Xiaoyang Li

In this paper, we study efficient codebook design for limited feedback in extremely large-scale multiple-input-multiple-output (XL-MIMO) frequency division duplexing (FDD) systems. It is worth noting that existing codebook designs for XL-MIMO, such as the polar-domain codebook, have not well taken into account user (location) distribution in practice, thereby incurring excessive feedback overhead. To address this issue, we propose in this paper a novel and efficient feedback codebook tailored to the user distribution. To this end, we first consider a typical scenario where users are uniformly distributed within a specific polar-region, based on which a sum-rate maximization problem is formulated to jointly optimize angle-range samples and bit allocation among angle/range feedback. This problem is challenging to solve due to the lack of a closed-form expression for the received power in terms of angle and range samples. By leveraging a Voronoi partitioning approach, we show that uniform angle sampling is optimal for received power maximization. For the more challenging range sampling design, we obtain a tight lower bound on the received power and show that geometric sampling, where the ratio between adjacent samples is constant, can maximize the lower bound and thus serves as a high-quality suboptimal solution. We then extend the proposed framework to accommodate more general non-uniform user distribution via an alternating sampling method. Furthermore, theoretical analysis reveals that as the array size increases, the optimal allocation of feedback bits increasingly favors range samples at the expense of angle samples. Finally, numerical results validate the superior rate performance and robustness of the proposed codebook design under various system setups, achieving significant gains over benchmark schemes, including the widely used polar-domain codebook.

We are interested in the mobile target state detection (TSD) based on multi-input-multi-output (MIMO) orthogonal-frequency-division-multiplexing (OFDM) communication signals. Yet, communication-based TSD is of challenge, due to complex problem structures and communication symbol randomness. To address this challenge, we exploit structured features of low-speed and narrow-band systems to decouple the target state parameters and random communication symbols, and then use coherent detection to equalize random symbols. As such, a simplified sensing model holding an explicit space-time-frequency-domain correlation structure with respect to target direction angle, radial speed and relative distance, respectively, is obtained. Then, an efficient MIMO OFDM waveform-based TSD method extracting space-time-frequency correlation features is devised. It is verified by simulations that the proposed TSD method outperforms state-of-the-art baselines, due to the above problem-specific algorithm design. In addition, we establish the closed-form boundaries of the maximum detectable speed and maximum detectable range for MIMO OFDM-based TSD, which are essentially subject to the limited coherent time and bandwidth, respectively. The impact of system parameters (e.g., signal bandwidth, subcarrier spacing and carrier frequency) on the detection capability boundaries is analysed to gain insights into the fundamental limits of MIMO OFDM communication-based TSD. This work does not only build a technical foundation for sensing-assisted communication design, but also provide a unified framework for understanding the potentials of MIMO OFDM communication-based sensing.

Wireless sensing has recently found widespread applications in diverse environments, including homes, offices, and public spaces, utilizing Channel State Information (CSI). However, CSI is highly sensitive to environmental changes, where even minor alterations can significantly distort the CSI patterns. This sensitivity often leads to performance degradation when applying models trained in one environment to another. To address this challenge, Domain Alignment Learning (DAL) has been widely adopted for cross-domain tasks, as it focuses on aligning the global distributions of the source and target domains in feature space. Despite its popularity, DAL often neglects inter-category relationships, which can lead to misalignment between categories across domains, even when global alignment is achieved. To overcome it, we propose KNN-MMD, a novel few-shot method for cross-domain wireless sensing. Our approach begins by constructing a "help set" using K-Nearest Neighbors (KNN) from the target domain, enabling local alignment between the source and target domains within each category using Maximum Mean Discrepancy (MMD). Additionally, most existing methods struggle to determine an optimal stopping point during training due to the absence of labeled data from the target domain. Our method resolves this by excluding the support set from the target domain during training and employing it as a validation set to determine the stopping criterion. We evaluate the proposed method across several cross-domain Wi-Fi sensing tasks, including gesture recognition, person identification, fall detection, and action recognition. In a one-shot scenario, our method achieves accuracy rates of 93.26%, 81.84%, 77.62%, and 75.30% for the respective tasks.

Channel state information (CSI) is a fundamental component in both wireless communication and sensing systems, enabling critical functions such as radio resource optimization and environmental perception. In wireless sensing, data scarcity and packet loss hinder efficient model training, while in wireless communication, high-dimensional CSI matrices and short coherent times caused by high mobility present challenges in CSI etimation. To address these issues, we propose a unified framework named CSIBERT2 for CSI prediction and classification tasks, built on our previous work CSIBERT. We introduce a two-stage training method that first uses a mask language model (MLM) to enable the model to learn general feature extraction from scarce datasets in an unsupervised manner, followed by fine-tuning for specific downstream tasks. Specifically, we extend MLM into a mask prediction model (MPM), which efficiently addresses the CSI prediction task. To further enhance the representation capacity of CSI data, we introduce an adaptive re-weighting layer (ARL) to enhance subcarrier representation and a MLP-based temporal embedding module to mitigate temporal information loss problem inherent in the original Transformer. Extensive experiments demonstrate that CSI-BERT2 achieves state-of-the-art performance across all tasks. Our results further show that CSI-BERT2 generalizes effectively across varying sampling rates and robustly handles discontinuous CSI sequences caused by packet loss—challenges that conventional methods fail to address.

The combination of Integrated Sensing and Communication (ISAC) and Mobile Edge Computing (MEC) enables devices to simultaneously sense the environment and offload data to the base stations (BS) for intelligent processing, thereby reducing local computational burdens. However, transmitting raw sensing data from ISAC devices to the BS often incurs substantial fronthaul overhead and latency. This paper investigates a three-tier collaborative inference framework enabled by Integrated Sensing, Communication, and Computing (ISCC), where cloud servers, MEC servers, and ISAC devices cooperatively execute different segments of a pre-trained deep neural network (DNN) for intelligent sensing. By offloading intermediate DNN features, the proposed framework can significantly reduce fronthaul transmission load. Furthermore, multiple-input multiple-output (MIMO) technology is employed to enhance both sensing quality and offloading efficiency. To minimize the overall sensing task inference latency across all ISAC devices, we jointly optimize the DNN partitioning strategy, ISAC beamforming, and computational resource allocation at the MEC servers and ISAC devices, subject to sensing beampattern constraints. We also propose an efficient two-layer optimization algorithm. In the inner layer, we derive closed-form solutions for computational resource allocation using the Karush-Kuhn-Tucker conditions. Moreover, we design the ISAC beamforming vectors via an iterative method based on the majorization–minimization and weighted minimum mean square error techniques. In the outer layer, we develop a cross-entropy-based probabilistic learning algorithm to determine an optimal DNN partitioning strategy. Simulation results demonstrate that the proposed framework substantially outperforms existing two-tier schemes in inference latency.

To support the unprecedented growth of the Internet of Things (IoT) networks, tremendous data need to be collected by the IoT devices and processed. Due to the limited computing capabilities of IoT devices, part of collected data need to be offloaded to the edge server for further processing. The conventional designs separately considering sensing, communication and computation processes lead to severe wastes of radio, energy, and computation resources. To overcome this drawback, an integrated sensing-communication-computation (ISCC) design is proposed in this paper, which aims at realizing energy efficient data processing by jointly determining the data offloading ratio together with the sensing and offloading rates according to the central processing units (CPU) profiles of IoT devices and servers. To deal with the resultant non-trivial optimization problem, the case with full-utilization of the server's CPU resource is considered, where the data offloading ratio is directly determined by the server's CPU profile, and the string-pulling (SP) algorithms are proposed to obtain the optimal sensing and offloading rates. As for the case with under-utilization of the server's CPU resource, a master-slave solving approach is proposed, where the master problem for optimizing the data offloading ratio can be solved by sub-gradient method, while the slave problem for optimizing the sensing and offloading rates can be solved by SP algorithms. The above solving approaches are further modified to account for the case with sensing busy time at which no data can be sensed by the IoT device. Simulations are conducted to verify the effectiveness of the proposed algorithms.

To facilitate the development of industrial Internet of Things applications, the wireless networked control system (WNCS) is envisioned to support real-time control and communication interactions performed in finite-time manner. A WNCS comprising multiple wirelessly interconnected sub-systems (SSs) is considered, wherein the sensed state information in each SS is transmitted to the controller via wireless links, thereby enabling timely decision-making processes. Following multiple operation periods of state sensing and transmission, the system identification (SI) is performed, leading to the formulation of optimal control policy. To improve the energy efficiency while guaranteeing the SI performance requirement within the allowed decision-making time, the communication and control co-design for WNCS is investigated, where the transmit power, transmission interval length, number of operation periods, coding block-length, and required transmission reliability are jointly optimized. Our investigation demonstrates the interrelationships among effective capacity, energy consumption, and communication parameters. Furthermore, it is found that the optimal communication parameters, such as transmit power and transmission interval length, should be determined by both communication and control requirements. Consequently, it is found that minimizing energy consumption is equivalent to minimize the number of operation periods while guaranteeing the SI performance with defined confidence, which can be effectively addressed by leveraging the non-decreasing property of controllability Gramian. Moreover, the co-design framework is extended to accommodate the scenarios involving link interruptions and overlapping time slots. Simulation results validate the necessity and effectiveness of exploring optimal system operational configurations from the perspective of the proposed co-design. It is also observed that although a 44.2% surge in energy consumption is associated with the proposed relay scheme in the link interruption case, the proposed time scheduling scheme brings a 23.4% reduction in the extra energy expenditure (from 44.2% to 20.8%).

We focus on joint multi-beam optimization (MBO) on both transmitter and receiver of 6G integrated sensing and communication (ISAC) systems, for achieving on-demand communication and sensing (C&S) performance tradeoff for diverse users. However, MBO is of great challenge due to inevitable self-interference (SI) of full-duplex antenna arrays and its non-convex optimization problem nature. Firstly, in order to address the SI challenge, we absorb SI alleviation requirements into problem modeling, and develop a novel SI-alleviated MBO framework. Secondly, in order to handle the non-convex optimization challenge, we resort to Lagrange dual transformation and fractional transformation for problem simplification, and extract structured models to yield an efficient alternating optimization-type MBO algorithm. We establish the convergence of the proposed MBO algorithm to justify our closed-form iterative optimization design. The proposed SI-alleviated MBO method can address different C&S requirements of diverse users, via joint transmitter and receiver beam steering, which paves the way for on-demand ISAC services. It is corroborated by simulations that our SI-alleviated MBO method outperforms state-of-the-art ISAC beamforming baselines, due to our problem-specific algorithm design.