. We also demonstrate an explicit capacity achieving transmission strategy for the physically degraded GBC with feedback, with the decay of error doubly exponential in the number of transmissions specific to a given message.

The kurtosis of a signal is a quantitative measure of how `peaky' it is. In this paper we consider two scenarios of communication over fading channels with kurtosis constraints: in the first, we analyze a non-coherent Rayleigh fading channel where the input signal is required to satisfy a kurtosis constraint in addition to a power constraint. In the second, we find the `worst' fading process that satisfies a kurtosis constraint and has a given second moment, while the fading coefficients are assumed to be known at the receiver. In both cases the transmitter is assumed ignorant of the instantaneous fading realization. The technique that enables our analysis is based on bounding mutual information between random variables which satisfy kurtosis and second moment constraints; the bound is tight in the low second moment regime and can be extended to multi-antenna communications.

We consider a relay channel with multiple receivers and a single relay, where each receiver is physically degraded with respect to the relay. We determine the capacity region of this channel