Performance of Uplink Multiuser Massive MIMO Systems

We study the performance of uplink transmission in a large-scale (massive) MIMO system, where all the transmitters have single antennas and the receiver (base station) has a large number of antennas. Specifically, we analyze achievable degrees of freedom of the system without assuming channel state information at the receiver. Also, we quantify the amount of power saving that is possible with increasing number of receive antennas.Comment: 5 pages, no figur

Joint Optimization of Power Allocation and Training Duration for Uplink Multiuser MIMO Communications

In this paper, we consider a multiuser multiple-input multiple-output (MU-MIMO) communication system between a base station equipped with multiple antennas and multiple mobile users each equipped with a single antenna. The uplink scenario is considered. The uplink channels are acquired by the base station through a training phase. Two linear processing schemes are considered, namely maximum-ratio combining (MRC) and zero-forcing (ZF). We optimize the training period and optimal training energy under the average and peak power constraint so that an achievable sum rate is maximized.Comment: Submitted to WCN

Multiple-Antenna Interference Channel with Receive Antenna Joint Processing and Real Interference Alignment

We consider a constant $K$-user Gaussian interference channel with $M$ antennas at each transmitter and $N$ antennas at each receiver, denoted as a $(K,M,N)$ channel. Relying on a result on simultaneous Diophantine approximation, a real interference alignment scheme with joint receive antenna processing is developed. The scheme is used to provide new proofs for two previously known results, namely 1) the total degrees of freedom (DoF) of a $(K, N, N)$ channel is $NK/2$; and 2) the total DoF of a $(K, M, N)$ channel is at least $KMN/(M+N)$. We also derive the DoF region of the $(K,N,N)$ channel, and an inner bound on the DoF region of the $(K,M,N)$ channel

A Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization: Convergence Analysis and Optimality

Symmetric nonnegative matrix factorization (SymNMF) has important applications in data analytics problems such as document clustering, community detection and image segmentation. In this paper, we propose a novel nonconvex variable splitting method for solving SymNMF. The proposed algorithm is guaranteed to converge to the set of Karush-Kuhn-Tucker (KKT) points of the nonconvex SymNMF problem. Furthermore, it achieves a global sublinear convergence rate. We also show that the algorithm can be efficiently implemented in parallel. Further, sufficient conditions are provided which guarantee the global and local optimality of the obtained solutions. Extensive numerical results performed on both synthetic and real data sets suggest that the proposed algorithm converges quickly to a local minimum solution.Comment: IEEE Transactions on Signal Processing (to appear