Grant: Discovery Grants Program - Individual

This proposal is for funding to support a continuing research program on digital and wireless communications, coding and information theory. The focus is on both sensing and sampling of the information as well as its transmission. For both cases, we are interested in devising efficient (low-complexity) algorithms and processes that can operate at very high speeds. We are also interested in implementing such algorithms in VLSI circuits. For sensing and sampling, our focus is a recent technique, called Compressed Sensing (CS). Using the graphical representation of the sensing process, we make connections to the widely popular and vastly explored area of iterative coding schemes, and propose new techniques to analyze CS algorithms, and devise new sensing matrices and recovery algorithms. The goal here is to reduce the complexity of the recovery process and yet be able to recover signals with relatively large fraction of nonzero (non-negligible) elements from a relatively small set of measurements. For information transmission, our proposal covers different layers of a communication system. At the physical layer, we devise iterative coding and processing schemes for low-complexity channel estimation/equalization/error correction. We focus on open problems in these areas such as the analysis of error floor and the design of codes with guaranteed low error floors. At the network layer, we analyze the performance of existing network coding (NC) solutions, and aim to devise new solutions with lower complexity and superior convergence behavior. Finally, we explore the cross-layer design involving two or more of the communication layers. One example would be the joint design of multiple access control (MAC) scheduling and NC for multicast scenarios in wireless multihop networks. The goal is to increase the throughput, reduce the consumed energy or decrease the delay. For the implementation part of this research, we are interested in devising new high-speed architectures and circuits with low power consumption for both sensing and processing of information. The outcome of this research translates to smaller faster more energy-efficient communication systems that are more reliable and provide a better quality of service.