Heterogeneously-Distributed Joint Radar Communications: Bayesian Resource Allocation

Abstract: Due to spectrum scarcity, the coexistence of radar and wireless communication has gained substantial research interest recently. Among many scenarios, the heterogeneouslydistributed joint radar-communication system is promising due to its flexibility and compatibility of existing architectures. In this paper, we focus on a heterogeneous radar and communication network (HRCN), which consists of various generic radars for multiple target tracking (MTT) and wireless communications for multiple users. We aim to improve the MTT performance and maintain good throughput levels for communication users by a well-designed resource allocation. The problem is formulated as a Bayesian Cramér-Rao bound (CRB) based minimization subjecting to resource budgets and throughput constraints. The formulated nonconvex problem is solved based on an alternating descent-ascent approach. Numerical results demonstrate the efficacy of the proposed allocation scheme for this heterogeneous network.

• The paper introduces a novel Bayesian resource allocation strategy that optimizes multiple target tracking and communication throughput in heterogeneous radar systems.
• It formulates the problem as a nonconvex maximin optimization using the Bayesian Cramér-Rao bound, solved via an alternating descent-ascent algorithm.
• Simulations demonstrate significant improvements in RMSE and tracking accuracy compared to uniform and random allocation methods.

Heterogeneously-Distributed Joint Radar Communications: Bayesian Resource Allocation

Introduction

The increasing demand for wireless spectrum has pushed the need for integrating radar systems with wireless communications, which have historically developed independently but share overlapping technical requirements. The paper "Heterogeneously-Distributed Joint Radar Communications: Bayesian Resource Allocation" (2107.13838) proposes a novel resource allocation strategy within a heterogeneous radar and communication network (HRCN) designed to improve multiple target tracking (MTT) and maintain throughput for communication users. The resource allocation problem is formulated as a constrained optimization problem based on Bayesian Cramér-Rao bound (CRB), solved via an alternating descent-ascent approach.

HRCN Configuration and Resource Allocation

Radar Network Configurations

The paper considers three types of radar systems within the network: MIMO radars (MMRs), phased array radars (PARs), and mechanical scanning radars (MSRs). Each radar system exhibits distinct operational characteristics:

• Colocated MIMO Radars (MMR): Utilizes multiple beams to simultaneously track multiple targets with uniform revisit intervals.
• Phased Array Radars (PAR): Sequentially illuminates targets with adaptive beam rotation, resulting in varied revisit intervals.
• Mechanical Scanning Radars (MSR): Sequentially scans targets with consistent dwell times and power levels, maintaining uniform revisit intervals across targets.

Communication System Configuration

The wireless communication subsystem features downlink users operated on distinct frequencies, assuming no mutual interference. Communication links are characterized by Gaussian transmission with persistent interference from radar systems, modeled by complex interference parameters.

Mutual Interference and Resource Allocation

The interference between radar signals and communication links is quantified using interference parameters. The resource allocation approach optimizes target tracking performance, quantified by a Bayesian CRB metric, while ensuring adequate communication throughput based on specified constraints and resource budgets.

Problem Formulation and Solution

The optimization problem is recast into a nonconvex maximin problem to achieve the intended balance between tracking performance and communication throughput. The paper pioneers the use of an alternating descent-ascent algorithm to address this complex optimization challenge. The efficacy of the proposed allocation algorithm is demonstrated through simulation, showing substantial improvements in tracking accuracy and communication reliability compared to traditional allocation methods.

Numerical Results and Simulation Analysis

The simulation results, illustrated in various figures and tables, reveal significant performance enhancements. For instance, the optimized allocation consistently outperforms uniform and randomly generated allocation strategies in terms of Bayesian CRB metrics. The simulation further illustrates improved RMSE performance across multiple fusion intervals, providing evidence of the allocation strategy's effectiveness in real-world scenarios.

Conclusion

The paper successfully introduces an innovative resource allocation strategy for heterogeneous radar-communication integration, addressing the spectrum scarcity challenge. The algorithm enhances MTT accuracy while ensuring reliable communication throughput. Potential future research could extend these methodologies to more complex networks or incorporate additional real-time constraints.

The research leverages a comprehensive set of prior works addressing radar and communication integration, resource allocation strategies, and optimization algorithms. Notable references include publications on power allocation in radar networks, multi-target tracking methods, and fundamental theories on radar measurement accuracy.