HiSAC: High-Resolution Sensing with Multiband Communication Signals

Integrated Sensing And Communication (ISAC ) systems are expected to perform accurate radar sensing while having minimal impact on communication. Ideally, sensing should only reuse communication resources, especially for spectrum which is contended by many applications. However, this poses a great challenge in that communication systems often operate on narrow subbands with low sensing resolution. Combining contiguous subbands has shown significant resolution gain in active localization. However, multiband ISAC remains unexplored due to communication subbands being highly sparse (non-contiguous) and affected by phase offsets that prevent their aggregation (incoherent). To tackle these problems, we design HiSAC, the first multiband ISAC system that combines diverse subbands across a wide frequency range to achieve super-resolved passive ranging. To solve the non-contiguity and incoherence of subbands, HiSAC combines them progressively, exploiting an anchor propagation path between transmitter and receiver in an optimization problem to achieve phase coherence. HiSAC fully reuses pilot signals in communication systems, it applies to different frequencies and can combine diverse technologies, e.g., 5G-NR and WiGig. We implement HiSAC on an experimental platform in the millimeter-wave unlicensed band and test it on objects and humans. Our results show it enhances the sensing resolution by up to 20 times compared to single-band processing while occupying the same spectrum.

Bistatic Doppler Frequency Estimation with Asynchronous Moving Devices for Integrated Sensing and Communications

In this letter, we present for the first time a method to estimate the bistatic Doppler frequency of a target with clock asynchronous and mobile Integrated Sensing And Communication (ISAC) devices. Existing approaches have separately tackled the presence of phase offsets due to clock asynchrony or the additional Doppler shift due to device movement. However, in real ISAC scenarios, these two sources of phase nuisance are concurrently present, making the estimation of the target's Doppler frequency particularly challenging. Our method solves the problem using the sole wireless signal at the receiver, by computing Channel Impulse Response (CIR) phase differences across different multipath components and subsequent time instants. In this way, we cancel out phase offsets. Then, we construct a system of equations that allows disentangling the target's Doppler frequency from that of the moving device. The proposed method is validated via simulation, exploring the impact of different system parameters. Numerical results show that our approach is a viable way of estimating Doppler frequency in bistatic asynchronous ISAC scenarios with mobile devices.

Sensing in Bi-Static ISAC Systems with Clock Asynchronism: A Signal Processing Perspective

Integrated Sensing and Communications (ISAC) has been identified as a pillar usage scenario for the impending 6G era. Bi-static sensing, a major type of sensing in \ac{isac}, is promising to expedite ISAC in the near future, as it requires minimal changes to the existing network infrastructure. However, a critical challenge for bi-static sensing is clock asynchronism due to the use of different clocks at far separated transmitter and receiver. This causes the received signal to be affected by time-varying random phase offsets, severely degrading, or even failing, direct sensing. Considerable research attention has been directed toward addressing the clock asynchronism issue in bi-static sensing. In this white paper, we endeavor to fill the gap by providing an overview of the issue and existing techniques developed in an ISAC background. Based on the review and comparison, we also draw insights into the future research directions and open problems, aiming to nurture the maturation of bi-static sensing in ISAC.

Learned Spike Encoding of the Channel Response for Low-Power Environment Sensing

Radio Frequency (RF) sensing holds the potential for enabling pervasive monitoring applications. However, modern sensing algorithms imply complex operations, which clash with the energy-constrained nature of edge sensing devices. This calls for the development of new processing and learning techniques that can strike a suitable balance between performance and energy efficiency. Spiking Neural Networks (SNNs) have recently emerged as an energy-efficient alternative to conventional neural networks for edge computing applications. They process information in the form of sparse binary spike trains, thus potentially reducing energy consumption by several orders of magnitude. Their fruitful use for RF signal processing critically depends on the representation of RF signals in the form of spike signals. We underline that existing spike encoding algorithms to do so generally produce inaccurate signal representations and dense (i.e., inefficient) spike trains. In this work, we propose a lightweight neural architecture that learns a tailored spike encoding representations of RF channel responses by jointly reconstructing the input and its spectral content. By leveraging a tunable regularization term, our approach enables fine-grained control over the performance-energy trade-off of the system. Our numerical results show that the proposed method outperforms existing encoding algorithms in terms of reconstruction error and sparsity of the obtained spike encodings.

An Experimental Prototype for Multistatic Asynchronous ISAC

We prototype and validate a multistatic mmWave ISAC system based on IEEE802.11ay. Compensation of the clock asynchrony between each TX and RX pair is performed using the sole LoS wireless signal propagation. As a result, our system provides concurrent target tracking and micro-Doppler estimation from multiple points of view, paving the way for practical multistatic data fusion. Our results on human movement sensing, complemented with precise, quantitative GT data, demonstrate the enhanced sensing capabilities of multistatic ISAC, due to the spatial diversity of the receiver nodes.

Enhanced Attention-Based Unrolling for Sparse Sequential micro-Doppler Reconstruction

The reconstruction of micro-Doppler signatures of human movements is a key enabler for fine-grained activity recognition with radio-frequency sensing. In this work, we focus on Joint Communication and Sensing (JCS) systems where, unlike in dedicated radar sensing systems, a suitable tradeoff between sensing accuracy and communication overhead has to be attained. It follows that the micro-Doppler has to be reconstructed from sparse and noisy channel estimates obtained from communication packets, limiting as much as possible the transmission of additional probing signals for the purpose of sensing. Existing approaches exploit compressed sensing, but produce very poor reconstructions when only a few channel measurements are available, which is often the case in real communication patterns. In addition, the large number of iterations they need to converge hinders their use in real-time systems. Here, we present STAR, a lightweight neural network that combines a single unrolled iterative hard-thresholding layer with an attention mechanism. Our new approach exploits the temporal correlation of the micro-Doppler to accurately reconstruct microDoppler sequences from human movement even from very sparse channel measurements. In doing so, it combines model-based and data-driven approaches into an interpretable and low-complexity architecture, which is amenable to real-time implementations. We evaluate STAR on a public JCS dataset of 60 GHz IEEE 802.11ay channel measurements of human activity traces. Experimental results show that it substantially outperforms state-of-the-art solutions in terms of the reconstructed microDoppler quality. Remarkably, STAR enables human activity recognition with satisfactory accuracy even with 90%-sparse channel measurements, for which existing techniques fail.

DISC: a Dataset for Integrated Sensing and Communication in mmWave Systems

In this paper we present DISC, a dataset of millimeter-wave channel impulse response measurements for integrated human activity sensing and communication. This is the first dataset collected with a software-defined radio testbed that transmits 60 GHz IEEE 802-11ay-compliant packets and estimates the channel response including reflections of the signal on the moving body parts of subjects moving in an indoor environment. The provided data contain the contribution of 7 subjects performing 4 different activities. Differently from available radar-based millimeter-wave sensing datasets, our measurements are collected using both uniform packet transmission times and sparse traffic patterns from real Wi-Fi deployments. Thanks to these unique characteristics, DISC serves as a multi-purpose benchmarking tool for machine learning-based human activity recognition, radio frequency gait analysis, and sparse sensing algorithms for next-generation integrated sensing and communication.

JUMP: Joint communication and sensing with Unsynchronized transceivers Made Practical

Wideband millimeter-wave communication systems can be extended to provide radar-like sensing capabilities on top of data communication, in a cost-effective manner. However, the development of joint communication and sensing technology is hindered by practical challenges, such as occlusions to the line-of-sight path and clock asynchrony between devices. The latter introduces time-varying timing and frequency offsets that prevent the estimation of sensing parameters and, in turn, the use of standard signal processing solutions. Existing approaches cannot be applied to commonly used phased-array receivers, as they build on stringent assumptions about the multipath environment, and are computationally complex. We present JUMP, the first system enabling practical bistatic and asynchronous joint communication and sensing, while achieving accurate target tracking and micro-Doppler extraction in realistic conditions. Our system compensates for the timing offset by exploiting the channel correlation across subsequent packets. Further, it tracks multipath reflections and eliminates frequency offsets by observing the phase of a dynamically-selected static reference path. JUMP has been implemented on a 60 GHz experimental platform, performing extensive evaluations of human motion sensing, including non-line-of-sight scenarios. In our results, JUMP attains comparable tracking performance to a full-duplex monostatic system and similar micro-Doppler quality with respect to a phase-locked bistatic receiver.

ORACLE: Occlusion-Resilient And self-Calibrating mmWave Radar Network for People Tracking

Millimeter wave (mmWave) radars are emerging as valid alternatives to cameras for the pervasive contactless monitoring of industrial environments, enabling very tight human-machine interactions. However, commercial mmWave radars feature a limited range (up to 6-8 m) and are subject to occlusion, which may constitute a significant drawback in large industrial settings containing machinery and obstacles. Thus, covering large indoor spaces requires multiple radars with known relative position and orientation. As a result, we necessitate algorithms to combine their outputs. In this work, we present ORACLE, an autonomous system that (i) integrates automatic relative position and orientation estimation from multiple radar devices by exploiting the trajectories of people moving freely in the radars' common fields of view and (ii) fuses the tracking information from multiple radars to obtain a unified tracking among all sensors. Our implementation and experimental evaluation of ORACLE results in median errors of 0.18 m and 2.86{\deg} for radars location and orientation estimates, respectively. The fused tracking improves upon single sensor tracking by 6%, in terms of mean tracking accuracy, while reaching a mean tracking error as low as 0.14 m. Finally, ORACLE is robust to fusion relative time synchronization mismatches between -20% and +50%.

Human Tracking with mmWave Radars: a Deep Learning Approach with Uncertainty Estimation

mmWave radars have recently gathered significant attention as a means to track human movement within indoor environments. Widely adopted Kalman filter tracking methods experience performance degradation when the underlying movement is highly non-linear or presents long-term temporal dependencies. As a solution, in this article we design a convolutional-recurrent Neural Network (NN) that learns to accurately estimate the position and the velocity of the monitored subjects from high dimensional radar data. The NN is trained as a probabilistic model, utilizing a Gaussian negative log-likelihood loss function, obtaining explicit uncertainty estimates at its output, in the form of time-varying error covariance matrices. A thorough experimental assessment is conducted using a 77 GHz FMCW radar. The proposed architecture, besides allowing one to gauge the uncertainty in the tracking process, also leads to greatly improved performance against the best approaches from the literature, i.e., Kalman filtering, lowering the average error against the ground truth from 32.8 to 7.59 cm and from 56.8 to 14 cm/s in terms of position and velocity tracking, respectively.