Cramér-Rao Bound Optimization for Massive MIMO DFRC Systems with 1-Bit DACs and ADCs

Jul 16, 2026

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Dual-function radar-communication (DFRC) design is developed for massive MIMO systems using 1-bit DACs at the transmitter and 1-bit ADCs at the receiver to achieve low-cost, power-efficient sensing and communications. In a downlink setting, the transmit signal matrix is optimized to enhance azimuth estimation of a point target while meeting communication QoS through symbol-level constructive interference constraints. The study minimizes the 1-bit Cramér–Rao bound, analyzes nonmonotonic 1-bit Fisher information versus SNR, and uses amplitude constraints plus continuous reformulation to solve a nonconvex binary–linear program via ALM and SPG with nonmonotone line search, refined by local search and cutting-plane methods.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

Introduction

DFRC and massive MIMO motivation

Role of 1-bit DACs/ADCs in reducing hardware cost

Challenges in joint 1-bit DFRC design

Related works

Cramr–Rao Bound Optimization for Massive MIMODFRC Systems with 1-Bit DACs and ADCs Chenfei Huang, Graduate Student Member, IEEE, Mingjie Shao, Member, IEEE, and Ya-Feng Liu, Senior Member, IEEE arXiv :2607 .02878v 1 [ cs .IT] 3 Jul 2026 Abstract—In this paper, we investigate the dual-function radar-communication (DFRC) design for massive multiple-input multiple-output (MIMO) systems equipped with 1-bit digital-toanalog converters (DACs) at the transmitter and 1-bit analogto-digital converters (ADCs) at the receiver, motivated by the need for low-cost and power-efficient implementations of massive MIMO systems. We consider a downlink scenario where the transmit signal matrix is optimized to enhance sensing performance while satisfying communication quality of service (QoS) requirements. Specifically, the objective is to minimize the 1-bit Cramr–Rao bound (CRB) for estimating the azimuth angle of a point-like target under symbol-level constructive interference (CI) constraints. We conduct an asymptotic analysis of the 1-bit Fisher information, revealing its nonmonotonicity with the signal-to-noise ratio (SNR), and introduce amplitude constraints to exclude regions where the objective function value is clearly suboptimal and facilitate convergence to high-quality solutions. The resulting problem is a nonconvex optimization challenge with coupled binary and linear constraints. We transform the discrete problem into a continuous constrained one, characterize its global and local minima, and tackle it via the augmented Lagrangian method (ALM) and a spectral projected gradient (SPG) method combined with nonmonotone line search. The solution is further refined via local search and cutting-plane techniques. Extensive numerical experiments verify our analysis, showing that the proposed approach exhibits promising DFRC performance compared to benchmark schemes. Index Terms—Dual-function radar-communication, multipleinput multiple-output, Cramr–Rao bound, 1-bit quantization, constructive interference. I. INTRODUCTION THE integration of radar and communication functional ities, referred to as dual-function radar-communication (DFRC) systems, represents an emerging research direction for joint hardware and spectrum sharing [1], [2] . By leveraging radio-frequency (RF) signals to simultaneously perform radar sensing and communication transmission, DFRC systems can effectively support concurrent data transmission and environmental sensing capabilities, including target detection, localization, and tracking [3] . The implementation of DFRC systems often relies on massive multiple-input multipleoutput (MIMO) techniques, which deploy a large number of antennas to improve both spectral efficiency and spatial resolution. However, the conventional fully digital massive Chenfei Huang and Mingjie Shao are with the State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China (email: huangchen[fei@lsec.cc.ac.cn](fei@lsec.cc.ac.cn); [mingjieshao@amss.ac.cn](mingjieshao@amss.ac.cn)). Ya-Feng Liu is with the Ministry of Education Key Laboratory of Mathematics and Information Networks, School of Mathematical Sciences, Beijing University of Posts and Telecommunications, Beijing 102206, China (e-mail: [yafengliu@bupt.edu.cn](yafengliu@bupt.edu.cn)). MIMO architectures demand one dedicated RF chain along with a pair of digital-to-analog converters (DACs) or analogto-digital converters (ADCs) for each antenna element. The power consumption of data converters (i.e., ADCs and DACs) scales exponentially with the number of quantization bits [4],[5] . In addition, the high dynamic range inherent to highresolution ADCs and DACs imposes an additional constraint on the RF chain, requiring it to maintain a large dynamic range to ensure signal integrity. Such high-performance RF chains are typically accompanied with high power consumption and high hardware costs. These c

FAQs

What DFRC optimization problem does the paper focus on?

It optimizes downlink transmit precoding for massive MIMO DFRC systems with 1-bit DACs and 1-bit ADCs to improve sensing performance while satisfying communication QoS via symbol-level constructive interference constraints.

How is sensing performance quantified in the optimization objective?

Sensing performance is quantified by minimizing the 1-bit Cramér–Rao bound for estimating the target azimuth angle.

Why is the optimization considered difficult to solve?

The formulation becomes a nonconvex optimization problem with coupled binary and linear constraints due to 1-bit quantization at both transmitter and receiver.

Jul 16, 2026

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