As a paradigm to recover the sparse signal from a small set of linear measurements, compressed sensing (CS) has stimulated a great deal of interest in recent years. In order to apply the CS techniques to wireless communication systems, there are a number of things to know and also several issues to be considered. However, it is not easy to come up with simple and easy answers to the issues raised while carrying out research on CS. The main purpose of this paper is to provide essential knowledge and useful tips that wireless communication researchers need to know when designing CS-based wireless systems.First, we present an overview of the CS technique, including basic setup, sparse recovery algorithm, and performance guarantee. Then, we describe three distinct subproblems of CS, viz., sparse estimation, support identification, and sparse detection, with various wireless communication applications. We also address main issues encountered in the design of CS-based wireless communication systems. These include potentials and limitations of CS techniques, useful tips that one should be aware of, subtle points that one should pay attention to, and some prior knowledge to achieve better performance. Our hope is that this article will be a useful guide for wireless communication researchers and even nonexperts to grasp the gist of CS techniques. DRAFT size being proportional to the sparsity level of the input signal are enough to reconstruct the original signal. In fact, in many real-world applications, signals of interest are sparse or can be approximated as a sparse vector in a properly chosen basis. Sparsity of underlying signals simplifies the acquisition process, reduces memory requirement and computational complexity, and further enables to solve the problem which has been believed to be unsolvable.In the last decade, CS techniques have spread rapidly in many disciplines such as medical imaging, machine learning, computer science, statistics, and many others. Also, various wireless communication applications exploiting the sparsity of a target signal have been proposed in recent years. Notable examples, among many others, include channel estimation, interference December 21, 2016 DRAFT also discuss the sparse signal recovery algorithm and performance guarantee of the CS technique under which accurate or stable recovery of the sparse signal is ensured. In Section III, we describe the basic wireless communication system model and then discuss each subproblems of CS related to wireless communications. Depending on the sparse structure of the desired signal vector, CS problems can be divided into three subproblems: sparse estimation, support identification, and sparse detection. We explain the subproblem with the specific wireless communication applications. Developing successful CS technique for the specific wireless application requires good understanding on key issues. These include properties of system matrix and input vector, algorithm selection/modification/design, system setup and performance requirements. I...

This paper addresses the problem of uplink and downlink channel estimation in FDD Massive MIMO systems. By utilizing sparse recovery and compressive sensing algorithms, we are able to improve the accuracy of the uplink/downlink channel estimation and reduce the number of uplink/downlink pilot symbols. Such successful channel estimation builds upon the assumption that the channel can be sparsely represented under some basis/dictionary. Previous works model the channel using some predefined basis/dictionary, while in this work, we present a dictionary learning based channel model such that a dictionary is learned from comprehensively collected channel measurements. The learned dictionary adapts specifically to the cell characteristics and promotes a more efficient and robust channel representation, which in turn improves the performance of the channel estimation. Furthermore, we extend the dictionary learning based channel model into a joint uplink/downlink learning framework by observing the reciprocity of the AOA/AOD between the uplink/downlink transmission, and propose a joint channel estimation algorithm that combines the uplink and downlink received training signals to obtain a more accurate channel estimate. In other words, the downlink training overhead, which is a bottleneck in FDD Massive MIMO system, can be reduced by utilizing the information from simpler uplink training.

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