We propose four hybrid combiner/precoder for downlink mmWave massive MU-MIMO systems. The design of a hybrid combiner/precoder is divided in two parts, analog and digital. The system baseband model shows that the signal processed by the mobile statio
n can be interpreted as a received signal in the presence of colored Gaussian noise, therefore, since the digital part of the combiner and precoder do not have constraints for their generation, their designs can be based on any traditional signal processing that takes into account this kind of noise. To the best of our knowledge, this was not considered by previous works. A more realistic and appropriate design is described in this paper. Also, the approaches adopted in the literature for the designing of the combiner/precoder analog parts do not try to avoid or even reduce the inter user/symbol interference, they concentrate on increasing the signal-to-noise ratio (SNR). We propose a simple solution that decreases the interference while maintaining large SNR. In addition, one of the proposed hybrid combiners reaches the maximum value of our objective function according with the Hadamards inequality. Numerical results illustrate the BER performance improvements resulting from our proposals. In addition, a simple detection approach can be used for data estimation without significant performance loss.
This work focuses on the downlink communication of a multiuser MIMO system where the base station antennas and the users receiving antennas are all active, but at each transmission, only a subset of the receive antennas is selected by the base statio
n to receive the information symbols, and the particular chosen subset (pattern) represents part of the information conveyed to the user. In this paper we present a mathematical model for the system and develop expressions that are fairly general and adequate for its analysis. Based on these expressions we propose a procedure to optimize the choice by the ERB of the sets of antenna patterns to be used in the transmissions to the different users, aiming at the maximization of the detection signal-to-noise ratio. Performance results, with and without the optimization procedure, are presented for different scenarios.
This letter presents a novel detection strategy for Spatially-Multiplexed Generalized Spatial Modulation systems. It is a multi-stage detection that produces a list of candidates of the transmitted signal vector, sorted according to the proximity of
the data vector to one of the possible vector subspaces. The quality metric and list-length metric selects the best candidate and manages the list length, respectively. Performance results show that it significantly reduces the performance gap to the optimal maximum likelihood detector, while maintaining significant computational cost reduction.
This letter proposes a novel sparsity-aware adaptive filtering scheme and algorithms based on an alternating optimization strategy with shrinkage. The proposed scheme employs a two-stage structure that consists of an alternating optimization of a dia
gonally-structured matrix that speeds up the convergence and an adaptive filter with a shrinkage function that forces the coefficients with small magnitudes to zero. We devise alternating optimization least-mean square (LMS) algorithms for the proposed scheme and analyze its mean-square error. Simulations for a system identification application show that the proposed scheme and algorithms outperform in convergence and tracking existing sparsity-aware algorithms.
This paper proposes a novel adaptive reduced-rank filtering scheme based on the joint iterative optimization of adaptive filters. The proposed scheme consists of a joint iterative optimization of a bank of full-rank adaptive filters that constitutes
the projection matrix and an adaptive reduced-rank filter that operates at the output of the bank of filters. We describe minimum mean-squared error (MMSE) expressions for the design of the projection matrix and the reduced-rank filter and simple least-mean squares (LMS) adaptive algorithms for its computationally efficient implementation. Simulation results for a CDMA interference suppression application reveals that the proposed scheme significantly outperforms the state-of-the-art reduced-rank schemes, while requiring a significantly lower computational complexity.
