Processing and Coding for Efficient Sensing and Transmission of Information

该提案旨在资助数字和无线通信，编码和信息理论的持续研究计划。重点是对信息的感知和采样以及信息的传输。对于这两种情况，我们感兴趣的是设计高效（低复杂度）的算法和过程，可以在非常高的速度运行。我们也有兴趣在VLSI电路中实现这样的算法。对于传感和采样，我们的重点是最近的技术，称为压缩传感（CS）。使用图形表示的传感过程中，我们广泛流行的和广泛探索的领域的迭代编码方案的连接，并提出新的技术来分析CS算法，并设计新的传感矩阵和恢复算法。这里的目标是降低恢复过程的复杂性，并且还能够从相对较小的测量集合中恢复具有相对较大比例的非零（不可忽略）元素的信号。对于信息传输，我们的建议涵盖了通信系统的不同层。在物理层，我们设计迭代编码和处理方案的低复杂度的信道估计/均衡/纠错。我们专注于开放的问题，在这些领域，如错误地板的分析和设计的代码，保证低错误地板。在网络层，我们分析了现有的网络编码（NC）解决方案的性能，并旨在设计新的解决方案，具有较低的复杂性和上级收敛行为。最后，我们将探讨涉及两个或更多通信层的跨层设计。一个例子是无线多跳网络中多播场景的多址接入控制（MAC）调度和NC的联合设计。其目标是增加吞吐量，减少消耗的能量或减少延迟。对于本研究的实施部分，我们有兴趣设计新的高速架构和低功耗的信息传感和处理电路。 这项研究的成果转化为更小、更快、更节能的通信系统，这些系统更可靠，并提供更好的服务质量。

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.