This paper considers and analyzes the performance of semiblind, training, and data-aided channel estimation schemes for multiple-input multiple-output (MIMO) filter bank multicarrier (FBMC) systems with offset quadrature amplitude modulation.

In this paper, we study blind channel-and-signal estimation by exploiting the burst-sparse structure of angular-domain propagation channels in massive MIMO systems. The state-of-the-art approach utilizes the structured channel sparsity by sampling the angular-domain channel representation with a uniform angle-sampling grid, a.k.a. virtual channel representation.

Linear data-detection algorithms that build on zero forcing (ZF) or linear minimum mean-square error (L-MMSE) equalization achieve near-optimal spectral efficiency in massive multi-user multiple-input multiple-output (MU-MIMO) systems. 

In this paper, we study the problem of beam alignment for millimeter wave (mmWave) communications, where a hybrid analog and digital beamforming structure is employed at the transmitter (i.e., base station), and an omni-directional antenna or an antenna array is used at the receiver (i.e., user).

Editor-in-Chief:
Brendt Wohlberg
Los Alamos National Laboratory, USA
Email EIC 
Email SPS Publications Office
Term Ends: 31 December 2025

This fully open access journal will publish high-quality, peer-reviewed papers covering the enabling technology for the generation, transformation, extraction, and interpretation of information. It comprises the theory, algorithms with associated architectures and implementations, and applications related to processing information contained in many different formats broadly designated as signals. Signal processing uses mathematical, statistical, computational, heuristic, and/or linguistic representations, formalisms, modeling techniques and algorithms for generating, transforming, transmitting, and learning from signals.