To support the development of low altitude economy, the air-ground integrated sensing and communication (ISAC) networks need to be constructed to provide reliable and robust communication and sensing services. In this paper, the sensing capabilities in the cooperative air-ground ISAC networks are evaluated in terms of area radar detection coverage probability under a constant false alarm rate, where the distribution of aggregated sensing interferences is analyzed as a key intermediate result. Compared with the analysis based on the strongest interferer approximation, taking the aggregated sensing interference into consideration is better suited for pico-cell scenarios with high base station density. Simulations are conducted to validate the analysis.

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 thatgeometricsampling, 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.

Wireless sensing has recently found widespread applications in diverse environments, including homes, offices, and public spaces. By analyzing patterns in channel state information (CSI), it is possible to infer human actions for tasks such as person identification, gesture recognition, and fall detection. 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 or outright failure when applying wireless sensing models trained in one environment to another. To address this challenge, Domain Alignment Learning (DAL) has been widely adopted for cross-domain classification 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 these limitations, we propose K-Nearest Neighbors Maximum Mean Discrepancy (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, we address a key instability issue commonly observed in cross-domain methods, where model performance fluctuates sharply between epochs. Further, 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 effectiveness of the proposed method across several cross-domain Wi-Fi sensing tasks, including gesture recognition, person identification, fall detection, and action recognition, using both a public dataset and a self-collected dataset. In a one-shot scenario, our method achieves accuracy rates of 93.26%, 81.84%, 77.62%, and 75.30% for the respective tasks. The dataset and code are publicly available athttps://github.com/RS2002/KNN-MMD.

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 estimation. To address these issues, we propose a unified framework named CSI-BERT2 for CSI prediction and classification tasks, built on our previous work CSI-BERT, which adapts BERT to capture the complex relationships among CSI sequences through a bidirectional self-attention mechanism. 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 modify the structure of the original CSI-BERT. We introduce an adaptive re-weighting layer (ARL) to enhance subcarrier representation and a multi-layer perceptron (MLP)-based temporal embedding module to mitigate temporal information loss problem inherent in the original Transformer. Extensive experiments on both real-world collected and simulated datasets 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 dataset and code are publicly available athttps://github.com/RS2002/CSI-BERT2.

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.

To facilitate the development of industrial Internet of Things applications, thewireless networked control system(WNCS) is envisioned to support real-time control and communication interactions performed in finite-time manner. A WNCS comprising multiple wirelessly interconnectedsub-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, thesystem 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%).

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.

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.

We focus on self-interference (SI) alleviated beamforming of 6 G full-duplex integrated sensing and communication (ISAC) systems, for achieving an on-demand sensing and communication performance tradeoff with suppressed SI for diverse user devices. However, SI-alleviated ISAC beamforming is of great challenge due to its complex problem structures and nonconvex optimization problem nature. In order to address this challenge, we propose to use the dual transformation framework for problem simplification, and exploit structured components of the problem model, such as convexity, linearity and fraction, for yielding an efficient iterative optimization solution. The proposed SI-alleviated beamforming method can gracefully take care of communication and sensing requirements with suppressed SI for diverse user devices, thus paving the way for an on-demand ISAC service. It is corroborated by numerical simulations that the proposed SI-alleviated beamforming method outperforms state-of-the-art ISAC beamforming baselines, due to our specially-tailored problem modeling and problem-specific algorithm design.

This paper considers downlink power allocation and beamforming (PABF) for integrated sensing and communications (ISAC) against multipath interference. Yet, ISAC-oriented PABF is of great difficulty, due to its parameter-coupling structure and non-convex problem nature. A novel PABF method is proposed to address this issue. Firstly, in order to handle its complex problem structure, the PABF problem is divided into three subproblems, where communication-end beamformer, sensing-end beamformer and multipath power vector are decoupled. Secondly, structured models of the complex problem are extracted to address the non-convexity challenge. An efficient alternating optimization-based PABF algorithm with closed-form iterations is obtained. At the sensing receiver end, our PABF method can focus beams at the line-of-sight direction, while form null beams at reflection directions for suppressing multipath interference. Simultaneously, at the communication transceiver ends, it can smartly adjust beam gains and transmitting power over multiple paths to maximize the communication performance while ensuring a promised sensing performance. The proposed PABF algorithm can strike an on-demand communication and sensing performance tradeoff, via adjusting the sensing performance requirement. We have verified the efficiency of our PABF method by numerical simulations.

In space-air-ground-sea networks, the conventional data processing 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 design is pro-posed 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 processor profiles of mobile devices and servers. It is proved that the data offloading ratio is determined by the server's processor profile, while the string-pulling algorithms are designed to obtain the optimal sensing and offloading rates. Simulations are conducted to verify the effectiveness of the proposed design.

Driven by the rapid development of industrial Internet of Things applications, the wireless networked control system (WNCS) is expected to support real-time control-communication interaction performed in finite-time, which is task-oriented. A WNCS composed of multiple wirelessly inter-connected subsystems (SSs) is considered in this paper. The sensed state information in each SS is transmitted to the controller via wireless links for decision-and-control tasks. After multiple operation periods of state sensing and trans-mission, the system identification (SI) is executed and the optimal control (OC) policy is made. The SI requirement for OC is analyzed via system-level synthesis (SLS) based on robust control theory. A communication and control co-design is investigated, aiming to improve the energy efficiency while guaranteeing the SI performance requirement within the allowed decision-making time. The transmit powers at each sensor and controller, transmission interval length as well as the number of operation periods are jointly optimized. Simulations are conducted to validate the performance of the proposed co-design.

Reconstructing accurate radio maps is crucial for optimizing wireless network performance and managing spectrum efficiently. In real-world scenarios, radio map data, often sparse and incompletely labelled, poses significant challenges to traditional learning techniques. Graph Neural Networks (GNNs) have become instrumental in efficiently reconstructing radio maps (RMR) in such environments by effectively encoding correlations in unstructured data. Existing GNN-based methods, however, are limited as they typically consider only single factors like location, environment, or transmitter characteristics during correlation encoding. To overcome this limitation, we introduce RadioGAT, a propagation model-based approach that comprehensively integrates these factors. We further utilize Graph Attention Networks to enable semi-supervised learning, enhancing the accuracy of radio map reconstruction. Our experimental results demonstrate the superiority and robustness of RadioGAT, particularly at low sampling rates, and highlight the importance of selecting appropriate correlation encoding methods based on the data availability for RMR.

In recent years, Wi-Fi sensing has garnered significant attention due to its numerous benefits, such as privacy protection, low cost, and penetration ability. Extensive research has been conducted in this field, focusing on areas, such as gesture recognition, people identification, and fall detection. However, many data-driven methods encounter challenges related to domain shift, where the model fails to perform well in environments different from the training data. One major factor contributing to this issue is the limited availability of Wi-Fi sensing datasets, which makes models learn excessive irrelevant information and over-fit to the training set. Unfortunately, collecting large-scale Wi-Fi sensing datasets across diverse scenarios is a challenging task. To address this problem, we propose CrossFi, a siamese network-based approach that excels in both in-domain scenario and cross-domain scenario, including few-shot, zero-shot scenarios, and even works in few-shot new-class scenario where testing set contains new categories. The core component of CrossFi is a sample-similarity calculation network called CSi-Net, which improves the structure of the siamese network by using an attention mechanism to capture similarity information, instead of simply calculating the distance or cosine similarity. Based on it, we develop an extra Weight-Net that can generate a template for each class, so that our CrossFi can work in different scenarios. Experimental results demonstrate that our CrossFi achieves state-of-the-art performance across various scenarios. In gesture recognition task, our CrossFi achieves an accuracy of 98.17% in in-domain scenario, 91.72% in one-shot cross-domain scenario, 64.81% in zero-shot cross-domain scenario, and 84.75% in one-shot new-class scenario. The code for our model is publicly available athttps://github.com/RS2002/CrossFi.

With the access to tens of gigahertz of bandwidth, terahertz (THz) wideband communication emerges as a promising technology for the upcoming next generation mobile networks. To deal with the severe path loss and blockage of THz signals, massive multiple-input multiple-output and intelligent reflecting surface (IRS) can be jointly employed. Due to the extremely large signal bandwidth, the beams generated by the transmit hybrid beamforming may point to different directions around the target direction at different frequencies, which results in the beam splitting effect (BSE). In this paper, a new system design namelyTHzCondenseris introduced to mitigate the BSE, where the signals generated by each transmit radio frequency (RF) chain are reflected by one of the distributed IRSs in a one-to-one manner via the joint transmit and IRS beamforming design, thus creating adjustable multi-path components to achieve both high spatial multiplexing gain and array gain. Moreover, for practical scenarios when the number of transmit RF chains is more than that of IRSs, each IRS may need to reflect the signals generated by multiple RF chains in a one-to-many manner. For the above two cases, the joint beamforming design problems are efficiently solved to maximize the achievable rate. Simulations are conducted to verify the effectiveness of the proposed algorithms for mitigating the BSE and improving the achievable rate in IRS-aided THz wideband communications.

To support the development of air-ground integrated sensing and communication (ISAC), network-level performance analysis is needed for providing an essential guide on the network design. Following the widely adopted orthogonal frequency-division multiplexing (OFDM) technology in existing wireless systems, a cooperative air-ground wireless network based on OFDM-ISAC is introduced in this paper, where the ISAC-enabled base stations (BSs) following the two-dimensional homogeneous Poisson point process (HPPP) distribution serve the terrestrial communication users while sensing the aerial targets. In particular, cooperative beamforming schemes are designed for mitigating the interference among ISAC BSs. First, we analyze the communication as well as sensing performances in terms of different metrics including area communication coverage probability, area communication spectral efficiency, area radar detection coverage probability, and average Cramér-Rao Bound. Simulation results are then presented to validate the theoretical analysis and illustrate the effects of key system parameters on the network performance. It is observed that both the communication and sensing (C&S) performances depend on the BS density and height, while the sensing performance also depends on the height of sensing target together with the numbers of OFDM subcarriers and symbols. Moreover, there exists a tradeoff between the C&S performances with respect to the BS density and height. The results of this paper provide useful guidance to the design and implementation of air-ground wireless network for harnessing the dual benefits of ISAC.

This work suggests a joint optimization of the aerial intelligent reflecting surface (AIRS) placement, passive beamforming, and base station (BS) association to improve the overall data throughput and fairness across downlink heterogeneous cellular networks. Differing from the related works in the literature that just seek to maximize signal-to-interference-plus-noise ratio (SINR), the paper takes into account the load balancing between macrocells and small cells. The resulting joint optimization problem is mixed continuous-discrete and has a highly bumpy landscape, so the traditional (sub)gradient-based tools are not suited. We propose a model-free approach based on adaptive particle swarm optimization (APSO) and blind beamforming, which recovers the solution from random explorations of the solution space. Simulations show that the proposed algorithm enables balanced traffic for the coexisting macro and small cells, and thereby achieves a higher network utility than the benchmark methods.

In the future sixth-generation wireless communications, wireless sensor networks (WSNs) are expected to support target sensing, information communication, and computational tasks concurrently over the same spectrum. Integrated sensing and communication (ISAC) and over-the-air computation (AirComp) arise as two promising techniques. In this article, we design an integrated sensing, communication, and computing framework to improve spectrum efficiency and quality of service in WSNs. We investigate omnidirectional and directional beampattern designs to minimize AirComp error. Leveraging the derived directional patterns, we further consider tradeoff beampatterns to balance sensing and AirComp performance under power constraints. A mismatch-based design is formulated to minimize AirComp errors. To solve these nonconvex problems, we propose an alternating optimization approach using singular value decomposition and projection techniques to jointly optimize sensor and access point beampatterns. In particular, we propose a low-complexity two-step projection-based gradient descent method to solve the convex problems with two norm-ball constraints. In addition, convergence and complexity analysis of the proposed methods is provided. Simulations demonstrate the performance of the proposed beampattern designs.

In the sixth-generation (6G) networks, massive low-power devices are expected to sense environment and deliver tremendous data. To enhance the radio resource efficiency, the integrated sensing and communication (ISAC) technique exploits the sensing and communication functionalities of signals, while the simultaneous wireless information and power transfer (SWIPT) techniques utilizes the same signals as the carriers for both information and power delivery. The further combination of ISAC and SWIPT leads to the advanced technology namely integrated sensing, communication, and power transfer (ISCPT). In this paper, a multi-user multiple-input multiple-output (MIMO) ISCPT system is considered, where a base station equipped with multiple antennas transmits messages to multiple information receivers (IRs), transfers power to multiple energy receivers (ERs), and senses a target simultaneously. The sensing target can be regarded as a point or an extended surface. When the locations of IRs and ERs are separated, the MIMO beamforming designs are optimized to improve the sensing performance while meeting the communication and power transfer requirements. The resultant non-convex optimization problems are solved based on a series of techniques including Schur complement transformation and rank reduction. Moreover, when the IRs and ERs are co-located, the power splitting factors are jointly optimized together with the beamformers to balance the performance of communication and power transfer. To better understand the performance of ISCPT, the target positioning problem is further investigated. Simulations are conducted to verify the effectiveness of our proposed designs, which also reveal a performance tradeoff among sensing, communication, and power transfer.

Integrated sensing and communication (ISAC) capability is envisioned as one key feature for future cellular networks. Classical half-duplex (HD) radar sensing is conducted in a “first-emit-then-listen” manner. One challenge to realize HD ISAC lies in the discrepancy of the two systems’ time scheduling for transmitting and receiving. This difficulty can be overcome by full-duplex (FD) transceivers. Besides, ISAC generally has to comprise its communication rate due to realizing sensing functionality. This loss can be compensated by the emerging reconfigurable intelligent surface (RIS) technology. This paper considers the joint design of beamforming, power allocation and signal processing in a FD uplink communication system aided by RIS, which is a highly nonconvex problem. To resolve this challenge, via leveraging the cutting-the-edge majorization-minimization (MM) and penalty-dual-decomposition (PDD) methods, we develop an iterative solution that optimizes all variables via using convex optimization techniques. Besides, by wisely exploiting alternative direction method of multipliers (ADMM) and optimality analysis, we further develop a low complexity solution that updates all variables analytically and runs highly efficiently. Numerical results are provided to verify the effectiveness and efficiency of our proposed algorithms and demonstrate the significant performance boosting by employing RIS in the FD ISAC system.

The unprecedented growth of industrial Internet of Things applications requires the evolution of wireless networked control system (WNCS). WNCSs are becoming the fundamental infrastructure technologies for critical wireless control applications due to the main benefits of the reduced deployment and maintenance cost, as well as the enhanced flexibility and safety. However, independent designs between communication and control without considering their tight interaction in conventional WNCS lead to poor overall system performance and efficiency. Co-designs are expected to achieve the target control performance while improving the wireless resource efficiency. In this paper, by considering how to allocate wireless resource while guaranteeing control performance, a co-design framework is established based on the finite-time wireless system identification (WSI) - a fundamental problem in systems theory and intelligent control. To this end, two design problems are investigated aiming at maximizing the communication throughput or minimizing the power consumption while guaranteeing the WSI performance. In the former design, the joint optimization of power and channel allocations leads to a non-convex integer combinatorial problem, which is iteratively solved by optimizing the power allocation via Lagrangian method and obtaining the optimal channel allocation via Hungarian algorithm. The minimum number of data samples for guaranteeing the WSI accuracy under confidence level is further derived by exploiting the relationship between WSI accuracy and the number of state sampling processes, which leads to the maximum throughput with respect to both the communication and control processes. In the latter design for energy-efficient WSI, by exploiting the relationship between the power consumption and channel allocation given the WSI performance requirement, the optimization problem can be simplified and solved by Hungarian algorithm. Simulations are conducted to verify the performance of the proposed solutions.

Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless communications for tasks such as spectrum management and network planning. However, traditional machine-learning-based MB-RMR methods, which rely heavily on simulated data or complete structured ground truth, face significant deployment challenges. These challenges stem from the differences between simulated and actual data, as well as the scarcity of real-world measurements. To address these challenges, our study presents RadioGAT, a novel framework based on Graph Attention Network (GAT) tailored for MB-RMR within a single area, eliminating the need for multi-region datasets. RadioGAT innovatively merges model-based spatial-spectral correlation encoding with data-driven radiomap generalization, thus minimizing the reliance on extensive data sources. The framework begins by transforming sparse multi-band data into a graph structure through an innovative encoding strategy that leverages radio propagation models to capture the spatial-spectral correlation inherent in the data. This graph-based representation not only simplifies data handling but also enables tailored label sampling during training, significantly enhancing the framework’s adaptability for deployment. Subsequently, The GAT is employed to generalize the radiomap information across various frequency bands. Extensive experiments using raytracing datasets based on real-world environments have demonstrated RadioGAT’s enhanced accuracy in supervised learning settings and its robustness in semi-supervised scenarios. These results underscore RadioGAT’s effectiveness and practicality for MB-RMR in environments with limited data availability.

To facilitate the data collection process, simultaneous wireless information and power transfer utilizes the same signal for powering the devices and delivering the information, while the integrated sensing and communication utilizes the same signal for data transmission and radar sensing. In next generation networks, the sensing, communication, and power transfer functionalities are expected to be integrated together to enhance the radio resource efficiency and enable the data collection by massive low-power devices, which leads to the new research direction namely integrating sensing, communication, and power transfer (ISCPT). The ISCPT beamforming design for multiple users is investigated in this paper to improve the sensing performance while guaranteeing the communication and power transfer requirements. The resultant non-convex optimization problem is solved by the approach based on semidefinite relaxation and rank reduction methods. Simulations are further conducted to verify the effectiveness of the proposed design.

The indoor location based services are fascinating in many applications such as commercial recommendation, surveillance, and navigation. In this paper, we propose a high-precision indoor single-link passive tracking method based on Unscented Kalman Filter (UKF) using WiFi channel state information (CSI), namely UKFWiTr. In this method, both the CSI-quotient and Space-Alternating Generalized Expectation-maximization algorithm are used to estimate Doppler frequency shift and Time-of-Flight. Then, an Arrival-of-Angle optimization method and a tracking accuracy improvement method both based on UKF are put forward in UKFWiTr. The experimental results show that the average tracking error in different environments is less than 1.3m, and can even achieve 0.49m in particular scenarios.

To support the unprecedented growth of the Internet of Things (IoT) applications, tremendous data need to be collected by the IoT devices and delivered to the server for further computation. By utilizing the same signals for both radar sensing and data transmission, theintegrated sensing and communication(ISAC) technique enables simultaneous data collection and delivery in the physical layer. By exploiting the analog-wave addition property in a multi-access channel,over-the-air computation(AirComp) has been proposed as a communication approach that also enables function computation. The promising performances of ISAC and AirComp motivate the current work on developing a framework calledintegrated sensing, communication, and computation over-the-air(ISCCO). Two schemes are designed to supportmultiple-input-multiple-output(MIMO) ISCCO simultaneously, namely theseparated and sharedschemes. The separated scheme splits antenna array for radar sensing and AirComp, while all the antennas transmit a joint waveform for both radar sensing and AirComp in the shared scheme. The performance of radar sensing is evaluated by themean squared error(MSE) of the estimated target response matrix, while the MSE of the estimated function is adopted as the metric to evaluate the performance of the coupled communication and computation in AirComp. The design challenge of MIMO ISCCO lies in the joint optimization of beamformers at both the IoT devices and the server, which results in a non-convex problem. To solve this problem, an algorithmic solution based on the technique of semidefinite relaxation is proposed. The results reveal that the beamformer at each sensor needs to account for supporting dual-functional signals in the shared scheme, while dedicated beamformers for sensing and AirComp are needed to mitigate the mutual interference between the two functionalities in the separated scheme. The application of ISCCO on target location estimation is further demonstrated via simulation.

The unprecedented growth of mobile data traffic has fueled the deployment of artificial intelligence (AI) at the network edge, while distilling the intelligence from raw data by machine learning requires tremendous labelling effort. To overcome this challenge, wireless crowd labelling (WCL) is proposed for efficient data labelling by exploiting billions of available mobile annotators and the multicasting property of wireless channels. A WCL system is considered in this paper where unlabelled data (objects) are multicast via fading channels to different clusters of annotators for repetition labelling to improve the accuracy. Given the desired labelling accuracy, the superposition coding technique together with the repetition labelling scheme give rise to a new tradeoff between radio-and-annotator resource consumption. Building on such tradeoff, the annotator clustering and transmit power control are jointly optimized to maximize the labelling throughput (i.e., the number of labelled objects) or minimize the power consumption, resulting in NP-hard integer programming problems. To solve these problems, the optimal structure of annotator clustering is derived by exploiting the property that the power allocation for multicasting objects tends to compensate for the worst channel among the annotators in each cluster. Based on such structure, the throughput maximization problem can be recognized as a longest-path problem and solved by means of branch-and-bound, while the power minimization problem can be recasted to a shortest-path problem and solved by means of forward dynamic programming. The solution approaches can be further simplified when the channels are symmetric by merging the same nodes and cutting the identical paths in the path graph. In addition, exact polices are derived for the special cases where either the annotators or power are constrained. Last, simulation results are presented to demonstrate the performance of our proposed joint designs.

Driven by the rapid growth of Internet of Things applications, tremendous data need to be collected by sensors and uploaded to the servers for further process. As a promising solution, mobile crowd sensing (MCS) enables controllable sensing and transmission processes of multiple types of data in a single device. Despite the appealing advantages, existing works on MCS have mostly simplified two design issues, namely joint control of sensing and transmission processes and corresponding energy consumption. To address the above issues, a single-user MCS system is considered with a typical MCS device sensing and transmitting data to a server in a given time duration. In particular, there exists a busy time interval when the device is incapable of sensing. To minimize the sensing-and-transmission energy consumption of the device, an optimization problem is formulated, where the sensing and transmission rates are jointly optimized over time subjecting to the constraints on the sensing data sizes, transmission data sizes, data casualty, and busy time of sensing. This problem is highly challenging due to the coupling between the rates as well as the existence of the busy time. To deal with this problem, we first show that it can be equivalently decomposed into two subproblems, corresponding to a search for the amount of data size that needs to be sensed before the busy time (referred to as the height), as well as the control of sensing and transmission rates given the height. Next, we show that the latter problem can be efficiently solved by using the classical string-pulling method, while an efficient algorithm is proposed to progressively find the optimal height without the exhaustive search. Moreover, the solution approach is extended to a more complex scenario where there is a finite-size buffer at the server for receiving data. Last, simulations are conducted to evaluate the performance of the proposed designs.

The rapid development of artificial intelligence together with the powerful computation capabilities of the advanced edge servers make it possible to deploy learning tasks at the wireless network edge, which is dubbed as edge intelligence (EI). The communication bottleneck between the data resource and the server results in deteriorated learning performance as well as tremendous energy consumption. To tackle this challenge, we explore a new paradigm called learning-and-energy-efficient (LEE) EI, which simultaneously maximizes the learning accuracies and energy efficiencies of multiple tasks via data partition and rate control. Mathematically, this results in a multi-objective optimization problem. Moreover, the continuous varying rates introduce infinite variables, which further complicates the problem. To solve this complex problem, the number of variables is reduced to a finite level by exploiting the optimality of constant-rate transmission in each epoch, based on which a string-pulling (SP) algorithm is proposed to obtain the numerical values. The performance of the proposed joint data partition and rate control design is examined by experiments based on public datasets.

The rapid development of artificial intelligence together with the powerful computation capabilities of the advanced edge servers make it possible to deploy learning tasks at the wireless network edge, which is dubbed as edge intelligence (EI). The communication bottleneck between the data resource and the server results in deteriorated learning performance as well as tremendous energy consumption. To tackle this challenge, we explore a new paradigm called learning-and-energy-efficient (LEE) EI, which simultaneously maximizes the learning accuracies and energy efficiencies of multiple tasks via data partition and rate control. Mathematically, this results in a multi-objective optimization problem. Moreover, the continuously varying communication rates introduce infinite variables, which further complicates the problem. To solve this complex problem, we consider the case with infinite server buffer capacity and one-shot data arrival at sensor. First, the number of variables is reduced to a finite level by exploiting the optimality of constant-rate transmission in each epoch. Second, the optimal solution of the multi-objective problem is found by applying the stratified sequencing or merging of objectives. By assuming higher priority of learning efficiency in stratified sequencing, the optimal data partition is derived in closed form by the Lagrange method, while the optimal rate control is proved to have the structure of directional water filling (DWF), based on which a string-pulling (SP) algorithm is proposed to obtain the numerical values. The DWF structure of rate control is also proved to be optimal in merging of objectives, which combines different objectives in a weighted manner. By exploiting the optimal rate changing properties, the SP algorithm is further extended to tackle the more challenging cases with limited server buffer capacity or bursty data arrival at sensor. The performance of the proposed joint data partition and rate control design is examined by extensive experiments based on public datasets.

The deployment of federated learning in a wireless network, calledfederated edge learning(FEEL), exploits low-latency access to distributed mobile data to efficiently train an AI model while preserving data privacy. In this work, we study the spatial (i.e., spatially averaged) learning performance of FEEL deployed in a large-scale cellular network with spatially random distributed devices. Both the schemes of digital and analog transmission are considered, providing support of error-free uploading and over-the-air aggregation of local model updates by devices. The derived spatial convergence rate for digital transmission is found to be constrained by a limited number of active devices regardless of device density and converges to the ground-true rate exponentially fast as the number grows. The population of active devices depends on network parameters such as processing gain and signal-to-interference threshold for decoding. On the other hand, the limit does not exist for uncoded analog transmission. In this case, the spatial convergence rate is slowed down due to the direct exposure of signals to the perturbation of inter-cell interference. Nevertheless, the effect diminishes when devices are dense as interference is averaged out by aggressive over-the-air aggregation. In terms of learning latency (in second), analog transmission is preferred to the digital scheme as the former dramatically reduces multi-access latency by enabling simultaneous access.

The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), while tremendous labels are required for AI model training via supervised learning. To tackle this challenge, a novel framework of wireless crowd labelling is proposed that downloads data to many imperfect mobile annotators for repetition labelling by exploiting multicasting in wireless networks. The integration of the rate-distortion theory and the principle of repetition labelling gives rise to a new tradeoff between radio-and-annotator resources under a constraint on labelling accuracy. Aiming at maximizing the labelling throughput, this work focuses on optimizing the joint annotator-and-spectrum allocation (JASA). To develop an efficient solution approach, an optimal sequential annotator-clustering scheme is derived. Thereby, the optimal JASA policy can be found by an efficient tree search.

The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), automating the operations of our society ranging from transportation to healthcare. The implementation of ubiquitous AI, however, entails labelling of an enormous amount of data prior to the training of AI models via supervised learning. To tackle this challenge, we explore a new direction called wireless crowd labelling, which involves downloading data to many imperfect mobile annotators for repetition labelling with an aim of exploiting multicasting in wireless networks. In this cross-disciplinary area, the rate-distortion theory and the principle of repetition labelling for accuracy improvement together give rise to a new tradeoff between radio-and-annotator resources under a constraint on labelling accuracy. Building on the tradeoff and aiming at maximizing the labelling throughput, this work focuses on the joint optimization of encoding rate, annotator clustering, and sub-channel allocation, which results in an NP-hard integer programming problem. To devise an efficient solution approach, we establish an optimal sequential annotator-clustering scheme based on the order of decreasing signal-to-noise ratios, thereby allowing the optimal solution to be found by an efficient tree search. This solution can be further simplified when the channels are symmetric. Alternatively, the optimization problem can be recognized as a knapsack problem, which can be efficiently solved in pseudo-polynomial time by means of dynamic programming. In addition, the optimal polices are derived for the annotator constrained and spectrum constrained cases. Last, simulation results are presented to demonstrate the significant throughput gains based on the optimal solution compared with decoupled allocation of the two types of resources.

Leveraging massive numbers of sensors in user equipment as well as opportunistic human mobility, mobile crowd sensing (MCS) has emerged as a powerful paradigm, where prolonging battery life of constrained devices and motivating human involvement are two key design challenges. To address these, we envision a novel framework, named wirelessly powered crowd sensing (WPCS), which integrates MCS with wireless power transfer for supplying the involved devices with extra energy and thus facilitating user incentivization. This paper considers a multiuser WPCS system where an access point (AP) transfers energy to multiple mobile sensors (MSs), each of which performing data sensing, compression, and transmission. Assuming lossless (data) compression, an optimization problem is formulated to simultaneously maximize data utility and minimize energy consumption at the operator side, by jointly controlling wireless-power allocation at the AP as well as sensing-data sizes, compression ratios, and sensor-transmission durations at the MSs. Given fixed compression ratios, the proposed optimal power allocation policy has the threshold-based structure with respect to a defined crowd-sensing priority function for each MS depending on both the operator configuration and the MS information. Further, for fixed sensing-data sizes, the optimal compression policy suggests that compression can reduce the total energy consumption at each MS only if the sensing-data size is sufficiently large. Our solution is also extended to the case of lossy compression, while extensive simulations are offered to confirm the efficiency of the contributed mechanisms.

As a revolution in networking, the Internet of Things (IoT) aims at automating the operations of our societies by connecting and leveraging an enormous number of distributed devices (e.g., sensors and actuators). One design challenge is efficient wireless data aggregation (WDA) over the dense IoT devices. This can enable a series of the IoT applications ranging from latency-sensitive high-mobility sensing to data-intensive distributed machine learning. Over-the-air (function) computation (AirComp) has emerged to be a promising solution that merges computing and communication by exploiting analog-wave addition in the air. Another IoT design challenge is battery recharging for dense sensors which can be tackled by wireless power transfer (WPT). The coexisting of AirComp and WPT in the IoT system calls for their integration to enhance the performance and efficiency of WDA. This motivates the current work on developing the wirelessly powered AirComp (WP-AirComp) framework by jointly optimizing wireless power control, energy and (data) aggregation beamforming to minimize the AirComp error. To derive a practical solution, we recast the non-convex joint optimization problem into the equivalent outer and inner sub-problems for (inner) wireless power control and energy beamforming, and (outer) the efficient aggregation beamforming, respectively. The former is solved in closed form while the latter is efficiently solved using the semidefinite relaxation technique. The results reveal that the optimal energy beams point to the dominant Eigen-directions of the WPT channels, and the optimal power allocation tends to equalize the close-loop (down-link WPT and up-link AirComp) effective channels of different sensors. The simulation demonstrates that the controlling WPT provides additional design dimensions for substantially reducing the AirComp error.