Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Towards Dual-functional Radar-Communication Systems: Optimal Waveform Design

Fan Liu, Longfei Zhou, Christos Masouros, Ang Li, Wu Luo, Athina Petropulu

TL;DR

This work focuses on a dual-functional multi-input-multi-output (MIMO) radar-communication system, where a single transmitter with multiple antennas communicates with downlink cellular users and detects radar targets simultaneously and proposes a branch-and-bound algorithm that obtains a globally optimal solution.

Abstract

We focus on a dual-functional multi-input-multi-output (MIMO) radar-communication (RadCom) system, where a single transmitter communicates with downlink cellular users and detects radar targets simultaneously. Several design criteria are considered for minimizing the downlink multi-user interference. First, we consider both the omnidirectional and directional beampattern design problems, where the closed-form globally optimal solutions are obtained. Based on these waveforms, we further consider a weighted optimization to enable a flexible trade-off between radar and communications performance and introduce a low-complexity algorithm. The computational costs of the above three designs are shown to be similar to the conventional zero-forcing (ZF) precoding. Moreover, to address the more practical constant modulus waveform design problem, we propose a branch-and-bound algorithm that obtains a globally optimal solution and derive its worst-case complexity as a function of the maximum iteration number. Finally, we assess the effectiveness of the proposed waveform design approaches by numerical results.

Towards Dual-functional Radar-Communication Systems: Optimal Waveform Design

TL;DR

This work focuses on a dual-functional multi-input-multi-output (MIMO) radar-communication system, where a single transmitter with multiple antennas communicates with downlink cellular users and detects radar targets simultaneously and proposes a branch-and-bound algorithm that obtains a globally optimal solution.

Abstract

We focus on a dual-functional multi-input-multi-output (MIMO) radar-communication (RadCom) system, where a single transmitter communicates with downlink cellular users and detects radar targets simultaneously. Several design criteria are considered for minimizing the downlink multi-user interference. First, we consider both the omnidirectional and directional beampattern design problems, where the closed-form globally optimal solutions are obtained. Based on these waveforms, we further consider a weighted optimization to enable a flexible trade-off between radar and communications performance and introduce a low-complexity algorithm. The computational costs of the above three designs are shown to be similar to the conventional zero-forcing (ZF) precoding. Moreover, to address the more practical constant modulus waveform design problem, we propose a branch-and-bound algorithm that obtains a globally optimal solution and derive its worst-case complexity as a function of the maximum iteration number. Finally, we assess the effectiveness of the proposed waveform design approaches by numerical results.

Paper Structure

This paper contains 21 sections, 3 theorems, 61 equations, 10 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Communication Model
  4. Radar Model
  5. Closed-form Waveform Design for Given Radar Beampatterns
  6. Omnidirectional Beampattern Design
  7. Directional Beampattern Design
  8. Complexity Analysis
  9. Trade-off Between Radar and Communication Performance
  10. Problem Formulation
  11. Low-complexity Algorithm
  12. Complexity Analysis
  13. Constant Modulus Waveform Design
  14. Problem Formulation
  15. The Branch-and-bound Framework
  16. ...and 6 more sections

Key Result

Lemma 1

As $\phi_{\max}$ or $d_{\max}$ goes to zero, the difference between $UB$ and $LB$ uniformly converges to zero, i.e.,

Figures (10)

  • Figure 1: Dual-functional Radar-Communication System.
  • Figure 2: Feasible region and convex hull of problem (28).
  • Figure 3: Sum-rate comparison for different approaches, $N = 16, K = 4$.
  • Figure 4: Radar beampatterns obtained by different approaches, $N = 16, K = 4$.
  • Figure 5: Trade-off between the achievable rate per user and the radar detection probability for omnidirectional beampattern design, $N = 16, \text{radar}\;\;{\text{SNR}} = -6\text{dB}, P_{FA} = 10^{-7}$.
  • ...and 5 more figures

Theorems & Definitions (6)

  • Lemma 1
  • proof
  • Theorem 1
  • proof
  • Theorem 2
  • proof