Non-Adaptive Distributed Compression in Networks

In this paper, we discuss non-adaptive distributed compression of inter-node correlated real-valued messages. To do so, we discuss the performance of conventional packet forwarding via routing, in terms of the total network load versus the resulting quality of service (distortion level). As a better alternative for packet forwarding, we briefly describe our previously proposed one-step Quantized Network Coding (QNC), and make motivating arguments on its advantage when the appropriate marginal rates for distributed source coding are not available at the encoder source nodes. We also derive analytic guarantees on the resulting distortion of our one-step QNC scenario. Finally, we conclude the paper by providing a mathematical comparison between the total network loads of one-step QNC and conventional packet forwarding, showing a significant reduction in the case of one-step QNC.Comment: Submitted for 2013 IEEE International Symposium on Information Theor

Sparse Signal Processing Concepts for Efficient 5G System Design

As it becomes increasingly apparent that 4G will not be able to meet the emerging demands of future mobile communication systems, the question what could make up a 5G system, what are the crucial challenges and what are the key drivers is part of intensive, ongoing discussions. Partly due to the advent of compressive sensing, methods that can optimally exploit sparsity in signals have received tremendous attention in recent years. In this paper we will describe a variety of scenarios in which signal sparsity arises naturally in 5G wireless systems. Signal sparsity and the associated rich collection of tools and algorithms will thus be a viable source for innovation in 5G wireless system design. We will discribe applications of this sparse signal processing paradigm in MIMO random access, cloud radio access networks, compressive channel-source network coding, and embedded security. We will also emphasize important open problem that may arise in 5G system design, for which sparsity will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces

Distributed sparse signal recovery in networked systems

In this dissertation, two classes of distributed algorithms are developed for sparse signal recovery in large sensor networks. All the proposed approaches consist of local computation (LC) and global computation (GC) steps carried out by a group of distributed local sensors, and do not require the local sensors to know the global sensing matrix. These algorithms are based on the original approximate message passing (AMP) and iterative hard thresholding (IHT) algorithms in the area of compressed sensing (CS), also known as sparse signal recovery. For distributed AMP (DiAMP), we develop a communication-efficient algorithm GCAMP. Numerical results demonstrate that it outperforms the modified thresholding algorithm (MTA), another popular GC algorithm for Top-K query from distributed large databases. For distributed IHT (DIHT), there is a step size $\mu$ which depends on the $\ell_2$ norm of the global sensing matrix A. The exact computation of $\|A\|_2$ is non-separable. We propose a new method, based on the random matrix theory (RMT), to give a very tight statistical upper bound of $\|A\|_2$, and the calculation of that upper bound is separable without any communication cost. In the GC step of DIHT, we develop another algorithm named GC.K, which is also communication-efficient and outperforms MTA. Then, by adjusting the metric of communication cost, which enables transmission of quantized data, and taking advantage of the correlation of data in adjacent iterations, we develop quantized adaptive GCAMP (Q-A-GCAMP) and quantized adaptive GC.K (Q-A-GC.K) algorithms, leading to a significant improvement on communication savings. Furthermore, we prove that state evolution (SE), a fundamental property of AMP that in high dimensionality limit, the output data are asymptotically Gaussian regardless of the distribution of input data, also holds for DiAMP. In addition, compared with the most recent theoretical results that SE holds for sensing matrices with independent subgaussian entries, we prove that the universality of SE can be extended to far more general sensing matrices. These two theoretical results provide strong guarantee of AMP\u27s performance, and greatly broaden its potential applications