Chameleon: Integrated Sensing and Communication with Sub-Symbol Beam Switching in mmWave Networks

Next-generation cellular networks are envisioned to integrate sensing capabilities with communication, particularly in the millimeter-wave (mmWave) spectrum, where beamforming using large-scale antenna arrays enables directional signal transmissions for improved spatial multiplexing. In current 5G networks, however, beamforming is typically designed either for communication or sensing (e.g., beam training during link establishment). In this paper, we present Chameleon, a novel framework that augments and rapidly switches beamformers during each demodulation reference signal (DMRS) symbol to achieve integrated sensing and communication (ISAC) in 5G mmWave networks. Each beamformer introduces an additional sensing beam toward target angles while maintaining the communication beams toward multiple users. We implement Chameleon on a 28 GHz software-defined radio testbed supporting over-the-air 5G physical downlink shared channel (PDSCH) transmissions. Extensive experiments in open environments show that Chameleon achieves multi-user communication with a sum data rate of up to 0.80 Gbps across two users. Simultaneously, Chameleon employs a beamformer switching interval of only 0.24 {\mu}s, therefore producing a 31x31-point 2D imaging within just 0.875 ms. Leveraging machine learning, Chameleon further enables object localization with median errors of 0.14 m (distance) and 0.24{\deg} (angle), and material classification with 99.0% accuracy.

BatStation: Toward In-Situ Radar Sensing on 5G Base Stations with Zero-Shot Template Generation

The coexistence between incumbent radar signals and commercial 5G signals necessitates a versatile and ubiquitous radar sensing for efficient and adaptive spectrum sharing. In this context, leveraging the densely deployed 5G base stations (BS) for radar sensing is particularly promising, offering both wide coverage and immediate feedback to 5G scheduling. However, the targeting radar signals are superimposed with concurrent 5G uplink transmissions received by the BS, and practical deployment also demands a lightweight, portable radar sensing model. This paper presents BatStation, a lightweight, in-situ radar sensing framework seamlessly integrated into 5G BSs. BatStation leverages uplink resource grids to extract radar signals through three key components: (i) radar signal separation to cancel concurrent 5G transmissions and reveal the radar signals, (ii) resource grid reshaping to align time-frequency resolution with radar pulse characteristics, and (iii) zero-shot template correlation based on a portable model trained purely on synthetic data that supports detection, classification, and localization of radar pulses without fine-tuning using experimental data. We implement BatStation on a software-defined radio (SDR) testbed and evaluate its performance with real 5G traffic in the CBRS band. Results show robust performance across diverse radar types, achieving detection probabilities of 97.02% (PUCCH) and 79.23% (PUSCH), classification accuracy up to 97.00%, and median localization errors of 2.68-6.20 MHz (frequency) and 24.6-32.4 microseconds (time). Notably, BatStation achieves this performance with a runtime latency of only 0.11/0.94 ms on GPU/CPU, meeting the real-time requirement of 5G networks.