Multi-Beam Object-Localization for Millimeter-Wave ISAC-Aided Connected Autonomous Vehicles

TL;DR

This paper introduces a multi-beam object-localization model for mmWave MIMO systems in connected autonomous vehicles, enhancing sensing capabilities by optimizing beam patterns while considering communication constraints.

Contribution

It proposes a novel multi-beam localization framework with a penalty-based optimization algorithm for hybrid beamforming in mmWave CAV systems.

Findings

Enhanced sensing beampattern gain for adjacent objects

Effective hybrid beamformer design under SINR and power constraints

Simulation results demonstrate improved localization accuracy

Abstract

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems capable of integrated sensing and communication (ISAC) constitute a key technology for connected autonomous vehicles (CAVs). In this context, we propose a multi-beam object-localization (MBOL) model for enhancing the sensing beampattern (SBP) gain of adjacent objects in CAV scenarios. Given the ultra-narrow beams of mmWave MIMO systems, a single pencil beam is unsuitable for closely located objects, which tend to require multiple beams. Hence, we formulate the SBP gain maximization problem, considering also the constraints on the signal-to-interference and noise ratio (SINR) of the communication users (CUs), on the transmit power, and the constant modulus of the phase-shifters in the mmWave hybrid transceiver. To solve this non-convex problem, we propose a penalty-based triple alternating optimization algorithm to design the hybrid beamformer. Finally, simulation results are provided for demonstrating the efficacy of the proposed model.