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MU-MIMO Communications with MIMO Radar: From Co-existence to Joint Transmission

Fan Liu, Christos Masouros, Ang Li, Huafei Sun, Lajos Hanzo

TL;DR

Numerical results show that the shared deployment outperforms the separated case significantly, and the proposed weighted optimizations achieve a similar performance to the original optimizations, despite their significantly lower computational complexity.

Abstract

Beamforming techniques are proposed for a joint multi-input-multi-output (MIMO) radar-communication (RadCom) system, where a single device acts both as a radar and a communication base station (BS) by simultaneously communicating with downlink users and detecting radar targets. Two operational options are considered, where we first split the antennas into two groups, one for radar and the other for communication. Under this deployment, the radar signal is designed to fall into the null-space of the downlink channel. The communication beamformer is optimized such that the beampattern obtained matches the radar's beampattern while satisfying the communication performance requirements. To reduce the optimizations' constraints, we consider a second operational option, where all the antennas transmit a joint waveform that is shared by both radar and communications. In this case, we formulate an appropriate probing beampattern, while guaranteeing the performance of the downlink communications. By incorporating the SINR constraints into objective functions as penalty terms, we further simplify the original beamforming designs to weighted optimizations, and solve them by efficient manifold algorithms. Numerical results show that the shared deployment outperforms the separated case significantly, and the proposed weighted optimizations achieve a similar performance to the original optimizations, despite their significantly lower computational complexity.

MU-MIMO Communications with MIMO Radar: From Co-existence to Joint Transmission

TL;DR

Numerical results show that the shared deployment outperforms the separated case significantly, and the proposed weighted optimizations achieve a similar performance to the original optimizations, despite their significantly lower computational complexity.

Abstract

Beamforming techniques are proposed for a joint multi-input-multi-output (MIMO) radar-communication (RadCom) system, where a single device acts both as a radar and a communication base station (BS) by simultaneously communicating with downlink users and detecting radar targets. Two operational options are considered, where we first split the antennas into two groups, one for radar and the other for communication. Under this deployment, the radar signal is designed to fall into the null-space of the downlink channel. The communication beamformer is optimized such that the beampattern obtained matches the radar's beampattern while satisfying the communication performance requirements. To reduce the optimizations' constraints, we consider a second operational option, where all the antennas transmit a joint waveform that is shared by both radar and communications. In this case, we formulate an appropriate probing beampattern, while guaranteeing the performance of the downlink communications. By incorporating the SINR constraints into objective functions as penalty terms, we further simplify the original beamforming designs to weighted optimizations, and solve them by efficient manifold algorithms. Numerical results show that the shared deployment outperforms the separated case significantly, and the proposed weighted optimizations achieve a similar performance to the original optimizations, despite their significantly lower computational complexity.

Paper Structure

This paper contains 25 sections, 65 equations, 12 figures, 3 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Co-existence of Radar and Communications Systems
  3. Co-design of Dual-functional RadCom Systems
  4. The Contribution of Our Work
  5. System Model
  6. Separated Deployment
  7. Shared Deployment
  8. Problem Formulation
  9. MIMO Radar Beampattern Design
  10. Zero-forcing Beamforming for Separated Deployment
  11. Beamforming for Shared Deployment
  12. SINR Penalty Terms
  13. Beamforming Optimization on the Manifold
  14. Preliminaries on Riemannian Manifold Optimization
  15. Beamforming under Total Power Constraint
  16. ...and 10 more sections

Figures (12)

  • Figure 1: Antenna deployments for the collocated radar and communications BS. (a) Separated deployment; (b) Shared deployment.
  • Figure 2: Riemannian conjugate gradient algorithm.
  • Figure 3: Multi-beam beampatterns comparisons for $\Gamma = 10\text{dB}, K = 4$. (a) Separated deployment; (b) Shared deployment.
  • Figure 4: 3dB beampatterns comparisons for $\Gamma = 10\text{dB}, K = 4$. (a) Separated deployment; (b) Shared deployment.
  • Figure 5: Trade-off between PSLR and SINR level, $P_0 = 20\text{dBm}, K = 4$.
  • ...and 7 more figures