Rate Balancing in Full-Duplex MIMO Two-Way Relay Networks

Maximizing the minimum rate for a full-duplex multiple-input multiple-output (MIMO) wireless network encompassing two sources and a two-way (TW) relay operating in a two hop manner is investigated. To improve the overall performance, using a zero-forcing approach at the relay to suppress the residual self-interference arising from full-duplex (FD) operation, the underlying max-min problem is cast as an optimization problem which is non-convex. To circumvent this issue, semidefinite relaxation technique is employed, leading to upper and lower bound solutions for the optimization problem. Numerical results verify that the upper and lower bound solutions closely follow each other, showing that the proposed approach results in a close-to-optimal solution. In addition, the impact of residual self-interference upon the overall performance of the network in terms of the minimum rate is illustrated by numerical results, and for low residual self-interference scenarios the superiority of the proposed method compared to an analogous half-duplex (HD) counterpart is shown

A Kronecker-Based Sparse Compressive Sensing Matrix for Millimeter Wave Beam Alignment

Millimeter wave beam alignment (BA) is a challenging problem especially for large number of antennas. Compressed sensing (CS) tools have been exploited due to the sparse nature of such channels. This paper presents a novel deterministic CS approach for BA. Our proposed sensing matrix which has a Kronecker-based structure is sparse, which means it is computationally efficient. We show that our proposed sensing matrix satisfies the restricted isometry property (RIP) condition, which guarantees the reconstruction of the sparse vector. Our approach outperforms existing random beamforming techniques in practical low signal to noise ratio (SNR) scenarios.Comment: Accepted to 13th International Conference on Signal Processing and Communication Systems (ICSPCS'2019

Millimeter wave beam alignment using deterministic compressive sensing

Empirical thesis.Bibliography: pages 50-54.1. Introduction -- 2. An overview of compressive sensing -- 3. System model -- 4. Pilot beam pattern design -- 5. Simulation results -- 6. Conclusion -- References.Designing the beamforming vectors for channel estimation in mmWave systems is challenging becauseof the narrow beams required and the small number of useful directions. The state of the art employs random or structured random beamforming to leverage compressive sensing techniques to solve this problem using a small number of measurements. In this dissertation, inspired by existing deterministic sensing matrices from the theory of compressive sensing, two novel deterministic compressive sensing approaches are proposed for channel estimation in mmWave systems. In the proposed approaches, the Kronecker product or row-by-row Kronecker product of existing deterministic sensing matrices are used to design the structure of pilot beam patterns for the beam alignment process. These approaches not only result in significant overhead reduction, but also present improvement in terms of performance for some scenarios.Mode of access: World wide web1 online resource (viii, 54 pages) colour illustration