Sibi Raj Bhaskaran

Maximizing the Sum Rate in Symmetric Networks of Interfering Links

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.

Downlink scheduling using compressed sensing

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.

Gaussian Broadcast Channel With Feedback

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.

Channel estimation and user selection in the MIMO broadcast channel

In this paper, we propose a method for user selection and channel estimation for the multiple-input multiple-output (MIMO) broadcast channel for the downlink of a cellular mobile or local-area wireless communication system. A distributed self-selection procedure is combined with a code-division multiple access (CDMA) uplink signaling strategy to reduce the uplink signaling bandwidth, and the computational complexity of user selection at the base station. We exploit recent advances in sparse signal recovery, which we apply to the uplink multi-user detection and channel estimation problems to reduce the signaling bandwidth. 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 a compressed-sensing technique 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.

Number of measurements in sparse signal recovery

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 subgaussian 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.

Gaussian Degraded Relay Broadcast Channel

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.

Multi-Antenna Downlink Broadcast Using Compressed-Sensed Medium Access

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 basestation 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.

Forward decoding over a relay channel

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.

Broadcasting with Feedback

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.

Maximizing the sum rate in symmetric networks of interfering links under flat power constraints

We consider the power control problem of maximizing the sum rate of a symmetric network of interfering links in Gaussian noise. We consider a static network: there is no time-varying fading and the power allocation is also mandated to be time and frequency flat. All transmitters have a maximum allowable average transmit power, the same for all transmitters. We solve this nonconvex problem by identifying some underlying convex structure, and show that the solution is either one link blasting at full power, or all links blasting at full power. We provide a characterization of the solution in terms of the level of cross-gain between the interfering links. There is a phase transition between these two states, as the cross-gain traverses a threshold.