Large Artificial Intelligence Models (LAMs) possess powerful learning capabilities and are regarded as key technologies for addressing communication challenges in the future sixth-generation (6G) wireless networks. However, their massive parameters make them difficult to deploy on computation resource-constrained end nodes. Recently, large-small model collaboration has been extensively studied, but most works assume homogeneous computational resources across end nodes. This assumption neglects the heterogeneity among nodes, potentially causing significant performance degradation or even system failures due to improper resource allocation and task partitioning. To address this challenge, we propose a large-small model collaboration framework that accounts for heterogeneous computational resources and limited wireless communication bandwidth. In this proposed framework, end nodes are responsible for data collection and local inference using small models. They also cooperate with the edge server that provides large model inference and model update. We design a joint optimization strategy that considers data transmission optimization and transmission resource allocation. The primary objective of this strategy is to enhance the inference accuracy of the framework by maximizing the mean average precision (mAP). Furthermore, we derive a closed-form lower bound for the mAP of the proposed framework. Simulations based on object detection experiments demonstrate that the proposed framework significantly outperforms existing frameworks under different communication bandwidths and data scales.

Covert communications provide a stronger privacy protection than cryptography and physical-layer security (PLS). However, previous works on covert communications have implicitly assumed the validity of channel reciprocity, i.e., wireless channels remain constant or approximately constant during their coherence time. In this work, we investigate covert communications in the presence of a disco RIS (DRIS) deployed by the warden Willie, where the DRIS with random and time-varying reflective coefficients acts as a “disco ball”, introducing time-varying fully-passive jamming (FPJ). Consequently, the channel reciprocity assumption no longer holds. The DRIS not only jams the covert transmissions between Alice and Bob, but also decreases the error probabilities of Willie’s detections, without either Bob’s channel knowledge or additional jamming power. To quantify the impact of the DRIS on covert communications, we first design a detection rule for the warden Willie in the presence of time-varying FPJ introduced by the DRIS. Then, we define the detection error probabilities, i.e., the false alarm rate (FAR) and the missed detection rate (MDR), as the monitoring performance metrics for Willie’s detections, and the signal-to-jamming-plus-noise ratio (SJNR) as a communication performance metric for the covert transmissions between Alice and Bob. Based on the detection rule, we derive the detection threshold for the warden Willie to detect whether communications between Alice and Bob is ongoing, considering the time-varying DRIS-based FPJ. Moreover, we conduct theoretical analyses of the FAR and the MDR at the warden Willie, as well as SJNR at Bob, and then present unique properties of the DRIS-based FPJ in covert communications. We present numerical results to validate the derived theoretical analyses and evaluate the impact of DRIS on covert communications.

A radio map captures the spatial distribution of wireless channel parameters, such as the strength of the signal received, across a geographic area. The problem of fine-grained three-dimensional (3D) radio map construction involves inferring a high-resolution radio map for the two-dimensional (2D) area at an arbitrary target height within a 3D region of interest, using radio samples collected by sensors sparsely distributed in that 3D region. Solutions to the problem are crucial for efficient spectrum management in 3D spaces, particularly for drones in the rapidly developing low-altitude economy. However, this problem is challenging due to ultra-sparse sampling, where the number of collected radio samples is far fewer than the desired resolution of the radio map to be estimated. In this paper, we design RadioLAM, a fine-grained 3D radio map construction paradigm built on generative Large Artificial Intelligence Models (LAMs). RadioLAM employs the creative power and the strong generalization capability of LAM to address the ultra-sparse sampling challenge. It consists of three key blocks: 1) an augmentation block, using the radio propagation model to project the radio samples collected at different heights to the 2D area at the target height; 2) a generation block, leveraging a diffusion-based LAM under an Mixture of Experts (MoE) architecture to generate a candidate set of fine-grained radio maps for the target 2D area; and 3) an election block, utilizing the radio propagation model as a guide to find the best map from the candidate set. Extensive simulations show that RadioLAM is able to solve the fine-grained 3D radio map construction problem efficiently from an ultra-low sampling rate of 0.1%, and significantly outperforms state-of-the-art (SOTA). Furthermore, real-world experiments also confirm that RadioLAM achieves superior performance compared to SOTA.

Space simultaneous information and power transfer (SSIPT) extends simultaneous wireless information and power transfer (SWIPT) from terrestrial to space-based scenarios for efficient energy utilization beyond the limitations of photovoltaic systems. A large-aperture antenna is required to provide sufficient gain to compensate for the severe path loss caused by the extremely long transmission distance in the SSIPT system. However, most existing antennas employ phased-array (PA) architectures that depend on costly hardware components, making it difficult to achieve the aforementioned requirements under constrained budgets. In this paper, we propose a reconfigurable holographic surface (RHS)-assisted SSIPT system. Specifically, RHSs, which can be implemented entirely with low-cost, commercially available components, offer a promising alternative to conventional PAs for realizing cost-efficient SSIPT. The serial-feed architecture of RHSs introduces radiation power coupling among adjacent elements, which significantly affects the transmission characteristics and invalidates conventional PA-based SWIPT analytical models. To address this issue, we develop a new analytical SSIPT framework that accurately captures the serial coupling effect and design amplitude-controlled beamforming schemes that depart fundamentally from phase-shift-based approaches, which improve rate-energy (R-E) performance. In simulations, we demonstrate that under the same hardware cost, the RHS achieves an expanded R-E region compared with the PA. Therefore, the compact and low-cost characteristics of RHSs make them more suitable for SSIPT scenarios with limited payload and cost.

Radio simultaneously localization and mapping (SLAM) is indispensable for a wide range of wireless applications owing to its ability to provide both location and mapping information. Traditional radio SLAM systems use fixed-aperture antennas with invariant beamwidths, which cannot adapt to the varying requirements for beam gain and coverage in complex environments. In this paper, we propose an aperture-changeable reconfigurable holographic surface (RHS)-assisted SLAM system. By deactivating different numbers of RHS elements, the equivalent antenna aperture is changeable, leading to adaptive beamwidths across detection directions. However, the RHS should follow the leakage power constraint, which stipulates that the total radiated power of RHS elements cannot exceed the input power. Its impact on the elements’ radiated power makes the optimization of RHS equivalent aperture challenging. To address this issue, we formulate a detection probability maximization problem and then adopt a two-step decomposition method to solve it. In the first step, we relax the leakage power constraint to obtain a relaxed solution. In the second step, we refine the relaxed solution by incorporating the leakage power constraint. We analyze the impact of RHS equivalent aperture and leakage power constraint on the SLAM system and evaluate the complexity of the proposed algorithm. Simulation results demonstrate that the proposed SLAM system can effectively reduce agent localization error compared with benchmark schemes.

Recent years have witnessed growing interest in leveraging the bidirectional wave control of intelligent omni-surfaces (IOS) for integrated sensing and communication (ISAC) systems. Nevertheless, acquiring precise channel state information (CSI) is particularly challenging due to the inherent interplay between the electromagnetic properties of IOS and the dual functions of ISAC. In this paper, we propose a robust beamforming design for IOS-enabled ISAC systems. We jointly optimize the transmit beamforming, sensing waveform and IOS phase shifts to minimize the Cram´er-Rao bound (CRB) for sensing while ensuring communication reliability under an outage probability constraint. The resulting mixed-integer non-convex problem is tackled via a dual-loop penalty dual decomposition (PDD) algorithm. This framework solves the augmented Lagrangian (AL) subproblem in the inner loop, while the outer loop adjusts dual variables and penalty parameters to enforce constraint satisfaction. Simulation results demonstrate that our design substantially enhances sensing accuracy and communication reliability in scenarios with large CSI errors or fluctuating service requirements. Furthermore, it is shown that an optimal ratio between sensing and passive IOS elements must be maintained to balance energy utilization and spatial sampling capability in ISAC systems.

With the widespread adoption of location-based services (LBS), user-generated trajectory data shows strong spatiotemporal correlation, rendering it highly vulnerable to inference attacks that expose sensitive information. In particular, once semantic locations like “hospital” and “bank” are identified, the risk of trajectory leakage increases substantially. To address this issue, this paper formulates a personalized spatiotemporal trajectory privacy protection framework, which is designed to protect locations with varying semantic sensitivities on the trajectory from the attacker with spatiotemporal correlation information. We model the trajectory privacy protection problem as a Markov Decision Process (MDP) and introduce the reinforcement learning (RL) technique to adjust the privacy parameters dynamically. Specifically, we leverage the twin delayed deep deterministic policy gradient (TD3) algorithm to enhance the stability and accuracy of policy evaluation, enabling efficient learning of optimal policies in continuous action spaces. Furthermore, a safe exploration strategy is incorporated to continuously evaluate and avoid high-risk state-action pairs, thereby enhancing privacy protection. Simulation results demonstrate that the proposed mechanism significantly improves privacy protection while effectively reducing Quality of Service (QoS) loss, exhibiting better convergence and overall system utility.

Multi-access Edge Computing (MEC) enables users to handle resource-intensive and latency-sensitive tasks. However, the offloading behaviors, which are closely correlated with wireless channel conditions, can inadvertently reveal users' location information to untrustworthy MEC servers. Existing location privacy-aware task offloading (LPTO) mechanisms have not fully considered and comprehensively analyzed personalized location privacy protection requirements. To address this gap, this paper proposes a differential privacy (DP)-based personalized LPTO mechanism for MEC environments that jointly optimizes the perturbation region, privacy budget, and offloading rate while maximizing the offloading utility. We quantify personalized privacy requirements by incorporating task sensitivity, user privacy preference, and task priority. Then, we propose a two-timescale (2Ts) optimization framework to solve the complex personalized location privacy-aware task offloading optimization problem. Specifically, we optimize the perturbation region on a long timescale to align with long-term privacy requirements. In contrast, the offloading ratio and privacy budget are dynamically optimized on a short timescale based on instantaneous channel states and offloading workloads. Furthermore, we model the privacy-aware offloading problem as a Markov decision process (MDP) and develop a dual-agent deep reinforcement learning (DRL)-based personalized LPTO mechanism (DDPLM) to optimize strategies under dynamic MEC systems. Simulation results validate that the proposed DDPLM achieves personalized location privacy protection while reducing computational costs.

Radio map presents communication parameters of interest, e.g., received signal strength, across a geographical region in a specific frequency band. It can be leveraged to improve the efficiency of spectrum utilization. With the rapid development of radio technology and the proliferation of radioenabled devices, there is an increasing demand for finer granularity (higher resolution) in radio maps. However, the problem of fine-grained radio map construction is uniquely challenging, as it requires to utilize an extremely small number of radio samples collected by sparsely distributed sensor devices to infer the map. To address the challenge, we propose a hybrid device-edge-cloud collaboration paradigm called CollaboRadio. CollaboRadio first groups sensor devices into multiple clusters, with cluster locations optimized based on principles of radio propagation. Next, it leverages a small AI model on each edge server to generate a local radio map for the respective cluster region from radio samples collected by intra-cluster sensors. Finally, it employs a large AI model in the cloud to construct a global radio map for the entire region from the local maps produced by edge servers of different clusters. For the implementation of CollaboRadio, we develop an UNet-based small model for the edge server and a Transformerbased large model for the cloud. Extensive simulations show that CollaboRadio is capable of constructing fine-grained radio maps from an ultra-low sampling rate of 0.1%, and significantly outperforms state-of-the-art.

Edge intelligence in space-air-ground integrated networks (SAGINs) can enable worldwide network coverage beyond geographical limitations for users to access ubiquitous and low-latency intelligence services. Facing global coverage and complex environments in SAGINs, edge intelligence can provision large language models (LLMs) agents for users via edge servers at ground base stations (BSs) or cloud data centers relayed by satellites. As LLMs with billions of parameters are pretrained on vast datasets, LLM agents have few-shot learning capabilities, e.g., chain-of-thought (CoT) prompting for complex tasks, which raises a new trade-off between resource consumption and performance in SAGINs. In this paper, we propose a joint caching and inference framework for edge intelligence to provision sustainable and ubiquitous LLM agents in SAGINs. We introduce “cached model-as-a-resource” for offering LLMs with limited context windows and propose a novel optimization framework, i.e., joint model caching and inference, to utilize cached model resources for provisioning LLM agent services along with communication, computing, and storage resources.We design “age of thought” (AoT) considering the CoT prompting of LLMs, and propose a least AoT cached model replacement algorithm for optimizing the provisioning cost. We propose a deep Q-network-based modified second-bid (DQMSB) auction to incentivize satellite/ground network operators in real-time, which can enhance allocation efficiency by 23% while guaranteeing strategy-proofness and being free from adverse selection.

Intelligent omni-surfaces (IOSs) with 360° electromagnetic radiation significantly improves the performance of wireless systems, while an adversarial IOS also poses a significant potential risk for physical layer security. In this paper, we propose a “DISCO” IOS (DIOS) based fully-passive jammer (FPJ) that can launch omnidirectional fully-passive jamming attacks. In the proposed DIOS-based FPJ, the interrelated refractive and reflective (R&R) coefficients of the adversarial IOS are randomly generated, acting like a “DISCO ball” that distributes wireless energy radiated by the base station. By introducing active channel aging (ACA) during channel coherence time, the DIOS-based FPJ can perform omnidirectional fully-passive jamming without neither jamming power nor channel knowledge of legitimate users (LUs). To characterize the impact of the DIOS-based PFJ, we derive the statistical characteristics of DIOS-jammed channels based on two widely-used IOS models, i.e., the constant-amplitude model and the variable-amplitude model. Consequently, the asymptotic analysis of the ergodic achievable sum rates under the DIOS-based omnidirectional fully-passive jamming is given based on the derived stochastic characteristics for both the two IOS models. Based on the derived analysis, the omnidirectional jamming impact of the proposed DIOS-based FPJ implemented by a constant-amplitude IOS does not depend on either the quantization number or the stochastic distribution of the DIOS coefficients, while the conclusion does not hold on when a variable-amplitude IOS is used. Numerical results1based on one-bit quantization of the IOS phase shifts are provided to verify the effectiveness of the derived theoretical analysis. The proposed DIOS-based FPJ can not only launch omnidirectional fully-passive jamming, but also improve the jamming impact by about 55% at 10 dBm transmit power per LU.

In response to the escalating communication demand, transceivers are transforming from a data-oriented to an artificial intelligence (AI)-driven semantic-aware paradigm. However, current semantic-aware transceivers fail to simultaneously adapt to unseen data without labels and guarantee low data processing latency because strong generalization ability requires large-scale models with robust semantic understanding, while low latency leads to small model size and simple structure. To this end, we propose a feature-foundation model evolution framework deployed at cloud, edge, and users, where models with different scales can cooperate to address these issues. Specifically, the transmitter at edge sends images to the users by performing real-time semantic feature extraction and data encoding, and the small feature model is evolved with the aid of a large foundation model at cloud when unseen data occurs. To simultaneously update the feature model and avoid loss of previously acquired semantic understanding, we design a feature-foundation model evolution scheme where outputs of both foundation and feature models are leveraged. Additionally, to tackle coupled communication-computation resources for evolution, we formulate the resource scheduling problem and design algorithms to minimize the evolution latency. We conduct rigorously-designed simulation to validate the effectiveness of our framework.

The Low-Altitude Intelligent Network (LAIN) serves as a critical enabler for low-altitude economic development, showing significant potential in areas such as environmental monitoring. These applications typically require extensive computational resources to drive large-scale models, posing challenges to resource-constrained aerial platforms like unmanned aerial vehicles (UAVs). Although air-ground model collaboration has been studied by some early studies, most approaches assume homogeneous computational resources, overlooking the heterogeneity among UAVs as end nodes. To address this challenge, we introduce an air-ground model collaboration framework that considers heterogeneous computational resources among multiple UAVs and limited wireless transmission bandwidth between UAVs and ground servers. In this framework, UAVs working as end nodes handle data collection and local inference with small models, while the edge servers on the ground perform large model inference and updates. We propose a joint optimization strategy that optimizes both data transmission and resource allocation, with the goal of improving the framework’s inference accuracy by maximizing the mean average precision (mAP). Simulations on object detection tasks show that our framework outperforms existing methods under different communication bandwidths and data scales.

A radio map captures the spatial distribution of wireless channel parameters, such as the strength of the signal received, across a geographic area. The problem of fine-grained three-dimensional (3D) radio map estimation aims to infer a high-resolution radio map for the two-dimensional (2D) area at an arbitrary target height within a 3D region of interest, using radio samples collected by sensors sparsely distributed in that 3D region. Solutions to the problem are crucial for the improvement in drone spectrum utilization in the rapidly developing low-altitude economy. However, this problem is challenging due to ultra-sparse sampling, where the number of collected radio samples is far fewer than the desired resolution of the radio map to be estimated. In this paper, we design a generative framework called GenRadio which employs the creative power of generative AI to address the ultra-sparse sampling challenge and solve the problem. GenRadio consists of three key blocks: (1) an augmentation block that uses radio propagation models to project samples collected at different heights to the 2D area at the target height; (2) a generation block that employs a diffusion model under a Mixture of Experts (MoE) architecture to generate a diverse set of fine-grained radio map candidates; and (3) an election block that leverages radio propagation models to identify the best map candidate from the diverse set. Experimental results demonstrate that GenRadio efficiently solves the fine-grained 3D radio map estimation problem from an ultra-low sampling rate of 0.1%, and significantly outperforms state-of-the-art.

The widespread adoption of large artificial intelligence (AI) models has enabled numerous applications of the Internet of Things (IoT). However, large AI models require substantial computational and memory resources, which exceed the capabilities of resource-constrained IoT devices. End-edge collaboration paradigm is developed to address this issue, where a small model on the end device performs inference tasks, while a large model on the edge server assists with model updates. To improve the accuracy of the inference tasks, the data generated on the end devices will be periodically uploaded to edge server to update model, and a distilled model of the updated one will be transmitted back to the end device. Subjected to the limited bandwidth for the communication link between the end device and the edge server, it is important to investigate whether the system should allocate more bandwidth to data upload or to model transmission. In this paper, we characterize the impact of data upload and model transmission on inference accuracy. Subsequently, we formulate a bandwidth allocation problem. By solving this problem, we derive an efficient optimization framework for the end-edge collaboration system. The simulation results demonstrate our framework significantly enhances mean average precision (mAP) under various bandwidths and datasizes.

To satisfy the high data rate requirements, cellular systems will evolve towards the direction of higher carrier frequencies and larger antenna arrays. The conventional phased arrays are hard to fulfill such a vision due to its excessive power consumption induced by numerous phase shifters. To address this issue, Reconfigurable Refractive Surfaces (RRSs) provide a energy efficient solution without relying on phase shifters. With enlarged radiation aperture and increased working frequency, users are more likely to be located in the near field of the RRS. Moreover, the frequency selectivity of the RRS cannot be neglected given the wideband communications enabled by higher frequency bands. These two effects jointly aggravate the beam split problem where the signal strength of different frequency components cannot concentrate on the user, leading to a data rate degradation. In this paper, we study an RRS-based wideband near-field communication system with multiple users. Unlike most existing works, which only considered the beam split effect under near-field conditions, we jointly consider the influence of the frequency selectivity of RRS and near-field conditions on the beam split effect. To mitigate the beam split effect, the time-delay units are introduced in the RRS elements based on which a beamforming scheme is proposed to improve system data rate by jointly optimizing the digital beamformer, the phase shifts of RRS and the time-delay units. Simulation results demonstrate the effectiveness of our proposed scheme.

Wireless transmission is vulnerable to malicious jamming attacks due to the openness of wireless channels, posing a severe threat to wireless communications. Current anti-jamming studies primarily focus on either enhancing desired signals or mitigating jamming, resulting in limited performance. To address this issue, intelligent omni-surface (lOS) is a promising solution. By jointly designing its reflective and refractive properties, the lOS can simultaneously nullify jamming and enhance desired signals. In this paper, we consider an lOS-aided multi-user anti-jamming communication system, aiming to improve desired signals and nullify jamming by optimizing lOS phase shifts and transmit beamforming. However, this is challenging due to the coupled and discrete lOS reflection and refraction phase shifts, the unknown jammer's beamformer, and imperfect jammer-related channel state information. To tackle this, we relax lOS phase shifts to continuous states and optimize with a coupling-aware algorithm using the Cauchy-Schwarz inequality and S-procedure, followed by a local search to recover discrete states. Simulation results show that the proposed scheme significantly improves the sum rate amid jamming attacks.

The swift advancement of Location-Based Services (LBSs) raises the danger of trajectory privacy being breached, since the location semantic tags can easily disclose users' sensitive information. Additionally, attackers can exploit temporal correlations to infer sensitive personal information. This paper formulates a personalized semantic trajectory privacy protection framework designed to protect locations with varying sensitivities on the trajectory from the attacker with temporal correlation information. We model the trajectory privacy protection problem as a Markov Decision Process (MDP) and introduce the Reinforcement Learning (RL) technique to dynamically adjust the privacy parameters. Specifically, we leverage the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to enhance the stability and accuracy of policy evaluation, enabling efficient learning of optimal policies in continuous action spaces. Simulation results indicate that the TD3-based personalized semantic trajectory privacy protection mechanism effectively balances the Quality of Service and semantic trajectory privacy while realizing personalized trajectory privacy protection.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is expected to play an important role in future sixth generation (6G) networks. Most existing works in this area focus on single-polarized XL-MIMO, where transceivers transmit and receive signals in only one polarization direction, leading to degraded data rates. To improve multiplexing performance, in this paper, we investigate downlink XL-MIMO networks with dual-polarized antennas. However, unlike conventional dual-polarized massive MIMO, the cross-polarization discrimination (XPD) of channels vary across base station antennas in dual-polarized XL-MIMO due to the enlarged antenna aperture, leading to following two challenges. First, conventional near-far field boundary is insufficient as it only accounts for phase differences across array elements while irrespective of XPD differences. Second, existing transmit covariance optimization methods developed for dual-polarized massive MIMO cannot be directly utilized, since they are developed based on uniform XPD and pathloss assumptions. To address these challenges, we model the variations of XPD across antennas, based on which a non-uniform XPD distance is introduced to complement existing near-far field boundary. Based on the new distance criterion, we propose an efficient scheme for optimizing the transmit covariance, which considers the non-uniform XPD and pathloss. Numerical results validate our analysis and demonstrate the effectiveness of the proposed algorithm.

Intelligent omni-surface (IOS) has been identified as an innovative technology to achieve omnidirectional wireless coverage for mobile users. However, due to the passive characteristics of the IOS, accurate channel state information (CSI) is difficult to acquire in IOS-assisted communication systems. In this article, we investigate a novel IOS-assisted multiuser multiple-input-single-output (MISO) downlink communication system. Specifically, the cascaded channel errors on both sides of the IOS are modeled separately to improve the flexibility and stability of the robust beamforming schemes. Considering the diverse practical communication requirements posed by the bounded and statistical CSI error models, we formulated the system sum-rate maximization and transmission power minimization problems for the worst-case and outage-constrained robust beamforming, respectively.$\boldsymbol {S}$-Procedure and Bernstein-type inequality are introduced to approximate the original nonconvex problems. Finally, we decompose the transformed problem into two subproblems and present an alternate optimization (AO) algorithm based on the success convex approximation (SCA) technique and the branch and bound method. Simulation results demonstrate that our robust beamforming schemes can effectively mitigate the system performance degradation caused by CSI error and enhance the downlink transmission robustness of the IOS-assisted communication system.

The growth of the Internet of Things (IoT) has reshaped the way devices, systems, and applications connect, leading to an enormous surge in data generation across various domains. This expansion, paired with the exponential increase in IoT devices, requires advanced data analysis capabilities to manage the multimodal sensory data collected by the massive IoT devices in real time, such as sensor outputs, visual data, audios, and videos. To address this challenge, large generative artificial intelligent (AI) models are designed, showing promise in processing multimodal data. However, deploying these models on IoT devices is constrained by limited computational power, memory, and energy resources, preventing full realization of their potential for real-time IoT systems. To address these limitations, we propose an innovative end-edge collaborative model framework between end nodes and edge servers, designed to balance computational load and optimize resource use. This approach transmits both extracted features and residual mapping data from end nodes to edge servers, allowing for spectrum efficient data handling across the network. Our work formulates an optimization strategy to enhance mean average precision (mAP) by adjusting task distribution, bandwidth, and data quantization in response to real-time network and device conditions. Comprehensive simulations demonstrate the proposed approach’s superiority over conventional centralized edge model computing and distributed end model computing frameworks, achieving enhanced efficiency across various communication rates in real time.

The radio map presents communication parameters of interest, e.g., received signal strength, at every point across a geographical region. It can be leveraged to improve the efficiency of spectrum utilization in the region, particularly critical for unlicensed WiFi spectrum. The problem of fine-grained radio map estimation is to utilize radio samples collected by sensors sparsely distributed in the region to infer a high-resolution radio map. This problem is challenging due to the ultra-low sampling rate, i.e., because the number of available samples is far fewer than the high resolution required for radio map estimation. We propose WiFi-Diffusion – a novel generative framework for achieving fine-grained WiFi radio map estimation using diffusion models. WiFi-Diffusion employs the creative power of generative AI to address the ultra-low sampling rate challenge and consists of three blocks: 1) a boost block, using prior information such as the layout of obstacles to optimize the diffusion model; 2) a generation block, leveraging the diffusion model to generate a candidate set of fine-grained radio maps; and 3) an election block, utilizing the radio propagation model as a guide to find the best fine-grained radio map from the candidate set. Extensive simulations demonstrate that 1) the fine-grained radio map generated by WiFi-Diffusion is ten times better than those produced by state-of-the-art (SOTA) when they use the same ultra-low sampling rate; and 2) WiFi-Diffusion achieves comparable fine-grained radio map quality with only one-fifth of the sampling rate required by SOTA.

The future sixth-generation (6G) of wireless networks is expected to surpass its predecessors by offering ubiquitous coverage through integrated air-ground deployments in both communication and computing domains. In such networks, aerial platforms, such as unmanned aerial vehicles (UAVs), conduct artificial intelligence (AI) computations based on multi-modal data to support diverse applications including surveillance and environment construction. However, these multi-domain inference and content generation tasks require large AI models, demanding powerful computing capabilities and finely tuned inference models trained on rich datasets, thus posing significant challenges for UAVs. To tackle this problem, we propose an integrated air-ground edge-cloud model framework, in which UAVs serve as edge nodes for data collection and small model computation. Through wireless channels, UAVs collaborate with ground cloud servers providing large model computation and model updating for edge UAVs. With limited wireless communication bandwidth, the proposed framework faces the challenge of information exchange scheduling between the edge UAVs and the cloud server. To tackle this, we present joint task allocation, transmission resource allocation, transmission data quantization design, and edge model update design to enhance the inference accuracy of the integrated air-ground edge-cloud model evolution framework by mean average precision (mAP) maximization. A closed-form lower bound on the mAP of the proposed framework is derived based on the mAP of the edge model and mAP of the cloud model, and the solution to the mAP maximization problem is optimized accordingly. Simulations, based on results from vision-based classification experiments, consistently demonstrate that the mAP of the proposed integrated air-ground edge-cloud model evolution framework outperforms both a centralized cloud model framework and a distributed edge model framework across various communication bandwidths and data sizes.

To meet rising data rate demands, cellular systems are expected to evolve towards higher carrier frequencies and larger antenna arrays, but conventional phased arrays face challenges in supporting such a prospection due to their excessive power consumption induced by numerous phase shifters required. Reconfigurable Refractive Surface (RRS) is an energy efficient solution to address this issue without relying on phase shifters. However, the increased radiation aperture size extends the range of the Fresnel region, leading the users to lie in the near-field zone. Moreover, given the wideband communications in higher frequency bands, we cannot ignore the frequency selectivity of the RRS. These two effects collectively exacerbate the beam split issue, where different frequency components fail to converge on the user simultaneously, and finally result in a degradation of the data rate. In this paper, we investigate a RRS-based wideband near-field multi-user communication system. Unlike most existing studies on wideband communications, which consider the beam split effect only with the near-field condition, we study the beam split effect under the influence of both the near-field condition and the frequency selectivity of the RRS. To mitigate the beam split effect, we propose a Delayed-RRS structure, based on which a beamforming scheme is proposed to optimize the user’s data rate. Through theoretical analysis and simulation results, we analyze the influence of the RRS’s frequency selectivity, demonstrate the effectiveness of the proposed beamforming scheme, and reveal the importance of jointly considering the near-field condition and the frequency selectivity of RRS.

Vehicle Twins (VTs) as digital representations of vehicles can provide users with immersive experiences in vehicular metaverse applications, e.g., Augmented Reality (AR) navigation and embodied intelligence. VT migration is an effective way that migrates the VT when the locations of physical entities keep changing to maintain seamless immersive VT services. However, an efficient VT migration is challenging due to the rapid movement of vehicles, dynamic workloads of Roadside Units (RSUs), and heterogeneous resources of the RSUs. To achieve efficient migration decisions and a minimum latency for the VT migration, we propose a multi-agent split Deep Reinforcement Learning (DRL) framework combined with spatio-temporal trajectory generation. In this framework, multiple split DRL agents utilize split architecture to efficiently determine VT migration decisions. Furthermore, we propose a spatio-temporal trajectory generation algorithm based on trajectory datasets and road network data to simulate vehicle trajectories, enhancing the generalization of the proposed scheme for managing VT migration in dynamic network environments. Finally, experimental results demonstrate that the proposed scheme not only enhances the Quality of Experience (QoE) by 29% but also reduces the computational parameter count by approximately 25% while maintaining similar performances, enhancing users' immersive experiences in vehicular metaverses.

Due to the openness of wireless channels, wireless transmission is susceptible to malicious jamming attacks, posing a severe threat to wireless communications. Existing studies on anti-jamming mainly considered enhancing desired signals or mitigating jamming, leading to limited performance. To address this issue, intelligent omni-surface (IOS) is a promising solution, which can simultaneously nullify jamming and enhance desired signals by jointly manipulating its reflective and refractive properties. In this paper, we consider an IOS-aided multi-user anti-jamming communication system. We aim to improve desired signals and nullify jamming by joint IOS phase shifts and transmit beamforming optimization, which is challenging due to the coupled and discrete nature of IOS reflection and refraction phase shifts, unknown jammer’s beamformer, and imperfect jammer-related channel state information. To tackle this, we relax IOS phase shifts to continuous states and develop a coupling-aware algorithm using Cauchy-Schwarz inequality and S-procedure, followed by a local search to recover discrete states. Simulation results show that the proposed scheme significantly improves the sum rate in the presence of jamming attacks.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is expected to be an important technology in future sixth generation (6G) networks. Compared with conventional single-polarized XL-MIMO, where signals are transmitted and received in only one polarization direction, dual-polarized XL-MIMO systems achieve higher data rate by improving multiplexing performances, and thus are the focus of this paper. Due to enlarged aperture, near-field regions become non-negligible in XL-MIMO communications, necessitating accurate near-far field boundary characterizations. However, existing boundaries developed for single-polarized systems only consider phase or power differences across array elements while irrespective of cross-polarization discrimination (XPD) variances in dual-polarized XL-MIMO systems, deteriorating transmit covariance optimization performances. In this paper, we revisit near-far field boundaries for dual-polarized XL-MIMO systems by taking XPD differences into account, which faces the following challenge. Unlike existing near-far field boundaries, which only need to consider co-polarized channel components, deriving boundaries for dual-polarized XL-MIMO systems requires modeling joint effects of co-polarized and cross-polarized components. To address this issue, we model XPD variations across antennas and introduce a non-uniform XPD distance to complement existing near-far field boundaries. Based on the new distance criterion, we propose an efficient scheme to optimize transmit covariance. Numerical results validate our analysis and demonstrate the proposed algorithm’s effectiveness.

Reconfigurable holographic surfaces (RHSs) have been proposed as a cost-effective and power-efficient solution for extremely large-scale arrays, where the amplitude of electromagnetic waves radiated at each element is controlled to achieve high directive gain. However, the complexity of acquiring real-time channel state information (CSI) required for beamforming is prohibitively high, especially when the near-field expansion brought by the large-scale RHS is considered. In this paper, we propose a codebook-based beam training scheme for a large-scale RHS-enabled communication system to bypass CSI estimation. Unlike traditional phase-controlled arrays, the amplitude-controlled property of the RHS implies that each RHS element can be selectively activated. This motivates an array reconfiguration method where a scale-changeable RHS array is constructed to generate gain-flat beams with different coverage in the angle-range domain. A hierarchical RHS codebook is then proposed where the coverage of the codewords in each layer is progressively refined. To address the substantial beam search overhead in the near-far field, a two-stage beam training scheme is performed in the proposed codebook, thereby reducing the overhead to a logarithmic level of the element number. The simulation results show that the proposed scheme performs better than phased arrays given the same input power in terms of sum rate, and it also approaches the upper bound achieved by the exhaustive search at a significantly reduced overhead.

With the advances of Virtual Reality (VR) / Augmented Reality (AR), there arises a compelling need for transmission of point clouds over lossy channels (e.g., a 5G millimeter wave (mmWave) link that tends to be easily blocked). In this paper, we revisit the traditional Multiple Description Coding (MDC) concept and propose a simple point cloud MDC scheme that takes advantage of voxelization and is built upon a typical geometric point cloud compression codec. Our simulation study demonstrates the efficacy of the proposed scheme, as well as the tradeoff between compression efficiency and point cloud quality gain offered by MDC.

Illegitimate intelligent reflective surfaces (IRSs) can pose significant physical layer security risks on multi-user multiple-input single-output (MU-MISO) systems. Recently, a DISCO approach has been proposed an illegitimate IRS with random and time-varying reflection coefficients, referred to as a “disco” IRS (DIRS). Such DIRS can attack MU-MISO systems without relying on either jamming power or channel state information (CSI), and classical anti-jamming techniques are in-effective for the DIRS-based fully-passive jammers (DIRS-based FPJs). In this paper, we propose an IRS-enhanced anti-jamming precoder against DIRS-based FPJs that requires only statistical rather than instantaneous CSI of the DIRS-jammed channels. Specifically, a legitimate IRS is introduced to reduce the strength of the DIRS-based jamming relative to the transmit signals at a legitimate user (LU). In addition, the active beamforming at the legitimate access point (AP) is designed to maximize the signal-to-jamming-plus-noise ratios (SJNRs). Numerical results are presented to evaluate the effectiveness of the proposed IRS-enhanced anti-jamming precoder against DIRS-based FPJs.

Localization which uses holographic multiple input multiple output surface such as reconfigurable intelligent surface (RIS) has gained increasing attention due to its ability to accurately localize users in non-line-of-sight conditions. However, existing RIS-enabled localization methods assume the users at either the near-field (NF) or the far-field (FF) region, which re-sults in high complexity or low localization accuracy, respectively, when they are applied in the whole area. In this paper, a unified NF and FF localization method is proposed for the RIS-enabled localization system to overcome the above issue. Specifically, the NF and FF regions are both divided into grids. The RIS reflects the signals from the user to the base station (BS), and then the BS uses the received signals to determine the grid where the user is located. Compared with existing NF - or FF -only schemes, the design of the location estimation method and the RIS phase shift optimization algorithm is more challenging because they are based on a hybrid NF and FF model. To tackle these challenges, we formulate the optimization problems for location estimation and RIS phase shifts, and design two algorithms to effectively solve the formulated problems, respectively. The effectiveness of the proposed method is verified through simulations.

Location-based services (LBSs) in vehicular ad hoc networks (VANETs) offer users numerous conveniences. However, the extensive use of LBSs raises concerns about the privacy of users' trajectories, as adversaries can exploit temporal correlations between different locations to extract personal information. Additionally, users have varying privacy requirements depending on the time and location. To address these issues, this paper proposes a personalized trajectory privacy protection mechanism (PTPPM). This mechanism first uses the temporal correlation between trajectory locations to determine the possible location set for each time instant. We identify a protection location set (PLS) for each location by employing the Hilbert curve-based minimum distance search algorithm. This approach incor-porates the complementary features of geo-indistinguishability and distortion privacy. We put forth a novel Permute-and-Flip mechanism for location perturbation, which maps its initial application in data publishing privacy protection to a location perturbation mechanism. This mechanism generates fake locations with smaller perturbation distances while improving the balance between privacy and quality of service (QoS). Simulation results show that our mechanism outperforms the benchmark by providing enhanced privacy protection while meeting user's QoS requirements.

Holographic Multiple-Input Multiple-Output (HMIMO), which densely integrates numerous antennas into a limited space, is anticipated to provide higher rates for future 6G wireless communications. The increase in antenna aperture size makes the near-field region enlarge, causing some users to be located in the near-field region. Thus, we are facing a hybrid near-field and far-field communication problem, where conventional far-field modeling methods may not work well. In this paper, we propose a near-far field channel model that does not presuppose whether each path is near-field or far-field, different from the existing work requiring the ratio of the number of near-field paths to that of far-field paths as prior knowledge. However, this gives rise to a new challenge for accurately modeling the channel, as conventional methods of obtaining channel model parameters are not applicable to this model. Therefore, we propose a new method, Expectation-Maximization (EM)-based Near-Far Field Channel Modeling, to obtain channel model parameters, which considers whether each path is near-field or far-field as a hidden variable, and optimizes the hidden variables and channel model parameters through an alternating iteration method. Simulation results show that our method is superior to conventional near-field and far- field algorithms in fitting the near-far field channel in terms of outage probability.

As a crucial facilitator of future autonomous driving applications, wireless simultaneous localization and mapping (SLAM) has drawn growing attention recently. However, the accuracy of existing wireless SLAM schemes is limited because the antenna gain is constrained given the cost budget due to the expensive hardware components such as phase arrays. To address this issue, we propose a reconfigurable holographic surface (RHS)-aided SLAM system in this paper. The RHS is a novel type of low-cost antenna that can cut down the hardware cost by replacing phased arrays in conventional SLAM systems. However, compared with a phased array where the phase shifts of parallel-fed signals are adjusted, the RHS exhibits a different radiation model because its amplitude-controlled radiation elements are series-fed by surface waves, implying that traditional schemes cannot be applied directly. To address this challenge, we propose an RHS-aided beam steering method for sensing the surrounding environment and design the corresponding SLAM algorithm. Simulation results show that the proposed scheme can achieve more than there times the localization accuracy that traditional wireless SLAM with the same cost achieves.

Metaverse can obscure the boundary between the physical and virtual worlds. Specifically, for the human-centric Metaverse in vehicular networks, i.e., the vehicular Metaverse, vehicles are no longer isolated physical spaces but interfaces that extend the virtual worlds to the physical world. Accessing the human-centric Metaverse via autonomous vehicles (AVs), drivers and passengers can immerse in and interact with 3D virtual objects overlaying views of streets on head-up displays (HUD) via augmented reality (AR). The seamless, immersive, and interactive experience rather relies on real-time multi-dimensional data synchronization between physical entities, i.e., AVs, and virtual entities, i.e., Metaverse billboard providers (MBPs). However, mechanisms to allocate and match synchronizing AV and MBP pairs to roadside units (RSUs) in a synchronization service market, which consists of the physical and virtual submarkets, are vulnerable to adverse selection. In this paper, we propose an enhanced second-score auction-based mechanism, named EPViSA, to allocate physical and virtual entities in the synchronization service market of the vehicular Metaverse. The EPViSA mechanism can determine synchronizing AV and MBP pairs simultaneously while protecting participants from adverse selection and thus achieving high total social welfare. We propose a synchronization scoring rule to eliminate the external effects from the virtual submarkets. Then, a price scaling factor is introduced to enhance the allocation of synchronizing virtual entities in the virtual submarkets. Finally, rigorous analysis and extensive experiments demonstrate EPViSA can achieve at least 96% of the social welfare compared to the omniscient benchmark while ensuring strategy-proof and adverse selection free through a simulation testbed.

Intelligent surfaces (ISs) have emerged as a key technology to empower a wide range of appealing applications for wireless networks, due to their low cost, high energy efficiency, flexibility of deployment, and capability of constructing favorable wireless channels/radio environments. Moreover, the recent advent of several new IS architectures further expanded their electromagnetic functionalities from passive reflection to active amplification, simultaneous reflection, and refraction, as well as holographic beamforming. However, the research on ISs is still in rapid progress and there have been recent technological advances in ISs and their emerging applications that are worthy of a timely review. Thus, in this article, we provide a comprehensive survey on the recent development and advances of ISs-aided wireless networks. Specifically, we start with an overview on the anticipated use cases of ISs in future wireless networks such as 6G, followed by a summary of the recent standardization activities related to ISs. Then, the main design issues of the commonly adopted reflection-based IS and their state-of-the-art solutions are presented in detail, including reflection optimization, deployment, signal modulation, wireless sensing, and integrated sensing and communications. Finally, recent progress and new challenges in advanced IS architectures are discussed to inspire future research.

Emerging intelligent reflecting surfaces (IRSs) significantly improve system performance, but also pose a huge risk for physical layer security. Existing works have illustrated that a disco IRS (DIRS), i.e., an illegitimate IRS with random time-varying reflection properties (like a “disco ball”), can be employed by an attacker to actively age the channels of legitimate users (LUs). Such active channel aging (ACA) generated by the DIRS can be employed to jam multi-user multiple-input single-output (MU-MISO) systems without relying on either jamming power or LU channel state information (CSI). To address the significant threats posed by DIRS-based fully-passive jammers (FPJs), an anti-jamming precoder is proposed that requires only the statistical characteristics of the DIRS-based ACA channels instead of their CSI. The statistical characteristics of DIRS-jammed channels are first derived, and then the anti-jamming precoder is derived based on the statistical characteristics. Furthermore, we prove that the anti-jamming precoder can achieve the maximum signal-to-jamming-plus-noise ratio (SJNR). To acquire the ACA statistics without changing the system architecture or cooperating with the illegitimate DIRS, we design a data frame structure that the legitimate access point (AP) can use to estimate the statistical characteristics. During the designed data frame, the LUs only need to feed back their received power to the legitimate AP when they detect jamming attacks. Numerical results are also presented to evaluate the effectiveness of the proposed anti-jamming precoder against the DIRS-based FPJs and the feasibility of the designed data frame used by the legitimate AP to estimate the statistical characteristics.

Due to the open communications environment in wireless channels, wireless networks are vulnerable to jamming attacks. However, existing approaches for jamming rely on knowledge of the legitimate users’ (LUs’) channels, extra jamming power, or both. To raise concerns about the potential threats posed by illegitimate intelligent reflecting surfaces (IRSs), we propose an alternative method to launch jamming attacks on LUs without either LU channel state information (CSI) or jamming power. The proposed approach employs an adversarial IRS with random phase shifts, referred to as a “disco” IRS (DIRS), that acts like a “disco ball” to actively age the LUs’ channels. Such active channel aging (ACA) interference can be used to launch jamming attacks on multi-user multiple-input single-output (MU-MISO) systems. The proposed DIRS-based fully-passive jammer (FPJ) can jam LUs with no additional jamming power or knowledge of the LU CSI, and it can not be mitigated by classical anti-jamming approaches. A theoretical analysis of the proposed DIRS-based FPJ that provides an evaluation of the DIRS-based jamming attacks is derived. Based on this detailed theoretical analysis, some unique properties of the proposed DIRS-based FPJ can be obtained. Furthermore, a design example of the proposed DIRS-based FPJ based on one-bit quantization of the IRS phases is demonstrated to be sufficient for implementing the jamming attack. In addition, numerical results are provided to show the effectiveness of the derived theoretical analysis and the jamming impact of the proposed DIRS-based FPJ.

Internet of meta-material things (meta-IoT) is a network of sensors composed of meta-materials with the advantages of low cost, ultra-low power consumption, and robust, showing great potential for the coming 6G communications. However, existing meta-IoT systems assume specular reflection on meta-IoT sensors, which limits their applications. For example, in chemical factories with harsh environments, receivers are often deployed on mobile robots. Due to their mobility, when measuring the signals, it is infeasible to ensure that the receivers are located at a certain angle relative to the meta-IoT sensors. Therefore, it is necessary to broaden the angle range of reflected signal coverage. In this paper, we propose a meta-IoT system capable of supporting receivers deployed at arbitrary angles in a broadened angle range. To be specific, we first propose an inhomogeneous structural design for meta-IoT sensors to achieve reflection coverage broadening. Then, we establish the signal transmission model from the transmitter to the receiver, going through the proposed meta-IoT sensor. To maximize the reflection coverage while ensuring accurate sensing results, we formulate a joint meta-IoT structure and sensing function optimization problem and propose efficient algorithms to solve it. Simulation results verify the effectiveness of the design method for the proposed meta-IoT system to achieve reflection coverage broadening.

Holographic multiple-input-multiple output (HMIMO) technology, which is enabled by large-scale antenna arrays with quasi-continuous apertures, is expected to be an important technology in the forthcoming 6G wireless network. Reconfigurable intelligent surface (RIS)-based antennas provide an energy-efficient solution for implementing HMIMO. Most existing works in this area focus on single-polarized RIS-enabled HMIMO, where the RIS can only reflect signals in one polarization towards users and signals in the other polarization cannot be received by intended users, leading to degraded data rate. To improve multiplexing performance, in this paper, we consider a dual-polarized RIS-enabled single-user HMIMO network, aiming to optimize power allocations across polarizations and analyze corresponding maximum system capacity. However, due to interference between different polarizations, the dual-polarized system cannot be simply decomposed into two independent single-polarized ones. Therefore, existing methods developed for the single-polarized system cannot be directly applied, which makes the optimization and analysis of the dual-polarized system challenging. To cope with this issue, we derive an asymptotically tight upper bound on the ergodic capacity, based on which the power allocations across two polarizations are optimized. Potential gains achievable with such dual-polarized RIS are analyzed. Numerical results verify our analysis.

Holographic massive-input-massive-output (HMIMO) is expected to play an important role in 6G, which integrates numerous antennas or reconfigurable elements into a compact surface to form a continuous aperture. However, it is not energy efficient to implement the HMIMO with conventional phased arrays, since hundreds of energy-intensive phase shifters are required, leading to inevitably huge power consumption and degraded energy efficiency. Compared with the phased array, metasurface-based antennas, also referred to as reconfigurable refractive surface (RRS), can significantly improve the energy efficiency, since they are free of those energy-hungry phase shifters. In this paper, we consider an RRS-enabled multi-user HMIMO system, where the energy efficiency of the system is maximized by optimizing the size of the RRS. However, different from the traditional metasurfaces that locate far from the base station (BS) and work as relays, the RRS is much closer to the BS such that the BS antennas cannot be assumed to locate in the far field of the RRS. Therefore, it is challenging to maximize the energy efficiency of the RRS-aided system. To cope with this issue, the capacity and power consumption of this system are analyzed first, based on which the energy efficiency is maximized by optimizing the number of RRS elements. The maximized energy efficiency is then compared against that obtained by the phased array. Through theoretical analysis and simulations, we verify that the RRS is a more energy efficient solution to HMIMO than the phased array when the power consumption per RRS element is lower than a derived closed-form threshold.

Emerging intelligent reflecting surfaces (IRSs) significantly improve system performance, while also pose a huge risk for physical layer security. A disco IRS (DIRS), i.e., an illegitimate IRS with random time-varying reflection properties, can be employed by an attacker to actively age the channels of legitimate users (LUs). Such active channel aging (ACA) generated by the DIRS-based fully-passive jammer (FPJ) can be applied to jam multi-user multiple-input single-output (MU-MISO) systems without relying on either jamming power or LU channel state information (CSI). To address the significant threats posed by the DIRS-based FPJ, an anti-jamming strategy is proposed that requires only the statistical characteristics of DIRS-jammed channels instead of their CSI. Statistical characteristics of DIRS-jammed channels are first derived, and then the anti-jamming precoder is given based on the derived statistical characteristics. Numerical results are also presented to evaluate the effectiveness of the proposed anti-jamming precoder against the DIRS-based FPJ.

Authenticating the communications among drones operating as a network (or a swarm) is crucial for the control of the network. When drones are in turn supporting communications with other ground devices (e.g., in non-terrestrial networks), all nodes in the network need to be authenticated for end-to-end security. The absence of a reliable fixed network architecture among drones, which are only connected by wireless links, calls for new authentication mechanisms that can complement or be used as alternatives to those offered by cryptography. We propose a challenge-response (CR) physical-layer authentication (PLA) mechanism, where, upon a transmission request from a transmitting drone, referred to as Alice, Bob either asks Alice to move in a specific (randomly chosen) position or moves to a (randomly chosen) position: in both cases, changes in the propagation environment are controlled by Bob. Then, the message is transmitted and Bob estimates the channel from the received signal and verifies that it is compatible with the positions assumed by Alice and Bob. Note that Bob may represent a group of drones that cooperate for authentication. We discuss several security challenges to this CR PLA mechanism and compare them with existing approaches. Preliminary results on the performance of the proposed authentication scheme are presented, showing the advantage of the CR PLA approach.

The Internet of Meta-material Things (Meta-IoT) has significant potential for future IoT applications due to its ability to function without a battery and its ease of maintenance. Specially, the environmental information can be obtained by analyzing the frequency response of Meta-IoT sensor from the reflected signals. Through the reflection, these Meta-IoT sensors can also provide additional paths for improving communication performance while performing sensing. In this paper, we design a dual-functional Meta-IoT network for integrated sensing and communication (ISAC), i.e., the Meta-IoT sensors can sense environmental conditions and facilitate communication simultaneously. However, ensuring the quality of both sensing and communication for multiple users is not trivial since the impact of the sensor structure on sensing and communication at the same time. Moreover, a customized beamforming scheme is required to balance the performance of two functions. To address these challenges, we optimize the sensor structure and the transmit beamforming. Simulation results show that the communication performance can be significantly improved with the proposed scheme while the sensing performance is guaranteed.

With the rapid development of artificial general intelligence (AGI), various multimedia services based on pretrained foundation models (PFMs) need to be effectively deployed. With edge servers that have cloud-level computing power, edge intelligence can extend the capabilities of AGI to mobile edge networks. However, compared with cloud data centers, resource-limited edge servers can only cache and execute a small number of PFMs, which typically consist of billions of parameters and require intensive computing power and GPU memory during inference. To address this challenge, in this paper, we propose a joint foundation model caching and inference framework that aims to balance the tradeoff among inference latency, accuracy, and resource consumption by managing cached PFMs and user requests efficiently during the provisioning of generative AI services. Specifically, considering the in-context learning ability of PFMs, a new metric named the Age of Context (AoC), is proposed to model the freshness and relevance between examples in past demonstrations and current service requests. Based on the AoC, we propose a least context caching algorithm to manage cached PFMs at edge servers with historical prompts and inference results. The numerical results demonstrate that the proposed algorithm can reduce system costs compared with existing baselines by effectively utilizing contextual information.

Holographic communication is recently envisioned to be a promising technology to handle the exponentially increasing data transmission demands, utilizing a large number of compact and tunable antenna elements. In this paper, we consider a holographic communication system where the beam-forming scheme is developed by the codebook design and beam training to avoid the high overhead of acquiring perfect channel state information. Given the large-scale antenna array, users are expected to be distributed in both the near and far fields of the base station, and thus, we design a near-far field codebook to apply to all users in unknown locations. However, the fine-grained beam training using narrow beams capable of enhancing received signal power at the expense of a high overhead which occupies significant time resources. To tackle such a conflict, we propose the adaptive beam training that leverages user feedback to train user-densely distributed regions at a fine-grained level and user-sparsely distributed ones in a coarse manner, thereby improving the throughput. Under a general setting including both the near-field and far-field users, simulation results show that the proposed scheme achieves a higher sum rate and throughput compared to the state-of-the-art schemes.

Ultra-massive multiple-input multiple-output (MIMO) is expected to be one of the key enablers in the forthcoming 6G networks to handle various user demands by exploiting spatial diversity. In this paper, a new paradigm termed holographic radio is considered for ultra-massive MIMO via integrating numerous antenna elements into a compact space, thereby achieving a spatially quasi-continuous aperture and realizing high beampattern gain. We propose a practical path to implement holographic radio by a novel metasurface-based antenna called a reconfigurable holographic surface (RHS). Specifically, the RHS is capable of holographic beamforming over the spatially quasi-continuous apertures by incorporating densely packed tunable metamaterial elements with low power consumption. To enhance the performance of the RHS as an antenna array for achieving ultra-massive MIMO, a holographic beamforming optimization algorithm is developed for beampattern gain maximization based on the hardware design and full-wave analyses of RHSs. We then implement a prototype of an RHS and build an RHS-aided communication platform to further substantiate the feasibility of RHS-enabled holographic radio. Both simulation and experimental results verify the effectiveness of the proposed holographic beamforming optimization algorithm. It is also proved that the RHS-aided communication platform is capable of supporting real-time transmission of high-definition video.

Integrated sensing and communication (ISAC) is a key enabler for next-generation wireless communication systems to improve spectral efficiency. However, the coexistence of sensing and communication functionalities can cause harmful interference. In this paper, we propose to use a reconfigurable intelligent surface (RIS) in conjunction with ISAC to address this issue. The RIS is composed of a large number of low-cost elements that can adjust the amplitude and phase shift of impinging signals, thus providing a relatively high beamforming gain. To maximize the sum-rate of the communication system, we jointly optimize the beamformer at the base station (BS) and the phase shifts at the RIS, subject to a threshold on the interference power, the unit-norm constraint of the transmit power, and the unit modulus constraint of the RIS phase shifts. To efficiently tackle this NP-hard problem, we first reformulate the problem into a more tractable form using the fractional programming (FP) technique. Then, we exploit the geometrical properties of the constraints and adopt an alternating manifold-based optimization to compute the optimal active beamformer and the RIS phase shifts, respectively. Simulation results demonstrate that the proposed RIS-assisted design significantly reduces the mutual interference and improves the system sum-rate for the communication system.

Difficulties in controlling IRSs to form the optimized passive beamforming have rarely been considered in intelligent reflecting surface (IRS)-aided systems, which are summarized as follows: 1) sending the optimized passive precoding vectors to the IRS controller incurs significant control overheads; 2) implementing the optimized passive precoding needs to set massive modes in the IRS control circuit. To address these issues, we investigate codebook-based passive beamforming for multi-IRS-aided millimeter-wave (mmWave) multi-user multiple-input single-output (MU-MISO) systems, where the control overheads are reduced to several scalars and the number of modes set in the IRS control circuit is reduced to that of codewords. Moreover, we formulate a joint passive and active precoding problem in the multi-IRS-aided mmWave MU-MISO system as a mixed-integer nonlinear programming (MINLP) problem, and then develop a generalized Benders decomposition (GBD)-based joint passive and active precoding algorithm. The proposed algorithm offers near-optimal performance ($\ge99.9$%) with significantly-reduced computational complexity. Simulation results show that the proposed algorithm achieves energy savings of up to 50% and 95%, compared to the benchmark by the maximum ratio transmission and that without IRSs, respectively. In addition, the energy savings increase with the number of reflecting elements packed on each IRS as well as that of codewords.

Using widely deployed Internet of Things (IoT) sensors to perceive the environmental distribution is crucial in many IoT applications, such as intelligent healthcare and smart home. As using traditional sensors will lead to high costs and maintenance, it is important to design the next-generation IoT sensor to reduce the cost of ubiquitous deployment. For this purpose, we propose a novel IoT system based on low-cost and fully passive meta-material sensors. Specifically, the meta-material sensors can sense multiple environmental conditions such as temperature and humidity levels, and transmit back the information by signal reflection, simultaneously. With the information contained in the received signals, a wireless receiver can obtain detailed environmental distributions. However, it is not trivial to achieve high sensing accuracy in the meta-material sensor based IoT system because the structure of the meta-materials, the deployment positions of sensors, and the reconstruction function for environmental distributions need to be jointly optimized. To handle this challenge, we propose an algorithm to design the meta-material based IoT system with the help of a deep learning approach. Simulation results verify that the proposed algorithm effectively maximizes the sensing accuracy. Experimental evaluations also show that the proposed scheme can obtain humidity distribution with an accuracy of over 93%.

Wireless simultaneous localization and mapping (SLAM) has attracted much attention as a promising technique to empower location based services. However, the accuracy of traditional wireless SLAM systems is limited as the wireless signals are easily disturbed by the uncontrollable radio environments. To mitigate this issue, in this paper, we propose a MetaSLAM system where multiple reconfigurable intelligent surfaces (RISs) are deployed to customize the wireless environments. To be specific, through adjusting the phase shifts of these RISs, the strength of reflected signals can be enhanced in order to resist the variance of radio environments. However, it is challenging to coordinate multiple RISs and optimize their phase shifts especially when their locations are unknown to the agent. In order to address these challenges, we formulate a MetaSLAM optimization problem, and design a two-stage optimization algorithm based on the genetic and particle filter algorithms to solve the formulated problem. Analysis of the complexity and the positioning error bound of the proposed SLAM system are provided. Simulation results show that compared with the benchmark schemes, the positioning error obtained by the MetaSLAM system is reduced by at least 31%.

Holographic Multiple Input Multiple Output (HMIMO), which integrates massive antenna elements into a compact space, has been considered as a promising enabling technique for future wireless networks. For the HMIMO implemented by traditional phased arrays, the system capacity is insufficient to satisfy the requirement of future networks since the phased array requires energy-consuming phase shifters. Compared with the phased array, reconfigurable refractive surface (RRS), which is free of phase shifters, can significantly improve the system capacity given the same power budget. In this paper, we consider a multi-user RRS-based HMIMO system. Unlike traditional metasurfaces working as passive relays, the RRS is used as transmit antennas, indicating that the RRS is much closer to the feeds. Therefore, the far-field approximation no longer holds, urging a new performance analysis framework. To address the above challenge, we first derive the system capacity, which is then compared against that obtained by the phased array. Simulation results verify our analysis and show that the RRS can bring higher system capacity.

Holographic multiple-input multiple-output (HMI-MO) has recently motivated its potential use to handle the exponentially increasing data transmission demands by achieving a spatially continuous aperture. With densely-packed metamaterial elements, the reconfigurable refractive surfaces (RRSs) serving as antennas emerge to enable HMIMO. In this paper, we consider a multi-user system where an RRS is employed as the transmit antenna array at the base station (BS). To avoid the high overhead of acquiring perfect channel state information (CSI), a codebook and beam training mechanism is required to develop the beamforming scheme. Given the large physical dimension of the RRS, users are likely to distribute in both the near field and the far field of the BS, making the codebook design more difficult. To address this issue, we design a hybrid near-far field codebook which applies to all users in any location with low overhead. To further reduce the training overhead, each codeword is designed to cover multiple spatial areas, enabling a multi-user beam training mechanism which can be performed for all users simultaneously. Under a general setting including both the near-field and far-field users, simulation results show that the proposed scheme significantly reduces the overhead and achieves a higher sum rate compared to the state-of-the-art codebooks, which performs very close to that of the perfect CSI case.

Obtaining fine-grained structural information about building through ubiquitous sensors is crucial for assessing their aging and damage. However, due to the energy requirements, traditional sensors deployed in the building structure need frequent maintenance works which are easy to produce irreversible damage to the building. Besides, the larger volume of sensors also brings the difficulty of deployment in buildings with complex structures. To solve these problems, we propose a novel sensing system to obtain fine-grained structural information based on ubiquitous deployed meta-material sensors. Specifically, meta-material sensors are small pieces of PCB printed with metal structure, which work without a power supply and suit wide deployment. The experiment realizes the humidity sensing with a spatial resolution of 0.5m, while existing methods for dispersing sensors achieve space intervals of 10m at the same cost. With this framework, the need of providing information support for assessing structural failure can be met.

Holographic multiple-input multiple-output (HMIMO) with a spatially continuous aperture is a promising solution for future radio access to handle the explosively increasing data demands. As a key enabler of HMIMO, the reconfigurable refractive surface (RRS) can serve as an antenna array with numerous programmable radiation elements. In this paper, we consider a multi-user system with an RRS-aided base station (BS) where the transmit signal is refracted by the RRS towards the users. A beamforming scheme is developed via codebook design and beam training. A larger codebook size implies more codewords, each corresponding to a directional beam. When the codebook size increases, the directivity of the refracted beam is enhanced, bringing a higher data rate. However, it also leads to an exponential growth of the training overhead. To achieve the critical tradeoff between the data rate and overhead, we evaluate the system throughput and model the relation between the codebook size of the RRS and the throughput mathematically. The optimal codebook size is then derived given different user distributions. Simulation results verify our theoretical analysis and show the influence of both codebook size and RRS size on the throughput.

Recently, the intelligent omni-surface (IOS) has been proposed as a novel instance of metasurface to achieve full-dimensional communications by jointly engineering its reflective and refractive properties. However, optimal beamforming scheme for the IOS is hard to obtain due to the difficulty in acquiring perfect channel state information (CSI). To address this issue, in this paper, we consider an IOS aided system where the beamforming scheme is designed via beam training with codebooks at the base station (BS), the IOS, and users. Given that the refractive/reflective signals are closely related to both incident signals from the BS and phase shifts of IOS elements, the codebooks at the BS and the IOS are designed jointly. Based on the joint BS-IOS codebook, a multi-lobe beam training mechanism is proposed to perform beam training for multiple users simultaneously, thereby reducing the training overhead. Simulation results indicate that our proposed scheme achieves a higher sum rate than the state-of-the-art beam training schemes and performs close to the perfect CSI case.

Abstract Due to the capability of providing flexible network coverage, unmanned aerial vehicles (UAVs) are a promising solution to support the Internet‐of‐Things (IoT) applications, especially for the area without terrestrial network coverage. In this paper, a UAV‐assisted IoT network is considered, where a UAV serves as an aerial base station to serve ground IoT devices. To support the massive access, grant‐free non‐orthogonal communications are also adopted, where packets generated by IoT devices can skip the handshake process and share a channel with other packets for transmission. With the aim to maintain the information freshness, the age of information (AoI) for the network is first derived, and an AoI minimization problem is formulated as a Stackelberg game where the UAV is the leader and ground IoT devices are followers. However, solving such a problem is challenging as the number of involved followers is large. To simplify the problem, a mean‐field game based approach is proposed, where the average behaviours of followers are considered instead of individual strategies. Simulation results show that the computational complexity of the proposed scheme is almost unchanged with the number of IoT devices while the performance is close to the optimal one.

International audience

In this article, we consider a cellular-based healthcare Internet of Things (IoT) system with imperfect channel state information (CSI), where a healthcare IoT device first receives radio frequency (RF) energy from the small cell base station (SBS) and then transmits physiological status updates to the corresponding SBS as timely as possible. A newly proposed metric, named Age of Information (AoI), will be introduced to characterize the data freshness, which is determined by the status updates generation probability and the information transmission outage probability. To minimize the average AoI, we formulate a distributionally robust optimization problem under an energy harvesting probability (chance) constraint and an information transmission probability constraint. Since the distributionally robust probability constraints are nonconvex, we use the conditional value-at-risk (CVaR)-based method to express constraint specifications related to distributional ambiguity. To tackle the NP-hard problem efficiently, we decompose the AoI minimization problem into two subproblems and propose a low-complexity iterative algorithm to obtain a suboptimal solution. Simulation results show that there exists an AoI-energy tradeoff in the considered healthcare IoT, and the CVaR-based method can achieve a better performance than the nonrobust method.

Ultra-dense low-Earth-orbit (LEO) satellite communication networks have significant potential for providing high-speed data services. To compensate the severe path loss in satellite communications, a key conceptual enabler is the holographic multiple input multiple output (HMIMO) with a spatially continuous aperture which can achieve a high directive gain with a small antenna size. In this paper, we consider a novel metamaterial antenna called a reconfigurable holographic surface (RHS) integrated with a user terminal (UT) to support LEO satellite communications. Composing of densely packing sub-wavelength metamaterial elements, the RHS can realize continuous or quasi-continuous apertures and provide a practical way towards the implementation of HMIMO. To obtain the desired beam directions towards the satellites, we propose a LEO satellite tracking scheme based on the temporal variation law such that frequent satellite positioning can be avoided. A holographic beamforming algorithm for sum rate maximization is then developed where a closed-form for the optimal holographic beamformer is derived. The robustness of the algorithm against the tracking errors of the satellites’ positions is also proved. Simulation results verify the theoretical analysis and show that the RHS outperforms the traditional phased array of the same physical dimension in terms of the sum rate when the compact element spacing of the RHS leads to much more RHS elements. Moreover, the RHS also provides a more cost-effective solution for pursuing high data rate compared with the phased array.

The next generation wireless communications aiming at enhancing capacity and massive connectivity significantly over high-frequency bands urge the development of novel multiple access technologies. In this paper, we propose a new type of space-division multiple access (SDMA), called holographic-pattern division multiple access (HDMA). We develop the principle for HDMA with the main idea of mapping the intended signals for receivers to a superposed holographic pattern. The multi-user holographic beamforming scheme for HDMA is then presented. Based on the theoretical analysis, we find that there exists an optimal holographic pattern such that the sum rate with simple zero-forcing precoding can achieve the asymptotic capacity of the HDMA system. Simulation results verify the theoretical analysis and show that the HDMA scheme outperforms the traditional SDMA scheme in terms of both the cost-efficiency and the sum rate.

To overcome spectrum congestion, a promising approach is to integrate sensing and communication (ISAC) functions in one hardware platform. Recently, metamaterial antennas, whose tunable radiation elements are arranged more densely than those of traditional multiple-input-multiple-output (MIMO) arrays, have been developed to enhance the sensing and communication performance by offering a finer controllability of the antenna beampattern. In this paper, we propose a holographic beamforming scheme, which is enabled by metamaterial antennas with tunable radiated amplitudes, that jointly performs sensing and communication. However, it is challenging to design the beamformer for ISAC functions by taking into account the unique amplitude-controlled structure of holographic beamforming. To address this challenge, we formulate an integrated sensing and communication problem to optimize the beamformer, and design a holographic beamforming optimization algorithm to efficiently solve the formulated problem. A lower bound for the maximum beampattern gain is provided through theoretical analysis, which reveals the potential performance enhancement gain that is obtained by densely deploying several elements in a metamaterial antenna. Simulation results substantiate the theoretical analysis and show that the maximum beamforming gain of a metamaterial antenna that utilizes the proposed holographic beamforming scheme can be increased by at least 50% compared with that of a traditional MIMO array of the same size. In addition, the cost of the proposed scheme is lower than that of a traditional MIMO scheme while providing the same ISAC performance.

In future cellular systems, wireless localization and sensing functions will be built-in for specific applications, e.g., navigation, transportation, and healthcare, and to support flexible and seamless connectivity. Driven by this trend, the need for fine-resolution sensing solutions and centimeter-level localization accuracy arises, while the accuracy of current wireless systems is limited by the quality of the propagation environment. Recently, with the development of new materials, reconfigurable intelligent surfaces (RISs) provide an opportunity to reshape and control the electromagnetic characteristics of the environment, which can be utilized to improve the performance of wireless sensing and localization. In this tutorial, we will first review the background and motivation for utilizing wireless signals for sensing and localization. Next, we will introduce how to incorporate RIS into applications of sensing and localization, including key challenges and enabling techniques, and then, some case studies will be presented. Finally, future research directions will also be discussed.

For many applications envisioned for the Internet of Things (IoT), it is expected that the sensors will have very low costs and zero power, which can be satisfied by meta-material sensor based IoT, i.e., meta-IoT. As their constituent meta-materials can reflect wireless signals with environment-sensitive reflection coefficients, meta-IoT sensors can achieve simultaneous sensing and transmission without any active modulation. However, to maximize the sensing accuracy, the structures of meta-IoT sensors need to be optimized considering their joint influence on sensing and transmission, which is challenging due to the high computational complexity in evaluating the influence, especially given a large number of sensors. In this paper, we propose a joint sensing and transmission design method for meta-IoT systems with a large number of meta-IoT sensors, which can efficiently optimize the sensing accuracy of the system. Specifically, a computationally efficient received signal model is established to evaluate the joint influence of meta-material structure on sensing and transmission. Then, a sensing algorithm based on deep unsupervised learning is designed to obtain accurate sensing results in a robust manner. Experiments with a prototype verify that the system has a higher sensitivity and a longer transmission range compared to existing designs, and can sense environmental anomalies correctly within 2 meters.

This paper considers a resource allocation problem where several Internet-of-Things (IoT) devices send data to a base station (BS) with or without the help of the reconfigurable intelligent surface (RIS) assisted cellular network. The objective is to maximize the sum rate of all IoT devices by finding the optimal RIS and spreading factor (SF) for each device. Since these IoT devices lack prior information of the RISs or the channel state information (CSI), a distributed resource allocation framework with low complexity and learning features is required to achieve this goal. Therefore, we model this problem as a two-stage multi-player multi-armed bandit (MPMAB) framework to learn the optimal RIS and SF sequentially. Then, we put forth an exploration and exploitation boosting (E2Boost) algorithm to solve this two-stage MPMAB problem by combining the$\epsilon $-greedy algorithm, Thompson sampling (TS) algorithm, and non-cooperation game method. We derive an upper regret bound for the proposed algorithm, i.e.,$\mathcal {O}(\log ^{1+\delta }_{2} T)$, increasing logarithmically with the time horizon$T$. Numerical results show that the E2Boost algorithm has the best performance among the existing methods and exhibits a fast convergence rate. More importantly, the proposed algorithm is not sensitive to the number of combinations of the RISs and SFs thanks to the two-stage allocation mechanism, which can benefit the high-density networks.

The intelligent omni-surface (IOS) is a dynamic metasurface that has recently been proposed to achieve full-dimensional communications by realizing the dual function of anomalous reflection and anomalous refraction. Existing research works provide only simplified models for the reflection and refraction responses of the IOS, which do not explicitly depend on the physical structure of the IOS and the angle of incidence of the electromagnetic (EM) waves. Therefore, the available reflection-refraction models are insufficient to characterize the performance of full-dimensional communications. In this paper, we propose a complete and detailed circuit-based reflection-refraction model for the IOS, which is formulated in terms of the physical structure and equivalent circuits of the IOS elements, as well as we validate it with the aid of full-wave EM simulations. Based on the proposed circuit-based model for the IOS, we analyze the asymmetry between the reflection and transmission coefficients. Moreover, the proposed circuit-based model is utilized for optimizing the hybrid beamforming of IOS-assisted networks and hence improving the system performance. To verify the circuit-based model, the theoretical findings, and to evaluate the performance of full-dimensional beamforming, we implement a prototype of IOS and deploy an IOS-assisted wireless communication testbed to experimentally measure the beam patterns and to quantify the achievable rate. The obtained experimental results validate the theoretical findings and the accuracy of the proposed circuit-based reflection-refraction model for IOSs.

Indoor localization has drawn much attention owing to its potential for supporting location based services. Among various indoor localization techniques, the received signal strength (RSS) based technique is widely researched. However, in conventional RSS based systems where the radio environment is unconfigurable, adjacent locations may have similar RSS values, which limits the localization precision. In this paper, we present MetaRadar, which explores reconfigurable radio reflection with a surface/plane made of metamaterial units for multi-user localization. By changing the reflectivity of metamaterial, MetaRadar modifies the radio channels at different locations, and improves localization accuracy by making RSS values at adjacent locations have significant differences. However, in MetaRadar, it is challenging to build radio maps for all the radio environments generated by metamaterial units and select suitable maps from all the possible maps to realize a high accuracy localization. To tackle this challenge, we propose a compressive construction technique which can predict all the possible radio maps, and propose a configuration optimization algorithm to select favorable metamaterial reflectivities and the corresponding radio maps. The experimental results show a significant improvement from a decimeter-level localization error in the traditional RSS-based systems to a centimeter-level one in MetaRadar.

The future sixth generation (6G) networks look forward to providing revolutionary mobile connectivity and high-throughput data services through low-cost communication devices. Benefiting from the programmability and tunability of metamaterials, the reconfigurable holographic surface (RHS) inlaid with numerous metamaterial radiation elements shows its great potential to achieve such a bold vision. By leveraging the holographic principle, the RHS serves as an ultra-thin and lightweight antenna integrated with the transceiver to generate desirable directional beams with low hardware cost and power consumption. In this paper, we consider a downlink RHS-aided multi-user communication system where a base station (BS) equipped with an RHS transmits signals to users. We propose an RHS-based hybrid beamforming scheme where the digital beamforming and the holographic beamforming are performed at the BS and RHS, respectively, together with the receive combining at each user. We formulate a sum-rate maximization problem for RHS-based hybrid beamforming and decompose it into three sub-problems. A joint sum-rate maximization algorithm is then developed to solve the sub-problems in an alternating manner. Simulation results show that based on the proposed RHS-aided hybrid beamforming scheme, a moderate-sized RHS is enough to achieve a satisfactory sum-rate, which is also higher than a same-sized phased array can achieve.

Semantic segmentation is a process of partitioning an image into segments for recognizing regions of humans and objects, which can be widely applied in scenarios such as healthcare and safety monitoring. To avoid privacy violation, using radio frequency (RF) signals instead of photos for semantic segmentation has gained increasing attention. However, traditional human and object recognition by using RF signals is a passive signal collection and analysis process without changing the radio environment. The recognition accuracy is restricted significantly by unwanted multi-path fading, and/or the limited number of independent channels between RF transceivers. This paper introduces MetaSketch, a novel RF-sensing system that performs semantic recognition and segmentation for humans and objects by making the radio environment reconfigurable. A metamaterial-based reconfigurable intelligent surface is incorporated to diversify the information carried by RF signals. Using compressive sensing techniques, MetaSketch reconstructs a point cloud consisting of the reflection coefficients of humans and objects at different spatial points, and recognizes the semantic meaning of the points by using symmetric multilayer perceptron groups. Our evaluation results show that MetaSketch is capable of generating favorable radio environments, extracting exact point clouds, and labeling the semantic meaning of the points with an average error rate of less than 1% in an indoor space.

In the coming 6G communications, the internet of things (IoT) will be a fundamental enabler for ubiquitous environment perception, which requires the IoT sensors to consume near-zero power and have the lowest cost. For this purpose, the IoT sensors are expected to perform simultaneous sensing and transmission, so that energy and hardware costs due to signal modulation can be saved. In this paper, we propose the concept of meta-IoT, i.e., the IoT with sensors composed of specially designed meta-materials, which can achieve simultaneous sensing and transmission without supplied power. The basic idea of meta-IoT sensors is that the signal reflection on the sensors is sensitive to environmental conditions, which can be captured by a wireless receiver. In order to optimize the sensing systems with meta-IoT sensors, we establish the mathematical model of meta-IoT sensors’ sensing and transmission and then jointly optimize the sensors’ structure and the environment estimation at the receiver. We design and implement a practical meta-IoT sensing system for monitoring temperature and humidity levels. Simulation results show that the proposed technique can obtain the optimal sensor structure, and the experimental results verify that the designed meta-IoT sensing system achieves low measurement errors.

Indoor location-based services (LBS) are widely used in large-scale indoor buildings, such as high-rise hospitals and multi-story shopping malls. At the same time, location privacy protection in such three-dimensional (3D) space has recently attracted considerable attention. Currently, most existing location privacy protection schemes focus on two-dimensional (2D) location protection and fail to prevent location inference attacks when the user’s location data include height dimension, i.e., 3D geolocation. Enlightened by the concept of differential privacy, in this paper we first study the impact factors of the degree of indistinguishability of 3D geolocations. Then, we quantify location privacy for LBS applications in the 3D space with geo-indistinguishability (3D-GI) rigorously and provably. We develop a mechanism of three-variates Laplacian to generate perturbed locations considering the locations’ X, Y, and Z-coordinates simultaneously, guaranteeing geo-indistinguishability. Furthermore, the discretization noise-adding mechanism is studied to satisfy geo-indistinguishability in the 3D space under the finite precision of hardware/devices. Considering the discretized mechanism can only satisfy geo-indistinguishability in finite 3D space and users visit the limited regions, we further study the truncation of the Laplacian mechanism to limit the generated perturbed locations within a specific region. Simulation results demonstrate that the proposed 3D-GI outperforms the benchmarks while guaranteeing privacy regardless of the adversary’s prior knowledge.

Recently, the intelligent omni-surface (IOS) has been proposed as a novel instance of metasurface to achieve full-dimensional communications by jointly engineering its reflective and refractive properties. However, optimal beamforming scheme for the IOS is hard to obtain due to the difficulty in acquiring perfect channel state information (CSI). To address this issue, in this paper, we consider an IOS aided system where the beamforming scheme is designed via beam training with codebooks at the base station (BS), the IOS, and users. Given that the refractive/reflective signals are closely related to both incident signals from the BS and phase shifts of IOS elements, the codebooks at the BS and the IOS are designed jointly. Based on the joint BS-IOS codebook, a multi-lobe beam training mechanism is proposed to perform beam training for multiple users simultaneously, thereby reducing the training overhead. The training/feedback overhead of the proposed beam training and the impact of the codebook size are then analyzed theoretically. Simulation results indicate that the proposed scheme achieves a higher sum rate than the state-of-the-art beam training schemes and performs close to the perfect CSI case.

Recently, reconfigurable intelligent surfaces (RISs) have been proposed as a novel solution to enhance wireless communications such as suppressing inter-cell interference. However, signals arriving at a conventional reflecting-type RIS can only be reflected towards one side, leading to a limited service coverage, especially in an indoor environment involving potential obstacles. In this paper, we consider an intelligent omni-surface (IOS) which can provide services for users on both sides by enabling simultaneous signal reflection and transmission. Specifically, we propose an IOS aided indoor communication system where an IOS is embedded in a wall between two independent access points (APs) to suppress inter-cell interference. Due to the independence of the APs, we design a distributed hybrid beamforming scheme consisting of digital beamforming at APs and IOS-based analog beamforming to maximize the sum rate without any exchange of channel state information (CSI) between APs. Simulation results indicate that the proposed system performs very close to an optimal centralized scheme, and has a better sum rate performance compared to existing schemes.

As a widely used localization and sensing technique, radars will play an important role in future wireless networks. However, the wireless channels between the radar and the targets are passively adopted by traditional radars, which limits the performance of target detection. To address this issue, we propose to use the reconfigurable intelligent surface (RIS) to improve the detection accuracy of radar systems due to its capability to customize channel conditions by adjusting its phase shifts, which is referred to as MetaRadar. In such a system, it is challenging to jointly optimize both radar waveforms and RIS phase shifts in order to improve the multi-target detection performance. To tackle this challenge, we design a waveform and phase shift optimization (WPSO) algorithm to effectively solve the multi-target detection problem, and also analyze the performance of the proposed MetaRadar scheme theoretically. Simulation results show that the detection performance of the MetaRadar scheme is significantly better than that of the traditional radar schemes.

Intelligent reflecting surfaces (IRSs), which are capable of adjusting radio propagation conditions by controlling the phase shifts of the waves that impinge on the surface, have been widely analyzed for enhancing the performance of wireless systems. However, the reflective properties of widely studied IRSs restrict the service coverage to only one side of the surface. In this paper, to extend the wireless coverage of communication systems, we introduce the concept of intelligent omni-surface (IOS)-assisted communication. More precisely, an IOS is an important instance of a reconfigurable intelligent surface (RIS) that can provide service coverage to mobile users (MUs) in a reflective and a refractive manner. We consider a downlink IOS-assisted communication system, where a multi-antenna small base station (SBS) and an IOS jointly perform beamforming, for improving the received power of multiple MUs on both sides of the IOS, through different reflective/refractive channels. To maximize the sum-rate, we formulate a joint IOS phase shift design and SBS beamforming optimization problem, and propose an iterative algorithm to efficiently solve the resulting non-convex program. Both theoretical analysis and simulation results show that an IOS significantly extends the service coverage of the SBS when compared to an IRS.

In the coming 6G communications, the internet of things (IoT) serves as a key enabler to collect environmental information and is expected to achieve ubiquitous deployment. However, it is challenging for traditional IoT sensors to meet this expectation because of their requirements of power supplies and frequent maintenance, which are due to their power-demanding sense and transmit modules. To address this challenge, we propose a meta-IoT sensing system, where the IoT sensors are based on specially designed meta-materials. The meta-IoT sensors achieve simultaneous sensing and transmission by physical reflection and require no power supplies. In order to design a meta-IoT sensing system with optimal sensing accuracy, we jointly consider the sensing and transmission of meta-IoT sensors and propose efficient algorithms to optimize the meta-IoT structure and the sensing function at the receiver. As an example, we apply the meta-IoT system to sensing environmental temperature and humidity levels. Simulation results show that by using the proposed algorithm, the sensing accuracy can be largely increased.

In this paper, we propose a Meta-IoT system to achieve ubiquitous deployment and pervasive sensing for future Internet of Things (IoT). In such a system, sensors are composed of dedicated passive meta-materials whose frequency response for wireless signals is sensitive to environmental conditions. Therefore, we can remove the energys-suppliers in the future IoT by obtaining sensing results from the reflected signals of Meta-IoT devices. Nevertheless, it remains a challenge to reconstruct 3D environmental condition distributions by using the Meta-IoT system. Because of the interferences among the reflected signals, it requires the optimization of the deployment of the meta-IoT devices to ensure the sensing accuracy. To handle this challenge, we establish a mathematical model of Meta-IoT devices' sensing and transmission to calculate the interference between Meta-IoT devices. Then, an algorithm is proposed to minimize the interference and reconstruction error by optimizing the Meta-IoT devices' position and the estimation function. The simulation results verify that the proposed system can obtain a 3D environmental conditions' distribution with high accuracy.

Next-generation wireless communication systems are believed to share the same spectrum previously allocated to radar applications. The coexisting communication system will cause harmful interference to the radar system. In this paper, we investigate the deployment of the Reconfigurable intelligent surface (RIS) to improve the performance of a Multiple-Input Multiple-Output (MIMO) Radar and Communication Coexis-tence (RCC) system. The RIS consists of a large number of nearly passive, and low-cost elements, which provides passive, and a relatively high beamforming gain by controlling the reflecting elements' reflection coefficients. Moreover, the RIS can eliminate the mutual interference between the radar and communication systems. To improve the sum-rate of the communication system subjected to the radar performance constraints, we design the transmit beamforming and the phase shifts for the RIS elements by using the local search approach. Numerical results verify the effectiveness of the utilization of the RIS.

Indoor wireless simultaneous localization and mapping (SLAM) is considered as a promising technique to provide positioning services in future 6G systems. However, the accuracy of traditional wireless SLAM system heavily relies on the quality of propagation paths, which is limited by the uncontrollable wireless environment. In this paper, we propose a novel SLAM system assisted by a reconfigurable intelligent surface (RIS) to address this issue. By configuring the phase shifts of the RIS, the strength of received signals can be enhanced to resist the disturbance of noise. However, the selection of phase shifts heavily influences the localization and mapping phase, which makes the design very challenging. To tackle this challenge, we formulate the RIS-assisted indoor SLAM optimization problem and design an error minimization algorithm for it. Simulations show that the RIS assisted SLAM system can decrease the positioning error by at least 31% compared with benchmark schemes.

Reconfigurable intelligent surfaces (RISs), which enable tunable anomalous reflection, have appeared as a promising method to enhance wireless systems. In this paper, we propose to use an RIS as a spatial equalizer to address the well-known multi-path fading phenomenon. By introducing some controllable paths artificially against the multi-path fading through the RIS, we can perform equalization during the transmission process instead of at the receiver, and thus all the users can share the same equalizer. Unlike the beam-forming application of the RIS, which aims to maximize the received energy at receivers, the objective of the equalization application is to reduce the inter-symbol interference (ISI), which makes phase shifts at the RIS different. To this end, we formulate the phase shift optimization problem and propose an iterative algorithm to solve it. Simulation results show that the multi-path fading effect can be eliminated effectively compared to benchmark schemes.

With the increasing deployment of mobile vehicles, such as mobile robots and unmanned aerial vehicles (UAVs), it is foreseen that they will play an important role in mobile crowd sensing (MCS). Specifically, mobile vehicles equipped with sensors and computing devices are able to collect massive data due to their fast and flexible mobility in MCS systems. In this paper, we consider a mobile vehicle-based MCS system where vehicles owned by different operators or individuals compete against others for limited sensing resources. We investigate the joint task selection and route planning problem for such an MCS system. However, since the structural complexity and computational complexity of the original problem is very high, we propose a multi-population Mean-Field Game (MFG) problem by simplifying the interaction between vehicles as a distribution over their strategy space, known as the mean-field term. To solve the multi-population MFG problem efficiently, we propose a G-prox primal-dual hybrid gradient method (PDHG) algorithm whose computational complexity is independent of the number of vehicles. Numerical results show that the proposed multi-population MFG scheme and algorithm are of effectiveness and efficiency.

In this paper, we consider a real-time E-Health Internet of Things (IoT) system with the uncertainty of channel state information (CSI), in which a wearable device collects radio frequency (RF) energy from a Personal Digital Assistant (PDA), and then transmits healthcare data status updates to the corresponding PDA promptly. The Peak Age of Information (PAoI) is considered as a parameter to measure the freshness of information. Our goal is to minimize the average PAoI under non-convex constraints related to an uncertain CSI mismatch model. Only mean and variance information is specified in the distributional ambiguity set. This distributionally robust optimization problem is transformed into a tractable semi-definite programming (SDP) problem using the Conditional Value-at-Risk (CVaR) based method. To solve this NP-hard problem effectively, we decompose the PAoI minimization problem into two subproblems, and propose a low complexity iterative algorithm to derive a suboptimal solution. Simulation results show an average PAoI-energy tradeoff in the considered healthcare IoT, and the CVaR based method can achieve a better performance than a non-robust method.

Emerging unmanned aerial vehicles (UAVs) are playing an increasingly important role in military, public, and civilian applications. More recently, UAVs have become a topic of central research interest in the wireless communication community. For example, the 3rd Generation Partnership Project (3GPP) standardisation body has recently worked on a study item to facilitate seamless integration of UAVs into future cellular networks, which is called the cellular Internet of UAVs. UAVs can be exploited in different ways to enhance cellular communications. On the one hand, dedicated UAVs can be used as airborne wireless access points or relay nodes to further improve terrestrial communications, which is referred to as UAV-assisted cellular communications. On the other hand, UAVs may be exploited for sensing purposes by leveraging their advantages such as on-demand deployment, larger service coverage compared with the conventional fixed sensor nodes, and flexible spatial network architecture. We refer to this category of UAV applications as cellular-assisted UAV sensing. Unlike terrestrial cellular networks, UAV communications have many distinctive features such as high dynamic network topologies and weakly connected communication links. Besides, they also suffer from some practical constraints such as battery power, no-fly zones, and sensing requirements. Therefore, it is essential to develop novel communication and signal-processing techniques in support of ultra-reliable and real-time sensing applications. This special issue aims to create a platform for researchers from both academia and industry to disseminate state-of-the-art results and to advance the integration of UAVs into cellular networks. In total, 12 excellent papers were accepted after a rigorous multi-round review process. These papers can be divided into two topics: UAV-assisted cellular communications and cellular-assisted UAV sensing. In the following, we will introduce these papers and highlight their contributions. In their survey paper 'A survey on unmanned aerial vehicle relaying networks', Li et al. comprehensively summarise UAV relaying communications, which is an important paradigm of UAV-assisted cellular communications, and introduce its application scenarios. Key challenges are presented and corresponding technologies to address these challenges are discussed. Furthermore, they also show the research opportunities of UAV relaying communications. Yuan et al., in their paper 'Interference coordination and throughput maximisation in an unmanned aerial vehicle-assisted cellular: User association and three-dimensional trajectory optimisation', consider a UAV as an aerial base station (BS) to serve ground users. To reduce the interference between the UAV and terrestrial BSs, they propose a joint user association and 3D trajectory optimisation method. An improved block successive upper-bound minimisation based penalty algorithm is proposed. In 'Age-optimal path planning for finite-battery UAV-assisted data dissemination in IoT networks', Changizi and Emadi consider using UAVs to assist wireless sensor networks to deliver information with the aim to explore the freshness of data. An UAV trajectory planning for data dissemination is proposed, taking into account both maximal use of energy and the freshness of data. The effect of limited energy for UAVs is also discussed. In 'metaheuristic-based optimal 3D positioning of UAVs forming aerial mesh network to provide emergency communication services', Gupta and Varma study the optimal placement of UAVs to facilitate post-disaster emergency communication services. Coverage, quality-of-services, energy consumption, equal load distribution over UAVs, and fault tolerance are all considered for improving network connectivity and lifetime. Two metaheuristic-based hybrid optimisation algorithms are proposed to integrate these objectives together. Sun et al., in their paper 'An efficient data collection framework in the sky: An affine transformation approach based on Internet of unmanned aerial vehicles', use a UAV as a data collector to collect data from sensors. An efficient data collection framework is proposed and a min-maximum data processing strategy is adopted based on data value to store the collected time-series data. Moreover, an efficient affine transformation method is proposed to improve the efficiency of the system. In their paper 'Advanced squirrel algorithm-trained neural network for efficient spectrum sensing in cognitive radio-based air traffic control application', Eappen et al. utilise a cognitive radio manner to establish a connection between the UAV and the ground controller. A neural network trained by Advanced Squirrel Algorithm (ASA) is proposed for efficient spectrum sensing. Simulation-based evaluation shows that the proposed method is capable of efficiently detecting the spectrum holes with high convergence rate. In 'Blockchain-assisted secure UAV communication in 6G environment: Architecture, opportunities, and challenges', Gupta et al. investigate the security and privacy issues in UAV sensing applications. They propose an Interplanetary File System and blockchain-based secure UAV communication scheme. The proposed scheme ensures data security and privacy, reduces data storage cost, and enhances network performance. Wu et al., in their paper 'Optimisation of virtual cooperative spectrum sensing for UAV-based interweave cognitive radio system', consider UAVs equipped with spectrum sensing for data transmission. Based on a virtual cooperative spectrum sensing model, the authors propose an energy-efficient virtual cooperative spectrum sensing with the sequential 0/1 fusion rule to reduce the average number of decisions without any loss in the detection performance. Moreover, the optimisation problem of virtual cooperative spectrum sensing for UAV-based interweave cognitive ratio systems is formulated and solved. In 'Cellular UAV-to-device communications: Joint trajectory, speed, and power optimisation', Liu et al. study two communication modes for the UAV sensing applications, that is, UAVs can transmit through the BS or to the corresponding mobile devices directly. The authors propose a joint sensing and transmission protocol to schedule UAV sensing and transmission, and formulate an energy utility maximisation problem. A joint trajectory, speed, and transmit power optimisation algorithm is proposed to obtain a suboptimal solution. Ren et al., in their paper 'Computation offloading game in multiple unmanned aerial vehicle-enabled mobile edge computing networks', use mobile edge computing to offload the computation tasks for UAVs. To obtain the minimum computing time, the offloading percentage and the transmission power is optimised through a game theory modelling. Numerical results verify that the proposed schemes can effectively decrease the computing time and energy consumption, especially for a large number of UAVs. In 'An enhanced genetic algorithm for unmanned aerial vehicle logistics scheduling', Yuan et al. examines a scheduling problem in consideration of the loading capacity, the maximum flight time, and the flight speed. A genetic-based algorithm framework is presented for solving the scheduling problem. Moreover, in order to reduce the search space and accelerate the execution of this algorithm, a weight-based loading method is adopted. Finally, in their paper 'Multi-channel underdetermined blind source separation for recorded audio mixture signals using an unmanned aerial vehicle', Xie et al. apply UAVs for locating sound-emitting targets and study the source separation problem when the number of sources is more than the number of sensors. An underdetermined blind source separation algorithm to separate the multi-channel audio mixture signals recorded by an unmanned aerial vehicle is proposed. As a result, the frequency-domain sources are estimated through Wiener filtering and time-domain sources are obtained via inverse short-time Fourier transform. We would like to express our sincere thanks to all the authors for submitting their papers and to the reviewers for their valuable comments and suggestions that significantly enhanced the quality of these articles. We are also grateful to Prof. Liuqing Yang, the Editor-in-Chief of the IET Communications, for her great support throughout the whole review and publication process of this special issue, and, of course, to all the editorial staff. Hongliang Zhang received the B.S. and Ph.D. degrees at the School of Electrical Engineering and Computer Science at Peking University, China, in 2014 and 2019, respectively. Currently, he is a postdoctoral associate in the Department of Electrical Engineering at Princeton University, USA. His current research interest includes reconfigurable intelligent surfaces, aerial access networks, and game theory. He received the best doctoral thesis award from the Chinese Institute of Electronics in 2019. He is an exemplary reviewer for IEEE Transactions on Communications in 2020. He has served as a TPC Member for many IEEE conferences, such as Globecom, ICC, and WCNC. He is currently an associate editor for IET Communications and Frontiers in Signal Processing. He also serves as a Guest Editor for IEEE IoT-J special issue on Internet of UAVs over cellular networks. Walid Saad received the Ph.D. degree from the University of Oslo, Norway, in 2010. He is currently a professor with the Department of Electrical and Computer Engineering, Virginia Tech, USA, where he leads the Network science, Wireless, and Security (NEWS) Laboratory. His research interests include wireless networks, machine learning, game theory, security, unmanned aerial vehicles, cyber-physical systems, and network science. Dr. Saad is a recipient of the NSF CAREER Award in 2013, the AFOSR Summer Faculty Fellowship in 2014, and the Young Investigator Award from the Office of Naval Research (ONR) in 2015. He has authored or co-authored 10 conference best paper awards at WiOpt in 2009, ICIMP in 2010, IEEE WCNC in 2012, IEEE PIMRC in 2015, IEEE SmartGridComm in 2015, EuCNC in 2017, IEEE GLOBECOM in 2018, IFIP NTMS in 2019, IEEE ICC in 2020, and IEEE GLOBECOM in 2020. He is also a recipient of the 2015 Fred W. Ellersick Prize from the IEEE Communications Society, the 2017 IEEE ComSoc Best Young Professional in Academia Award, the 2018 IEEE ComSoc Radio Communications Committee Early Achievement Award, and the 2019 IEEE ComSoc Communication Theory Technical Committee. He has also co-authored the 2019 IEEE Communications Society Young Author Best Paper. From 2015 to 2017 he was named the Stephen O. Lane Junior Faculty Fellow at Virginia Tech and in 2017 he was named College of Engineering Faculty Fellow. He received the Dean's award for research excellence from Virginia Tech in 2019. He currently serves as an editor for IEEE Transactions on Mobile Computing and IEEE Transactions on Cognitive Communications and Networking. He is an Editor-at-Large of IEEE Transactions on Communications. He is an IEEE Distinguished Lecturer. Mérouane Debbah received the M.Sc. and Ph.D. degrees from Ecole Normale Supérieure Paris-Saclay, France. In 1996, he joined Ecole Normale Supérieure Paris-Saclay. He was with Motorola Labs, France, from 1999 to 2002, and also with the Vienna Research Center for Telecommunications, Austria, until 2003. From 2003 to 2007, he was an assistant professor with the Mobile Communications Department, Institut Eurecom, France. From 2007 to 2014, he was the director of the Alcatel-Lucent Chair on flexible radio. Since 2007, he has been a full professor with CentraleSupelec, France. He has managed eight EU projects and more than 24 national and international projects. His research interests include fundamental mathematics, algorithms, statistics, information, and communication sciences research. He was a recipient of the ERC Grant MORE (Advanced Mathematical Tools for Complex Network Engineering) from 2012 to 2017. He received 20 best paper awards, among which the 2015 IEEE Communications Society Leonard G. Abraham Prize, the 2016 IEEE Communications Society Best Tutorial Paper Award, and the 2018 IEEE Marconi Prize Paper Award. He is an associate editor-in-chief of the journal Random Matrix: Theory and Applications. He was an associate area editor and a senior area editor of IEEE Transactions on Signal Processing from 2011 to 2013 and from 2013 to 2014, respectively. Lingyang Song received the Ph.D. from the University of York, UK, in 2007, where he received the K.M. Stott Prize for excellent research. He worked as a postdoctoral research fellow at the University of Oslo, Norway, and Harvard University, USA, until rejoining Philips Research UK in March 2008. In May 2009, he joined the School of Electronics Engineering and Computer Science, Peking University, China, as a full professor. His main research interests include cooperative and cognitive communications, physical layer security, and wireless ad hoc/sensor networks. He has published extensively, writing six textbooks, and is co-inventor of a number of patents (standard contributions). He received nine paper awards in IEEE journals and conferences including IEEE JSAC 2016, IEEE WCNC 2012, ICC 2014, Globecom 2014, and ICC 2015. He is currently on the editorial board of IEEE Transactions on Wireless Communications and Journal of Network and Computer Applications. He served as the TPC co-chairs for the International Conference on Ubiquitous and Future Networks (ICUFN2011/2012), symposium co-chairs in the International Wireless Communications and Mobile Computing Conference (IWCMC 2009/2010), IEEE International Conference on Communication Technology (ICCT2011), and IEEE International Conference on Communications (ICC 2014, 2015). He is the recipient of the 2012 IEEE Asia Pacific (AP) Young Researcher Award. Dr. Song is a fellow of IEEE and IEEE ComSoc distinguished lecturer since 2015.

Reconfigurable intelligent surface (RIS) is a promising reflective radio technology for improving the coverage and rate of future wireless systems by reconfiguring the wireless propagation environment. The current work mainly focuses on the physical layer design of RIS. However, enabling multiple devices to communicate with the assistance of RIS is a crucial challenging problem. Motivated by this, we explore RIS-assisted communications at the medium access control (MAC) layer and propose an RIS-assisted MAC framework. In particular, RIS-assisted transmissions are implemented by prenegotiation and a multidimension reservation (MDR) scheme. Based on this, we investigate RIS-assisted single-channel multiuser (SCMU) communications. Wherein the RIS regarded as a whole unity can be reserved by one user to support the multiple data transmissions, thus achieving high efficient RIS-assisted connections at the user. Moreover, under frequency-selective channels, implementing the MDR scheme on the RIS group division, RIS-assisted multichannel multiuser (MCMU) communications are further explored to improve the service efficiency of the RIS and decrease the computation complexity. Besides, a Markov chain is built based on the proposed RIS-assisted MAC framework to analyze the system performance of SCMU/MCMU. Then the optimization problem is formulated to maximize the overall system capacity of SCMU/MCMU with energy-efficient constraint. The performance evaluations demonstrate the feasibility and effectiveness of each.

The Emerging unmanned aerial vehicles (UAVs) have been widely exploited for sensing purposes due to the larger service coverage compared with the conventional fixed sensor nodes. However, due to the limited computation capability of UAVs, real-time sensory data needs to be transmitted to the BS/server for real-time data processing. In this regard, the cellular networks are necessary to support the data transmission for UAVs, which is called the Internet of UAVs. Very recently, 3GPP has approved a study item on the enhanced support to seamlessly integrate UAVs into future cellular networks.

With the increasing popularity of mobile vehicles, such as unmanned aerial vehicles (UAVs) and mobile robots, it is foreseen that they will play an important role in Internet-of-Things (IoT) networks due to their high mobility and rapid deployment. Specifically, mobile vehicles equipped with sensors act as IoT devices and can be dispatched to several sensing regions to perform sensing tasks. In this article, we consider mobile vehicles for sensing applications and investigate the corresponding joint task assignment and collision-free trajectory optimization problem. This problem is challenging as the number of involved vehicles can be very large, and to tackle the problem efficiently, we reformulate the original optimization problem into a mean-field-game (MFG) problem by simplifying the interaction between vehicles as a distribution over their state space, known as the mean-field term. To solve the MFG problem efficiently, we propose a G-prox primal-dual hybrid gradient (PDHG) algorithm that transforms the MFG problem into a saddle-point problem by defining a Lagrangian functional with a proximal operator. The complexity of this algorithm is shown to be linear with the total number of grid points in the proposed MFG problem. We provide a comprehensive theoretical analysis of the proposed model and algorithm. Numerical results together with the practical implementation on real mobile robots show that our proposed system model and algorithm are of significant effectiveness and efficiency.

Using RF signals for wireless sensing has gained increasing attention. However, due to the unwanted multi-path fading in uncontrollable radio environments, the accuracy of RF sensing is limited. Instead of passively adapting to the environment, in this paper, we consider the scenario where an intelligent metasurface is deployed for sensing the existence and locations of 3D objects. By programming its beamformer patterns, the metasurface can provide desirable propagation properties. However, achieving a high sensing accuracy is challenging, since it requires the joint optimization of the beamformer patterns and mapping of the received signals to the sensed outcome. To tackle this challenge, we formulate an optimization problem for minimizing the cross-entropy loss of the sensing outcome, and propose a deep reinforcement learning algorithm to jointly compute the optimal beamformer patterns and the mapping of the received signals. Simulation results verify the effectiveness of the proposed algorithm and show how the size of the metasurface and the target space influence the sensing accuracy.

In recent years, unmanned aerial vehicles (UAVs) have unlocked numerous sensing applications, which are expected to add billions of dollars to the world economy in the next decade. To further improve the Quality-of-Service in these applications, the 3rd Generation Partnership Project has considered the use of terrestrial cellular networks to support UAV sensing services, also known as the cellular Internet of UAVs. In this paper, we consider a cellular Internet of UAVs, where the sensory data can be transmitted either to the base station via cellular links, or to the mobile devices by underlay UAV-to-Device (U2D) communications. To evaluate the freshness of the sensory data, the concept of age of information (AoI) is adopted, in which a lower AoI implies fresher data. Since UAVs' AoIs are determined by their trajectories during sensing and transmission, we investigate the AoI minimization problem for UAVs by designing their trajectories. This problem is a Markov decision problem with an infinite state-action space, and thus we utilize multi-agent deep reinforcement learning to approximate the state-action space. Then, we propose a multi-UAV trajectory design algorithm to solve this problem. Simulation results show that our proposed algorithm can achieve a lower AoI than a greedy algorithm, policy gradient algorithm, and overlay U2D scheme.

Grant-free access, in which channels are accessed without undergoing assignment through a handshake process, is a promising solution to support massive connectivity needed for Internet-of-Things (IoT) networks. In this paper, we consider uplink grant-free massive access for an IoT network with multiple channels. To be specific, the IoT devices generate short packets and have grant-free non-orthogonal access to a channel to transmit the generated packets to a base station (BS). With the aim of keeping the information fresh at the BS, we first derive the age of information (AoI) for grant-free short-packet communications, and then formulate the AoI minimization problem. However, the problem is challenging as the number of users involved is large, and to tackle this problem efficiently, we propose a mean-field evolutionary game-based approach. In this approach, the average behavior of the IoT devices is considered rather than their individual behaviors, and the dynamics of the strategies of the IoT devices are modeled by an evolutionary process. Simulation results verify the effectiveness of the proposed mean-field evolutionary game-based approach.

With the evolution of 5G, 6G and beyond, device-to-device (D2D) communications have been developed as an energy-, and spectrum-efficient solution. However, D2D links are allowed to share the same spectrum resources with cellular links, which will bring significant interference to those cellular links. Fortunately, an emerging technique called reconfigurable intelligent surface (RIS), can mitigate aggravated interference caused by D2D links by adjusting phase shifts of the surface to create favorable beam steering. In this paper, we study an RIS-assisted single cell uplink communication scenario, where a cellular link and multiple D2D links share the same spectrum and an RIS is adopted to mitigate the mutual interference. The problem of maximizing total system rate is formulated by jointly optimizing transmission powers of all links and discrete phase shifts of the surface. To obtain practical solutions, we capitalize on alternating maximization and the problem is decomposed into two sub-problems. For the power allocation, the problem is a difference of concave functions (DC) problem, which is solved with the gradient descent method. For the phase shift optimization, a local search algorithm is utilized. Simulation results show that deploying the RIS with optimized phase shifts can effectively eliminate the interference in D2D networks.

Recently, the ultra-dense low Earth orbit (LEO) satellite constellation over high-frequency band has served as a potential solution for high-capacity backhaul data services. In this paper, we consider an ultra-dense LEO-based terrestrial-satellite network where terrestrial users can access the network through the LEO-assisted backhaul. We aim to minimize the number of satellites in the constellation while satisfying the backhaul requirement of each user terminal (UT). We first derive the average total backhaul capacity of each UT, based on which a three-dimensional constellation optimization algorithm is proposed to minimize the number of satellites in the constellation. Simulation results verify our theoretical capacity analysis and show that for any given coverage ratio requirement, the corresponding optimized LEO satellite constellation can be obtained by the proposed three-dimensional constellation optimization algorithm. Given the same number of deployed LEO satellites, the average coverage ratio of the proposed LEO satellite constellation is at least 10 percentage points higher than that of Telesat constellation.

In this paper, we consider a single-cell multi-user orthogonal frequency division multiple access (OFDMA) network with one unmanned aerial vehicle (UAV), which works as an amplify-and-forward relay to improve the quality-of-service (QoS) of the user equipments (UEs) in the cell edge. Aiming to improve the throughput while guaranteeing the user fairness, we jointly optimize the communication mode, subchannel allocation, power allocation, and UAV trajectory, which is an NP-hard problem. To design the UAV trajectory and resource allocation efficiently, we first decompose the problem into three subproblems, i.e., mode selection and subchannel allocation, trajectory optimization, and power allocation, and then solve these subproblems iteratively. Simulation results show that the proposed algorithm outperforms the random algorithm and the cellular scheme.

The received signal strength (RSS) based technique is extensively utilized for localization in the indoor environments. Since the RSS values of neighboring locations may be similar, the localization accuracy of the RSS based technique is limited. To tackle this problem, in this paper, we propose to utilize reconfigurable intelligent surface (RIS) for the RSS based multi-user localization. As the RIS is able to customize the radio channels by adjusting the phase shifts of the signals reflected at the surface, the localization accuracy in the RIS aided scheme can be improved by choosing the proper phase shifts with significant differences of RSS values among adjacent locations. However, it is challenging to select the optimal phase shifts because the decision function for location estimation and the phase shifts are coupled. To tackle this challenge, we formulate the optimization problem for the RIS-aided localization, derive the optimal decision function, and design the phase shift optimization (PSO) algorithm to solve the formulated problem efficiently. Analysis of the proposed RIS aided technique is provided, and the effectiveness is validated through simulation.

In this paper, we focus on the reconfigurable intelligent surface (RIS) assisted single-cell uplink communication network scenario. In the network, one cellular link and multiple device-to-device (D2D) links sharing the same spectrum resources combine direct and reflective channel transmissions with the assistance of the RIS, which is adopted to alleviate the interference by fully using the beamforming capability. Subjected to qualityof-service (QoS) and total power constraints, a system sumrate maximization problem is formulated by jointly optimizing transmission powers of all links and discrete phase shifts of all RIS elements. Since it is a mixed integer non-convex non-linear problem, we decompose it into two sub-problems, and apply the alternating optimization to obtain a sub-optimal solution efficiently. For the power allocation sub-problem, it is a difference of concave functions (DC) problem, which is transformed to a convex one by the multivariate Taylor expansion and then solved with the gradient descent method. For the phase shift subproblem, a local search algorithm is utilized. Simulation results verify that our proposed scheme can eliminate the interference of D2D networks better than the scheme without RIS and other benchmark schemes.

Recently, the ultra-dense low earth orbit (LEO)satellite communication networks over high-frequency band have served as a potential solution for high-capacity backhaul data services. In this paper, we consider an ultra-dense LEO-based terrestrial-satellite network where terrestrial users can access the network through the LEO-assisted backhaul. We aim to minimize the number of satellites in the LEO satellite constellation while satisfying the backhaul requirement of each terrestrial-satellite terminal (TST). We first derive the average total backhaul capacity of each TST, based on which a three-dimensional constellation optimization algorithm is proposed to minimize the number of satellites in the LEO satellite constellation. Simulation results verify our theoretical capacity analysis and show that for any given coverage percentage requirement, the corresponding optimized LEO satellite constellation can be obtained by the proposed three-dimensional constellation optimization algorithm.

In recent years, there has been a growing interest in utilizing mobile robot swarm to execute several tasks at the same time. However, how to assign tasks to the swarm and optimize the trajectory of the robots scientifically to minimize energy consumption is still a big challenge. In this paper, we consider a mobile robot swarm system where a large number of robots are deployed by a centralized controller to execute a series of tasks, such as target detection tasks, cooperatively. The controller controls the velocity strategy of each robot, and makes corresponding task assignment decisions to minimize the overall cost of the robot swarm. Since the number of involving robots is large, it will be extremely difficult to consider the interaction between them. In this regard, we adopt the concept of mean-field term to approximate the behaviors and states of the robots, and formulate the joint task assignment and trajectory optimization problem as a mean-field game. To solve the problem efficiently, a primal-dual hybrid gradient algorithm is proposed to find the optimal trajectory and corresponding task assignment decisions for each robot. The numerical simulation results show the effectiveness of the proposed algorithm.

In this paper, we consider a cellular Internet of UAVs, where the sensory data can be transmitted either to the base station via cellular links, or to the mobile devices by underlay UAV-to-Device communications. To evaluate the freshness of the sensory data, the age of information (AoI) is adopted, in which a lower AoI implies fresher data. Since UAVs' AoIs are determined by their trajectories during sensing and transmission, we aim to minimize the AoIs of UAVs by designing their trajectories. This problem is a Markov decision problem with an infinite state-action space, and thus, we propose a multi-UAV trajectory design algorithm by leveraging multi-agent deep reinforcement learning to solve it. Simulation results show that our proposed algorithm outperforms both a greedy algorithm and a policy gradient algorithm.

In this paper, we consider a cellular Internet of UAVs with a massive number of UAVs, where the sensory data can be transmitted to the mobile devices by UAV-toDevice (U2D) communications, or to the base station (BS) by UAV-to-Network (U2N) communications. To improve the spectral efficiency, the U2D links can share the spectrum with U2N links. Since the sensing and transmission of the UAV are coupled by the trajectory, it is necessary to optimize the trajectory. Due to the underlay property, the trajectories of UAVs will have an impact on each other, which makes the trajectory optimization more challenging when the number of UAVs is large. To tackle this challenge, mean-field approximation is an efficient method to approximate the interactions among UAVs. Since the UAVs in the cellular Internet of UAVs are distinguishable, we propose a mean-field-type (MFT) control method to solve the trajectory optimization problem, where the interactions among. The simulation results verify the effectiveness of our proposed method.

Grant-free (GF) access, where channels are accessed without undergoing assignment through a handshake process, is a promising solution to support the massive connectivity for IoT networks. In this paper, we consider uplink GF massive access for an IoT network. IoT devices generate short packets and transmit the generated packets by GF non-orthogonal multiple access (NOMA) communications. To keep the information fresh, we first derive the age of information (AoI) in the GF short-packet communications and then formulate the AoI minimization problem. However, the AoI minimization problem is challenging to solve since the number of users involved is large. To tackle this problem efficiently, we propose a mean-field evolutionary game-based scheme where the average behavior of the IoT nodes will be considered rather than their individual behavior to reduce the complexity. Simulation results verify the effectiveness of the proposed mean-field evolutionary game-based algorithm.

In this paper, a satellite-aerial integrated computing (SAIC) architecture in disasters is proposed, where the computation tasks from two-tier users, i.e., ground/aerial user equipments, are either locally executed at the high-altitude platforms (HAPs), or offloaded to and computed by the Low Earth Orbit (LEO) satellite. With the SAIC architecture, we study the problem of joint two-tier user association and offloading decision aiming at the maximization of the sum rate. The problem is formulated as a 0-1 integer linear programming problem which is NP-complete. A weighted 3-uniform hypergraph model is obtained to solve this problem by capturing the 3D mapping relation for two-tier users, HAPs, and the LEO satellite. Then, a 3D hypergraph matching algorithm using the local search is developed to find a maximum-weight subset of vertex-disjoint hyperedges. Simulation results show that the proposed algorithm has improved the sum rate when compared with the conventional greedy algorithm.

In this paper, we consider the cellular Internet of unmanned aerial vehicles (UAVs), where UAVs sense data for multiple tasks and transmit the data to the base station (BS). To quantify the “freshness” of the data at the BS, we bring in the concept of the age of information (AoI). The AoI is determined by the time for UAV sensing and that for UAV transmission, and gives rise to a trade-off within a given period. To minimize the AoI, we formulate a joint sensing time, transmission time, UAV trajectory, and task scheduling optimization problem. To solve this problem, we first propose an iterative algorithm to optimize the sensing time, transmission time, and UAV trajectory for completing a specific task. Afterwards, we design the order in which the UAV performs data updates for multiple sensing tasks. The convergence and complexity of the proposed algorithm, together with the trade-off between UAV sensing and UAV transmission, are analyzed. Simulation results verify the effectiveness of our proposed algorithm.

Reconfigurable intelligent surfaces (RISs) have drawn considerable attention from the research community recently. RISs create favorable propagation conditions by controlling the phase shifts of reflected waves at the surface, thereby enhancing wireless transmissions. In this paper, we study a downlink multi-user system where the transmission from a multi-antenna base station (BS) to various users is achieved by an RIS reflecting the incident signals of the BS towards the users. Unlike most existing works, we consider the practical case where only a limited number of discrete phase shifts can be realized by a finite-sized RIS. A hybrid beamforming scheme is proposed and the sum-rate maximization problem is formulated. Specifically, continuous digital beamforming and discrete RIS-based analog beamforming are performed at the BS and the RIS, respectively, and an iterative algorithm is designed to solve this problem. Both theoretical analysis and numerical validations show that the RIS-based system can achieve good sum-rate performance by setting a reasonable size of the RIS and a small number of discrete phase shifts.

Using radio-frequency (RF) sensing techniques for human posture recognition has attracted growing interest due to its advantages of pervasiveness, contact-free observation, and privacy protection. Conventional RF sensing techniques are constrained by their radio environments, which limit the number of transmission channels to carry multi-dimensional information about human postures. Instead of passively adapting to the environment, in this paper, we design an RF sensing system for posture recognition based on reconfigurable intelligent surfaces (RISs). The proposed system can actively customize the environments to provide desirable propagation properties and diverse transmission channels. However, achieving high recognition accuracy requires the optimization of RIS configuration, which is a challenging problem. To tackle this challenge, we formulate the optimization problem, decompose it into two subproblems, and propose algorithms to solve them. Based on the developed algorithms, we implement the system and carry out practical experiments. Both simulation and experimental results verify the effectiveness of the designed algorithms and system. Compared to the random configuration and non-configurable environment cases, the designed system can greatly improve the recognition accuracy.

As distributed energy resources (DERs) are widely deployed, DC packetized power microgrids have been considered as a promising solution to incorporate DERs effectively. In this paper, we consider a DC packetized power microgrid, where the energy is dispatched in the form of power packets with the assistance of a power router. However, the benefits of the microgrid can only be realized when energy subscribers (ESs) equipped with DERs actively participate in the energy market. Therefore, peer-to-peer (P2P) energy trading is necessary in the DC packetized power microgrid to encourage the usage of DERs. Different from P2P energy trading in AC microgrids, the dispatching capability of the router needs to be considered in DC microgrids, which will complicate the trading problem. To tackle this challenge, we formulate the P2P trading problem as an auction game, in which the demander ESs submit bids to compete for power packets, and a controller decides the energy allocation and power packet scheduling. Analysis of the proposed scheme is provided, and its effectiveness is validated through simulation.

Due to the advantages of flexible deployment and extensive coverage, unmanned aerial vehicles (UAVs) have significant potential for sensing applications in the next generation of cellular networks, which will give rise to a cellular Internet of UAVs. In this article, we consider a cellular Internet of UAVs, where the UAVs execute sensing tasks through cooperative sensing and transmission to minimize the age of information (AoI). However, the cooperative sensing and transmission is tightly coupled with the UAVs' trajectories, which makes the trajectory design challenging. To tackle this challenge, we propose a distributed sense-and-send protocol, where the UAVs determine the trajectories by selecting from a discrete set of tasks and a continuous set of locations for sensing and transmission. Based on this protocol, we formulate the trajectory design problem for AoI minimization and propose a compound-action actor-critic (CA2C) algorithm to solve it based on deep reinforcement learning. The CA2C algorithm can learn the optimal policies for actions involving both continuous and discrete variables and is suited for the trajectory design. Our simulation results show that the CA2C algorithm outperforms four baseline algorithms. Also, we show that by dividing the tasks, cooperative UAVs can achieve a lower AoI compared to non-cooperative UAVs.

In the current unmanned aircraft systems (UASs) for sensing services, unmanned aerial vehicles (UAVs) transmit their sensory data to terrestrial mobile devices over the unlicensed spectrum. However, the interference from surrounding terminals is uncontrollable due to the opportunistic channel access. In this paper, we consider a cellular Internet of UAVs to guarantee the Quality-of-Service (QoS), where the sensory data can be transmitted to the mobile devices either by UAV-to-Device (U2D) communications over cellular networks, or directly through the base station (BS). Since UAVs' sensing and transmission may influence their trajectories, we study the trajectory design problem for UAVs in consideration of their sensing and transmission. This is a Markov decision problem (MDP) with a large state-action space, and thus, we utilize multi-agent deep reinforcement learning (DRL) to approximate the state-action space, and then propose a multi-UAV trajectory design algorithm to solve this problem. Simulation results show that our proposed algorithm can achieve a higher total utility than policy gradient algorithm and single-agent algorithm.

In this paper, we consider the cellular Internet of unmanned aerial vehicles (UAVs), where UAVs sense data with onboard sensors for multiple sensing tasks and transmit the data to the base station (BS). To quantify the “freshness” of the data at the BS, we bring in the concept of the age of information (AoI). The AoI is determined by the time for UAV sensing and that for UAV transmission, which gives rise to a trade-off within a given period. To minimize the AoI, we formulate a joint sensing time, transmission time, UAV trajectory, and task scheduling optimization problem. This NP-hard problem can be decoupled into two subproblems. We first propose an iterative algorithm to optimize the sensing time, transmission time, and UAV velocity for completing a specific task. Afterwards, we design the order in which the UAV performs data updates for multiple sensing tasks. The convergence and complexity of the proposed algorithm, together with the trade-off between UAV sensing and UAV transmission, are analyzed. Simulation results show that the AoI with the proposed algorithm is about 15% lower than that of the greedy algorithm, and over 40% lower than that of the random algorithm.

In this paper, we consider a cellular Internet of UAVs, where UAVs execute multiple sensing tasks continuously and cooperatively through sensing and transmission with the objective to minimize the age of information (AoI). However, the cooperative sensing and transmission is coupled with the trajectories of the UAVs, which makes the trajectory design a challenging problem. To tackle this challenge, we first propose a distributed sense-and-send protocol to coordinate the UAVs. Based on this protocol, we formulate the trajectory design problem for AoI minimization and propose a deep reinforcement learning algorithm to solve it, which we refer to as the compound-action actor-critic (CA2C) algorithm. Simulation results show that the CA2C algorithm outperforms two baseline algorithms for AoI minimization.

In this paper, we consider a cellular Internet of unmanned aerial vehicles (UAVs) where the sensory data can be transmitted to the mobile devices directly by overlaying UAV-to-Device (U2D) communications, or through the base station (BS) by cellular communications. Since the transmission modes of UAVs may influence their trajectories, we study the trajectory design problem for UAVs aiming to maximize the total utility in consideration of their transmission modes. This problem is a Markov decision problem (MDP) with a large state-action space, and thus, we propose a multi-UAV trajectory design algorithm using multi- agent deep reinforcement learning (DRL) to solve this problem. Simulation results show that our proposed algorithm can achieve a higher total utility than the single-agent method.

In this paper, we consider a single-cell multi-user orthogonal frequency division multiple access (OFDMA) network with one unmanned aerial vehicle (UAV), which works as an amplify-and-forward relay to improve the quality-of-service (QoS) of the user equipments (UEs) in the cell edge. Aiming to improve the throughput while guaranteeing user fairness, we formulate a joint mode selection, subchannel allocation, trajectory optimization, and power allocation problem, which is NP-hard. To solve the problem efficiently, we first decompose it into three subproblems, i.e., mode selection and subchannel allocation, trajectory optimization, and power allocation. Then we propose a joint mode selection and subchannel allocation, trajectory optimization, and power allocation (JMS-T-P) algorithm where these subproblems are solved iteratively. Simulation results show that the JMS- T-P algorithm outperforms the random algorithm and the cellular scheme.

As distributed energy resources (DERs) are widely deployed, the DC packetized power microgrid is a promising solution to incorporate DERs effectively and steadily. In this paper, we consider a DC microgrid, where the energy is dispatched by a power router in the form of power packets. Since energy subscribers (ESs) with DERs in the microgrid can generate surplus electricity, the peer-to-peer (P2P) energy trading is an effective way in order to balance the energy. Different from the P2P trading in AC smart grids, the dispatching capability of the router in the DC microgrid needs to be considered, which will make the trading problem more complicated. To tackle this challenge, we formulate the P2P trading problem as an auction game, where the demander ESs submit bids to compete for power packets, and a controller decides the energy allocation and power packet scheduling. The effectiveness of the proposed scheme is validated through simulations.

In this paper, the energy efficient virtual machine (VM) placement and virtual resource allocation problem for the mobile edge computing (MEC) system is explored. Particularly, we develop an optimization framework of energy consumption minimization for computing and offloading by jointly optimizing the VM placement matrix and the number of physical machines (PMs). To resolve this problem, we transform the optimization problem into a non-uniform weighted hypergraph model. In this model, the weight of hyperedge is defined as the negative of the accumulated energy consumption for computing at VM instances hosted by one PM. Based on the hypergraph model, a hypergraph matching algorithm by utilizing the local search policy is proposed for finding the maximum-weight subset of vertex-disjoint hyperedges, aiming to obtain an optimal VM placement, i.e., (M*)-perfect matching. Furthermore, the virtualized resources are further allocated to user equipments (UEs) in the form of multiple VM instances via the optimal VM placement to meet the requirement of workloads. Simulation results are presented to demonstrate the effectiveness of the proposed hypergraph matching algorithm over the alternative benchmark algorithm.

In this paper, we study a multi-lane cooperative platoon scenario, where platoons move cooperatively and communicate with each other by cellular vehicle-to-everything communication. The platoon formation is important and difficult in multi-platoon scenario, for the platoon size, power and resource allocation of each platoon interact with each other and influence those of other platoons. To this end, we propose a two-step resource allocation strategy in consideration of platoon formation, including the resource allocation at the base station and within each platoon, respectively. A branch and bound algorithm is utilized for the resource allocation at the BS. We design a distributed dynamic programming based subchannel allocation and power control algorithm for the joint optimization of platoon size, power and intra-platoon resource allocation. Simulation results evaluate the influence of the penalty factor and latency constraint on the system performance.

In this paper, we consider a device-to-device (D2D) communications underlaying cellular network where Long Term Evolution (LTE) and D2D users are allowed to communicate over both licensed and unlicensed bands for spectrum efficiency improvement. LTE users utilize spectrum orthogonally and share it with D2D users. To maximize the total throughput of this D2D system, we leverage stochastic geometry to derive the throughput for each kind of users by modeling the deployment of users as Poisson point processes (PPPs), and investigate the mode selection problem for D2D users. Since the problem is NP-hard, we propose a sequential quadratic programming (SQP) based algorithm to obtain the corresponding suboptimal solutions. Theoretically, we evaluate the system performance by analyzing the throughput regions. Simulation results validate the accuracy of the geometric analysis and verify the effectiveness of the proposed algorithm.

The DC power packet transmission contributes to a reliable integration of distributed energy resources (DERs) in power grids and reduces the load fluctuation. In this paper, we consider a DC packetized-power microgrid for the integration of DERs and propose a power packet dispatching protocol to regulate the peer to peer power interchange within the microgrid. We formulate the joint subscriber matching and energy allocation problem to optimize the subscribers' benefits, which is proved to be NP-hard. Based on the matching theory, we associate the problem equivalent to a many-to-many matching problem and design a two-sided matching algorithm to solve it. We then design a graph coloring based algorithm to schedule the energy transmissions of the matched energy subscribers. Simulation results validate the effectiveness of the proposed protocol in achieving a steady and efficient microgrid power dispatching.

In this poster, we consider the Peer-to-Peer (P2P) energy trading in a local area packetized power network (LAPPN), where demander energy subscribers (ESs) can buy energy from supplier ESs or the utility grid (UG). Since the energy is transmitted in the form of power packets in the power channels in a time division multiplex (TDM) manner, the limited channel resources should be considered in the trading. The trading problem is formulated, in which selfish demander ESs compete for power packets and channels to maximize their own utilities, while the controller maximizes the total revenue. To tackle this problem, an iterative auction scheme is proposed, and its effectiveness is validated by simulation.

Recently, the unmanned aerial vehicles (UAVs) have been widely used in real-time sensing applications over cellular networks. The performance of a UAV is determined by both its sensing and transmission processes, which are influenced by the trajectory of the UAV. However, it is challenging for the UAV to determine its trajectory, since it works in a dynamic environment, where other UAVs determine their trajectories dynamically and compete for the limited spectrum resources in the same time. To tackle this challenge, we adopt the reinforcement learning to solve the UAV trajectory design problem in a decentralized manner. To coordinate multiple UAVs performing real-time sensing tasks, we first propose a sense-and-send protocol, and analyze the probability for successful valid data transmission using nested Markov chains. Then, we propose an enhanced multi-UAV Q-learning algorithm to solve the decentralized UAV trajectory design problem. Simulation results show that the proposed algorithm converges faster and achieves higher utilities for the UAVs, compared to traditional singleand multi-agent Q-learning algorithms.

In this paper, we consider a cellular controlled unmanned aerial vehicle (UAV) network in which multiple UAVs cooperatively complete each sensing task. We first propose a sense-and-send protocol where the UAVs collect sensory data of the tasks and transmit the collected data to the base station. We then formulate a joint trajectory, sensing location, and UAV scheduling optimization problem that minimizes the completion time for all the sensing tasks in the network. To solve this NP-hard problem efficiently, we decouple it into three subproblems: 1) trajectory optimization; 2) sensing location optimization; and 3) UAV scheduling. An iterative trajectory, sensing, and scheduling optimization (ITSSO) algorithm is proposed to solve these subproblems jointly. The convergence and complexity of the ITSSO algorithm, together with the system performance are analyzed. Simulation results show that the proposed ITSSO algorithm saves the task completion time by 15% compared to the noncooperative scheme.

In this paper, we consider device-to-device (D2D) communications as an underlay to cellular networks over both licensed and unlicensed spectrums, where long-term evolution (LTE) users utilize the spectrum orthogonally while D2D users share the spectrum with LTE users. In the system, each LTE and D2D user can access the licensed or unlicensed band for communications. To maximize the total throughput of the system, we leverage stochastic geometry to derive the throughput for each kind of user by modeling the deployment of users as Poisson point processes (PPPs), and investigate the spectrum access problem for these users. Since the problem is NP-hard, we propose a sequential quadratic programming (SQP)-based algorithm to obtain the corresponding suboptimal solutions. Theoretically, we evaluate the system performance by analyzing the throughput regions. Simulation results validate the accuracy of the geometric analysis and verify the effectiveness of the proposed algorithm.

In this paper, we propose a terrestrial-satellite network (TSN) architecture to integrate the ultra-dense low earth orbit (LEO) networks and the terrestrial networks to achieve efficient data offloading. In TSN, each ground user can access the network over C-band via a macro cell, a traditional small cell, or a LEO-backhauled small cell (LSC). Each LSC is then scheduled to upload the received data via multiple satellites over Ka-band. We aim to maximize the sum data rate and the number of accessed users while satisfying the varying backhaul capacity constraints jointly determined by the LEO satellite-based backhaul links. The optimization problem is then decomposed into two closely connected subproblems and solved by our proposed matching algorithms. The simulation results show that the integrated network significantly outperforms the non-integrated ones in terms of the sum data rate. The influence of the traffic load and LEO constellation on the system performance is also discussed.

In this paper, we study the platoon cooperation in the multi-lane cooperative platoon scenario, where platoons move cooperatively and communicate with each other by cellular vehicle-to-everything (V2X) communication. The platoon cooperation is important for the interference management and communication reliability enhancement, yet it is difficult, since the platoon formation, subchannel allocation, and power control of each platoon interact with each other and influence those of other platoons. To increase the number of vehicles in the platoon and reduce the power consumption, we propose a two-step resource allocation strategy in consideration of platoon formation, i.e., the resource allocation at the base station (BS) and within each platoon. A branch and bound algorithm is utilized for the resource allocation at the BS. We then design a distributed dynamic programming-based subchannel allocation and power control algorithm for the joint optimization of platoon formation, subchannel allocation, and power control. The simulation results evaluate the impact of the penalty factor and the latency requirement on the system performance.

In this paper, we consider an uplink cellular Internet-of-Things (IoT) network, where a cellular user (CU) can serve as the mobile data aggregator for a cluster of IoT devices. To be specific, the IoT devices can either transmit the sensory data to the base station (BS) directly by cellular communications, or first aggregate the data to a CU through machine-to-machine (M2M) communications before the CU uploads the aggregated data to the BS. To support massive connections, the IoT devices can leverage the unlicensed spectrum for M2M communications, referred to as IoT unlicensed (IoT-U). Aiming to maximize the number of scheduled IoT devices and meanwhile associate each IoT device with the right CU or BS with the minimum transmit power, we first introduce a single-stage formulation that captures these objectives simultaneously. To tackle the NP-hard problem efficiently, we decouple the problem into two subproblems, which are solved by successive linear programming and convex optimization techniques, respectively. The simulation results show that the proposed IoT-U scheme can support more IoT devices than that only using the licensed spectrum.

In this paper, we consider a single-cell cellular network with a number of cellular users (CUs) and unmanned aerial vehicles (UAVs), in which multiple UAVs upload their collected data to the base station (BS). Two transmission modes are considered to support the multi-UAV communications, i.e., UAV-to-network (U2N) and UAV-to-UAV (U2U) communications. Specifically, the UAV with a high signal-to-noise ratio (SNR) for the U2N link uploads its collected data directly to the BS through U2N communication, while the UAV with a low SNR for the U2N link can transmit data to a nearby UAV through underlaying U2U communication for the sake of quality of service. We first propose a cooperative UAV sense-and-send protocol to enable the UAV-to-X communications, and then formulate the subchannel allocation and UAV speed optimization problem to maximize the uplink sum-rate. To solve this NP-hard problem efficiently, we decouple it into three sub-problems: U2N and cellular user (CU) subchannel allocation, U2U subchannel allocation, and UAV speed optimization. An iterative subchannel allocation and speed optimization algorithm (ISASOA) is proposed to solve these sub-problems jointly. The simulation results show that the proposed ISASOA can upload 10% more data than the greedy algorithm.

In this paper, we propose a terrestrial-satellite network (TSN) architecture to integrate the ultra- dense low earth orbit (LEO) networks and the terrestrial networks for data offloading. In TSN, each user can access the network over C-band via a macro cell, a traditional small cell, or a LEO- backhauled small cell (LSC). Each LSC is scheduled to upload the received data via multiple satellites over Ka-band. We aim to maximize the sum data rate while satisfying the varying backhaul capacity constraints jointly determined by the LEO satellite based backhaul links. The optimization problem is then solved by our proposed matching algorithm. Simulation results show that the integrated network significantly outperforms the non-integrated one in terms of the sum data rate.

With the large penetration of distributed energy resources, DC power packet transmission is a potential technique to achieve efficient peer to peer (P2P) power dispatching. In this paper, a multi-router local area packetized power network (LAPPN) is considered, where multiple power routers are employed to dispatch power packets among demander and supplier energy subscribers (ESs) connected to different routers. To achieve the efficient P2P power transmission in the LAPPN, a power packet dispatching protocol is developed, in which the routes of power packets are optimized first to maximize the power packets utilization efficiency. Then the transmission schedule is determined by allowing different power packets to transmit on different power channels concurrently to meet the variety of urgency requirements of demander ESs. Simulation results demonstrate the effectiveness of the proposed LAPPN power dispatching protocols in achieving high power packet utilization.

In this paper, we consider a single-cell cellular network with a number of cellular users (CUs) and unmanned aerial vehicles (UAVs), in which multiple UAVs upload their collected data to the base station (BS). Two communication modes are considered to support the multi-UAV communications, i.e., UAV-to-infrastructure (U2I) and UAV-to-UAV (U2U) communications. We then formulate the subcarrier allocation and trajectory design problem to maximize the uplink sum-rate taking the delay of sensing tasks into consideration. To solve this NP-hard problem efficiently, we decouple it into three sub-problems: U2I and cellular user (CU) subcarrier allocation, U2U subcarrier allocation, and UAV trajectory design. An iterative subcarrier allocation and trajectory design algorithm (ISATCA) is proposed to solve these sub-problems jointly. Simulation results show that the proposed ISATCA can upload 20% more data than the one without U2U communication.

In this paper, we consider a cellular controlled unmanned aerial vehicle (UAV) sensing network in which multiple UAVs cooperatively complete each sensing task. We formulate a joint trajectory, sensing location, and UAV scheduling optimization problem that minimizes the completion time for all the sensing tasks in the network. To solve this NP-hard problem efficiently, we decouple it into three sub-problems: trajectory optimization, sensing location optimization, and UAV scheduling. An iterative trajectory, sensing, and scheduling optimization (ITSSO) algorithm is proposed to solve these sub-problems jointly. The convergence of the proposed algorithm and the dominated factors on the system performance are analysed. Simulation results show that the task completion time obtained by the proposed ITSSO algorithm is 15% less than that by non- cooperative scheme.

With the increasing demand for vehicular data transmission, limited dedicated cellular spectrum becomes a bottleneck to satisfy the requirements of cellular vehicle-to-everything (V2X) users. To address this issue, we study the coexistence problem between cellular V2X users and vehicular ad-hoc network (VANET) users over the unlicensed spectrum. To facilitate the coexistence, we design an energy sensing based spectrum sharing scheme, where cellular V2X users are able to access the unlicensed channels fairly, thereby reducing the collisions of data transmission. In order to maximize the number of active cellular V2X users, we formulate the scheduling and resource allocation problem as a two-sided many-to-many matching with peer effects. A dynamic vehicle-resource matching algorithm (DV-RMA) is then proposed to solve this problem. Simulation results show that the proposed scheme outperforms the existing approaches in supporting the massive connectivity.

With the increasing demand for vehicular data transmission, limited dedicated cellular spectrum becomes a bottleneck to satisfying the requirements of all cellular vehicle-to-everything (V2X) users. To address this issue, unlicensed spectrum is considered to serve as the complement to support cellular V2X users. In this paper, we study the coexistence problem of cellular V2X users and vehicular ad hoc network (VANET) users over the unlicensed spectrum. To facilitate the coexistence, we design an energy sensing-based spectrum sharing scheme, where cellular V2X users are able to access the unlicensed channels fairly while reducing the data transmission collisions between cellular V2X and VANET users. In order to maximize the number of active cellular V2X users, we formulate the scheduling and resource allocation problem as a two-sided many-to-many matching with peer effects. We then propose a dynamic vehicle-resource matching algorithm and present the analytical results on the convergence time and computational complexity. Simulation results show that the proposed algorithm outperforms existing approaches in terms of the performance of the cellular V2X system when the unlicensed spectrum is utilized.

Load balancing is an effective approach to address the spatial-temporal fluctuation problem of mobile data traffic for cellular networks. The existing schemes that focus on channel borrowing from neighboring cells cannot be directly applied to the future 5G wireless networks, because the neighboring cells will reuse the same spectrum band in 5G systems. In this paper, we consider an orthogonal frequency division multiple access ultra-dense small cell network, where device-to-device (D2D) communication is advocated to facilitate load balancing without extra spectrum. Specifically, the data traffic can be effectively offloaded from a congested small cell to other underutilized small cells by D2D communications. The problem is naturally formulated as a joint resource allocation and D2D routing problem that maximizes the system sum-rate. To efficiently solve the problem, we decouple the problem into a resource allocation subproblem and a D2D routing subproblem. The two subproblems are solved iteratively as a monotonic optimization problem and a complementary geometric programming problem, respectively. Simulation results show that the data sum-rate in the neighboring small cells increases 20% on average by offloading the data traffic in the congested small cell to the neighboring small cell base stations.

Device-to-Device (D2D) communication, which enables direct communication between nearby mobile devices, is an attractive technique to improve spectrum efficiency by reusing licensed spectrum. Nowadays, LTE-unlicensed (LTE-U) emerges to extend the cellular network to the unlicensed spectrum to alleviate the spectrum scarcity issue. In this paper, D2D communication is allowed to work in unlicensed spectrum (D2D-U) as an underlay of the cellular network for further booming the network capacity. A sensing-based protocol is designed to support the unlicensed channel access for both LTE and D2D users, based on which we investigate the subchannel allocation problem to maximize the total sum rate while taking into account their interference to the existing Wi-Fi systems. Specifically, we formulate the subchannel allocation as a many-to-many matching problem with externalities, and develop an iterative usersubchannel swap algorithm. Analytical and simulation results show that the proposed D2D-U scheme can significantly improve the network capacity.

Load balancing is an effective approach to address the spatial-temporal fluctuation of mobile traffic in cellular networks. However, existing schemes that rely on channel borrowing from neighboring cells cannot be directly applied to current LTE-A systems where all cells are deployed with the same spectrum band. In this paper, we consider a multi-cell OFDMA network, where Device-to-Device (D2D) communication is opted for load balancing without the need of extra spectrum. Specifically, the traffic can be steered from a congested cell to another underutilized cells by D2D communications. The problem is naturally formulated as a joint resource allocation and D2D routing problem that maximizes the system sum-rate. In particular, we formulate the resource allocation as a Monotonic optimization problem and the D2D routing as a complementary Geometric Programming problem. The two subproblems are solved iteratively to obtain the joint resource allocation and D2D routing solution. Simulation results demonstrate that the D2D load balancing can improve the system sum-rate efficiently.

Device-to-device (D2D) communication, which enables direct communication between nearby mobile devices, is an attractive add-on component to improve spectrum efficiency and user experience by reusing licensed cellular spectrum in 5G system. In this paper, we propose to enable D2D communication in unlicensed spectrum (D2D-U) as an underlay of the uplink LTE network for further booming the network capacity. A sensing-based protocol is designed to support the unlicensed channel access for both LTE and D2D users. We further investigate the subchannel allocation problem to maximize the sum rate of LTE and D2D users while considering their interference to the existing Wi-Fi systems. Specifically, we formulate the subchannel allocation as a many-to-many matching problem with externalities, and develop an iterative user-subchannel swap algorithm. Analytical and simulation results show that the proposed D2D-U scheme can significantly improve the system sum rate.

Device-to-Device (D2D) communication is an important component for 5G networks. Traditional D2D communications are mainly within the same cell. However, due to the reduced cell sizes, and the long communication range among mobile devices, the issues in D2D communication across cells have not yet been well addressed. In this paper, we first introduce an operation protocol to support cross-cell D2D communications underlaying cellular networks, then propose a hypergraph based resource allocation scheme to optimize the sum rate over the shared resource. In a hypergraph, the D2D and cellular users are regarded as vertices, and hyperedges represent mutual interference. For efficient interference coordination, the hypergraph is partitioned into different clusters corresponding to different channels. Simulation results demonstrate that the scheme efficiently leads to a good performance on the sum rate.

Device-to-device (D2D) communication has been recognized as a promising technique to offload the traffic for the evolved Node B (eNB). However, D2D transmission as an underlay causes severe interference to both the cellular and other D2D links, which imposes a great technical challenge to radio resource allocation. Conventional graph based resource allocation methods typically consider the interference between two user equipments (UEs), but they cannot model the interference from multiple UEs to completely characterize the interference. In this paper, we study channel allocation using hypergraph theory to coordinate the interference between D2D pairs and cellular UEs, where an arbitrary number of D2D pairs are allowed to share the uplink channels with the cellular UEs. Hypergraph coloring is used to model the cumulative interference from multiple D2D pairs, and thus, eliminate the mutual interference. Simulation results show that the system capacity is significantly improved using the proposed hypergraph method in comparison to the conventional graph based one.

WiFi Direct is a new technology proposed by WiFi Alliance to enable direct device to device communications using the same spectrum as WiFi. In some scenarios, communication distance between two devices cannot be covered by one hop, and thus, multihop Device to Device (D2D) communication is required to solve this problem. However, existing WiFi Direct protocol doesn't provide multihop message routing. In this paper, we propose a multihop D2D communication protocol based on WiFi Direct, and implement it on Android smartphone. Devices will form a group in consideration of power and topology condition, delay-tolerant routing between two groups is supported by the protocol.