CommRad: Context-Aware Sensing-Driven Millimeter-Wave Networks

TL;DR

CommRad addresses the overhead and reliability challenges of maintaining mmWave links under mobility and blockage by introducing a context-aware, sensing-driven framework that couples a base-station radar (mono-static) with a bi-static radio sensing channel. The three-stage workflow defines the necessary contextual information, acquires it via periodic radio beam training, and leverages radar to continuously track direct and reflected paths while predicting and mitigating blockages. The end-to-end implementation on a 28 GHz mmWave testbed with a 24 GHz radar demonstrates a 2.5x median throughput improvement and up to 8x gains in the lower tail, with overhead reduced to as low as 0.5%, validating robust, high-throughput mobile mmWave operation in diverse environments. The work’s significance lies in providing contextual awareness to radar through radio collaboration, enabling reliable, low-overhead beam management that generalizes to additional sensing modalities and deployment scenarios.

Abstract

Millimeter-wave (mmWave) technology is pivotal for next-generation wireless networks, enabling high-data-rate and low-latency applications such as autonomous vehicles and XR streaming. However, maintaining directional mmWave links in dynamic mobile environments is challenging due to mobility-induced disruptions and blockage. While effective, the current 5G NR beam training methods incur significant overhead and scalability issues in multi-user scenarios. To address this, we introduce CommRad, a sensing-driven solution incorporating a radar sensor at the base station to track mobile users and maintain directional beams even under blockages. While radar provides high-resolution object tracking, it suffers from a fundamental challenge of lack of context, i.e., it cannot discern which objects in the environment represent active users, reflectors, or blockers. To obtain this contextual awareness, CommRad unites wireless sensing capabilities of bi-static radio communication with the mono-static radar sensor, allowing radios to provide initial context to radar sensors. Subsequently, the radar aids in user tracking and sustains mobile links even in obstructed scenarios, resulting in robust and high-throughput directional connections for all mobile users at all times. We evaluate this collaborative radar-radio framework using a 28 GHz mmWave testbed integrated with a radar sensor in various indoor and outdoor scenarios, demonstrating a 2.5x improvement in median throughput compared to a non-collaborative baseline.

Figures (19)

• Figure 1: CommRad is a collaborative learning framework for context-aware sensing-driven mmWave communication.
• Figure 2: CommRad's bi-directional radar-radio collaborative learning framework to improve data communication efficiency by reducing radio beam scan overhead.
• Figure 3: Overview of CommRad's end-end system implementation of collaborative bi-directional learning with radar+radio integration.
• Figure 4: Challenges with lack of context for user identification
• Figure 5: Challenges with lack of context for reflector identification.