Sparse Array Architectures for Wireless Communication and Radar Applications
Doctoral thesis, 2021
We first develop a multi-user (MU) multiple-input multiple-output (MIMO) simulation platform which incorporates both antenna and channel effects based on standard network theory. The combined transmitter-channel-receiver is modeled by cascading Z-matrices to interrelate the port voltages/currents to one another in the linear network model. The herein formulated channel matrix includes physical antenna and channel effects and thus enables us to compute the actual port powers. This is in contrast with the assumptions of isotropic radiators without mutual coupling effects which are commonly being used in the Wireless Community.
Since it is observed in our model that the sum-rate of a MU-MIMO system can be adversely affected by antenna gain pattern variations, a novel BSA configuration is proposed by combining field-of-view (FOV) sectorization, array panelization and array sparsification. A multi-panel BSA, equipped with sparse arrays in each panel, is presented with the aim of reducing the implementation complexities and maintaining or even improving the sum-rate.
We also propose a capacity-driven array synthesis in the presence of mutual coupling for a MU-MIMO system. We show that the appearance of grating lobes is degrading the system capacity and cannot be disregarded in a MU communication, where space division multiple access (SDMA) is applied. With the aid of sparsity and aperiodicity, the adverse effects of grating lobes and mutual coupling are suppressed and capacity is enhanced. This is performed by proposing a two-phase optimization. In Phase I, the problem is relaxed to a convex optimization by ignoring the mutual coupling and weakening the constraints. The solution of Phase I is used as the initial guess for the genetic algorithm (GA) in phase II, where the mutual coupling is taken into account. The proposed hybrid algorithm outperforms the conventional GA with random initialization.
A novel sparse MIMO radar is presented for high-resolution single snapshot DOA estimation. Both transmit and receive arrays are divided into two uniform arrays with increased inter-element spacings to generate two uniform sparse virtual arrays. Since virtual arrays are uniform, conventional spatial smoothing can be applied for temporal correlation suppression among sources. Afterwards, the spatially smoothed virtual arrays satisfy the co-primality concept to avoid DOA ambiguities. Physical antenna effects are incorporated in the received signal model and their effects on the DOA estimation performance are investigated.
base station antenna (BSA)
multi-user (MU) MIMO
direction-of-arrival (DOA) estimation
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks, Antennas
Array Configuration Effect on the Spatial Correlation of MU-MIMO Channels in NLoS Environments
2020 14th European Conference on Antennas and Propagation (EuCAP),; Vol. 2020(2020)
Paper in proceeding
Multi-Panel Sparse Base Station Design with Physical Antenna Effects in Massive MU-MIMO
IEEE Transactions on Vehicular Technology,; Vol. 69(2020)p. 6500-6510
Per-Antenna Power Distribution of a Zero-Forcing Beamformed ULA in pure LOS MU-MIMO
IEEE Communications Letters,; Vol. 22(2018)p. 2515-2518
Network model of a 5G MIMO base station antenna in a downlink multi-user scenario
IET Conference Publications,; Vol. 2018(2018)
Paper in proceeding
Silicon-based Ka-band massive MIMO antenna systems for new telecommunication services (SILIKA)
European Commission (EC) (EC/H2020/721732), 2016-09-01 -- 2020-08-31.
Areas of Advance
Information and Communication Technology
Innovation and entrepreneurship
Onsala Space Observatory
Electrical Engineering, Electronic Engineering, Information Engineering
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5039
Chalmers University of Technology
Online participation via Zoom
Opponent: Prof. Michael Allen Jensen