Joint Compression and Feedback of CSI in Correlated multiuser MISO Channels

The potential gains of multiple antennas in wireless systems can be limited by the channel state information imperfections. In this context, this paper tackles the feedback in multiuser correlated multiple input single output (MU-MISO). We propose a framework to feedback the minimum number of bits without performance degradation. This framework is based on decorrelating the channel state information by compression and then quantize the compressed (CSI) and feedback it to the base station (BS). We characterize the rate loss resulted from the proposed framework. An upper bound on the rate loss is derived in terms of the amount of feedback and the statistics of the channel. Based on this characterization, we propose am adaptive bit allocation algorithm that takes into the account the channel statistics to reduce the rate loss induced by the quantization. Moreover, in order to maintain a constant rate loss with respect to the perfect CSIT case, it is shown that the number of feedback bits should scale linearly with the SNR (in dB) and to the rank of the user transmit correlation matrix. We validate the proposed framework by Monte-carlo simulations

A Novel Transmission Scheme for the KK-user Broadcast Channel with Delayed CSIT

The state-dependent $K$-user memoryless Broadcast Channel~(BC) with state feedback is investigated. We propose a novel transmission scheme and derive its corresponding achievable rate region, which, compared to some general schemes that deal with feedback, has the advantage of being relatively simple and thus is easy to evaluate. In particular, it is shown that the capacity region of the symmetric erasure BC with an arbitrary input alphabet size is achievable with the proposed scheme. For the fading Gaussian BC, we derive a symmetric achievable rate as a function of the signal-to-noise ratio~(SNR) and a small set of parameters. Besides achieving the optimal degrees of freedom at high SNR, the proposed scheme is shown, through numerical results, to outperform existing schemes from the literature in the finite SNR regime.Comment: 30 pages, 3 figures, submitted to IEEE Transactions on Wireless Communications (revised version

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