A novel Integrated Sensing-Communication (ISAC) system is proposed that can accommodate high mobility scenarios while making efficient use of bandwidth for both communication and sensing. The system comprises a monostatic multiple-input multiple-output (MIMO) radar that transmits orthogonal time frequency space (OTFS) waveforms. Bandwidth efficiency is achieved by making Doppler-delay (DD) domain bins available for shared use by the transmit antennas. For maximum communication rate, all DD-domain bins are used as shared, but in this case, the target resolution is limited by the aperture of the receive array. A low-complexity method is proposed for obtaining coarse estimates of the radar targets parameters in that case. A novel approach is also proposed to construct a virtual array (VA) for achieving a target resolution higher than that allowed by the receive array. The VA is formed by enforcing zeros on certain time-frequency (TF) domain bins, thereby creating private bins assigned to specific transmit antennas. The TF signals received on these private bins are orthogonal, enabling the synthesis of a VA. When combined with coarse target estimates, this approach provides high-accuracy target estimation. To preserve DD-domain information, the introduction of private bins requires reducing the number of DD-domain symbols, resulting in a trade-off between communication rate and sensing performance. However, even a small number of private bins is sufficient to achieve significant sensing gains with minimal communication rate loss.

With the extensive deployment of smart environments and IoT devices, WiFi sensing has proven its significant convenience and contact-free sensing capabilities in supporting a wide range of applications. However, designing a ubiquitous WiFi sensing system for diverse real-world scenarios presents a substantial dilemma, as the system performance deteriorates when the testing data diverges significantly from the training data due to domain variations. To address this dilemma, existing studies need extra efforts to develop new features or even retrain the original model under environmental variations. However, these approaches have not efficiently resolved the dilemma. In this study, we conduct a comprehensive study on the domain variation problem to make WiFi sensing robust and accurate in practical applications. Our definition of domains is comprehensive and includes environmental conditions, surrounding settings, user differences, user orientations, user's positions relative to WiFi sensors, and user participation time frames. We design a novel conformal prediction framework that quantifies the conformity (i.e., similarity) between the testing and training WiFi samples, then labels the testing samples with the most probable class(es). Unlike traditional conformal prediction which relies on data from a single domain, we develop a new statistical (Type I) approach to assess the conformity of the testing WiFi samples to individual training domains and aggregate the outcomes. To further improve the framework's generalization, we design a (Type II) fusion approach that utilizes the inter-relationships among domains for more accurate conformity quantification. Built upon these two methods, kernel density estimation-based and SVM-based methods are developed to compute the conformity scores for new testing samples to make conformal predictions. Extensive experiments, utilizing both self-collected and publicly available datasets show that our framework can improve prediction accuracies ranging from 20.1% to 77.1% in three of the most representative WiFi-based applications across six types of domain variations.

A novel dual-function radar communication (DFRC) system is proposed that can accommodate high mobility scenarios while making efficient use of bandwidth for both communication and sensing. The system comprises a monostatic multiple-input multiple-output (MIMO) radar that transmits orthogonal time frequency space (OTFS) waveforms. Bandwidth efficiency is achieved by making all Doppler-delay (DD) domain bins available for all transmit antennas. While this shared use of DD bins allows for high communication rate, it makes target estimation challenging. A low complexity method is proposed to estimate the radar parameters at the receiver. The proposed system not only exhibits the advantages of other multi-carrier waveforms, but is also more robust to Doppler frequency shifts that arise in high mobility scenarios. Simulation results demonstrate the promise of the proposed approach in 6G system.

With the wide deployment of smart environments and IoT devices, WiFi sensing has demonstrated its great convenience and contactless sensing capabilities in supporting a broad array of applications. However, designing a ubiquitous WiFi sensing system for heterogeneous scenarios in practice is still a big dilemma as the system performs poorly when the testing data is significantly different from the training data caused by domain variations. To address this dilemma, existing studies involve extra efforts to develop new features or even to retrain the original model under environmental variations. However, none of them can resolve the dilemma completely. In this work, we conduct a comprehensive study on the domain variation problem to make WiFi sensing robust and accurate in reality. Our definition of domains is comprehensive and includes environments, surrounding settings, user differences, user's facing directions, user's positions relative to WiFi sensors, and user participating time frames. Our innovation is to achieve reliable WiFi sensing across all the domains based on the conformal prediction framework. Our approach quantifies the conformity (i.e., similarity) between the testing WiFi samples and the training samples, then labels the testing samples with the most probable class(es). We develop a novel cross-domain transformal prediction scheme based on the multivariate kernel density estimation to effectively assess and learn the conformity of each domain in the training data. To meet various application-specific requirements, we further develop two approaches to fuse the knowledge of conformity derived from the training domains to perform predictions. Extensive experiments with both self-collected and public datasets show that our framework can improve prediction accuracies from 30% to 74% improvements in three most representative WiFi-based applications across six types of domain variations.