Accurate beam prediction is essential for maintaining reliable links and high spectral efficiency in dynamic low-altitude wireless networks. However, existing approaches often fail to capture the deep correlations across heterogeneous sensing modalities, limiting their adaptability in complex three-dimensional environments. To overcome these challenges, we propose a multi-modal predictive beamforming method based on a cross-attention fusion mechanism that jointly leverages visual and structured sensor data. The proposed model utilizes a Convolutional Neural Network (CNN) to learn multi-scale spatial feature hierarchies from visual images and a Transformer encoder to capture cross-dimensional dependencies within sensor data. Then, a cross-attention fusion module is introduced to integrate complementary information between the two modalities, generating a unified and discriminative representation for accurate beam prediction. Through experimental evaluations conducted on a real-world dataset, our method reaches 79.7% Top-1 accuracy and 99.3% Top-3 accuracy, surpassing the 3D ResNet-Transformer baseline by 4.4%-23.2% across Top-1 to Top-5 metrics. These results verify that multi-modal cross-attention fusion is effective for intelligent beam selection in dynamic low-altitude wireless networks.

We revisit the Gray-Wyner lossy source coding problem and derive the first-order asymptotic optimal rate-distortion-perception region when additional perception constraints are imposed on reproduced source sequences. The optimal trade-off is shown to be governed by a mutual information term involving common information and two conditional rate-distortion-perception functions. The perception constraint requires that the distribution of each reproduced sequence is close to that of the original source sequence, which is motivated by practical applications in image and video compression. Prior studies usually focus on the compression and reconstruction of a single source sequence. In this paper, we generalize the prior results for point-to-point systems to the representative multi-terminal setting of the Gray-Wyner problem with two correlated source sequences. In particular, we integrate the analyses of the distortion and the perception constraints by including the random circular shift operator in the encoding and decoding process directly.

In this paper, we investigate the problem of predictive beamforming design for tracking vehicles in an integrated sensing and communication (ISAC)-based near-field vehicle-toinfrastructure (V2I) system. The waveform design in near-field scenarios requires the joint consideration of both range and angle dimensions, posing new challenges to conventional beamforming and tracking strategies. To address this issue, we propose a predictive beamforming framework leveraging a large language model (LLM)-based neural network (LNN), which exploits historical channel state information (CSI) to facilitate accurate future beamforming decisions. Cramér–Rao bounds (CRBs) for angle and distance estimation, along with the achievable sum-rate, are applied as key metrics to evaluate the sensing and communication performance of the V2I system, respectively. Capitalizing on the derived performance metrics, we formulate the optimization problems aiming either to maximize the sum-rate subject to CRB constraints or to minimize the CRB while ensuring a required communication rate, thereby accommodating different design requirements. Moreover, to effectively capture the stochastic nature of vehicle driving behavior, the performance metrics are further expressed in expectation form over the distribution of possible driving states. Consequently, a data-driven optimization approach based on the LNN is adopted to handle the resulting intractable analytical expressions, and the underlying LNN is trained with task-specific loss functions. During the training process, low-rank adaptation (LoRA) is incorporated to fine-tune the pre-trained LLM, which significantly reduces the number of trainable parameters. Simulation results demonstrate that the proposed framework accurately predicts future vehicle kinematic parameters and effectively optimizes the power allocation across transmit links. As a result, it achieves superior and robust performance in both communication and sensing tasks, highlighting its potential as a vital solution for next-generation near-field V2I systems.

Integrated sensing and communications (ISAC) systems promise dual use of spectrum and hardware for data transmission and environmental awareness. However, extending ISAC to satellite networks is challenged by high path loss, long delays, and the overhead of channel estimation. To address these challenges, we propose a channel-agnostic predictive beamforming framework for satellite ISAC (S-ISAC) within a crowdsourced bistatic architecture. Unlike conventional bistatic architectures that require a dedicated sensing receiver, our design aggregates echoes from multiple ground internet of things (IoT) devices (GIDs) in a crowdsourced manner to improve sensing performance without introducing any additional sensing equipment. We propose a model termed Historical Geometric-based LLM (HG-LLM) as a realization of the channel-agnostic predictive beamforming framework. HG-LLM learns to map historical geometric information (HGI) of the satellite, sensing target, and GIDs directly to future beamforming matrices, eliminating the need for channel state information (CSI). We propose two key modules in HG-LLM, namely, the Histogeometric Encoder, which transforms spatial-temporal data into LLM-compatible embeddings, and the TokenBeamformer, which translates the LLM outputs into optimized beamforming weights. Moreover, the backbone LLM is fine-tuned using low-rank adaptation for efficient adaptation to predictive beamforming tasks. Extensive simulations demonstrate that HG-LLM achieves performance levels comparable to channel-based methods across diverse settings, despite relying solely on HGI without requiring explicit CSI.

Wireless jamming identification, which detects and classifies electromagnetic jamming from non-cooperative devices, is crucial for emerging low-altitude wireless networks consisting of many drone terminals that are highly susceptible to electromagnetic jamming. However, jamming identification schemes adopting deep learning (DL) are vulnerable to attacks involving carefully crafted adversarial samples, resulting in inevitable robustness degradation. To address this issue, we propose a differential transformer framework for wireless jamming identification. Firstly, we introduce a differential transformer network in order to distinguish jamming signals, which overcomes the attention noise when compared with its traditional counterpart by performing self-attention operations in a differential manner. Secondly, we propose a randomized masking training strategy to improve network robustness, which leverages the patch partitioning mechanism inherent to transformer architectures in order to create parallel feature extraction branches. Each branch operates on a distinct, randomly masked subset of patches, which fundamentally constrains the propagation of adversarial perturbations across the network. Additionally, the ensemble effect generated by fusing predictions from these diverse branches demonstrates superior resilience against adversarial attacks. Finally, we introduce a novel consistent training framework that significantly enhances adversarial robustness through dual-branch regularization. Simulation results demonstrate that our proposed methodology is superior to existing methods in boosting robustness to adversarial samples.

Simultaneous wireless information and power transfer (SWIPT) has emerged as a promising paradigm for enabling sustainable connectivity in battery-limited low-altitude wireless networks (LAWNs). This paper investigates a SWIPT-enabled LAWN system in which a multi-antenna base station (BS) simultaneously delivers control information and wireless energy to a fleet of uncrewed aircraft systems (UASs) via power splitting. In particular, the BS remotely guides the UASs to accurately track predefined reference trajectories toward their destinations while avoiding multiple mobile no-fly zones (NFZs). To guarantee collision-free path planning, we first construct smooth and safe reference trajectories using stream function theory. Then, a real-time optimization problem is formulated, which jointly takes into account the wireless control cost and energy sustainability by optimizing control inputs, transmit beamforming vectors, and the power splitting ratios. To address the resultant non-convex problem, a two-stage optimization framework is proposed. First, we develop a model predictive control (MPC)-based method to generate predictive control inputs. Subsequently, we derive a computationally efficient iterative algorithm to optimize the beamforming vectors and power splitting ratios by applying semidefinite relaxation (SDR) and successive convex approximation (SCA) techniques. We further prove that the SDR is tight for our formulation. Extensive numerical results demonstrate that our proposed design significantly outperforms benchmark schemes in terms of tracking accuracy and harvested energy, thereby validating its effectiveness for sustainable implementation in LAWN systems.

The space-air-ground integrated network (SAGIN) paradigm has emerged as a pivotal enabler for the evolution of next-generation wireless systems. This article proposes a novel framework for covert communication in SAGINs, wherein a high-altitude platform (HAP), equipped with multiple antennas, serves terrestrial communication users (CUs) under the surveillance of multiple non-colluding wardens, with satellite assistance for warden location updates via space-air links. To safeguard the communication from detection by the wardens, the HAP employs artificial noise (AN) and robust beamforming techniques, addressing the challenges posed by imperfect channel state information (CSI) of the wardens. Subsequently, we formulate a non-convex optimization problem aimed at maximizing the number of served users, subject to stringent covertness constraints, satellite-HAP link outage probabilities, and maximum available power budgets. By employing ℓ0-norm relaxation, we convert the original problem into a mixed-integer optimization framework and develop a computationally efficient alternating optimization approach that combines bisection search, successive convex approximation (SCA), and semidefinite relaxation (SDR) techniques to tackle satellite power allocation, user scheduling, and beamforming design. Numerical simulations demonstrate that the proposed scheme significantly improves the user coverage while maintaining covertness, revealing a trade-off between covert communication and CU coverage capability in resource-constrained aerial-terrestrial environments, even under imperfect CSI conditions.

Integrated sensing and communication (ISAC) has emerged as a pivotal technology for advancing low-altitude wireless networks (LAWNs), serving as a critical enabler for next-generation communication systems. This paper investigates the system design for energy-saving uncrewed aerial vehicle (UAV) formations in dual-functional LAWNs, where a ground base station (GBS) simultaneously wirelessly controls multiple UAV formations and performs sensing tasks. To enhance flight endurance, we exploit the aerodynamic upwash effects and propose a distributed energy-saving formation framework based on the adapt-then-combine (ATC) diffusion least mean square (LMS) algorithm. Specifically, each UAV updates the local position estimate by invoking the LMS algorithm, followed by refining it through cooperative information exchange with neighbors. This enables an optimized aerodynamic structure that minimizes the formation’s overall energy consumption. To ensure control stability and fairness, we formulate a maximum linear quadratic regulator (LQR) minimization problem, which is subject to both the available power budget and the required sensing beam pattern gain. To address this non-convex problem, we develop a two-step approach by first deriving a closed-form expression of LQR as a function of arbitrary beamformers. Subsequently, an efficient iterative algorithm that integrates successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques is proposed to obtain a sub-optimal dual-functional beamforming solution. Extensive simulation results confirm that the ‘V’-shaped formation is the most energy-efficient configuration and demonstrate the superiority of our proposed design over benchmark schemes in improving control performance.

This paper proposes a joint secure sensing and communication framework for full-link covert transmissions, which integrates an intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) system and compliant distributed cooperative jammers to enhance the communication quality of legitimate users while promoting the efficient utilization of limited resources. In the proposed scheme, upon sensing potential eavesdropper embodied by unmanned aerial vehicle (UAV), the dual-functional base station (BS) covertly transmits the acquired UAV state information to friendly jammers within relevant coverage area and issues activation commands promptly. Simultaneously, with IRS assistance, reconfigurable parameters such as signal phase in NOMA transmissions are adjusted to satisfy public user’s service requirements while facilitating covert communications for legitimate user. To ensure dynamic adaptability and link sustainability, the BS leverages historical sensing data to predict the UAV’s flight trajectory in real time and infer its movement intent. If the UAV exhibits a tendency to deviate from the currently effective jamming zone, the BS proactively activates friendly jammers in adjacent regions to maintain covert transmission rates and ensure robust system operation. To address the non-convex optimization challenge arising from jointly optimizing sensing beamforming, communication beamforming, and the IRS reflection matrix with highly coupled variables, we disassemble the problem into three subproblems. Correspondingly, an alternating optimization framework is designed by employing the semidefinite relaxation (SDR), Gaussian randomization, penalty-based methods, and Dinkelbach transformation to jointly maximize covert transmission rates while guaranteeing both sensing accuracy and communication quality of service (QoS). Simulation results demonstrate that the proposed scheme achieves superior covert transmission rates compared with benchmark schemes. Moreover, the dual-covertness mechanisms for sensing and communication further enhance the system security, validating the framework’s robustness in dynamic resource-constrained environments.

Security and reliability have always evolved alongside advancements in communication technologies. Facing imminent the sixth generation of mobile communication (6G) era, this work explores a jamming exploitation-enabled covert transmission framework for satellite-aerial-terrestrial integrated networks (SATINs), aiming to meet user privacy requirements in future complex adversarial scenarios characterized by stereoscopic coverage and multi-domain collaboration. The research scenario involves a three-dimensional space comprising four core elements: a satellite, an unmanned aerial vehicle (UAV) equipped with an active simultaneously transmitting and reflecting reconfigurable intelligent surface (active STAR-RIS), an eavesdropper possessing dual functionalities of jamming and detection, and a ground terminal. Upon sensing malicious jamming from the eavesdropper, the UAV aerial platform serving as a relay node utilizes its on-board active STAR-RIS to achieve the targeted reflection and manipulation of the malicious jamming signals while concurrently facilitating the effective forwarding of the legitimate signals. Namely, breaking through the conventional mindset of “jamming suppression”, it equivalently constructs a “self-interference loop” centered on the eavesdropper, thereby degrading the adversary’s detection sensitivity. For this process, we construct a covert analysis framework featuring the joint design of the static/dynamic scenarios and the active STAR-RIS reflection-transmission matrices dominated by the UAV’s limited power, derive the analytical expression of the Kullback-Leibler (KL) divergence, and establish rigorous covertness constraints for the system. To address the highly coupled non-convex problem in the joint optimization,we propose a solution combining semidefinite relaxation (SDR), Dinkelbach transformation, Gaussian randomization, and the proximal policy optimization (PPO) framework, maximizing the system covert transmission rate while satisfying various constraints. Numerical results demonstrate that, compared with benchmark schemes, the proposed scheme exhibits superior flexibility and covert transmission advantages in adversarial environments.

This paper investigates a general integrated sensing and covert communication (ISCC) system, where a base station (BS) transmits signals to covert users (CUs) while simultaneously sensing a dynamic target that acts as a warden (WA), maliciously attempting to eavesdrop on the covert communication. An essential task in realizing ISCC is the beamforming design, which however, is complicated by the dynamic nature of both the WA and the CUs in practice, i.e., (i) the rapid movement of the WA and (ii) the time-varying number of CUs. To address these challenges, in this paper, we develop a versatile recurrent graph neural network (RGNN)-based beamforming design framework, where the penalty method is first employed to transform the constrained optimization problem into an unconstrained one, and then an RGNN is customized to effectively output the beamforming vectors. Through implicitly learning features from the historical warden detection channels and the instantaneous channel state information among CUs and BS, the proposed approach could predict the next-time slot beamforming matrix while accommodating a scalable number of CUs, thus eliminating repeated WA channel estimation and re-optimization when handling dynamic scenarios. Moreover, a convolutional long short-term memory (CLSTM)-augmented message passing GNN (CL-MPGNN) is developed to realize the RGNN framework. In particular, a CLSTM module is first adopted to exploit the spatial-temporal features from the input to facilitate an effective predictive beamforming. Then, a set of message-passing layers is employed to guarantee the scalability of the beamforming design. Simulations verify the effectiveness of the proposed algorithm in terms of the covert communication performance, the covert communication-sensing tradeoff, and the generalizability, respectively.

Unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) is regarded as a key enabler for next-generation wireless systems. However, conventional fixed-position antennas limit the ability of UAVs to fully exploit their inherent potential. To overcome this limitation, we propose a UAV-enabled ISAC framework equipped with fluid antennas (FAs), where the mobility of antenna elements introduces additional spatial degrees of freedom to simultaneously enhance communication and sensing performance. A multi-objective optimization problem is formulated to maximize the communication rates of multiple users while minimizing the Cram´er-Rao bound (CRB) for the angle estimation of a single target. Due to excessively frequent updates of FA positions may lead to response delay, a three-timescale optimization framework is developed to jointly optimize transmit beamforming, FA positions, and UAV trajectory based on their characteristics. To solve the non-convexity of the problem, an alternating optimization-based algorithm is developed to obtain a sub-optimal solution. Numerical results show that the proposed scheme significantly outperforms various benchmark schemes, validating the effectiveness of integrating the FA technology into the UAV-enabled ISAC systems.

Motivated by the practical application of beam tracking of multiple devices in Multiple Input Multiple Output (MIMO) communication, we study the problem of non-adaptive twenty questions estimation for locating and tracking multiple moving targets under a query-dependent noisy channel. Specifically, we derive a non-asymptotic bound and a second-order asymptotic bound on resolution for optimal query procedures and provide numerical examples to illustrate our results. In particular, we demonstrate that the bound is achieved by a state estimator that thresholds the mutual information density over possible target locations. This single threshold decoding rule has reduced the computational complexity compared to the multiple threshold scheme proposed for locating multiple stationary targets (Zhou, Bai and Hero, TIT 2022). We discuss two special cases of our setting: the case with unknown initial location and known velocity, and the case with known initial location and unknown velocity. Both cases share the same theoretical benchmark that applies to stationary multiple target search in Zhou, Bai and Hero (TIT 2022) while the known initial location case is close to the theoretical benchmark for stationary target search when the maximal speed is inversely proportional to the number of queries. We also generalize our results to account for a piecewise constant velocity model introduced in Zhou and Hero (TIT 2023), where targets change velocity periodically. Finally, we illustrate our proposed algorithm for the application of beam tracking of multiple mobile transmitters in a 5G wireless network.

Semantic communication is increasingly recognized as a promising paradigm for enhancing the communication capabilities of wireless systems in the 6G era. Existing deep learning (DL)-based semantic methods typically enhance system robustness through module-centric strategies, where additional components are integrated into the model. Due to the significant computational overhead, these strategies are often unsuitable for resource-limited systems, such as the emerging low-altitude wireless networks (LAWNs). Moreover, most existing methods are optimized for fixed channel models and lack architectural adaptability across diverse environments, leading to repeated retraining and increased maintenance complexity. To address these challenges, we propose a novel Dual-Branch Architecture (DBA) for semantic communication. DBA employs a training-only auxiliary decoder branch to provide noise-free supervision for the main decoder, thereby enhancing robustness without adding deployment or inference overhead. Specifically, we introduce a contrastive learning mechanism to align the outputs of the noisy and noise-free branches, reinforcing semantic consistency. Building on this, we further propose the Extensible Multi-Channel Architecture (EMCA), a multi-branch design that incorporates multiple decoder branches optimized for different channel models and jointly trains them with a shared encoder and auxiliary branch, improving scalability without duplicating model parameters. Simulation results demonstrate that DBA and EMCA consistently outperform existing baselines in terms of semantic fidelity across additive white Gaussian noise (AWGN), Rayleigh, and Rician fading channels, without incurring any additional inference cost. Additionally, experiments conducted using a software-defined radio (SDR)-based platform with universal software radio peripheral (USRP) further validate the robustness and practicality of the proposed methods in practical environmental applications.

The rapid development of the low-altitude economy (LAE) has accelerated the deployment of cargo unmanned aerial vehicles (UAVs) for intelligent logistics and delivery services. However, large-scale UAV operations still face multiple practical challenges, including unstable communication connectivity, limited onboard battery energy, and strict customer time-window constraints. To address these issues, this paper investigates the trajectory and task scheduling optimization problem for multi-UAV cooperative cargo pick-up under joint communication, energy, and time-window constraints. We develop a collision-aware cooperative multi-UAV optimization algorithm (CACMO) that integrates a Dueling Deep Q-Network (D3QN) for communication-aware trajectory learning with a simulated annealing (SA) based global task-sequence planner and an explicit inter-UAV conflict-resolution mechanism. The D3QN module enables adaptive trajectory generation in unknown and time-varying radio environments without requiring an a priori radio map, maintaining stable connectivity while reducing flight cost, whereas the SA module determines efficient task orders and enforces safe coordination among multiple UAVs through collision-aware refinement. Simulation results demonstrate that the proposed CACMO algorithm framework achieves an optimal balance between task completion time (1,719 seconds) and user satisfaction (score of 0.9969) under typical operating conditions, delivering a 70–75% reduction in total weighted cost compared to representative baseline methods. Crucially, this substantial improvement is achieved while explicitly enforcing multi-UAV collision avoidance-a critical constraint absent in most baseline methods. The framework maintains zero communication outage and guarantees safe inter-UAV separation throughout the mission while satisfying all energy and time window constraints in realistic urban environments, confirming its robustness and scalability for cooperative multi-UAV logistics operations within the LAE.

Integrated sensing and communication (ISAC) utilizes existing communication devices for sensing and is emerging as a key technology in wireless networks, particularly for mobile applications such as vehicular networks. Most systems rely on path parameters, such as direction of arrival (DoA), for accurate sensing. However, commercial communication devices often adopt wider antenna spacings to enhance communication performance, which can lead to grating lobes and reduce DoA accuracy in mobile environments. To address this issue, we investigate the variation of grating lobes across OFDM subcarrier frequencies and propose a differential frequency array (DFA) model to suppress grating lobes through subcarrier cooperation. Furthermore, we develop an off-grid DoA estimation algorithm based on sparse Bayesian learning, tailored to the DFA structure. Simulation results show that the proposed method effectively suppresses grating lobes and significantly improves DoA estimation accuracy. Prototype experiments based on 5G picocells further confirm its feasibility in practical mobile ISAC scenarios.

Low-altitude wireless networks (LAWNs) have emerged as a viable solution for maritime communications. In these maritime LAWNs, uncrewed aerial vehicles (UAVs) serve as practical low-altitude platforms for wireless communications due to their flexibility and ease of deployment. However, the open and clear UAV communication channels make maritime LAWNs vulnerable to eavesdropping attacks. Existing security approaches often assume eavesdroppers follow predefined trajectories, which fail to capture the dynamic mobility patterns of eavesdroppers in realistic maritime environments. To address this challenge, we consider a low-altitude maritime communication system that employs intelligent jamming to counter dynamic eavesdroppers with uncertain positions to enhance the physical layer security. Since such a system requires balancing the conflicting performance metrics of the secrecy rate and energy consumption of UAVs, we formulate a secure and energy-efficient maritime communication multi-objective optimization problem (SEMCMOP). To solve this dynamic and long-term optimization problem, we first reformulate it as a partially observable Markov decision process (POMDP). We then propose a novel soft actor-critic with conditional variational autoencoder (SAC-CVAE) algorithm, which is a deep reinforcement learning algorithm improved by generative artificial intelligence. Specifically, the SAC-CVAE algorithm employs advantage-conditioned latent representations to disentangle and optimize policies, while enhancing computational efficiency by reducing the state space dimension. Simulation results demonstrate that our proposed intelligent jamming approach achieves secure and energy-efficient maritime communications. Furthermore, comparison results show that the proposed SAC-CVAE algorithm outperforms baseline methods across various eavesdropper movement patterns, simultaneously maximizing the secrecy rate and minimizing the energy consumption of UAVs.

Low-altitude wireless networks (LAWNs) have been envisioned as flexible and transformative platforms for enabling delay-sensitive control applications in Internet of Things (IoT) systems. In this work, we investigate the real-time wireless control over LAWNs, where an aerial drone is employed to serve multiple mobile automated guided vehicles (AGVs) via finite blocklength (FBL) transmission. Toward this end, we adopt the model predictive control (MPC) to ensure accurate trajectory tracking, while we analyze the communication reliability using the outage probability. Subsequently, we formulate an optimization problem to jointly determine control policy, transmit power allocation, and drone trajectory by accounting for the maximum travel distance and control input constraints. To address the resultant non-convex optimization problem, we first derive the closed-form expression of the outage probability under FBL transmission. Based on this, we reformulate the original problem as a quadratic programming (QP) problem, followed by developing an alternating optimization (AO) framework. Specifically, we employ the projected gradient descent (PGD) method and the successive convex approximation (SCA) technique to achieve computationally efficient sub-optimal solutions. Furthermore, we thoroughly analyze the convergence and computational complexity of the proposed algorithm. Extensive simulations and AirSim-based experiments are conducted to validate the superiority of our proposed approach compared to the baseline schemes in terms of control performance.

Despite the widespread deployment of terrestrial networks, providing reliable communication services to remote areas and maintaining connectivity during emergencies remains challenging. Low Earth orbit (LEO) satellite constellations offer promising solutions with their global coverage capabilities and reduced latency, yet struggle with intermittent coverage and limited communication windows due to orbital dynamics. This paper introduces an age of information (AoI)-aware space-air-ground integrated network (SAGIN) architecture that leverages a high-altitude platform (HAP) as intelligent relay between the LEO satellites and ground terminals. Our three-layer design employs hybrid free-space optical (FSO) links for high-capacity satellite-to-HAP communication and reliable radio frequency (RF) links for HAP-to-ground transmission, and thus addressing the temporal discontinuity in LEO satellite coverage while serving diverse user priorities. Specifically, we formulate a joint optimization problem to simultaneously minimize the AoI and satellite handover frequency through optimal transmit power distribution and satellite selection decisions. This highly dynamic, non-convex problem with time-coupled constraints presents significant computational challenges for traditional approaches. To address these difficulties, we propose a novel diffusion model (DM)-enhanced dueling double deep Q-network withaction decomposition andstate transformer encoder (DD3QN-AS) algorithm that incorporates transformer-based temporal feature extraction and employs a DM-based latent prompt generative module to refine state-action representations through conditional denoising. Simulation results highlight the superior performance of the proposed approach compared with policy-based methods and some other deep reinforcement learning (DRL) benchmarks. Moreover, performance analysis under various system settings verifies the robustness of the proposed approach.

Large language models are increasingly deployed across mobile and edge environments, where privacy-sensitive and heterogeneous user data raise critical concerns of copyright infringement, data leakage, and regulatory non-compliance. Ma chine unlearning has thus emerged as an essential capability to remove the influence of specific data without full retraining. However, two key challenges remain open: 1) how to quantify unlearning to enable data valuation without retraining, especially since the massive scale of pretraining makes it infeasible to evaluate the contribution of individual data samples in advance, and 2) how to verify the correctness without retraining to ensure that third-party auditors can efficiently confirm the complete removal of targeted data influence. To address the aforementioned challenges, in this paper, we design a dual-stage machine unlearning framework to quantify the contribution of forgotten data and certify data removal without full retraining, serving as an auditing layer for first-order unlearning methods. Specifically, we design a run-time Shapley value-based unlearned data evaluation mechanism that utilizes a first-order approximation strategy to estimate the marginal contribution of forgotten samples. Moreover, we propose a proof of unlearning mechanism that generates compact, auditable artifacts of the unlearning process to efficiently verify that the targeted data influence has been completely removed. Compared with five state-of-the-art unlearning baselines, our approach achieves effectiveness in data valuation, stronger guarantees of removal correctness, and lower computational overhead.

Accurate fall risk prediction is crucial for early intervention and prevention, effectively reducing the incidence of falls and the associated harm. This paper proposes a non-contact gait detection and fall risk prediction method based on the human electrostatic field and Stacking ensemble learning algorithm. A theoretical model for gait detection based on the human electrostatic field is established, and an experimental scheme is designed. The electrostatic gait measurement system is used to collect electrostatic gait signals from healthy young individuals, healthy elderly individuals, and elderly individuals with a history of falls. Gait features, including 28-dimensional quantifiable characteristics, are proposed for evaluating human balance and motor abilities, covering four aspects: gait time parameters, gait symmetry based on ratios and signal similarity, gait stability based on the maximum Lyapunov exponent and entropy information, and gait time parameter variability. A hybrid feature reduction method based on Particle Swarm Optimization (PSO) is used to obtain the optimal feature subset. Fall risk prediction models based on single classifiers (DT, SVM, KNN, and NB) are constructed using both the original feature set and the optimal feature subset. The single classifier based on the optimal feature subset achieves better classification performance. Furthermore, a Stacking ensemble learning model using LightGBM as the meta-learner is developed, achieving an accuracy of 97.78%. This study provides a novel approach for fall risk prediction that can predict the likelihood of falls and reduce the probability of their occurrence.

The accuracy of traditional localization methods significantly degrades when the direct path between the wireless transmitter and the target is blocked or non-penetrable. This paper proposesN LoS, a novel approach for precise non-line-of-sight (NLoS) localization using a single mmWave radar and a backscatter tag.N LoSleverages multipath reflections from both the tag and surrounding reflectors to accurately estimate the target's position.N LoSintroduces several key innovations. First, we designHFD(Hybrid Frequency-Hopping and Direct Sequence Spread Spectrum) to detect and differentiate reflectors from the target. Second, we enhance signal-to-noise ratio (SNR) by exploiting the correlation properties of the designed signals, improving detection robustness in complex environments. Third, we proposeFS-MUSIC(Frequency-Spatial Multiple Signal Classification), a super-resolution algorithm that extends the traditional MUSIC method by constructing a higher-rank signal matrix, enabling the resolution of additional multipath components. We evaluateN LoSusing a 24 GHz mmWave radar with 250 MHz bandwidth in three diverse environments: a laboratory, an office, and an around-the-corner corridor. Experimental results demonstrate thatN LoSachieves median localization errors of10.69 cm (X)and11.98 cm (Y)at a 5 m range in the laboratory setting, showcasing its effectiveness for real-world NLoS localization.

As terrestrial resources become increasingly saturated, the developing attention is gradually shifting from the ground to the low-altitude airspace, which supports many emerging applications such as urban air taxis and aerial inspection. For these applications, low-altitude wireless networks (LAWNs) are the foundation, with integrated sensing, communications, and computing (ISCC) being one of the core parts. However, the openness of low-altitude airspace poses a serious threat to communications, degrading ISCC performance and ultimately compromising the reliability of applications supported by LAWNs. To address these challenges, this paper studies joint performance optimization of ISCC while considering security of the communications. Specifically, we derive beampattern error, secrecy rate, and age of information (AoI) as performance metrics for sensing, secure communication, and computing. Building on these metrics, we formulate a multi-objective optimization problem, which aims to balance sensing and computing performance while enhancing the secrecy rate of communications. We then propose a deep Q-network (DQN)-based multi-objective evolutionary algorithm, which adaptively selects evolutionary operators according to the evolving optimization objectives, thereby leading to more effective solutions. Extensive simulations show that the proposed method brings an average performance gain of about 14% compared to existing methods, thereby ensuring ISCC performance for applications supported by LAWNs.

Wireless sensing enables contactless and accurate recognition of human activities and physiological states by using electromagnetic signals. As a promising enabler for sixth-generation (6 G) multi-functional networks, orthogonal time frequency space (OTFS) modulation exhibits strong resilience to high Doppler shifts in high-mobility environments, while also supporting precise human sensing in low-mobility scenarios. In this work, we propose a novel two-dimensional (2D) delay-Doppler motion profiling framework based on the OTFS waveform to extract distinctive features of human activities. To enhance recognition performance, a fractional-Doppler enhancement network is integrated with a convolutional neural network (CNN)-aided encoder-only Transformer architecture. Extensive experiments are conducted to assess the cross-domain generalization capability of the proposed OTFSensi system. Compared with existing classification models based on CNN, gated recurrent unit (GRU), and long short-term memory (LSTM) networks, OTFSensi demonstrates substantial improvements in adaptability across diverse environments and observation angles. Furthermore, a comparative analysis with various radio frequency (RF) sensing technologies confirms the superior classification performance achieved by OTFSensi.

Privacy security constitutes a critical challenge in low-altitude wireless communications. Motivated by the application requirements for stereoscopic coverage and multi-domain collaboration, this paper investigates a friendly jamming-assisted air-ground cooperative covert transmission scheme. In the considered system, an unmanned aerial vehicle (UAV) equipped with a reconfigurable intelligent surface (RIS) serves as a network hub. It relays confidential signals from an aerial hovering platform to ground users while cooperating with terrestrial jammer to realize environment-independent directional jamming. Benefiting from the UAV's relaying functionality, this architecture can significantly enhance the flexibility of the jamming mechanism and the security of the jamming node. With the objective of maximizing the UAV's energy efficiency associated with effective throughput, we formulate a joint optimization problem under strict covertness constraints. To solve this problem, we propose an algorithm that integrates semidefinite relaxation (SDR), the Dinkelbach method, and Gaussian randomization within a double deep Q-network (DDQN) framework. The UAV trajectory, onboard resource, user scheduling and RIS parameters are jointly optimized to simultaneously ensure the communication covertness and transmission performance. Numerical simulation results validate the superiority of the proposed scheme compared to benchmark solutions.

Falls remain a major cause of injury and death among older adults, which shows the need for reliable and non-intrusive monitoring solutions in healthcare environments. In this paper, we propose a novel Ambient Internet of Things (IoT) backscatter sensing system that utilizes a dense array of passive tags and a minimal number of reader antennas for cost-effective fall detection and localization. To fully exploit the spatial and temporal characteristics of ambient backscatter sensing data, we design a hierarchical multi-task spatio-temporal graph attention network (HM-STGAT), which jointly models the spatial relationships among tags and antennas as well as the temporal dynamics of human activities. The proposed unified framework simultaneously detects fall events and accurately estimates fall locations. We validate the proposed approach through a real-world experiment to collect a diverse dataset of fall and non-fall scenarios. Experimental results demonstrate that the proposed method achieves state-of-the-art performance in both fall detection accuracy and localization precision, highlighting its potential for practical deployment in healthcare monitoring applications.

The Low-Altitude Wireless Networks (LAWNs) has emerged as a cornerstone of next-generation mobile due to their flexibility and adaptability in providing on-demand connectivity. However, ensuring reliable and high-throughput aerial drone communication remains a major challenge, mainly due to the dynamic mobility of aerial drones and the complexity of the wireless propagation environment. Traditional LAWNs rely heavily on channel sampling and real-time feedback, which introduce latency and communication overhead. In this work, we proposeRadioRS, a novel sampling-free aerial drone communication framework that combines Radio Map (RM) prediction with Rate-Splitting Multiple Access (RSMA) to enable robust and efficient communication without requiring explicit channel estimation during flight. RadioRS leverages a RM that provides location-aware predictions of channel state. To enhance the accuracy and generalization of these predictions under complex propagation conditions, we develop a generative model based on the Mamba architecture, which efficiently captures fine-grained correlations in the radio environment. Building on the RM, RSMA is employed to flexibly manage interference and improve spectral efficiency. In addition, we design a Mamba-powered controller that adapts beamforming strategies from the RM directly, further improving link reliability and throughput. Comprehensive simulation results demonstrate that the proposed RadioRS framework significantly outperforms conventional channel-sampling-based approaches in terms of both communication reliability and throughput.

Vehicle-to-everything (V2X) networks face critical security challenges due to their dynamic nature, stringent latency requirements, and susceptibility to malicious attacks. Traditional trust management approaches often rely on centralized authorities or historical data, creating vulnerabilities and scalability limitations. This paper presents a new trust management system that leverages integrated sensing and communication (ISAC) technology and blockchain-based smart contracts to provide secure and decentralized trust evaluation in V2X networks. The proposed framework leverages real-time ISAC signal processing to compute five comprehensive trust metrics: behavior score, reputation score, safety score, uptime score, and response time score. These metrics are derived through advanced Kalman filtering and statistical anomaly detection applied to physical-layer measurements, enabling immediate detection of malicious activities that traditional approaches might miss. Trust records are securely stored and validated through smart contracts deployed on 5G base station blockchains, ensuring tamper-proof storage and automated policy enforcement. Numerical results demonstrate that the proposed protocol achieves faster trust convergence, higher communication reliability, significant reduction in false positive rates, improved detection accuracy, acceptable end-to-end latency, and lower computational overhead compared to state-of-the-art approaches.

This paper investigates the receiver design for clipped orthogonal time frequency space (OTFS) systems, where the user devices are equipped with power amplifiers (PAs) with low dynamic range. To improve power efficiency, the PAs have to work near the saturation points, which leads to unknown nonlinear distortions, thus making the signal detection more challenging. To solve this problem, techniques like intentional clipping or pre-distortion are adopted, thus approximating the outputs of the PAs as clipped signals. To further compensate for the unknown time-varying multipath channel and the clipping distortion at the receiver, the channel and clipping amplitude (CA) estimation, channel tracking, and signal detection are studied in this paper. Firstly, a receiver framework is developed for clipped OTFS. Secondly, by adopting the sparsity of the delay-Doppler (DD) domain channel and the piecewise linearized signal model with respect to CA, a novel sparse Bayesian learning (SBL) based joint channel and CA estimation scheme is proposed. Then, to further reduce the estimation error and bit error rate, a Kalman filter (KF) based channel tracking scheme and a minimum mean square error decision feedback blockwise equalization (MMSE-DFBE) based detection scheme are proposed. These two schemes are integrated in an expectation maximization (EM) based iterative tracking and detection algorithm. Finally, numerical simulations are conducted to demonstrate the superiority of the proposed schemes in terms of both estimation error and bit error rate.

Recently, index modulated orthogonal time frequency space modulation combined with multi-input and multi-output (MIMO-OTFS) has been introduced to get superior bit error rate (BER) performance than conventional MIMO-OTFS schemes. In this paper, we propose a novel transmission scheme called generalized space-delay-Doppler index modulated OTFS (GSDDIM-OTFS) to further utilize the multi-domain resources and explore the potential benefits of the index modulated MIMO-OTFS. In this scheme, additional information bits are transmitted through the combined space-delay-Doppler resource units. We also derive the analytical expressions of average bit error probability (ABEP) to evaluate the performance of the proposed scheme. For multi-domain index modulation schemes, the traditional detection suffers a supreme complexity with a large size of look-up table. To address this issue, we propose a coarse-to-fine (CTF) network for the GSDDIM-OTFS detection, called the CTFIM detector. In the proposed detector, the characteristic of the transmit constellation of index modulated schemes is fully utilized and we explore the coarse-to-fine strategy to capture the general features more efficiently from different dimensions. Specifically, the coarse module is used to capture features based on the index pattern and the fine classification to establish the global relationships in each GSDDIM-OTFS subblock. Furthermore, we also employ feature fusion to increase the feature dimensions. Simulation results demonstrate the enhanced performance of the GSDDIM-OTFS over doubly-selective fading channels and the proposed DL-based detectors under perfect and imperfect channel conditions.

In this paper, we investigate delay-Doppler (DD) sensing in a communication-centric integrated sensing and communication (ISAC) framework based on Zak transform-based orthogonal time frequency space (Zak-OTFS) modulation. Specifically, we consider target sensing with communication waveforms and propose a novel backpropagation (BP) algorithm for multi-target DD parameter estimation. We formulate the radar sensing task as a maximum likelihood parameter estimation problem, which is highly non-convex. By exploiting the structural analogy between parameter estimation and neural network training, the BP algorithm treats the DD parameters as tunable network weights and efficiently computes their gradients via the chain rule, enabling accurate and parallelized estimation. To facilitate the algorithm implementation, a successive interference cancellation method based on DD domain twisted convolution is developed to obtain coarse DD estimates. Furthermore, a constant false alarm rate based dynamic merging strategy is introduced to adaptively estimate the number of targets during the BP process. Comprehensive theoretical analyses are conducted, including the derivation of the Cramér–Rao bound (CRB) for Zak-OTFS systems and performance evaluation under various challenging sensing scenarios. Simulation results demonstrate that the proposed algorithm achieves high estimation accuracy and validates the theoretical analysis.

With the rapid expansion of Internet of Things (IoT) applications, the demand of wide coverage and massive connectivity is inevitable. In this context, this paper investigates massive unsourced random access (URA) paradigm for low earth orbit (LEO) satellite IoT applications, focusing on device separation and signal detection. By exploiting the structured Grassmannian constellation to generate the codebook, a tensor-based URA transmission scheme is provided, which models the separation and detection problem as a general canonical polyadic (CP) decomposition. Then, to evaluate the access capability of our considered URA scheme, a comprehensive uniqueness analysis considering both sufficient conditions and necessary conditions is presented. Accordingly, an efficient generalized line-search-accelerated alternating least squares (GLSA-ALS) method is proposed to conduct the device separation and signal detection, which can avoid a large number of inverse computations for large-scale matrices. To be specific, with the help of the relaxation factors during the iteration, our proposed method can converge at a fast speed with negligible performance loss, which facilitates a better trade-off between the detection accuracy and computational complexity. Furthermore, depending on the demand of a specific application scenario, the flexible selection of relaxation factors enables the proposed method to be compatible to the classical ALS method, which can enhance the performance at the cost of additional complexity. Finally, relying on the maximum likelihood (ML)-based detection approach, the message list transmitted by active devices from one common codebook can be recovered. Simulation results demonstrate that the proposed GLSA-ALS method outperforms the state-of-the-art methods for practical LEO satellite IoT applications.

Approximate message passing (AMP) iterative detection is recognized as a reliable and practical approach for multiple-input multiple-output (MIMO) systems. However, existing AMP detection algorithms face a critical challenge: high computational complexity due to redundant iterations, making them impractical for the coming 6G networks with increased data throughput demands. This paper addresses this challenge by investigating the mutual information (MI) update flow in AMP iterative MIMO detection and introducing a precise MI computation mechanism based on the error function, referred to as the EFA mechanism. Leveraging the EFA mechanism, we propose a convergence-based termination (CT) scheme to accurately track the convergent iteration number and eliminate redundant iterations in AMP iterative detection. Numerical results demonstrate that the MI flow calculated using the EFA mechanism is consistent with the convergence behavior of AMP iterative MIMO detection across different iterations and signal-to-noise ratios (SNRs). Specifically, the EFA mechanism can precisely identify the convergent iteration number and corresponding SNR. Additionally, the CT scheme achieves up to a 80% reduction in complexity compared to original AMP detection, while maintaining the expected BER performance.

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 meet the growing demand for communication capacity and address the scarcity of wireless spectrum resources, we propose a novel orthogonal time frequency space (OTFS) channel estimation scheme enhanced by visual environment semantics. This approach establishes a theoretical foundation for semantic-assisted channel estimation by modeling the potential relationship between the wireless communication channel and its surrounding environment. Leveraging a computer vision-based adaptive environment semantic sensing framework, the system extracts and processes environment features to infer channel characteristics. To tackle the challenge of capturing small-scale fading solely through visual environment semantics, we design two new pilot structures and the corresponding channel estimation methods. These are tailored to maximize the utility of information derived from the environment while minimizing pilot overhead. The simulation results demonstrate that the proposed scheme outperforms the conventional channel estimation method in terms of spectral efficiency and robustness in high-mobility and low-SNR scenarios with fewer pilot symbols.

Deep learning (DL) has become pivotal in advancing integrated sensing and communication (ISAC) systems. However, conventional DL models often require frequent updating or retraining to adapt to dynamic ISAC environments. To address these limitations, this work creatively proposes a large language model (LLM)-based ISAC system, called LLM-ISAC, to enable concurrent sensing-communication processing in a unified framework, with enhanced generalization and environmental robustness. To realize LLM-ISAC, we design a novel signal encoder to transform ISAC signals into LLM-compatible representations through a delay-Doppler-spatial transformer, enabling discriminative cross-domain signal feature extraction for downstream tasks. Moreover, we develop an innovative ISAC-specific context prompt to construct structured machine-readable prompts, dynamically guiding the LLM’s reasoning without retraining and ensuring robust generalization to unseen scenarios. To the best of the authors’ knowledge, this is the first work leveraging the property of LLM in ISAC systems. Extensive simulations demonstrate that LLMI-SAC achieves significant superiority in sensing accuracy, communication reliability, and environmental robustness, compared to state-of-the-art DL-based ISAC methods.

This paper investigates an integrated sensing and communication (ISAC) system employing delay-Doppler (DD) signaling. The sensing performance of both random and deterministic signaling schemes is evaluated based on the expected squared ambiguity function (AF), for which closed-form expressions are derived by leveraging the Zak transform-based orthogonal time-frequency space (Zak-OTFS) modulation framework. Our analysis highlights a key difference between the two signaling types: DD domain ISAC (DD-ISAC) with deterministic signaling yields a roughly periodic AF with prominent peaks and low sidelobes between adjacent peaks, whereas DD-ISAC with random signaling using a Quadrature Phase-Shift Keying (QPSK) constellation exhibits low sidelobe values periodically without prominent peaks. Furthermore, we demonstrate that DD-ISAC enables a flexible trade-off between delay and Doppler sidelobe levels by adjusting the number of delay and Doppler bins. The analytical findings are explicitly validated through numerical simulations.

In this paper, we propose a novel cross-domain channel estimation (CDCE) algorithm for orthogonal frequency division multiplexing (OFDM) systems, leveraging the unique characteristics of the delay-Doppler (DD) domain channel. Specifically, the proposed algorithm transforms the time-frequency (TF) domain pilot sequence of OFDM into the DD domain and applies a two-dimensional (2D) twisted-convolution for acquiring a coarse estimation of the underlying channel delay and Doppler. Then, the OFDM channel estimation is formulated as a sparse signal recovery problem in the TF domain according to the dictionary derived based on the obtained delay and Doppler estimates. Furthermore, a low-complexity ℓ1-regularized least-square estimator is proposed to effectively solve this problem. Moreover, we further develop a performance analysis framework of the proposed scheme based on the ambiguity function (AF) of the adopted pilot sequence. Our numerical results demonstrate noticeable estimation performance improvement compared to conventional OFDM channel estimation methods, particularly in the presence of high channel mobility.

Motivated by the practical application of beam tracking of multiple devices in Multiple Input Multiple Output (MIMO) communication, we study the problem of non-adaptive twenty questions estimation for locating and tracking multiple moving targets under a query-dependent noisy channel. Specifically, we derive a second-order asymptotic bound on resolution for optimal query procedures and provide numerical examples to illustrate our results. In particular, we demonstrate that a single threshold decoding rule achieves the asymptotic bound. The single threshold decoding rule has reduced the computational complexity compared to the multiple threshold method proposed for locating multiple stationary targets (Zhou, Bai and Hero, TIT 2022). Finally, we illustrate our proposed algorithm for the application of beam tracking of multiple mobile transmitters in a 5G wireless network.

Future 6G Integrated Sensing and Communication (ISAC) networks are expected to reuse data payload signals for both communication and sensing. However, the inherent randomness of these signals can degrade ranging accuracy. To address this challenge, this paper studies power allocation (PA) strategies for CP-OFDM-based ISAC systems operating under random signaling, with the goal of reducing the sidelobe levels in the periodic auto-correlation function (P-ACF) of the ISAC signal. Specifically, we first derive closed-form expressions for the average squared P-ACF, and then formulate an optimization problem that minimizes the expected integrated sidelobe level (EISL) under arbitrary constellation mappings. We rigorously prove that, across all constellations, a uniform PA scheme yields the lowest ranging sidelobe levels, both in terms of the EISL and at each individual lag. Additionally, we extend our analysis to scenarios involving frequency-domain zero-padding. In such cases, we show that uniform PA no longer guarantees optimal sidelobe suppression. To address this, we propose a projected gradient descent (PGD) algorithm to find a locally optimal PA scheme that minimizes the EISL. Finally, our theoretical results are substantiated by extensive simulations, which confirm the effectiveness of the proposed PA methods in suppressing the ranging sidelobe levels of random OFDM signals.

The rapid advancement of Internet of Things (IoT) networks has positioned unmanned aerial vehicles (UAV s) as critical enablers of next-generation wireless communication technologies. This paper focuses on orthogonal time frequency space (OTFS) modulation-assisted wireless control in UAV networks with finite blocklength (FBL) transmission. In particular, we in-vestigate the optimal power allocation that maximizes the fairness of control performance in terms of linear quadratic regulator (LQR) cost, subject to rate-LQR cost bounds and maximum available power budget constraints. To address the optimization problem, we first analyze the concave-convex property of the FBL rate function, followed by developing an efficient successive convex approximation (SCA)-based algorithm to obtain a sub-optimal solution. The convergence and computational complexity of the proposed algorithm are thoroughly analyzed. Simulation results validate the effectiveness of the proposed approach, offering promising insights for UAV-enabled wireless control systems.

Orthogonal Time Frequency Space (OTFS) modulation is expected to address the performance degradation of orthogonal frequency division multiplexing (OFDM) modulated signals, particularly due to issues like Doppler shifts in mobile communication environments. In this paper, we propose a lightweight deep learning-based framework for end-to-end joint channel estimation and data detection (JCEDD) in an OTFS communication system. To fully exploit the characteristics of OTFS modulation, we introduce a data padding preprocessing method and a slicing data augmentation technique. Furthermore, the performance of the proposed deep learning-based framework could be enhanced dramatically with only a small overhead compared to the superimposed pilot scheme. Ablation experiments demonstrate that the proposed data padding preprocessing method and the slicing data augmentation technique significantly improve the performance of the deep learning-based framework. Simulation results show that the proposed framework outperforms existing algorithms in terms of JCEDD performance, while maintaining a relatively low level of computational complexity.

The development of integrated sensing and communication (ISAC) technique makes it possible to exploit echoes of communication signals to localize aerial eavesdropper (AE) and enhance the secrecy performance. In this paper, we investigate the sensing-assisted secure precoding design in rate-splitting multiple access (RSMA) systems. In particular, we aim at maximizing the minimum achievable rate among all users while satisfying the Cramér-Rao bound (CRB) constraint for AE’s 2-dimensional angle estimation and protecting both common stream and private streams from being intercepted. We first consider the ideal case where perfect CSI is available and propose an iterative optimization algorithm, where successive convex approximation technique, fractional programming, and the Schur complement condition are leveraged to handle the non-convex constraints and objective function. This scenario is further extended to a more general case with channel estimation errors, for which we propose a robust precoding design algorithm to ensure worst-case performance. Simulation results validate the effectiveness of leveraging the sensing capability to enhance secrecy performance and show that the RSMA scheme is able to achieve higher user rates and lower eavesdropping rates compared to spatial division multiple access (SDMA)-based sensing-assisted secure communication system. Furthermore, we demonstrate the trade-off between achievable minimum user rate and sensing accuracy.

Orthogonal Time Frequency Space (OTFS) modulation technology which provides reliable communication and precise sensing in high-mobility scenarios, has emerged as a potential solution for various unmanned aerial vehicle (UAV)-related applications. In this paper, we consider an OTFS communication waveform-based UAV sensing situation. Due to random wind gusts and varying weather conditions, the sensing signals may experience sudden disturbances. To effectively address this challenge, we propose a deep learning (DL)-based framework to compensate the impulse interference, which leverages empirical information and generates real-time predictions to achieve accurate UAV sensing. Specifically, we develop a prediction-assisted estimation network (PAEnet) to implement the proposed framework. The core component of PAEnet, the estimation network (ESnet), is capable to directly extract fractional delay and Doppler from the transmitted OTFS frame, thereby reducing the complexity of the sensing process. Through comprehensive simulation results, we demonstrate the effectiveness of the compensation mechanism in unreliable sensing scenarios, while showcasing the PAEnet’s capability to achieve superior accuracy for OTFS-based UAV sensing.

In this paper, we focus on both the communication and sensing security in the proposed multiple-subcarrier (MC) non-orthogonal multiple access (NOMA)-assisted integrated sensing and communication (ISAC) systems, where an active eaves-dropper is considered with imperfect channel state information (CSI). We consider a secure ISAC design by adopting the proposed secrecy rate and secrecy Cramér-Rao bound (S-CRB) metrics under the bounded CSI errors model. The joint design of artificial noise (AN) and dual-functional radar communications (DFRC) signals beamforming as well as subcarrier allocation is formulated to maximize the minimum achievable rate, while ensuring the hierarchical confidentiality requirements for users and satisfying the leakage CRB constraint for the target. To handle the non-convex problem, we devise a low-complexity successive convex approximation (SCA)-based suboptimal algorithm, and its ε-optimality is validated via our proposed branch and bound (B&B) algorithm in simulations. Numerical results reveal the effectiveness and superiority of our proposed scheme compared to the other baselines. Moreover, the inherent win-win relationship between the sum secrecy rate and target S-CRB is demonstrated via various simulation results, which provides several insights into secure MC-NOMA ISAC network deployment.

Modern wireless systems face interference due to rising spectrum efficiency demands and increasingly aggressive network designs. Despite its optimality, the huge complexity of the maximum likelihood (ML) detection hinders its deployment in the future wireless communication systems, which require low latency and high energy efficiency. In this paper, we develop a novel generalized framework for data detection in interference channels. In particular, we factorize the joint likelihood function of the transmitted symbols to obtain the marginal distribution of a single symbol following the sum-product (SP) algorithm. Motivated by the fact that the complexity of the SP algorithm is dominated by the summation process, we introduce Gaussian and Gaussian mixture models to reduce the state space of symbols, which helps to reduce the detection complexity. The proposed hybrid detection framework consists of three kinds of symbol distributions, i.e., original discrete, Gaussian, and Gaussian mixture distributions. To strike a balance between complexity and error performance, we can simply modify the components of different symbol distributions, offering high flexibility in practical applications. Furthermore, we analyze the performance of our proposed detection scheme and discuss the design guidelines for the mixture Gaussian messages. Simulation results demonstrated the effectiveness of the proposed algorithm.

Large-aperturecoprime arrays(CAs) are expected to achieve higher sensing resolution than conventional dense arrays (DAs), yet with lower hardware and energy cost. However, existing CA far-field localization methods cannot be directly applied to near-field scenarios due to channel model mismatch. To address this issue, in this paper, we propose an efficient near-field localization method for CAs. Specifically, we first construct an effective covariance matrix, which allows to decouple the target angle-and-range estimation. Then, a customized two-phase multiple signal classification (MUSIC) method for CAs is proposed, which first detects all possible angles of targets by using an angular-domain MUSIC method, followed by a second phase to resolve the true angles of targets and their ranges by devising a range-domain MUSIC method. We show that the proposed method can achieve near-optimal multi-target localization performance as conventional two-dimensional (2D)-MUSIC method with much lower computational complexity. Additionally, we characterize the Cramér-Rao bounds for symmetric CAs and provide interesting insights. Finally, numerical results demonstrate that our proposed method is able to localize more targets than the existing subarray-based method as well as achieve lower root mean square error than DAs.

Orthogonal Time Frequency Space (OTFS) modulation holds significant potential for diverse applications in both sensing and communication fields. This paper mainly investigates waveform optimization for OTFS modulation, aiming to design pilot symbol matrices with low sidelobe levels and data symbol matrices with high communication rates under peak-to-average power ratio constraints. We first formulate the problem of minimizing the weighted integrated sidelobe levels and maximizing the communication rate in the delay-Doppler domain. Subsequently, to address the complicated optimization problem, a Majorization-Minimization based algorithm is proposed to decompose it into a series of subproblems. These subproblems are then reformulated as unconstrained optimization problems on the Stiefel manifold, and the Riemannian conjugate gradient method is employed to solve them efficiently. Moreover, a faster iterative algorithm is proposed based on second-order Taylor approximation to accelerate the convergence speed. Simulation results validate that the proposed algorithms effectively achieve pilot matrices with desirable ambiguity functions and data symbol matrices with high communication rates under various weighting factors.

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of the variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm.

This paper aims to answer a fundamental question in the area of Integrated Sensing and Communications (ISAC):What is the optimal communication-centric ISAC waveform for ranging?Towards that end, we first established a generic framework to analyze the sensing performance of communication-centric ISAC waveforms built upon orthonormal signaling bases and random data symbols. Then, we evaluated their ranging performance by adopting both the periodic and aperiodic auto-correlation functions (P-ACF and A-ACF), and defined the expectation of the integrated sidelobe level (EISL) as a sensing performance metric. On top of that, we proved that among all communication waveforms with cyclic prefix (CP), the orthogonal frequency division multiplexing (OFDM) modulation is the only globally optimal waveform that achieves the lowest ranging sidelobe for quadrature amplitude modulation (QAM) and phase shift keying (PSK) constellations, in terms of both the EISL and the sidelobe level at each individual lag of the P-ACF. As a step forward, we proved that among all communication waveforms without CP, OFDM is a locally optimal waveform for QAM/PSK in the sense that it achieves a local minimum of the EISL of the A-ACF. Finally, we demonstrated by numerical results that under QAM/PSK constellations, there is no other orthogonal communication-centric waveform that achieves a lower ranging sidelobe level than that of the OFDM, in terms of both P-ACF and A-ACF cases.

Wireless sensor networks (WSNs) are vital for addressing vehicle-related challenges information management, congestion, and safety in Intelligent Transportation Systems (ITS). Ensuring reliable communication is critical, particularly in urban environments where real-time data from roadside infrastructure enhances traffic flow efficiency and safety. This paper proposes R2Com, a reliable and resilient communication protocol for duty-cycled Software-Defined Wireless Sensor Networks (SDWSNs), specifically designed for urban traffic monitoring. By integrating reliable routing and adaptive duty cycling, R2Com ensures low-latency, energy-efficient data exchange between vehicle detection units and traffic control centers. The protocol leverages four attributes: direct trust, recommended trust, signal-to-interference noise ratio, and residual energy, considering their probability distributions to ensure reliability and resilience in the data plane communication. Secondly, the SDN controller calculates these attributes alongside the Expected Duty Cycled Wake-ups (EDC), enhancing reliability through flexible management and low latency. It then assigns communication strategies to each node through reliable nodes and limits the number of forwarding nodes per node to reduce packet duplication. Simulation results demonstrate that the proposed protocol significantly outperforms existing protocols in terms of average energy consumption, packet delivery ratio, average latency, network lifetime, communication overhead, packet success ratio, reliable coverage degree, coverage percentage, traffic density estimation accuracy, and intersection congestion levels.

The rapid advancement of Internet of Things (IoT) services and the evolution toward the sixth generation (6 G) have positioned unmanned aerial vehicles (UAVs) as critical enablers of low-altitude wireless networks (LAWNs). This work investigates the co-design of integrated sensing, communication, and control ($\mathbf {SC^{2}}$) for multi-UAV cooperative systems with finite blocklength (FBL) transmission. In particular, the UAVs continuously monitor the state of the field robots and transmit their observations to the robot controller to ensure stable control while cooperating to localize an unknown sensing target (ST). To this end, a weighted optimization problem is first formulated by jointly considering the control and localization performance in terms of the linear quadratic regulator (LQR) cost and the determinant of the Fisher information matrix (FIM), respectively. The resultant problem, optimizing resource allocations, the UAVs' deployment positions, and multi-user scheduling, is non-convex. To circumvent this challenge, we first derive a closed-form expression of the LQR cost with respect to other variables. Subsequently, the non-convex optimization problem is decomposed into a series of sub-problems by leveraging the alternating optimization (AO) approach, in which the difference of convex functions (DC) programming and projected gradient descent (PGD) method are employed to obtain an efficient near-optimal solution. Furthermore, the convergence and computational complexity of the proposed algorithm are thoroughly analyzed. Extensive simulation results are presented to validate the effectiveness of our proposed approach compared to the benchmark schemes and reveal the trade-off between control and sensing performance.

Extremely Large-scale Array (ELAA) is considered a frontier technology for future communication systems, playing a crucial role in enhancing the rate and spectral efficiency of wireless networks. As ELAA employs a multitude of antennas operating at higher frequencies, users are typically situated in the near-field region where the spherical wavefront propagates. Near-field beam training requires information on both angle and distance, which inevitably leads to a significant increase in the beam training overhead. To address this challenge, we propose a near-field beam training method based on deep learning. Specifically, we employ a convolutional neural network (CNN) to efficiently extract channel characteristics from historical data by strategically selecting padding and kernel sizes. The negative value of the user average achievable rate is utilized as the loss function to optimize the beamformer, maximizing the achievable rate in multi-user networks without relying on predefined beam codebooks. Once deployed, the model requires only pre-estimated channel state information (CSI) to compute the optimal beamforming vector. Simulation results demonstrate that the proposed scheme achieves more stable beamforming gains and substantially outperforms traditional beam training approaches. Furthermore, owing to the inherent traits of deep learning methodologies, this approach substantially diminishes the near-field beam training overhead.

Orthogonal Time-Frequency Space (OTFS) modulation is an emerging technique that characterizes wireless channels and transmits information in the delay-Doppler domain. This work focuses on estimating fundamental sensing parameters, i.e., the delay and Doppler shifts of individual propagation paths, which serve as critical enablers for downstream positioning techniques, such as time-difference-of-arrival (TDOA)-based localization. Specifically, we propose a parameter-inherited (PI) channel estimation method that integrates sparse Bayesian learning (SBL) with unitary approximate message passing (UAMP), achieving low computational complexity and high estimation robustness. To accelerate the convergence of the UAMP-based iterative estimation, we explore the strategy of initializing parameters by inheriting prior estimates from adjacent OTFS transmission blocks. Furthermore, the overall computational burden is significantly reduced by employing large-scale matrix operations via two-dimensional fast Fourier transform (2D FFT). The proposed algorithms are implemented and evaluated on a software-defined radio (SDR)-based ISAC platform. Experimental results demonstrate that the proposed dual-functional system outperforms existing benchmarks in both communication quality and sensing parameter accuracy.

This paper investigates the system design and experimental validation of integrated sensing and communication (ISAC) for environmental sensing, which is expected to be a critical enabler for next-generation wireless networks. We advocate exploiting orthogonal time frequency space (OTFS) modulation for its inherent sparsity and stability in delay- Doppler (DD) domain channels, facilitating a low-overhead environment sensing design. Moreover, a comprehensive environmental sensing framework is developed, encompassing DD domain channel estimation, target localization, and experimental validation. In particular, we first explore the OTFS channel estimation in the presence of fractional delay and Doppler shifts. Given the estimated parameters, we propose a three-ellipse positioning algorithm to localize the target's position, followed by determining the mobile transmitter's velocity. Additionally, to evaluate the performance of our proposed design, we conduct extensive simulations and experiments using a software-defined radio (SDR)-based platform with universal software radio peripheral (USRP). The experimental validations demonstrate that our proposed approach outperforms the benchmarks in terms of localization accuracy and velocity estimation, confirming its effectiveness in practical environmental sensing applications.

Integrated sensing and communications (ISAC) has emerged as a pivotal enabling technology for next-generation wireless networks. Despite the distinct signal design requirements of sensing and communication (S&C) systems, shifting the symbol-wise pulse shaping (SWiPS) framework from communication-only systems to ISAC poses significant challenges in signal design and processing This paper addresses these challenges by examining the ambiguity function (AF) of the SWiPS ISAC signal and introducing a novel pulse shaping design for single-carrier ISAC transmission. We formulate optimization problems to minimize the average integrated sidelobe level (ISL) of the AF, as well as the weighted ISL (WISL) while satisfying inter-symbol interference (ISI), out-of-band emission (OOBE), and power constraints. Our contributions include establishing the relationship between the AFs of both the random data symbols and signaling pulses, analyzing the statistical characteristics of the AF, and developing algorithmic frameworks for pulse shaping optimization using successive convex approximation (SCA) and alternating direction method of multipliers (ADMM) approaches. Numerical results are provided to validate our theoretical analysis, which demonstrate significant performance improvements in the proposed SWiPS design compared to the root-raised cosine (RRC) pulse shaping for conventional communication systems.

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection.

In symbiotic radio (SR), the secondary system not only shares the spectrum and power of the primary system but also enhances its performance by providing multipath gains, fostering a cooperative mutualism between the two systems. However, in high-mobility channels, time-frequency selective fading presents significant challenges for reliable SR communications. The recently introduced orthogonal time-frequency space (OTFS) technique, which processes signals in the delay-Doppler (DD) domain, is expected to improve SR communication performance in high-speed mobile scenarios. In this paper, we propose embedding primary information symbols in the DD domain using amplitude-phase modulation, while employing a combinatorial frequency (CF) modulation strategy for secondary information transmission. To obtain channel state information (CSI) and detect secondary symbols, for some special scenarios, we propose an off-grid sparse Bayesian learning (SBL)-based method. This method first estimates the equivalent CSI and then detects the symbols by leveraging the highly structured Doppler shifts. For more general scenarios, we introduce a model-driven equivalent CSI estimation-net (ECSIEst-Net) and a data-driven secondary symbol detection-Net (SSymDet-Net). Numerical results are provided to guide parameter selection and demonstrate the effectiveness of the proposed methods.

Non-orthogonal multiple access (NOMA) with successive detection receivers, e.g., successive interference cancellation (SIC), is a potential technology for satellite multi-user communication due to its lower complexity. However, within a spot beam, multi-user interference (MUI) is complicated by channel-induced time-frequency offset, while the path loss differences that the receiver relies on for MUI suppression almost disappear. To overcome the above obstacle, this paper exploits the delay-Doppler (D-D) domain circular shifting property under time-frequency offsets and proposes a D-D domain spectral shaping multiple access (SSMA) technique. By analyzing the influence of D-D domain spectrum on channel capacity, we identify that an enlarged inter-user power gap can be derived at the receiver by constructing a non-uniform D-D domain spectrum. Inspired by this, a D-D domain multi-branch structure-based shaping framework is proposed to flexibly construct the user-consistent power envelope. Meanwhile, two additional signal designs are introduced to ensure that the D-D domain information density and constellation fit to the constructed power envelope. First, by adjusting the transmission rate of the signal on each branch, we optimize the information density with a non-uniform pattern. Second, by introducing a branch-wise phase rotation and deploying an iterative variational approximation method, the shape of the composite constellation is reconstructed. In addition, we also design a branch-bundling-based successive detection receiver using an alternating direction method of multipliers. This receiver can flexibly combine detectable branch signals while maintaining the complexity close to the traditional SIC receiver. Analysis and simulation results reveal that the proposed D-D domain SSMA has a higher achievable rate and can provide$1\sim 4.5$dB bit error rate performance gain compared to the typical D-D domain NOMA.

Orthogonal time frequency space (OTFS) modulation is anticipated to be a promising candidate for supporting integrated sensing and communications (ISAC) systems, which is considered as a pivotal technique for realizing next-generation wireless networks. In this paper, we develop a minimum bit error rate (BER) precoder design for an OTFS-based ISAC system. In particular, the BER minimization problem takes into account the maximum available transmission power budget and the required sensing performance. Unlike previous studies that focused on ISAC in the time-frequency (TF) domain, we devise the precoder from the perspective of the delay-Doppler (DD) domain by exploiting the equivalent DD domain channel. The DD domain channel generally tends to be sparse and quasi-static, which is conducive to a low-complexity ISAC system design. To address the non-convex optimization design problem, we resort to optimizing the lower bound of the derived average BER by adopting Jensen’s inequality. Subsequently, the formulated problem is decoupled into two independent sub-problems via singular value decomposition (SVD) methodology. We then theoretically analyze the feasibility conditions of the proposed problem and present a low-complexity iterative solution via leveraging the Lagrangian duality approach. Simulation results verify the effectiveness of our proposed precoder compared to the benchmark schemes and reveal the interplay between sensing and communication for dual-functional precoder design, indicating a trade-off where transmission efficiency is sacrificed for increasing transmission reliability and sensing accuracy.

In this paper, we propose a novel symbiotic sensing and communication (SSAC) framework, comprising a base station (BS) and a passive sensing node. In particular, the BS transmits communication waveform to serve vehicle users (VUEs), while the sensing node is employed to execute sensing tasks based on the echoes in a bistatic manner, thereby avoiding the issue of self-interference. Besides the weak target of interest, the sensing node tracks VUEs and shares sensing results with BS to facilitate sensing-assisted beamforming. By considering both fully digital arrays and hybrid analog-digital (HAD) arrays, we investigate the beamforming design in the SSAC system. We first derive the Cramér-Rao lower bound (CRLB) of the two-dimensional angles of arrival estimation as the sensing metric. Next, we formulate an achievable sum rate maximization problem under the CRLB constraint, where the channel state information is reconstructed based on the sensing results. Then, we propose two penalty dual decomposition (PDD)-based alternating algorithms for fully digital and HAD arrays, respectively. Simulation results demonstrate that the proposed algorithms can achieve an outstanding data rate with effective localization capability for both VUEs and the weak target. In particular, the HAD beamforming design exhibits remarkable performance gain compared to conventional schemes, especially with fewer radio frequency chains.

Existing communication signal-based sensing systems mainly rely on the orthogonal frequency division multiplexing (OFDM) technique due to its remarkable communication performance. However, extracting Doppler shifts from the received signal is not straightforward for OFDM and usually requires additional operations. The recently emerging orthogonal time frequency space (OTFS) modulation, which employs the Delay-Doppler (DD) domain for data transmission, can reveal the physical wireless propagation environments and provide the DD information directly. This paper investigates the moving multi-target sensing problem based on OTFS signaling. In particular, we attempt to tackle sensing and data association tasks concurrently by using the time delay (TD) and Doppler information from OTFS channel estimation. To this end, we formulate a mixed-integer optimization problem and approximate it as a convex problem. Simulation results has demonstrated the effectiveness of the proposed method.

Extremely Large-scale Array (ELAA) is considered a frontier technology for future communication systems, pivotal in improving wireless systems’ rate and spectral efficiency. However, as ELAA employs a multitude of antennas operating at higher frequencies, users are typically situated in the near-field region. This inevitably leads to a significant increase in the overhead of beam training, requiring two-dimensional beam searching in both the angle and the distance domain. To address this problem, we propose a learning-based codebook-free near-field beamforming method. We strategically select padding and kernel size of convolutional neural network to efficiently extract complex channel state information features. We optimize the beamformers to maximize achievable rates in a multi-user network without predefined beam codebooks. Our solution requires only pre-estimated channel state information for optimal beamforming vector derivation during deployment. Simulation results demonstrate stable beamforming gain compared to baseline schemes, and the deep learning approach substantially reduces near-field beam training overhead.

This paper investigates the bit error rate (BER) minimum precoder design for an orthogonal time frequency space (OTFS)-based integrated sensing and communications (ISAC) system, which is considered as a promising technique for enabling future wireless networks. In particular, the BER minimum problem takes into account the maximized available transmission power and the required sensing performance. We devise the precoder from the perspective of delay-Doppler (DD) domain by exploiting the equivalent DD channel. To address the non-convex design problem, we resort to minimizing the lower bound of the derived average BER. Afterwards, we propose a computationally iterative method to solve the dual problem at low cost. Simulation results verify the effectiveness of our proposed precoder and reveal the interplay between sensing and communication for dual-functional precoder design.

In this paper, we introduce a novel communication-assisted sensing (CAS) framework that explores the potential coordination gains offered by the integrated sensing and communication technique. The CAS system endows users with beyond-line-of-the-sight sensing capabilities, supported by a dual-functional base station that enables simultaneous sensing and communication. To delve into the system's fundamental limits, we characterize the information-theoretic framework of the CAS system in terms of rate-distortion theory. We reveal the achievable overall distortion between the target's state and the reconstructions at the end-user, referred to as the sensing quality of service, within a special case where the distortion metric is separable for sensing and communication processes. As a case study, we employ a typical application to demonstrate distortion minimization under the ISAC signaling strategy, showcasing the potential of CAS in enhancing sensing capabilities.

In this paper, we study the parameter estimation performance for monostatic downlink integrated sensing and communications (ISAC) systems. In particular, we analyze the mean squared error (MSE) lower bound for target sensing in the downlink ISAC system that reveals the suboptimality in re-using the conventional communication waveform for sensing. To realize a practical dual-functional waveform, we propose a waveform augmentation strategy that imposes an extra signal structure, namely the degrees-of-freedom (DoF) completion method. The proposed approach is capable of improving the parameter estimation performance of the ISAC system and achieving the derived MSE lower bound. To improve the performance of the proposed strategy, we formulate an MSE minimization problem to design the ISAC precoder, subject to the communication users' signal-interference-plus-noise-ratio (SINR) constraints. Despite the non-convexity of the waveform design problem, we obtain its globally optimal solution via semi-definite relaxation (SDR) and the proposed constructive method. Simulation results validate the proposed DoF completion technology could achieve the derived MSE lower bound and the effectiveness of the MSE-based ISAC waveform design.

To leverage distributed data communication and learning in sensor networks effectively, edge learning (EL) methods have garnered significant attention. In the realm of distributed sensor networks, achieving consensus estimation of interested variables stands as a pivotal challenge. To address this challenge using EL methods, several approaches have been proposed combining message passing (MP) algorithms. In this article, we first describe the distributed consensus algorithm based on MP and summarize the sampling-based and parameter-based representation of the beliefs exchanged in the distributed MP algorithm. To improve the accuracy of estimation while retaining the low-complexity advantage of the parametric representation method, we propose a distributed consensus framework based on the Gaussian mixture model (GMM) MP. We approximate and keep the form beliefs as GMM in the iterations. Two different simulation scenarios are performed to shed light on the proposed distributed consensus estimation framework, i.e., static target localization and dynamic target tracking. Finally, simulation results show the performance advantages of the algorithm proposed.

It is anticipated that integrated sensing and communications (ISAC) would be one of the key enablers of next-generation wireless networks (such as beyond 5G (B5G) and 6G) for supporting a variety of emerging applications. In this paper, we provide a comprehensive review of the recent advances in ISAC systems, with a particular focus on their foundations, physical-layer system design, networking aspects and ISAC applications. Furthermore, we discuss the corresponding open questions of the above that emerged in each issue. Hence, we commence with the information theory of sensing and communications (S&C), followed by the information-theoretic limits of ISAC systems by shedding light on the fundamental performance metrics. Next, we discuss their clock synchronization and phase offset problems, the associated Pareto-optimal signaling strategies, as well as the associated super-resolution physical-layer ISAC system design. Moreover, we envision that ISAC ushers in a paradigm shift for the future cellular networks relying on network sensing, transforming the classic cellular architecture, cross-layer resource management methods, and transmission protocols. In ISAC applications, we further highlight the security and privacy issues of wireless sensing. Finally, we close by studying the recent advances in a representative ISAC use case, namely the multi-object multi-task (MOMT) recognition problem using wireless signals.

Fingerprint-based indoor localization holds great potential for the Internet of Things. Despite numerous studies focusing on its algorithmic and practical aspects, a notable gap exists in theoretical performance analysis in this domain. This paper aims to bridge this gap by deriving several lower bounds and approximations of mean square error (MSE) for fingerprint-based localization. These analyses offer different complexity and accuracy trade-offs. We derive the equivalent Fisher information matrix and its decomposed form based on a wireless propagation model, thus obtaining the Cramér-Rao bound (CRB). By approximating the Fisher information provided by constraint knowledge, we develop a constraint-aware CRB. To more accurately characterize nonlinear transformation and constraint information, we introduce the Ziv-Zakai bound (ZZB) and modify it for adapt deterministic parameters. The Gauss–Legendre quadrature method and the trust-region reflective algorithm are employed to make the calculation of ZZB tractable. We introduce a tighter extrapolated ZZB by fitting the quadrature function outside the well-defined domain based on the Q-function. For the constrained maximum likelihood estimator, an approximate MSE expression, which can characterize map constraints, is also developed. The simulation and experimental results validate the effectiveness of the proposed bounds and approximate MSE.

The formation control of multi-agent systems has increasingly drawn attention for fulfilling numerous emerging applications and services. To achieve high-accuracy formation, the location awareness of all agents becomes an essential requirement. In this paper, we address the problem of network localization and formation control in a cooperative system with asynchronous agents. In particular, we formulate the joint localization and synchronization of agents as a statistical inference problem. The underlying probabilistic model is represented by a factor graph from which a message-passing algorithm is designed that computes approximations of the marginals of unknown variables, i.e. agents’ locations and clock offsets. Due to the Euclidean-norm operator involved in their computation no parametric closed-form expressions of the messages exist. As a compromise, implemented message-passing methods therefore resort to approximations of these messages. Conventional methods rely either on a first-order Taylor expansion of the norm operation or on non-parametric representations, e.g. by means particle filters (PFs), to compute such approximations. However, the former approach suffers from poor performance while the latter one experiences high complexity. The proposed message-passing algorithm in this paper is parametric. Specifically, it passes Gaussian messages that can be essentially obtained by suitably augmenting the factor graph and applying on it a hybrid method for combining belief propagation and variational message passing. Subsequently, the agents can exploit the estimated locations for determining the control policy. Two types of control policy are designed based on the optimization of a generalized cost function. We show that the proposed scheme enjoys a reduced complexity for multi-agent localization while achieving the desired formation with excellent accuracy.

The emergence of the fifth-generation (5G) New Radio (NR) technology has provided unprecedented opportunities for vehicle-to-everything (V2X) networks, enabling enhanced quality of services. However, high-mobility V2X networks require frequent handovers and acquiring accurate channel state information (CSI) necessitates the utilization of pilot signals, leading to increased overhead and reduced communication throughput. To address this challenge, integrated sensing and communications (ISAC) techniques have been employed at the base station (gNB) within vehicle-to-infrastructure (V2I) networks, aiming to minimize overhead and improve spectral efficiency. In this study, we propose novel frame structures that incorporate ISAC signals for three crucial stages in the NR-V2X system: initial access, connected mode, and beam failure and recovery. These new frame structures employ 75% fewer pilots and reduce reference signals by 43.24%, capitalizing on the sensing capability of ISAC signals. Through extensive link-level simulations, we demonstrate that our proposed approach enables faster beam establishment during initial access, higher throughput and more precise beam tracking in connected mode with reduced overhead, and expedited detection and recovery from beam failures. Furthermore, the numerical results obtained from our simulations showcase enhanced spectrum efficiency, improved communication performance and minimal overhead, validating the effectiveness of the proposed ISAC-based techniques in NR V2I networks.

The internet of things (IoT) and wireless sensor networks (WSNs) face an energy shortage challenge that could be overcome by the novel wireless power transfer (WPT) technology. The combination of WSNs and WPT is known as wireless rechargeable sensor networks (WRSNs), with the charging efficiency and charging scheduling being the primary concerns. Therefore, this paper proposes a probabilistic on-demand charging scheduling for integrated sensing and communication (ISAC)-assisted WRSNs with multiple mobile charging vehicles (MCVs) that addresses three parts. First, it considers the four attributes with their probability distributions to balance the charging load on each MCV. The attributes are residual energy of charging node, distance from MCV to charging node, degree of charging node, and charging node betweenness centrality. Second, it considers the efficient charging factor strategy to partially charge network nodes. Finally, it employs the ISAC concept to efficiently utilize the wireless resources to reduce the traveling cost of each MCV and to avoid the charging conflicts between them. The simulation results show that the proposed protocol outperforms cutting-edge protocols in terms of energy usage efficiency, charging delay, charging coverage, survival rate, travel distance, queue length, and service time.

The orthogonal time-frequency space (OTFS) modulation is an effective technique to deal with the high-mobility challenge in vehicular wireless communications, whose data detection depends heavily on accurate channel estimation (CE). In pilot-aided CE, reducing guard symbols can achieve higher spectral efficiency. However, data interference is inevitable, especially in fractional Doppler channels. Therefore, this paper investigates the impact of data interference on CE, and aims to mitigate such data interference by adding a non-rectangular window in the time-frequency (TF) domain. To fulfill this goal, a Cramer-Rao lower bound (CRLB) is derived by considering the presence of data interference and a non-rectangular window for CE in OTFS. Different from the existing CRLB analysis for embedded-pilot OTFS, data interference is considered and treated as noise interference, resulting in a tighter derived CRLB that effectively reflects the impact of data interference on CE. By minimizing the derived CRLB with the rectangular window, a new pilot sequence is obtained, which can achieve lower CRLB and better BER performance compared to the Zadoff-Chu (ZC) sequence. On the other hand, windowing can loosen the CRLB due to its ability to suppress data interference. Under the same main lobe width, it is found that the Kaiser window and Slepian window are more effective in reducing data interference than Dolph-Chebyshev (DC) window and rectangular window. Our simulation results indicate that the interference from data to the pilot can be significantly reduced by employing appropriate windowing techniques, and the windowing functions employed play an important role in pilot-aided CE.

Vehicle-to-infrastructure (V2I) communications have been regarded as an emerging application in next-generation wireless networks. However, guaranteeing high-quality wireless communications in high-mobility scenarios remains a major challenge. In this paper, we investigate the deployment of reconfigurable intelligent surface (RIS) for improving the communication performance of V2I systems. In particular, integrated sensing and communication (ISAC) signals are exploited to facilitate sensing-assisted beamforming. Aiming at maximizing the achievable rate, two deep learning-based predictive beamforming mechanisms are proposed. First, a two-stage beamforming design is devised, where the channel state information (CSI) is estimated based on the echo signals and predicted by a dedicated neural network for time-varying channels. Then, the transmit beamforming vector at the base station (BS) and the reflect beamforming matrix at the RIS are jointly optimized. To further reduce the computational complexities, we develop an end-to-end beamforming design by employing the parameter sharing mechanism and weighted loss function. Simulation results demonstrate that the proposed algorithms can achieve an outstanding data rate that approaches the upper bound exploiting perfect CSI. In particular, the end-to-end design exhibits remarkable robustness against the impact of noise and achieves outstanding sensing-assisted beamforming performance, especially at the low signal-to-noise ratio region.

Existing Rate-Splitting Multiple Access (RSMA) techniques offer a promise for Non-Terrestrial Networks (NTNs) by managing interference and ensuring reliable data transmission. However, precoder design remains a crucial bottleneck, demanding accurate Channel State Information (CSI) feedback and complex optimization, which are challenging in practical deployment. Motivated by this, this paper proposes a novel Deep Learning (DL)-based method to predict the precoder design from the historical CSI directly. In particular, we first establish a predictive beamforming protocol for precoder design using historical CSI, bypassing the need for constant feedback and reducing complexity. Subsequently, we formulate a general problem for precoder design, with the Weighted Ergodic Sum Rate (WESR) serving as the objective function. Solving this problem is particularly challenging due to the dynamic nature of wireless channels in NTNs. To address this, we designed a fusion model, named TranCN, which harnesses the strengths of Transformers and Convolutional Neural Networks (CNNs) to extract spatial-temporal features from historical CSI, thereby enhancing precoder performance. Simulation results demonstrate that our predictive beamforming scheme enables RSMA to adapt to dynamic channel conditions using historical CSI, surpassing baseline methods and improving data transmission resilience.

Integrated sensing and communications (ISAC), supported by massive multiple-input multiple-output (MIMO), can provide simultaneously improvement of sensing capability and communication capacity. However, employing the conventional fully digital beamforming architecture with a large-scale antenna array will incur the prohibitively high hardware cost and power consumption. In this paper, we propose a hybrid beamforming design with the overlapped subarrays (OSA)-based hybrid architecture for massive MIMO-ISAC systems. We design the analog and digital beamformers by jointly optimizing the spectral efficiency of communication and beampattern mean squared error of sensing under the specific constraints of OSA structures, power budget, and constant modulus. To tackle the resulting non-convex problem, we relax it as a weighted summation minimization problem, where the Euclidean distance between the designed hybrid beamformers and the optimal communication/desired sensing beamformers is minimized. We further decompose the formulated problem into three subproblems and develop an effective alternating minimization algorithm. Numerical simulations demonstrate the effectiveness and flexibility of the proposed OSA-based hybrid beamforming design in terms of spectral efficiency and sensing beampattern performance.

In this paper, we study the pulse shaping for delay-Doppler (DD) communications. We start with constructing a basis function in the DD domain following the properties of the Zak transform. Particularly, we show that the constructed basis functions are globally quasi-periodic while locally twisted-shifted, and their significance in time and frequency domains are then revealed. We further analyze the ambiguity function of the basis function, and show that fully localized ambiguity function can be achieved by constructing the basis function using periodic signals. More importantly, we prove that time and frequency truncating such basis functions naturally leads to approximate delay and Doppler orthogonalities, if the truncating windows are periodic within the support. Motivated by this, we propose a DD Nyquist pulse shaping scheme considering signals with periodicity. Finally, our conclusions are verified by using various strictly or approximately periodic pulses.

The internet of things (IoT) based wireless sensor networks (WSNs) face an energy shortage challenge that could be overcome by the novel wireless power transfer (WPT) technology. The combination of WSNs and WPT is known as wireless rechargeable sensor networks (WRSNs), with the charging efficiency and charging scheduling being the primary concerns. Therefore, this paper proposes a probabilistic on-demand charging scheduling for integrated sensing and communication (ISAC)-assisted WRSNs with multiple mobile charging vehicles (MCVs) that addresses three parts. First, it considers the four attributes with their probability distributions to balance the charging load on each MCV. The distributions are residual energy of charging node, distance from MCV to charging node, degree of charging node, and charging node betweenness centrality. Second, it considers the efficient charging factor strategy to partially charge network nodes. Finally, it employs the ISAC concept to efficiently utilize the wireless resources to reduce the traveling cost of each MCV and to avoid the charging conflicts between them. The simulation results show that the proposed protocol outperforms cutting-edge protocols in terms of energy usage efficiency, charging delay, survival rate, and travel distance.

The orthogonal time frequency space (OTFS) technique is an innovative modulation scheme that provides significant advantages in terms of channel delay and Doppler shifts. In this work, we study the sparse delay and Doppler channel estimation problem for OTFS and consider the impact of inheriting initial and iterative parameters on adjacent estimated channel corresponding to previous OTFS transmitted blocks. We propose a parameter-inherited sparse Bayesian learning (SBL) channel estimation algorithm based on unitary approximate message passing (UAMP). Simulation results show that compared to the state-of-art SBL-based algorithms, the proposed algorithm has faster convergence speed and higher accuracy. Furthermore, by exploiting the block circulant matrix with circulant blocks (BCCB) matrix property, we replace the matrix multiplication with two-dimensional (2D) fast Fourier transform (FFT), which leads to a low complexity.

Extremely large-scale array (XL-array) has emerged as a promising technology to enhance the spectrum efficiency and spatial resolution in future wireless networks, leading to a fundamental paradigm shift from conventional far-field communications towards the near-field communications. Different from the existing works that mostly considered simultaneous wireless information and power transfer (SWIPT) in the far field, we consider in this paper a new and practical scenario, called mixed near- and far-field SWIPT, in which energy harvesting (EH) and information decoding (ID) receivers are located in the near- and far-field regions of the XL-array base station (BS), respectively. Specifically, we formulate an optimization problem to maximize the weighted sum-power harvested at all EH receivers by jointly designing the BS beam scheduling and power allocation, under the constraints on the ID sum-rate and BS transmit power. To solve this non-convex optimization problem, an efficient algorithm is proposed to obtain a suboptimal solution by leveraging the binary variable elimination and successive convex approximation methods. Numerical results demonstrate that our proposed joint design achieves substantial performance gain over other benchmark schemes.

To guarantee excellent reliability performance in ultra-reliable low-latency communications (URLLC), pragmatic precoder design is an effective approach. However, an efficient precoder design highly depends on the accurate instantaneous channel state information at the transmitter (ICSIT), which however, is not always available in practice. To overcome this problem, in this paper, we focus on the orthogonal time frequency space (OTFS)-based URLLC system and adopt a deep learning (DL) approach to directly predict the precoder for the next time frame to minimize the frame error rate (FER) via implicitly exploiting the features from estimated historical channels in the delay-Doppler domain. By doing this, we can guarantee the system reliability even without the knowledge of ICSIT. To this end, a general precoder design problem is formulated where a closed-form theoretical FER expression is specifically derived to characterize the system reliability. Then, a delay-Doppler domain channels-aware convolutional long short-term memory (CLSTM) network (DDCL-Net) is proposed for predictive precoder design. In particular, both the convolutional neural network and LSTM modules are adopted in the proposed neural network to exploit the spatial-temporal features of wireless channels for improving the learning performance. Finally, simulation results demonstrated that the FER performance of the proposed method approaches that of the perfect ICSI-aided scheme.

The recently proposed orthogonal time frequency space (OTFS) modulation multiplexes data symbols in the delay-Doppler (DD) domain. Since the range and velocity, which can be derived from the delay and Doppler shifts, are the parameters of interest for radar sensing, it is natural to consider implementing DD signal processing for radar sensing. In this paper, we investigate the potential connections between the OTFS and DD domain radar signal processing. Our analysis shows that the range-Doppler matrix computing process in radar sensing is exactly the demodulation of OTFS with a rectangular pulse shaping filter. Furthermore, we propose a two-dimensional (2D) correlation-based algorithm to estimate the fractional delay and Doppler parameters for radar sensing. Simulation results show that the proposed algorithm can efficiently obtain the delay and Doppler shifts associated with multiple targets.

In this paper, we propose a rate-splitting design and characterize the sum-degrees-of-freedom (DoF) for the K-user multiple-input-single-output (MISO) broadcast channel with mixed channel state information at the transmitter (CSIT) and order-(K − 1) messages, where mixed CSIT refers to the delayed and imperfect-current CSIT, and order-(K − 1) message refers to the message desired by K − 1 users simultaneously. In particular, for the sum-DoF lower bound, we propose a rate-splitting scheme embedding with retrospective interference alignment. In addition, we propose a matching sum-DoF upper bound via genie signalings and extremal inequality. Opposed to existing works for K = 2, our results show that the sum-DoF is saturated with CSIT quality when CSIT quality thresholds are satisfied for K > 2.

Orthogonal time frequency space (OTFS) modulation which multiplexes data symbols in the delay Doppler (DD) domain has been proved to be an effective scheme for high-mobility scenarios. To realize the full time and frequency diversity promised, some promising detectors have been developed. The existence of fractional Doppler imposes new challenges to the performance and complexity of the detector. In this paper, we proposed a hybrid message passing detector for OTFS in the presence of the fractional OTFS. Based on the system model, we derive the probabilistic model of OTFS detector and represent it by a factor graph. For one Doppler index, we choose the path with the highest gain which is referred to as the main path. Then, a hybrid message passing (MP) scheme is developed, where we apply the non-approximate MP algorithm for the main paths while the Gaussian approximation-based MP algorithm is executed for the remaining paths.

In this paper, we propose an efficient channel estimation algorithm for orthogonal time frequency space (OTFS) systems in the presence of fractional Doppler. The proposed algorithm first employs the well-known threshold-based estimator to obtain the effective channel response. With the effective channel matrix in hand, we then utilize the linear system to recover the Doppler shifts and channel gains of different resolvable paths. The interference between different paths is also considered. Our simulation results verify that, by selecting appropriate samples in the effective channel matrix, the Doppler shifts and channel gains can be estimated robustly even in poor signal-to-noise ratio (SNR) conditions.

Orthogonal time frequency space (OTFS) modulation has shown promising application perspectives, thanks to its strong delay and Doppler resilience. Furthermore, the delay-Doppler domain channel response directly reflects the physical attributes of channel scatterers, which provides fundamentally new perspectives for channel estimation (CE) and radar sensing. The success of OTFS has stimulated various CE and equalization algorithms with promising performance. However, only few of them were validated by hardware experiments. In this paper, we develop an OTFS communication and sensing (C&S) system using software defined radio (SDR), which invokes the off-grid target sensing and minimum mean square error (MMSE) channel equalization. In particular, we design and emulate the high-mobility wireless channel with multiple scatterers (sensing targets) and conduct the channel equalization with MMSE for data detection. Moreover, we study the influence of transceiver impairments, such as in-phase and quadrature (IQ) imbalance, DC offset, and carrier frequency offsets (CFO). With real-time experiments, the results show that the addition of scatterers engenders the distortion of the DD domain signals which curtail the BER performance of the communication system and further trims the MSE of sensing parameters with the increasing number of scatterers.

This paper explores an orthogonal time frequency space (OTFS)-assisted integrated sensing and communication (ISAC) system in vehicular networks. We present a deep learning (DL)-based framework for the OTFS-assisted ISAC system, leveraging the advantages offered by the Delay-Doppler representation of the time-variant channel. The communication channel matrix is utilized within the framework to infer motion parameters, thereby enabling the establishment of an effective transmission protocol. Therefore, it is crucial to design a channel estimation method that simultaneously fulfills both sensing and communication performance requirements. To this end, a DL-based channel estimation approach is designed to obtain accurate channel state information (CSI), due to the powerful capability of neural networks [1]. Specifically, we model the channel estimation as a denoising problem from the embedded pilot scheme and employ a self-adaptive threshold submodule to eliminate irrelevant features. Finally, simulation results demonstrate that our proposed method can obtain accurate CSI with the available sensing performance.

In this article, we study the uplink transmission scheme and channel estimation design for reconfigurable intelligent surfaces (RIS)-aided orthogonal time–frequency space (OTFS) systems in high-mobility scenarios. To this end, we first propose an efficient and reliable transmission scheme that utilizes the delay-Doppler (DD) information in OTFS to facilitate the configuration of RIS. Specifically, the proposed scheme exploits the estimated delay and Doppler shifts of the cascaded channel to sense the channel parameters, and the sensing parameters are then used for RIS passive beamforming. It is noteworthy that we estimate the channel state information (CSI) by employing only one OTFS frame and configure the RIS based on the predicted channel parameters, leading to substantially reduced channel training overhead and more real-time RIS configuration. To obtain the essential information for channel information sensing, we then propose a low-complexity algorithm which determines the Doppler and delay shifts of the channel between the user and RIS based on linear systems and the mapping relationship of the DD pairs, respectively. With the DD information in hand, a user localization algorithm constructed by the least square (LS) and a channel tracking method relying on extended Kalman filter (EKF) are then presented to obtain the spatial angle information. By making use of the channel parameters acquired at the base station (BS), the RIS reflection vector is designed to maximize the achievable rate. The results obtained from the simulation experiments affirm the efficacy of the proposed scheme, thereby confirming its capability to attain efficient communications under high Doppler channels.

The unmanned aerial vehicles (UAVs) are envisioned as promising aerial facilities for providing advanced communication services as well as sensing functionalities in the next-generation wireless system. This article considers a UAV-enabled integrated sensing and communications (ISACs) system, where a moving ground user (GU) is simultaneously tracked by multiple UAVs and receives the downlink communication information transmitted from the UAV. In particular, to jointly enhance the sensing and communication (S&C) performance, optimizing the UAV moving trajectory is demanded. To achieve this goal, we first harness the extended Kalman filtering (EKF) method for predicting and tracking the motion parameters of GU at each time slot, which relies on the range measurements extracted from the sensing echoes at the base station (BS). Afterward, we formulate a weighted optimization problem that addresses the design of UAV trajectories and GU-UAV association simultaneously, incorporating the consideration of real-time downlink communication rates and the Cramér–Rao bound (CRB) for GU tracking. The problem further is constrained by the maximum consumed power, maximum traveling distance, and minimum collision avoidance distance. As a step forward, to address the resultant nonconvex problem, we develop an efficient iterative algorithm to obtain a near-optimal solution by utilizing the successive convex approximate (SCA) technique. Specifically, we alternately solve the GU-UAV association and the real-time trajectory design problem at each time slot. Finally, our numerical simulations illustrate that our proposed algorithm can track the GU accurately while meeting the sensing-centric and/or communication-centric requirements.

Indoor localization is of pivotal significance for a wide variety of services in the context of the Internet of Things (IoT). Both ranging-based and fingerprint-based localization techniques are promising for employment in harsh indoor environments. Hence, we propose a unified framework based on factor graphs for ubiquitous high-accuracy indoor localization. Our unified framework efficiently integrates ranging and fingerprinting for striking an appealing accuracy versus deployment cost tradeoff, where the crowdsourcing required for the construction of fingerprinting databases can also be addressed with little human intervention. By intrinsically amalgamating the global grid sampling and the regularized importance-resampling techniques, a nonparametric belief propagation algorithm is proposed for achieving the accurate position estimation at the cost of a moderate computational complexity. For improving the robustness to environmental variations, a likelihood-ratio-based approach is employed to detect ranging outliers. Moreover, a low-complexity serial scheduling scheme defined over factor graphs is designed for real-time localization. We design a hybrid ultrawide bandwidth and Wi-Fi localization system relying on off-the-shelf commercial devices and evaluate the proposed unified framework in a typical office building. Our experimental results show that the proposed algorithm outperforms the existing state-of-the-art methods and it is capable of achieving submeter localization accuracy.

The digital twin (DT), which effectively represents the actual real-world physical system or process, has reshaped the classic manufacturing, construction, as well as healthcare industry. As for realizing DT, both sensing and communication functionalities are demanded, which fully builds the connectivity between the physical world and the digital world. We first conducted a survey on the current situation of DT combined with communication and sensing. Inspired from this survey and the current development of communication and sensing, in this paper, we attempt to study the communication annd sensing technologies of physical layer in DT, to reduce the hardware and spectrum overhead. First, we studied the degree of freedom (DoF) problem in general communication and sensing system, and contribute to the DoF definition in the sensing system. Then, in order to improve the spectrum efficiency in DT system, we proposed an iterative optimization framework to address the coexistence of communication and sensing, and some examples are provided. Finally, in order to pursue a better integration gain, we proposed a new waveform design method based on DoF completion. The proposed optimization method can achieve the mean square error (MSE) lower bound. Simulation results demonstrate the effectiveness of various problems in the above scenarios.

This paper investigates the orthogonal time frequency space (OTFS) transmission for enabling ultra-reliable low-latency communications (URLLC). To guarantee excellent reliability performance, pragmatic precoder design is an effective and indispensable solution. However, the design requires accurate instantaneous channel state information at the transmitter (ICSIT) which is not always available in practice. Motivated by this, we adopt a deep learning (DL) approach to exploit implicit features from estimated historical delay-Doppler domain channels (DDCs) to directly predict the precoder to be adopted in the next time frame for minimizing the frame error rate (FER), that can further improve the system reliability without the acquisition of ICSIT. To this end, we first establish a predictive transmission protocol and formulate a general problem for the precoder design where a closed-form theoretical FER expression is derived serving as the objective function to characterize the system reliability. Then, we propose a DL-based predictive precoder design framework which exploits an unsupervised learning mechanism to improve the practicability of the proposed scheme. As a realization of the proposed framework, we design a DDCs-aware convolutional long short-term memory (CLSTM) network for the precoder design, where both the convolutional neural network and LSTM modules are adopted to facilitate the spatial-temporal feature extraction from the estimated historical DDCs to further enhance the precoder performance. Simulation results demonstrate that the proposed scheme facilitates a flexible reliability-latency tradeoff and achieves an excellent FER performance that approaches the lower bound obtained by a genie-aided benchmark requiring perfect ICSI at both the transmitter and receiver.

With the continuous evolution of emerging technologies such as mobile network, machine learning (ML), 5G, etc., digital twins (DT) bursts out great potential by its capacity of data analysis, data tracking, data prediction, etc, building a bridge between the physical and information world. Meanwhile, mobile network is moving towards data-driven paradigm, the issue of data privacy and data security seem to be a bottleneck. As a result, federated learning (FL) and mobile network are deeply converging. However, the mobile network is time-varying and the parameters of FL-empowered mobile network is huge and continue to increase with exponential growth of wireless terminals, result in the failure of traditional modeling. In the mobile networks, DT is conducive to prototyping, testing, and optimization, enabling mobile networks to be modelled more efficiently in a virtual environment and thus providing guidance for practical application. To this end, a communication-assisted sensing scenario is considered in this paper with FL in DT-empowered mobile networks. More specifically, two communication-assisted sensing architectures are proposed to improve communication efficiency of mobile network, namely, centralized architecture of federated transfer learning (FTL) and decentralized architecture of FTL. For centralized architecture of FTL, feature extraction of sensing information is conducted by FL between partial nodes and central server while the remaining nodes are used to train the fully connected layers at the central server. Considering data safety during the communication between sensing nodes, a decentralized architecture is designed based on FTL and Blockchain, where the feature extraction module is obtained by the fusion of sharing model (by Blockchain) and local model. The performance of proposed schemes is evaluated and demonstrated by the simulations.

Rate-Splitting Multiple Access (RSMA) has recently found favour in the multi-antenna-aided wireless downlink, as a benefit of relaxing the accuracy of Channel State Information at the Transmitter (CSIT), while in achieving high spectral efficiency and providing security guarantees. These benefits are particularly important in high-velocity vehicular platoons since their high Doppler affects the estimation accuracy of the CSIT. To tackle this challenge, we propose an RSMA-based Internet of Vehicles (IoV) solution that jointly considers platoon control and FEderated Edge Learning (FEEL) in the downlink. Specifically, the proposed framework is designed for transmitting the unicast control messages within the IoV platoon, as well as for privacy-preserving FEEL-aided downlink Non-Orthogonal Unicasting and Multicasting (NOUM). Given this sophisticated framework, a multi-objective optimization problem is formulated to minimize both the latency of the FEEL downlink and the deviation of the vehicles within the platoon. To efficiently solve this problem, a Block Coordinate Descent (BCD) framework is developed for decoupling the main multi-objective problem into two sub-problems. Then, for solving these non-convex sub-problems, a Successive Convex Approximation (SCA) and Model Predictive Control (MPC) method is developed for solving the FEEL-based downlink problem and platoon control problem, respectively. Our simulation results show that the proposed RSMA-based IoV system outperforms both the popular Multi-User Linear Precoding (MU–LP) and the conventional Non-Orthogonal Multiple Access (NOMA) system. Finally, the BCD framework is shown to generate near-optimal solutions at reduced complexity.

Integrated Sensing and Communication (ISAC) is recognized as a promising technology for the next-generation wireless networks, which provides significant performance gains over individual sensing and communications (S&C) systems via the shared use of wireless resources. The characterization of the S&C performance tradeoff is at the core of the theoretical foundation of ISAC. In this paper, we consider a point-to-point (P2P) ISAC model under vector Gaussian channels, and propose to use the Cramér-Rao bound (CRB)-rate region as a basic tool for depicting the fundamental S&C tradeoff. In particular, we consider the scenario where a unified ISAC waveform is emitted from a dual-functional ISAC transmitter (Tx), which simultaneously communicates information to a communication receiver (Rx) and senses targets with the help of a sensing Rx. In order to perform both S&C tasks, the ISAC waveform is required to be random to convey communication information, with realizations being perfectly known at both the ISAC Tx and the sensing Rx as a reference sensing signal as in typical radar systems. In this context, we treat the ISAC waveform as a random but known nuisance parameter in the sensing signal model, and define a Miller-Chang type CRB for the analysis of the sensing performance. As the main contribution of this paper, we characterize the S&C performance at the two corner points of the CRB-rate region, namely,$P_{\mathrm{ SC}}$indicating the maximum achievable communication rate constrained by the minimum CRB, and$P_{\mathrm{ CS}}$indicating the minimum achievable CRB constrained by the maximum communication rate. In particular, we derive the high-SNR communication capacity at$P_{\mathrm{ SC}}$, and provide lower and upper bounds for the sensing CRB at$P_{\mathrm{ CS}}$. We show that these two points can be achieved by the conventional Gaussian signalling and a novel strategy relying on the uniform distribution over the set of semi-unitary matrices, i.e., the Stiefel manifold, respectively. Based on the above-mentioned analysis, we provide an outer bound and various inner bounds for the achievable CRB-rate regions. Our main results reveal a two-fold tradeoff in ISAC systems, consisting of the subspace tradeoff (ST) and the deterministic-random tradeoff (DRT) that depend on the resource allocation and data modulation schemes employed for S&C, respectively. Within this framework, we examine the state-of-the-art ISAC signalling strategies and study a number of illustrative examples, which are validated through numerical simulations.

We investigate sensing-assisted beamforming for vehicle-to-infrastructure (V2I) communication by exploiting integrated sensing and communications (ISAC) functionalities at the roadside unit (RSU). The RSU deploys a massive multi-input-multi-output (mMIMO) array at mmWave. The pencil-sharp mMIMO beams and fine range-resolution implicate that the point-target assumption is impractical, as the vehicle’s geometry becomes essential. Therefore, the communication receiver (CR) may never lie in the beam, even when the vehicle is accurately tracked. To tackle this problem, we consider the extended target with two novel schemes. For the first scheme, the beamwidth is adjusted in real-time to cover the entire vehicle, followed by an extended Kalman filter to predict and track the position of CR according to resolved scatterers. An upgraded scheme is proposed by splitting each transmission block into two stages. The first stage is exploited for ISAC with a wide beam. Based on the sensed results at the first stage, the second stage is dedicated to communication with a pencil-sharp beam, yielding significant communication improvements. We reveal the inherent tradeoff between the two stages in terms of their durations, and develop an optimal allocation strategy that maximizes the average achievable rate. Finally, simulations verify the superiorities of proposed schemes over state-of-the-art methods.

In this paper, we propose sensing-assisted beamforming designs for vehicles on arbitrarily shaped roads by relying on integrated sensing and communication (ISAC) signalling. Specifically, we aim to address the limitations of conventional ISAC beam-tracking schemes that do not apply to complex road geometries. To improve the tracking accuracy and communication quality of service (QoS) in vehicle to infrastructure (V2I) networks, it is essential to model the complicated roadway geometry. To that end, we impose the curvilinear coordinate system (CCS) in an interacting multiple model extended Kalman filter (IMM-EKF) framework. By doing so, both the position and the motion of the vehicle on a complicated road can be explicitly modeled and precisely tracked attributing to the benefits from the CCS. Furthermore, an optimization problem is formulated to maximize the array gain by dynamically adjusting the array size and thereby controlling the beamwidth, which takes the performance loss caused by beam misalignment into account. Numerical simulations demonstrate that the roadway geometry-aware ISAC beamforming approach outperforms the communication-only-based and ISAC kinematic-only-based technique in tracking performance. Moreover, the effectiveness of the dynamic beamwidth design is also verified by our numerical results.

This paper studies a downlink system that combines orthogonal-time-frequency-space (OTFS) modulation and sparse code multiple access (SCMA) to support massive connectivity in high-mobility environments. We propose a cross-domain receiver for the considered OTFS-SCMA system which efficiently carries out OTFS symbol estimation and SCMA decoding in a joint manner. This is done by iteratively passing the extrinsic information between the time domain and the delay-Doppler (DD) domain via the corresponding unitary transformation to ensure the principal orthogonality of errors from each domain. We show that the proposed OTFS-SCMA detection algorithm exists at a fixed point in the state evolution when it converges. To further enhance the error performance of the proposed OTFS-SCMA system, we investigate the cooperation between downlink users to exploit the diversity gains and develop a distributed cooperative detection (DCD) algorithm with the aid of belief consensus. Our numerical results demonstrate the effectiveness and convergence of the proposed algorithm and show an increased spectral efficiency compared to the conventional OTFS transmission.

Integrated Sensing and Communication (ISAC) is recognized as a promising technology for the next-generation wireless networks. In this paper, we provide a general framework to reveal the fundamental tradeoff between sensing and communications (S&C), where a unified ISAC waveform is exploited to perform dual-functional tasks. In particular, we define the Cramér-Rao bound (CRB)-rate region to characterize the S&C tradeoff, and propose a pentagon inner bound of the region. We show that the two corner points of the CRB-rate region can be achieved by the conventional Gaussian waveform and a novel strategy corresponding to the uniform distribution over the Stiefel manifold, respectively. Moreover, we also offer our insights into transmission approaches achieving the boundary of the CRB-rate region, namely the Shannon-Fisher information flow.

In this paper, we study the potentials of spatially-spread orthogonal time frequency space (SS-OTFS) modulation for integrated sensing and communication (ISAC) transmissions. The most favourable feature of SS-OTFS modulation is that it forms beams according to a pre-determined angular grid, which is different from the conventional beamforming, where dedicated beams are formed according to the a priori information on the angle of departures (AoDs). According to the delay-Doppler domain channel characteristics, we first derive the input-output relationships for SS-OTFS-enabled ISAC system in a typical downlink multi-user MIMO (MU-MIMO) scenario. Based on those relationships, we further study the angular domain channel features and discuss the system design. Our numerical results have demonstrated the advantages of the proposed scheme over the conventional beamforming counterpart in terms of the signal-to-interference-plus-noise ratio (SINR).

The implementation of integrated sensing and communication (ISAC) highly depends on the effective beamforming design exploiting accurate instantaneous channel state information (ICSI). However, channel tracking in ISAC requires large amount of training overhead and prohibitively large computational complexity. To address this problem, in this paper, we focus on ISAC-assisted vehicular networks and exploit a deep learning approach to implicitly learn the features of historical channels and directly predict the beamforming matrix for the next time slot to maximize the average achievable sum-rate of system, thus bypassing the need of explicit channel tracking for reducing the system signaling overhead. To this end, a general sum-rate maximization problem with Cramer-Rao lower bounds-based sensing constraints is first formulated for the considered ISAC system. Then, a historical channels-based convolutional long short-term memory network is designed for predictive beamforming that can exploit the spatial and temporal dependencies of communication channels to further improve the learning performance. Finally, simulation results show that the proposed method can satisfy the requirement of sensing performance, while its achievable sum-rate can approach the upper bound obtained by a genie-aided scheme with perfect ICSI available.

This paper investigates the integrated sensing and communication (ISAC) in vehicle-to-infrastructure (V2I) networks. To realize ISAC, an effective beamforming design is essential which however, highly depends on the availability of accurate channel tracking requiring large training overhead and computational complexity. Motivated by this, we adopt a deep learning (DL) approach to implicitly learn the features of historical channels and directly predict the beamforming matrix to be adopted for the next time slot to maximize the average achievable sum-rate of an ISAC system. The proposed method can bypass the need of explicit channel tracking process and reduce the signaling overhead significantly. To this end, a general sum-rate maximization problem with Cramer-Rao lower bounds-based sensing constraints is first formulated for the considered ISAC system taking into account the multiple access interference. Then, by exploiting the penalty method, a versatile unsupervised DL-based predictive beamforming design framework is developed to address the formulated design problem. As a realization of the developed framework, a historical channels-based convolutional long short-term memory (LSTM) network (HCL-Net) is devised for predictive beamforming in the ISAC-based V2I network. Specifically, the convolution and LSTM modules are successively adopted in the proposed HCL-Net to exploit the spatial and temporal dependencies of communication channels to further improve the learning performance. Finally, simulation results show that the proposed predictive method not only guarantees the required sensing performance, but also achieves a satisfactory sum-rate that can approach the upper bound obtained by the genie-aided scheme with the perfect instantaneous channel state information available.

Faster-than-Nyquist (FTN) signaling aided non-orthogonal multiple access (NOMA) is conceived and its achievable rate is quantified in the presence ofrandomlink delays of the different users. We reveal that exploiting the link delays may potentially lead to a signal-to-interference-plus-noise ratio (SINR) gain, while transmitting the data symbols at FTN rates has the potential of increasing the degree-of-freedom (DoF). We then unveil the fundamental trade-off between the SINR and DoF. In particular, at a sufficiently high symbol rate, the SINR gain vanishes while the DoF gain achieves its maximum, where the achievable rate is almost$(1+\beta)$times higher than that of the conventional synchronous NOMA transmission in the high signal-to-noise ratio (SNR) regime, with$\beta $being the roll-off factor of the signaling pulse. Our simulation results verify our analysis and demonstrate considerable rate improvements over the conventional power-domain NOMA scheme.

In this paper, we propose a novel integrated sensing and communication (ISAC) transmission framework based on the spatially spread orthogonal time frequency space (SS-OTFS) modulation by considering the fact that communication channel strengths cannot be directly obtained from radar sensing. We first propose the concept of SS-OTFS modulation, where the key novelty is the angular domain discretization enabled by the spatial spreading/de-spreading. This discretization gives rise to simple and insightful effective models for both radar sensing and communication, which results in simplified designs for the related estimation and detection problems. In particular, we design simple beam tracking, angle estimation, and power allocation schemes for radar sensing, by utilizing the special structure of the effective radar sensing matrix. Meanwhile, we provide a detailed analysis on the pair-wise error probability (PEP) for communication, which unveils the key conditions for both precoding and power allocation designs for communication. Based on those conditions, we design a symbol-wise precoding scheme for communication based only on the delay, Doppler, and angle estimates from radar sensing, without thea prioriknowledge of the communication channel fading coefficients, and also propose a suitable power allocation. Furthermore, we notice that radar sensing and communication requires different power allocations. Therefore, we discuss the performances of both the radar sensing and communication with different power allocations and show that the power allocation should be designed leaning towards radar sensing in practical scenarios. The effectiveness of the proposed ISAC transmission framework is verified by our numerical results, which also agree with our analysis and discussions.

Recently proposed orthogonal time frequency space (OTFS) modulation has been considered as a promising candidate for accommodating various emerging communication and sensing applications in high-mobility environments. In this paper, we propose a novel cross domain iterative detection algorithm to enhance the error performance of OTFS modulation. Different from conventional OTFS detection methods, the proposed algorithm applies basic estimation/detection approaches to both the time domain and delay-Doppler (DD) domain and iteratively updates the extrinsic information from two domains with the unitary transformation. In doing so, the proposed algorithm exploits the time domain channel sparsity and the DD domain symbol constellation constraints. We evaluate the estimation/detection error variance in each domain for each iteration and derive the state evolution to investigate the detection error performance. We show that the performance gain due to iterations comes from the non-Gaussian constellation constraint in the DD domain. More importantly, we prove that the proposed algorithm can indeed converge and, in the convergence, the proposed algorithm can achieve almost the same error performance as the maximum-likelihood sequence detection even in the presence of fractional Doppler shifts. Furthermore, the computational complexity associated with the domain transformation is low, thanks to the structure of the discrete Fourier transform (DFT) kernel. Simulation results are consistent with our analysis and demonstrate a significant performance improvement compared to conventional OTFS detection methods.

This paper proposes an off-grid channel estimation scheme for orthogonal time-frequency space (OTFS) systems adopting the sparse Bayesian learning (SBL) framework. To avoid channel spreading caused by the fractional delay and Doppler shifts and to fully exploit the channel sparsity in the delay-Doppler (DD) domain, we estimate the original DD domain channel response rather than the effective DD domain channel response as commonly adopted in the literature. OTFS channel estimation is firstly formulated as a one-dimensional (1D) off-grid sparse signal recovery (SSR) problem based on a virtual sampling grid defined in the DD space, where the on-grid and off-grid components of the delay and Doppler shifts are separated for estimation. In particular, the on-grid components of the delay and Doppler shifts are jointly determined by the entry indices with significant values in the recovered sparse vector. Then, the corresponding off-grid components are modeled as hyper-parameters in the proposed SBL framework, which can be estimated via the expectation-maximization method. To strike a balance between channel estimation performance and computational complexity, we further propose a two-dimensional (2D) off-grid SSR problem via decoupling the delay and Doppler shift estimations. In our developed 1D and 2D off-grid SBL-based channel estimation algorithms, the hyper-parameters are updated alternatively for computing the conditional posterior distribution of channels, which can be exploited to reconstruct the effective DD domain channel. Compared with the 1D method, the proposed 2D method enjoys a much lower computational complexity while only suffers a slight performance degradation. Simulation results verify the superior performance of the proposed channel estimation schemes over state-of-the-art schemes.

The orthogonal-time-frequency-space (OTFS) modulation has emerged as a promising modulation scheme for high mobility wireless communications. To harvest the time and frequency diversity promised by OTFS, some promising detectors, especially message passing based ones, have been developed by taking advantage of the sparsity of the channel in the delay-Doppler domain. However, when the number of channel paths is relatively large or fractional Doppler shifts have to be considered, the complexity of existing detectors is a concern, and the existing message passing based detectors suffer from performance loss. In this work, we investigate the design of OTFS detectors based on the approximate message passing (AMP). In particular, leveraging the unitary AMP (UAMP), we design new detectors that enjoy the structure of the channel matrix and allow efficient implementation. In addition, the estimation of noise variance is incorporated into the UAMP-based detectors. Thanks to the robustness of UAMP relative to AMP, the UAMP-based detectors deliver superior performance, and outperform state-of-the-art detectors significantly. We also investigate iterative joint detection and decoding in a coded OTFS system, where the OTFS detectors are integrated into a powerful turbo receiver, leading to considerable performance gains.

This paper proposes an off-grid channel estimation scheme for orthogonal time-frequency space (OTFS) systems adopting the sparse Bayesian learning (SBL) framework. To avoid channel spreading caused by the fractional delay and Doppler shifts and to fully exploit the channel sparsity in the delay-Doppler (DD) domain, we estimate the original DD domain channel response rather than the effective DD domain channel response as commonly adopted in the literature. The OTFS channel estimation problem is formulated as an off-grid sparse signal recovery problem based on a virtual sampling grid defined in the DD space, where the on-grid and off-grid components of the delay and Doppler shifts are separated for estimation. In particular, the on-grid components of the delay and Doppler shifts are jointly determined by the entry indices with significant values in the recovered sparse vector. Then, the corresponding off-grid components are modeled as hyper-parameters in the proposed SBL framework, which can be estimated via the expectation-maximization method. Simulation results verify that compared with the on-grid approach, our proposed off-grid OTFS channel estimation scheme enjoys a 1.5 dB lower normalized mean square error.

In this paper, we investigate the impacts of transmitter and receiver windows on orthogonal time-frequency space (OTFS) modulation and propose a window design to improve the OTFS channel estimation performance. Assuming ideal pulse shaping filters at the transceiver, we first identify the role of window in effective channel and the reduced channel sparsity with conventional rectangular window. Then, we characterize the impacts of windowing on the effective channel estimation performance for OTFS modulation. Based on the revealed insights, we propose to apply a Dolph-Chebyshev (DC) window at either the transmitter or the receiver to effectively enhance the sparsity of the effective channel. As such, the channel spread due to the fractional Doppler is significantly reduced, which leads to a lower error floor in channel estimation compared with that of the rectangular window. Simulation results verify the accuracy of the obtained analytical results and confirm the superiority of the proposed window designs in improving the channel estimation performance over the conventional rectangular or Sine windows.

Non-orthogonal multiple access (NOMA) has been widely recognized as a promising multiple access scheme for realizing next generation wireless communications. Unlike existing NOMA schemes assuming perfectly time synchronized user's signals received at the base station (BS), in this paper, we investigate the achievable rates of uplink NOMA with asynchronized transmission. By invoking Szegö's Theorem, we derive both the upper- and lower-bounds of the achievable rates of asynchronized NOMA (aNOMA) systems. In particular, we reveal that the derived lower-bound is essentially the achievable rate for conventional synchronized NOMA systems, which indicates that the asynchronization is not necessarily a foe. More specifically, we show that aNOMA systems are superior to conventional NOMA systems in terms of the achievable rates with non-sinc shaping pulses. Important insights are also unveiled based on the derived bounds. Simulation results confirm the validity of our derived analysis and demonstrate considerable achievable rates gains of aNOMA systems over conventional NOMA systems.

In this paper, we investigate the impacts of transmitter and receiver windows on the performance of orthogonal time-frequency space (OTFS) modulation and propose window designs to improve the OTFS channel estimation and data detection performance. In particular, assuming ideal pulse shaping filters at the transceiver, we derive the impacts of windowing on the effective channel and its estimation performance in the delay-Doppler (DD) domain, the total average transmit power, and the effective noise covariance matrix. When the channel state information (CSI) is available at the transceiver, we analyze the minimum squared error (MSE) of data detection and propose an optimal transmitter window to minimize the detection MSE. The proposed optimal transmitter window can be interpreted as a mercury/water-filling power allocation scheme, where the mercury is firstly filled before pouring water to pre-equalize the time-frequency (TF) domain channels. When the CSI is not available at the transmitter but can be estimated at the receiver, we propose to apply a Dolph-Chebyshev (DC) window at either the transmitter or the receiver, which can effectively enhance the sparsity of the effective channel in the DD domain. Thanks to the enhanced DD domain channel sparsity, the channel spread due to the fractional Doppler is significantly reduced, which leads to a lower error floor in both channel estimation and data detection compared with that of rectangular window. Simulation results verify the accuracy of the obtained analytical results and confirm the superiority of the proposed window designs in improving the channel estimation and data detection performance over the conventional rectangular window design.

Orthogonal time frequency space (OTFS) modulation is a recently developed multi-carrier multi-slot transmission scheme for wireless communications in high-mobility environments. In this paper, the error performance of coded OTFS modulation over high-mobility channels is investigated. We start from the study of conditional pairwise-error probability (PEP) of the OTFS scheme, based on which its performance upper bound of the coded OTFS system is derived. Then, we show that the coding improvement for OTFS systems depends on the squared Euclidean distance among codeword pairs and the number of independent resolvable paths of the channel. More importantly, we show that there exists a fundamental trade-off between the coding gain and the diversity gain for OTFS systems, i.e., the diversity gain of OTFS systems improves with the number of resolvable paths, while the coding gain declines. Furthermore, based on our analysis, the impact of channel coding parameters on the performance of the coded OTFS systems is unveiled. The error performance of various coded OTFS systems over high-mobility channels is then evaluated. Simulation results demonstrate a significant performance improvement for OTFS modulation over the conventional orthogonal frequency division multiplexing (OFDM) modulation over high-mobility channels. Analytical results and the effectiveness of the proposed code design are also verified by simulations with the application of both classical and modern codes for OTFS systems.

The development of dual-functional radar-communication (DFRC) systems, where vehicle localization and tracking can be combined with vehicular communication, will lead to more efficient future vehicular networks. In this paper, we develop a predictive beamforming scheme in the context of DFRC systems. We consider a system model where the road-side unit estimates and predicts the motion parameters of vehicles based on the echoes of the DFRC signal. Compared to the conventional feedback-based beam tracking approaches, the proposed method can reduce the signaling overhead and improve the accuracy of the angle estimation. To accurately estimate the motion parameters of vehicles in real-time, we propose a novel message passing algorithm based on factor graph, which yields a near optimal performance achieved by the maximum a posteriori estimation. The beamformers are then designed based on the predicted angles for establishing the communication links. With the employment of appropriate approximations, all messages on the factor graph can be derived in a closed-form, thus reduce the complexity. Simulation results show that the proposed DFRC based beamforming scheme is superior to the feedback-based approach in terms of both estimation and communication performance. Moreover, the proposed message passing algorithm achieves a similar performance of the high-complexity particle filtering-based methods.

Location awareness becomes an essential requirement for numerous applications and services in the future wireless communications. This paper addresses the problem of joint localization and synchronization in a network with cooperative nodes. The focus of this work is on the design of a low-complexity yet near-optimal message-passing implementations. To avoid the high-complexity of applying particle filtering-based approaches, we suitably augment the factor graph by introducing auxiliary variables. Then we propose a hybrid method that combines belief propagation (BP) and mean field (MF) message passing, which are used for message updating in the synchronization and localization parts of the factor graph, respectively. As a result, all messages on factor graph can be represented in parametric forms such that the proposed algorithm features a significantly low complexity while achieving near-optimal positioning performance.

Both faster than Nyquist (FTN) signaling and non-orthogonal multiple access (NOMA) are promising next generation wireless communications techniques as a benefit of their capability of improving the system's spectral efficiency. This paper considers an uplink system that combines the advantages of FTN and NOMA. Consequently, an improved spectral efficiency is achieved by deliberately introducing both inter-symbol interference (ISI) and inter-user interference (IUI). More specifically, we propose a grant-free transmission scheme to reduce the signaling overhead and transmission latency of the considered NOMA system. To distinguish the active and inactive users, we develop a novel message passing receiver that jointly estimates the channel state, detects the user activity, and performs decoding. We conclude by quantifying the significant spectral efficiency gain achieved by our amalgamated FTN-NOMA scheme compared to the orthogonal transmission system, which is up to 87.5%.

Spectrally efficient frequency division multiplexing (SEFDM) relying on index modulation (IM) has emerged as a promising multicarrier technique. In this paper, we develop a joint channel estimation and equalization method based on factor graphs for SEFDM-IM signaling over frequency-selective fading channels. By approximating the interference in the frequency domain, we reformulate the problem to obey a linear state-space model and construct a multi-layer factor graph. To support a reconfigurable architecture, non-orthogonal demodulation is adopted and the colored noise encountered is approximated by a complex auto-regressive (CAR) model. For deriving a low-complexity parametric Gaussian message passing (GMP)-based method, we exploit an expectation propagation (EP)-based technique for approximating the discrete a posteriori distributions of the transmitted symbols in a Gaussian form. To further simplify the result, variational message passing (VMP) is applied to an equivalent soft node to obtain a Gaussian form. Moreover, we also derive the Cramér-Rao lower bound (CRLB) in closed-form. The overall complexity only grows linearly with the number of subcarriers and logarithmically with the length of the channel's memory. Compared to its Nyquist signaling based counterpart, SEFDM-IM signaling relying on the proposed algorithm exhibits up to 25% higher bandwidth efficiency without any bit error rate (BER) performance degradation.

Given the requirements of increased data rate and massive connectivity in the Internet-of-things (IoT) applications of the fifth-generation communication systems (5G), non-orthogonal multiple access (NOMA) was shown to be capable of supporting more users than OMA. As a further potential enhancement, the faster-than-Nyquist (FTN) signaling is also capable of increasing the symbol rate. Since NOMA and FTN signaling impose non-orthogonalities from different perspectives, it is possible to achieve further increased spectral efficiency by exploiting both. Hence we investigate the FTN-NOMA uplink in the context of random access. Although random access schemes reduce the signaling overheads as well as latency, they require the base station to identify active users before performing data detection. As both inter-symbol and inter-user interferences exist, performing optimal detection requires a prohibitively high complexity. Moreover, in typical mobile communication environments, the channel envelope of users fluctuates violently, which imposes challenges on the receiver design. To tackle this problem, we propose a joint user activity tracking and data detection algorithm based on the factor graph framework, which relies on a sophisticated amalgam of expectation maximization (EM) and hybrid message passing algorithms. The complexity of the algorithm advocated only increases linearly with the number of active users. Our simulation results show that the proposed algorithm is effective in tracking user activity and detecting data symbols in dynamic random access systems.

In vehicular networks of the future, sensing and communication functionalities will be intertwined. In this article, we investigate a radar-assisted predictive beamforming design for vehicle-to-infrastructure (V2I) communication by exploiting the dual-functional radar-communication (DFRC) technique. Aiming for realizing joint sensing and communication functionalities at road side units (RSUs), we present a novel extended Kalman filtering (EKF) framework to track and predict kinematic parameters of each vehicle. By exploiting the radar functionality of the RSU we show that the communication beam tracking overheads can be drastically reduced. To improve the sensing accuracy while guaranteeing the downlink communication sum-rate, we further propose a power allocation scheme for multiple vehicles. Numerical results have shown that the proposed DFRC based beam tracking approach significantly outperforms the communication-only feedback based technique in the tracking performance. Furthermore, the designed power allocation method is able to achieve a favorable performance trade-off between sensing and communication.

The sparse code multiple access (SCMA) is a promising candidate for bandwidth-efficient next generation wireless communications, since it can support more users than the number of resource elements. On the same note, faster-than-Nyquist (FTN) signaling can also be used to improve the spectral efficiency. Hence in this paper, we consider a combined uplink FTN-SCMA system in which the data symbols corresponding to a user are further packed using FTN signaling. As a result, a higher spectral efficiency is achieved at the cost of introducing intentional inter-symbol interference (ISI). To perform joint channel estimation and detection, we design a low complexity iterative receiver based on the factor graph framework. In addition, to reduce the signaling overhead and transmission latency of our SCMA system, we intrinsically amalgamate it with grant-free scheme. Consequently, the active and inactive users should be distinguished. To address this problem, we extend the aforementioned receiver and develop a new algorithm for jointly estimating the channel state information, detecting the user activity and for performs data detection. In order to further reduce the complexity, an energy minimization based approximation is employed for restricting the user state to Gaussian. Finally, a hybrid message passing algorithm is conceived. Our Simulation results show that the FTN-SCMA system relying on the proposed receiver design has a higher throughput than conventional SCMA scheme at a negligible performance loss.

Faster-than-Nyquist (FTN) signaling with index modulation (IM) is an attractive non-orthogonal transmission scheme characterized by high spectral efficiency and energy efficiency. In this paper, we develop a hybrid belief propagation (BP) and expectation propagation (EP) based iterative receiver for FTN-IM systems. To approach the optimal maximum a posteriori (MAP) receiver, we derive the factorization of marginal posterior probability and construct the corresponding factor graph by ignoring trivial interferences. To address the inherent colored noise imposed by FTN signaling, we employ autoregressive (AR) model to approximate the correlated noise samples. To further design low-complexity parametric message passing receiver, we resort to expectation propagation (EP) to derive Gaussian approximation of discrete transmitted symbols containing specific inactivated zeros. As a result, the overall complexity grows linearly with the number of transmitted symbols. Simulation results show that the coded FTN-IM system relying on the proposed iterative receiver can improve the spectral efficiency up to 43% without performance loss. For identical spectral efficiency with the Nyquist counterpart, FTN-IM signaling achieves 0.80 dB performance gain with proper packing factor and coding rate.

Passive localization based on time of arrival (TOA) measurements is investigated, where the transmitted signal is reflected by a passive target and then received at several distributed receivers. After collecting all measurements at receivers, we can determine the target location. The aim of this paper is to provide a unified factor graph-based framework for passive localization in wireless sensor networks based on TOA measurements. Relying on the linearization of range measurements, we construct a Forney-style factor graph model and conceive the corresponding Gaussian message passing algorithm to obtain the target location. It is shown that the factor graph can be readily modified for handling challenging scenarios such as uncertain receiver positions and link failures. Moreover, a distributed localization method based on consensus-aided operation is proposed for a large-scale resource constrained network operating without a fusion center. Furthermore, we derive the Cramér-Rao bound (CRB) to evaluate the performance of the proposed algorithm. Our simulation results verify the efficiency of the proposed unified approach and of its distributed implementation.

This paper considers a passive localization scenario relying on a single transmitter, several receivers, and multiple moving targets to be located. The so-called “passive” targets equipped with RFID reflectors are capable of reflecting the signals from the transmitter to the receivers. Existing approaches assume that the transmitter and receivers are synchronous or quasi-synchronous, which is not always realistic in practical scenarios. Hence, an asynchronous wireless network is considered, where different clock offsets are assumed at different receivers. We propose a centralized expectation-maximization-based passive localization method for asynchronous receivers (EMpLaR) by treating the clock offsets as hidden variables. Thereby, the proposed algorithm makes use of Taylor expansions to arrive at a closed-form maximization. Furthermore, to improve the robustness to link failures and to reduce the energy consumption, we propose a distributed localization approach based on average consensus formulation to locate the target at each receiver. By applying a quadratic polynomial approximation of the function on which consensus has to be reached, both the computational complexity and the communications overhead are significantly reduced. The Cramér-Rao bound of the target location is derived as a benchmark of our proposed algorithms. Our simulation results show that the proposed centralized and distributed EMpLaR algorithms match the Cramér-Rao bound and significantly improve the localization performance compared with the conventional methods.

This paper considers the issue of passive localization based on time of arrival (TOA) measurement in the presence of receiver detection failures. In passive localization, the signal sent from the transmitter is reflected or relayed by "passive" target and then received at several distributed receivers. The target's position can be determined by collecting range mea- surements from all receivers. With a linearized model for range measurements, we build a factor graph model and implement Gaussian message passing algorithm to obtain target location and detect link failures. The Cramer-rao bound (CRB) is also derived to evaluate the performance of proposed algorithm. Simulation results verify the effectiveness of proposed factor graph approach.

The rapid development of mobile communications requires even higher spectral efficiency. Non-orthogonal multiple access (NOMA) has emerged as a promising technology to further increase the access efficiency of wireless networks. Among several NOMA schemes, it has been shown that sparse code multiple access (SCMA) is able to achieve better performance. In this paper, we consider a downlink MIMO-SCMA system over frequency selective fading channels. For optimal detection, the complexity increases exponentially with the product of the number of users, the number of antennas and the channel length. To tackle this challenge, we propose near optimal low-complexity iterative receivers based on factor graph. By introducing auxiliary variables, a stretched factor graph is constructed and a hybrid belief propagation (BP) and expectation propagation (EP) receiver, named stretch-BP-EP, is proposed. Considering the convergence problem of BP algorithm on loopy factor graph, we convexify the Bethe free energy and propose a convergence-guaranteed BP-EP receiver, named conv-BP-EP. We further consider cooperative network and propose two distributed cooperative detection schemes to exploit the diversity gain, namely, belief consensus-based algorithm and the Bregman alternative direction method of multipliers (ADMM)-based method. Simulation results verify the superior performance of the proposed conv-BP-EP receiver compared with other methods. The two proposed distributed cooperative detection schemes can improve the bit error rate performance by exploiting the diversity gain. Moreover, Bregman ADMM method outperforms the belief consensus-based algorithm in noisy inter-user links.

As the mobile communications develop rapidly, ever higher spectral efficiency is required. The sparse code multiple access (SCMA) has been recognized as a promising technology to further increase the access efficiency of wireless networks. In this paper, we consider the receiver design problem for SCMA system over frequency selective channels. The conventional minimum mean squared error (MMSE) detection method suffers from huge complexity due to the the multi-user and inter-symbol interferences. To this end, we propose a near optimal low complexity message passing receiver. By approximating the discrete log-likelihood ratio as Gaussian random variable, all messages on factor graph can be obtained as Gaussian distributions. Furthermore, we propose to introduce auxiliary variables to the factor graph and develop a novel hybrid belief propagation (BP) and expectation propagation (EP) receiver. Simulation results show that the proposed hybrid BP-EP method performs close to the MMSE-based receiver with reduced complexity. Also, compared to the orthogonal multiple access scheme, the considered SCMA system with the proposed receiver is able to support 50% more users.

Modern mobile communication applications raise the requirement of high quality support for high mobility users. In this paper, we present a Bayesian graphical model based frequency domain equalization method for faster-than-Nyquist (FTN) signaling in doubly selective channels. The conventional frequency domain minimum mean squared error (FD-MMSE) equalizer suffers high complexity due to the interferences induced by adjacent frequency symbols. To tackle this problem, a low complexity iterative message passing method namely, belief propagation is employed on the Bayesian graphical model to detect the FTN symbols. Compared to the low complexity variational inference method, the proposed algorithm considers the conditional dependencies between symbols and therefore can improve the performance. Simulation results show that the proposed equalization method has similar performance of the MMSE equalizer and outperforms the variational inference method.

Obtaining the location of a “passive” target in wireless sensor networks has attracted numerous interest in recent years. This paper considers the passive localization based on time-of-arrival measurements in an asynchronous sensor network where the receivers are with both clock skew and offset. Based on the factor graph model, the beliefs (approximated marginal) of target location and clock parameters can be obtained by executing iterative message passing algorithms. To reduce the huge complexity of particle based method, we propose two approximate approaches to determine parametric Gaussian message passing. Simulation results show that the proposed low complexity algorithm performs close to the particle-based method and attain the Cramer-Rao bound.

Oscillator phase noise (PHN) can result in significant performance loss in coherent communication systems if not compensated appropriately. Most existing studies focus on either PHN estimation over additive white Gaussian noise channels or channel impulse response (CIR) estimation in the absence of PHN. In this study, joint CIR estimation and decoding over time‐selective flat‐fading channels impacted by PHN is studied. Both the time evolutions of CIR and PHN are approximated by autoregressive models. Building on this, factor graph of the joint a posteriori probability function is constructed and the sum–product algorithm is applied to derive messages on factor graph. Due to the non‐linearity of PHN, no closed‐form expressions of the messages can be obtained. To this end, the authors use canonical distribution approach, which approximates the messages by Gaussian and Tikhonov probability density functions on the sub‐graphs of CIR and PHN, respectively. Accordingly, the messages can be calculated by updating the parameters of the canonical distributions. A mixed serial‐parallel message passing schedule is presented to implement the algorithm, which enables the compromise between the bit error rate performance and the processing throughput. Simulation results show that the proposed joint estimation and decoding algorithm significantly outperforms the existing methods in fading channels impacted by PHN.

This work deals with frequency-domain equalization for faster-than-Nyquist (FTN) signaling in doubly selective channels (DSCs). To handle the interference of frequency-domain symbols, the minimum mean square error (MMSE) equalizer involves high complexity in DSCs. To overcome the problem, we propose low-complexity receivers based on two variational methods, i.e., mean field (MF) and Bethe approximations. Compared with the MF method, the Bethe approximation takes into account the conditional dependencies of pairwise symbols. By only considering a small set of the frequency-domain symbols that have strong interference to each other, the complexity of the proposed algorithms increases linearly with the block length. Simulation results demonstrate that the proposed algorithms for FTN signaling are able to perform close to the MMSE equalizer in DSCs while with significantly reduced computational complexity.

In this paper, we study the time of arrival (TOA)-based distributed passive localization in asynchronous wireless network. Performing synchronization between receivers before target localization is possible but costs extra energy and bandwidth. To this end, We propose an expectation maximization (EM) algorithm to locate the passive target in the presence of receivers' clock offsets. To improve the robustness of the proposed algorithm, we employ the average consensus scheme to obtain the location of target at each receiver in a distributed way. A quadratic polynomial approximation is proposed to reduce the communication overhead and computational complexity. To evaluate the performance of the proposed algorithm, the Cramer-Rao bound (CRB) of the target's position estimation is derived. Simulation results show that the proposed distributed EM algorithm performs close to the centralized counterpart. It outperforms the conventional two step estimation method and the one based on time difference of arrival. Moreover, the proposed algorithm can attain the derived CRB, which demonstrates the effectiveness of the algorithm.

In wireless sensor networks, acquiring accurate timing information is a crucial requirement for time-based sensor localization. Utilizing a joint localization and synchronization method in sensor networks can improve positioning speed and accuracy. In this paper, we present a unified factor graph framework based on time of arrival (TOA) measurements to solve the problem of joint localization and time synchronization. A novel distributed cooperative joint estimation method based on belief propagation (BP) is proposed. We linearize the nonlinear terms in messages on factor graph in order to obtain a closed Gaussian form solution of message update. Accordingly, only the means and variances have to be updated and transmitted, which significantly reduce the communication overhead and computational complexity. To further reduce the communication overhead, we propose a message passing schedule. Simulation results show that the proposed BP method reach close performance to particle-based approaches with lower complexity.