Co-Designing Statistical MIMO Radar and In-band Full-Duplex Multi-User MIMO Communications -- Part II: Joint Precoder, Radar Code, and Receive Filters Design

TL;DR

The paper addresses spectral sharing between a distributed statistical MIMO radar and an in-band full-duplex MU-MIMO system by formulating a joint design objective based on the compounded-and-weighted sum mutual information $I_{ ext{CWSM}}$. It develops a WMMSE-based reformulation and a three-block alternating optimization (BCD-AP) algorithm to jointly optimize radar codes, UL/DL precoders, and receive filters under UL/DL power, QoS, and radar-PAR constraints. The approach yields monotonic convergence, mitigates uplink interference, and demonstrates robust, stable data rates across a range of SNRs, CSI imperfections, noise, and self-interference; Part III extends to multiple targets and tracking performance. Overall, the work provides a practical, convergent framework for high-performance spectral co-design of radar sensing and IBFD MU-MIMO communications in shared bands.

Abstract

We address the challenge of spectral sharing between a statistical multiple-input multiple-output (MIMO) radar and an in-band full-duplex (IBFD) multi-user MIMO (MU-MIMO) communications system operating simultaneously in the same frequency band. Existing research on joint MIMO-radar-MIMO-communications (MRMC) systems has limitations, such as focusing on colocated MIMO radars, half-duplex MIMO communications, single-user scenarios, neglecting practical constraints, or employing separate transmit/receive units for MRMC coexistence. This paper, along with companion papers (Part I and III), proposes a comprehensive MRMC framework that addresses all these challenges. In the previous companion paper (Part I), we presented signal processing techniques for a distributed IBFD MRMC system. In this paper, we introduce joint design of statistical MIMO radar codes, uplink/downlink precoders, and corresponding receive filters using a novel metric called compounded-and-weighted sum mutual information. To solve the resulting highly non-convex problem, we employ a combination of block coordinate descent (BCD) and alternating projection methods. Numerical experiments show convergence of our algorithm, mitigation of uplink interference, and stable data rates under varying noise levels, channel estimate imperfections, and self-interference. The subsequent companion paper (Part III) extends the discussion to multiple targets and evaluates the tracking performance of our MRMC system.

Figures (5)

• Figure 1: The overlaid receive signal timing diagram during ${k}{\text{-th}}$ radar PRI and ${k}{\text{-th}}$ communications frame in the observation window; noise trails have been excluded. The purple bin with more opacity indicates the DL signal reflected from the target and observed in the radar CUT, i.e., $\mathbf{y}^{\left({n_\mathrm{t}}\right)}_{\textrm{Bt},n_\mathrm{r}}{\left [{k}\right ]}$.
• Figure 2: Convergence behaviors of the BCD-AP MRMC algorithm with two initialization methods and multiple $\mathrm{SNR}_\textrm{r}$s.
• Figure 3: Proposed co-design compared with the conventional communications precoding and radar coding techniques under varying radar SNRs with and without CSI errors.
• Figure 4: Joint radar and communications analyses: (a) IBFD MU-MIMO performance versus CNRs. (b) ROC curves with varying numbers of UL/DL UEs.
• Figure 5: Impact of the FD SI on the proposed radar-communications co-design.

Theorems & Definitions (4)

• Theorem 1
• proof
• Proposition 1
• proof