Interference management is a central bottleneck in dense multi-antenna wireless networks. In this study, we present a digital precoding-free hierarchical rate-splitting multiple access (HRSMA) architecture assisted by a stacked intelligent metasurface (SIM) to achieve high spectral efficiency and user fairness with reduced hardware complexity. In the proposed system, the base station performs only scalar power allocation, whereas a multi-layer SIM acts as a wave-domain processor that spatially separates users and mitigates interference via nonlinear wavefront reconfiguration. This design eliminates the need for digital or hybrid precoding, drastically reducing the baseband computations. A joint optimization problem is formulated to maximize the minimum user rate by jointly optimizing SIM phase shifts, power allocation, and user grouping. To efficiently solve the resulting non-convex problem, an alternating optimization algorithm is developed, combining simultaneous perturbation stochastic approximation (SPSA) for SIM configuration and power control with clustering-based grouping refinement. Simulation results demonstrate that the proposed SIM-aided HRSMA achieves substantial gains in both spectral efficiency and fairness compared to hybrid beamforming and non-precoding baselines. Specifically, SIM-aided HRSMA attains comparable or superior minimum rates with significantly fewer active antennas by exploiting the additional wave-domain degrees of freedom facilitated using multi-layer SIMs. These findings highlight the potential of SIM-aided HRSMA as a low-cost, energy-efficient, and scalable solution for beyond-6G networks.

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.

Recently, reconfigurable holographic surfaces (RHSs) have been proposed as a cost-efficient approach for achieving ultra-massive multiple-input multiple-output to satisfy the growing data rate requirements. Unlike existing antennas, the RHS follows a serial transmission characteristic where the electromagnetic (EM) wave propagates along the RHS element and is radiated outward one after another, concurrently with the gradual decrease of the EM wave energy. Thus, there exists a minimum RHS size that satisfies the data rate requirement in the RHS-enabled communication system without excessive antenna elements. In this paper, we first propose a serial propagation model of RHS to depict the coupling effect among RHS elements that is induced by the excitation of EM waves carrying signals one after another. Based on such a model, we derive the closed-form expression of the minimum required RHS size by estimating the upper and lower bounds of the achievable rate. Simulation results verify the theoretical analysis and reveal the relationship between the radiation characteristics of the RHS and the required minimum size.

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.

The fourth industrial revolution drives AI-powered smart manufacturing through cloud-edge computing, enabling intelligent production processes and data-driven automation. To handle security concerns arising from massive IoT deployments, attribute-based access control (ABAC) has become essential for smart factories. It offers flexibility in dynamic environments by utilizing attributes of users, devices, and contextual conditions to decide whether an access request should be permitted or denied. However, the proliferation of IoT devices drastically increases the number of attributes, causing exponential growth in policy complexity and severe decision latency at resource-constrained edge nodes. To address this issue, we propose PRUNE, a cloud–edge collaborative framework for ABAC policy pruning. The framework adaptively determines pruning strategies based on the real-time security state of the factory. Specifically, it employs deep reinforcement learning (DRL) for coarse-grained control in highly dynamic environments, while switching to a Deterministic Policy Optimizer (DPO) for fine-grained adjustment under quasi-static conditions. The pruned lightweight ABAC policy subset is then deployed on edge nodes for real-time access decisions. By continuously monitoring factory conditions and analyzing historical access requests, PRUNE dynamically adjusts pruning strategies. Simulation results on our containerized digital-twin testbed show that PRUNE reduces security response latency by 22% and improves operational efficiency by 30%, while maintaining robust security with anomaly rates consistently below 10%.

Reconfigurable intelligent surface (RIS) has been considered as an effective approach to achieve extremely large-scale MIMO (XL-MIMO). To mitigate the high-complexity of channel information acquisition brought by the large number of RIS elements, beam training has been applied to select an optimal beam from a predefined codebook for beamforming in large-scale RIS-aided systems. However, practical limitations of RIS, such as its macrocell structure and finite phase shifts, result in the overlap of codeword coverage, i.e., non-orthogonal beams, which degrades the accuracy of beam training. In this paper, we propose a self-correcting hierarchical beam training scheme, where we model the beam training as a sequential process based on a tailored long short-term memory network. Received powers of sequentially selected codewords layer by layer are used to construct quasi-orthogonality for optimizing the beam selection. Unlike traditional deep learning methods relying on sufficient data, which takes substantial overhead to collect, we design a diffusion-empowered generation module given the general data-constrained conditions. The generation module is capable of synthesizing codebook power profiles to enhance the beam training performance. Simulation results demonstrate that our proposed method outperforms existing beam training approaches in terms of accuracy and sum rate, even in the presence of dataset limitations.

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.

This paper considers an intelligent reflecting surface (IRS)-assisted bi-static localization architecture for the sixth-generation (6G) integrated sensing and communication (ISAC) network. The system consists of a transmit user, a receive base station (BS), an IRS, and multiple passive targets in either the far-field or near-field region of the IRS. In particular, we focus on the challenging scenario where the line-of-sight (LOS) paths between targets and the BS are blocked, such that the emitted orthogonal frequency division multiplexing (OFDM) signals from the user reach the BS merely via the user-target-IRS-BS path. Our objective is to localize the targets by estimating their relative positions to the IRS from the received signal at the BS, instead of the BS. We show that subspace-based methods, such as the multiple signal classification (MUSIC) algorithm, can be applied to estimate the relative states from the targets to the IRS, while the spectrum ambiguity exhibits caused by the low-rank IRS-BS channel. To overcome this issue, we propose a novel spatiotemporal IRS phase profile and create a virtual signal model by concatenating the temporal signals over multiple OFDM symbols. Furthermore, we rigorously prove that the spectrum ambiguity issue can be resolved almost surely, if the MUSIC algorithm is applied to our properly constructed temporal-domain signals. Numerical results verify the effectiveness and efficiency of our proposed IRS-assisted localization scheme over the other localization counterparts. Our paper demonstrates the potential of employing passive anchors, i.e., IRSs, to improve the sensing coverage of the active anchors, i.e., BSs.

Amodal sensing enables the shape reconstruction of occluded objects, facilitating a wide range of sensing applications in complex environments. However, traditional amodal sensing methods based on cameras or LiDAR suffer from privacy issues and performance degradation under poor weather conditions. In this demo, we present a wireless amodal sensing system that leverages mmWave signals to improve robustness and protect privacy. The system first segments the obtained mmWave point clouds into individual object instances, and then reconstructs their complete shapes. Unlike camera and LiDAR-based methods, it is challenging to realize wireless amodal sensing due to the measurement errors caused by wireless channel noise and the data sparsity. To address these challenges, we first design an RCS-enhanced error suppression module to mitigate measurement errors by leveraging the negative correlation between radar cross-section (RCS) values and noise. For the data sparsity, we utilize a modified Transformer architecture to extract diverse geometric features at multiple scales, and incorporate a fine-tuned vision-language model (VLM) to generate semantic features that describe object classes. The geometric and semantic features are finally fused to reconstruct complete object shapes using pre-trained generative models. The effectiveness of the proposed system is demonstrated through extensive experiments in real-world scenarios.

Reconfigurable intelligent surface (RIS) has emerged as a promising solution to enable ultra-massive multiple-input multiple-output (MIMO) for 6G wireless communications. To reduce pilot overhead, multi-resolution codebook-based beam training has been proposed for RIS-aided communication systems. However, the hardware constraints of the RIS such as the limited control capability, mutual coupling, and manufacturing tolerances have not been fully considered, which may result in non-ideal beam patterns and misleading beam search directions, thereby degrading the overall beam training performance. To address this issue, in this paper, we revisit the multi-resolution codebook design and beam training scheme by taking the RIS hardware constraints into account. First, we propose a wide-beam generation method via multi-beam superposition, where the RIS reflection coefficients are optimized to achieve high beam gain with minimal fluctuations. Then, a posterior-based beam training scheme is proposed to adaptively correct erroneous beam search directions, thereby improving the beam training accuracy. We implement a prototype RIS with 32x32 reflective elements and deploy a 26 GHz millimeter-wave RIS-aided wireless communication testbed to experimentally evaluate the proposed scheme. The results indicate that our approach achieves low training overhead and a data rate close to the exhaustive search method.

With the development of the Internet of Things (IoT), mobile edge computing (MEC) and energy harvesting (EH) technologies are applied to handle numerous computing-intensive and latency-sensitive IoT applications by offloading data to MEC servers as well as achieving sustainable operation. In this paper, we investigate a distributed EH-enhanced MEC system consisting of multiple MEC servers and user mobile devices (MDs). The MEC servers, as service providers, inherently control the price and availability of resources. MDs reactively optimize their task offloading and battery energy management strategies. Considering the hierarchical structure and large optimization dimension of MEC, a Stackelberg game-based bi-level multi-agent deep deterministic policy gradient (BL-MADDPG) algorithm is proposed which treats the MD and joint MEC servers as follower and leader, respectively. Different from the parallel decision of conventional multi-agent deep reinforcement learning, the BL-MADDPG algorithm establishes a two-stage decision process and can achieve an energy efficient offloading strategy by taking MDs’ limited EH power into account. Simulation results are carried out to validate the effectiveness of the proposed scheme.

In future wireless networks, the proliferation of multimodal foundation models has created new opportunities for applications with high precision or real-time requirements, such as surveillance, disaster response and environmental construction. However, deploying these models directly on personal devices remains a formidable challenge, as their extensive parameter scales and intensive computational needs far exceed the capabilities of most edge devices. In this paper, we propose a latency minimization scheme within the edge-cloud model evolution framework. In this framework, edge nodes, i.e., personal devices, perform feature extraction on locally collected data, while cloud servers leverage foundation models to manage retraining and model updates. To address the constraints of limited wireless bandwidth and computational resources for regular model updates, we formulate a joint optimization problem targeting multi-user transmission strategies, resource allocation, and cloud training frequency to minimize overall latency. Closed-form solutions are derived for scenarios with separately fixed uplink and downlink bandwidths. In image segmentation tasks, simulations show that the foundation model-enabled cloud server can update edge models in real time, achieving minimal latency without compromising edge inference performance.

Recently, holographic integrated sensing and communication (ISAC) has been proposed as a promising paradigm to support dual functionalities of sensing and communications. It employs reconfigurable holographic surfaces (RHSs), a cost-effective solution for extremely large-scale antenna arrays, to provide unprecedented spatial degrees of freedom for beam management in ISAC. However, as a type of leaky-wave antenna, the radiation power of an RHS is inherently featured by the leakage power constraint, i.e., the sum of leaky wave power radiated by the serially-fed metamaterial elements cannot exceed the input power of the RHS. To this end, we consider a holographic ISAC system with leakage power constraints in this paper, where two RHSs are applied for signal transmission and reception. In such a system, it is challenging to optimize the RHS beamformers because the radiation amplitudes of all the elements are coupled under the leakage power constraint. To tackle these challenges, we formulate the holographic beamforming optimization problem with leakage power constraints and develop a joint transmit and receive beamforming optimization algorithm to solve the formulated problem. Theoretical analysis and simulation results validate the effectiveness of the proposed scheme and demonstrate that the leakage power constraints on the transmit and receive RHSs at the BS have different impacts on the overall ISAC performance.

In smart factories, the integration of cyber-physical systems and IoT devices has revolutionized manufacturing processes, enabling agile and highly customized production. However, the hyper-connected nature of these environments introduces significant security challenges, particularly in access control. Attribute-based access control (ABAC) is widely adopted for its flexibility in handling dynamic and complex access policies. Yet, ABAC suffers from rule explosion, where the exponential growth of policy combinations leads to high computational overhead and delays, adversely impacting production efficiency. To address these, we propose ABAC-Prune, an environment-adaptive DRL-rule fused ABAC pruning framework, where deep reinforcement learning (DRL) drives coarse-grained adaptation and rule-based policies enable fine-tuning. ABAC-Prune continuously monitors factory conditions, analyzes historical access patterns, and adjusts pruning strategies in real-time to balance security and production efficiency. Our evaluation in a simulated smart factory environment demonstrates that ABAC-Prune achieves a 22% reduction in security response latency and high improvement in production yield, while maintaining robust security by keeping the anomaly rate (illicit requests allowed) below a predefined threshold.

Reconfigurable holographic surfaces (RHSs) have recently emerged as a cost-effective and energy-efficient solution for extremely large-scale MIMO (XL-MIMO). High-gain directional holographic patterns can be generated by adjusting the amplitude responses of RHS elements. However, for XL-MIMO systems, the complexity of obtaining real-time channel state information (CSI) for beamforming is extremely high, especially when users are distributed in the near-field region of the RHS. To tackle this problem, this paper proposes a one-shot multi-user beam training scheme with low complexity for large-scale RHS-assisted systems, which is applicable to both near-and far-field users. The proposed beam training scheme includes two phases that are performed simultaneously for all users: angle search and distance search. In the angle search phase, an RHS angular codebook is designed according to holographic principles. Each codeword in this codebook covers multiple angles in both the near-field and far-field regions, enabling simultaneous angular search for all users. For the distance search, by taking advantage of the additivity of holographic beamformers, we construct distance-adaptive codewords in real time, which can cover all the candidate angles of users. Simulation results show that the proposed scheme achieves higher system throughput compared with traditional beam training schemes. The beam training accuracy approaches exhaustive search while significantly reducing the overhead.

Millimeter-wave (mmWave) radar has emerged as a promising sensing technology for various applications due to its capabilities of all-weather operation and direct velocity measurement. However, existing mmWave radar schemes exhibit significant shape reconstruction errors when the target is partially occluded by obstacles. To address this issue, we propose a wireless amodal sensing paradigm that supports the shape reconstruction of the occluded target using a single mmWave radar. The basic idea is to first extract the features from the mmWave point cloud of the occluded target, and then leverage pre-trained generative models to complete the whole shape based on these features. New challenges have arisen that mmWave radar point clouds are inherently sparse, containing measurement noise that reduces the accuracy of feature extraction, thus resulting in degraded shape reconstruction quality. To tackle these challenges, we propose WASNet that incorporates a radar cross section (RCS)-enhanced geometric feature extraction module to suppress measurement noise and a Vision-Language Model (VLM)-based semantic injection module to enhance the shape reconstruction accuracy by extracting semantic features. Experiments demonstrate the effectiveness and robustness of the proposed scheme, which achieves a 70.5% reduction in point cloud reconstruction error compared with baselines.

Reconfigurable intelligent surfaces (RISs) are considered as an efficient solution for the implementation of extremely large-scale MIMO (XL-MIMO). To mitigate the complexity of channel information acquisition, beam training is identified as an effective solution by selecting the optimal beam from a predefined codebook. However, practical hardware limitations of RIS result in the overlap of codeword coverage, i.e., non-orthogonal beams, which degrades the accuracy of optimal beam selection. In this paper, we propose a self-correcting hierarchical beam training scheme, where beam training is designed as a sequential process based on a tailored long short-term memory network. Received powers of sequentially selected multi-layer codewords are integrated to construct quasi-orthogonality for optimizing the beam selection. Beam training is thus reformulated as a sequential codeword classification problem. A codeword priority adjustment procedure is then designed to prevent the beam selection from looping among layers according to the visit frequency to each codeword of the current selection path. Simulation results show that our proposed method outperforms existing beam training approaches in terms of beam selection accuracy and sum rate in the presence of hardware limitations.

Large-scale reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for signal strength enhancement and coverage extension in 6G networks. However, as the antenna scale and the bandwidth increase, large-scale RIS-assisted wideband orthogonal frequency division multiplexing (OFDM) communication systems face new challenges due to the near-field range expansion and the beam split effect, complicating the acquisition of channel state information (CSI). To tackle these challenges, in this paper, we present a wideband beam training scheme by utilizing the beam split effect to bypass the CSI acquisition. Specifically, by analyzing the beam split effect in RIS-assisted OFDM communication systems, we propose a beam-split-aware codebook capable of covering both the near and far fields. The coverage of codewords in the proposed codebook is expanded benefiting from the beam split effect, leading to fewer codewords compared to conventional narrow-band codebooks. Utilizing such a codebook, a three-stage beam training mechanism is performed to obtain the optimal codeword with low time overhead, thereby facilitating the beamforming. Simulation results demonstrate that the proposed scheme outperforms existing codebook-based beam training schemes in terms of training overhead and sum rate in the hybrid near-far field.

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.

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.

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.

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 suggested as an energy-efficient solution for extremely large-scale arrays. By controlling the amplitude of RHS elements, high-gain directional holographic patterns can be achieved. However, the complexity of acquiring real-time channel state information (CSI) for beamforming is exceedingly high, particularly in large-scale RHS-assisted communications, where users may be distributed in the near-field region of RHS. This paper proposes a one-shot multi-user beam training scheme in large-scale RHS-assisted systems applicable to both near and far fields. The proposed beam training scheme comprises two phases: angle search and distance search, both conducted simultaneously for all users. For the angle search, an RHS angular codebook is designed based on holographic principles so that each codeword covers multiple angles in both near-field and far-field regions, enabling simultaneous angular search for all users. For the distance search, we construct the distance-adaptive codewords covering all candidate angles of users in a real-time way by leveraging the additivity of holographic patterns, which is different from the traditional phase array case. Simulation results demonstrate that the proposed scheme achieves higher system throughput compared to traditional beam training schemes. The beam training accuracy approaches the upper bound of exhaustive search at a significantly reduced overhead.

As a typical instance of the metasurface, intelligent omni-surfaces (IOSs) emerge as a potential technique for coverage extension benefiting from the symmetric reflection and refraction. For large-scale IOSs, the enlarged near-field region allows users to be randomly distributed in both the near and far fields of IOSs. To perform beamforming in such anear-far (NF) field communicationsystem, in this paper, we propose an NF-field codebook design and beam training scheme, which avoids the high complexity of accurate channel state information (CSI) acquisition. We reveal that the traditional Rayleigh distance based NF-field boundary may cause redundant training overhead. Thus, an effective NF-field boundary is introduced in terms of the beamforming gain. We then utilize this NF-field boundary and the symmetric characteristic of the IOS reflective-refractive signals to design an IOS-tailored codebook consisting of multiple codewords covering both the near and far fields. On this basis, a joint reflective-refractive beam training mechanism for IOS-aided systems is presented, where beam training is simultaneously performed in the symmetric regions of the IOS, thereby reducing training overhead. Simulation results show that the proposed scheme achieves a higher sum rate than the traditional codebooks given the same number of codewords, and performs close to the perfect CSI case.

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.

As a key feature that has the potential to enable many advanced applications, the integration of sensing functionality is considered essential in the 6G network, which motivates the design of integrated sensing and communication (ISAC) systems. However, traditional ISAC systems based on non-overlapped resource allocation face the challenge of poor energy and spectral efficiency. In this paper, we implement an ISAC system named Horus based on reconfigurable intelligent surfaces, which provides an energy-efficient solution to sense objects in a wide range of blind areas. With a carefully designed ISAC protocol, Horus can transmit sensing information to the receiver with high spectral efficiency. We have verified the ability of the proposed system in two case studies of multi-modal sensing and around-corner radar early warning, respectively. Our demonstration video can be found in [11].

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.

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.

With the development of millimeter-wave (mmWave) communication systems, large-scale reconfigurable intelligent surfaces (RISs) have gained considerable attention as a promising technology for signal strength enhancement and coverage extension. However, as the antenna scale and bandwidth increase, RIS-assisted wideband orthogonal frequency division multiplexing (OFDM) communication systems face challenges due to the near-field range expansion and the beam split effect over the high-frequency band, complicating the acquisition of channel state information (CSI). To tackle these challenges, we present a codebook-based three-stage beam training scheme by using the beam split effect to bypass CSI estimation. Specifically, by analyzing the beam split effect in RIS-assisted OFDM communication systems, we propose a beam-split-aware codebook capable of covering both the near and far fields with fewer codewords compared to conventional narrow-band codebooks. Using such a codebook, a three-stage beam training mechanism is adopted to obtain the optimal codeword with low time overhead, thereby facilitating subsequent beamforming. Simulation results demonstrate that the proposed scheme outperforms existing near- and far-field codebook-based schemes in terms of the beam training resolution and sum rate in the hybrid near–far field.

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.

With the 5G systems being highly developed, the urge of the next generation networks is increasingly necessary, which demands extremely high data rates and low latency. As an emerging technology capable of reflecting and refracting the incident signals on both sides simultaneously, recently the intelligent omnidirectional surface (IOS) has been used to enhance the capacity of wireless networks. However, it is challenging to design an IOS-enabled beamforming scheme that can respond quickly in a varying mobile environment due to its high complexity. In this paper, we aim to maximize the sum rate in an IOS-aided multi-user system given dynamically changing channel states and user mobility. A novel meta-critic reinforcement learning framework named meta-critic deep deterministic policy gradient algorithm is proposed to design the IOS-enabled beamforming scheme. We propose a meta-critic network that can recognize the environment change and automatically perform the self-renewal of the learning model. A stochastic explore-and-reload procedure is also tailored to reduce the high-dimensional action space problem. Simulation results demonstrate that our proposed method outperforms other benchmarks including the state-of-the-art reinforcement learning method in both achievable sum rate and convergence speed.

In the future 6G integrated sensing and communication (ISAC) cellular systems, networked sensing is a promising technique that can leverage the cooperation among the base stations (BSs) to perform high-resolution localization. However, a dense deployment of BSs to fully reap the networked sensing gain is not a cost-efficient solution in practice. Motivated by the advance in the intelligent reflecting surface (IRS) technology for 6G communication, this paper examines the feasibility of deploying the low-cost IRSs to enhance the anchor density for networked sensing. Specifically, we propose a novel heterogeneous networked sensing architecture, which consists of both the active anchors, i.e., the BSs, and the passive anchors, i.e., the IRSs. Under this framework, the BSs emit the orthogonal frequency division multiplexing (OFDM) communication signals in the downlink for localizing the targets based on their echoes reflected via/not via the IRSs. However, there are two challenges for using passive anchors in localization. First, it is impossible to utilize the round-trip signal between a passive IRS and a passive target for estimating their distance. Second, before localizing a target, we do not know which IRS is closest to it and serves as its anchor. In this paper, we show that the distance between a target and its associated IRS can be indirectly estimated based on the length of the BS-target-BS path and the BS-target-IRS-BS path. Moreover, we propose an efficient data association method to match each target to its associated IRS. Numerical results are given to validate the feasibility and effectiveness of our proposed heterogeneous networked sensing architecture with both active and passive anchors.

Holographic MIMO communications, enabled by large-scale antenna arrays with quasi-continuous apertures, are potential technology for spectrum efficiency improvement. However, the increased antenna aperture size extends the range of the Fresnel region, leading to a hybrid near-far field communication mode. The users and scatterers randomly lie in near-field and far-field zones, and thus, conventional far-field-only and near-field-only channel estimation methods may not work. To tackle this challenge, we demonstrate the existence of the power diffusion (PD) effect, which leads to a mismatch between the hybrid-field channel and existing channel estimation methods. Specifically, in far-field and near-field transform domains, the power of one channel path may diffuse to other positions, thus generating fake paths. This renders the conventional techniques unable to detect those real paths. We propose a PD-aware orthogonal matching pursuit (PD-OMP) algorithm to eliminate the influence of the PD effect by identifying the PD range, within which the path power diffuses to other positions. PD-OMP fits a general case without prior knowledge of respective numbers of near-field and far-field paths and the user’s location. Simulation results show that PD-OMP can accurately estimate the channel when antenna spacing is below half wavelength and outperform current state-of-the-art hybrid-field channel estimation methods.

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.

Reconfigurable metasurfaces integrating numerous elements are one promising solution for empowering high-accuracy positioning applications, benefiting from their high spatial resolution, low power consumption, and low cost. In this paper, we investigate two typical types of metasurfaces, i.e., reconfigurable holographic surfaces (RHSs) and reconfigurable intelligent surfaces (RISs), for target detection and positioning. Specifically, an RHS is a leaky-wave surface antenna with an embedded feed, while an RIS is a type of reflective metasurface whose feed is positioned outside the surface. Due to their distinct structures and working principles, RHSs and RISs may be suitable for different scenarios for target detection and positioning. To determine their best working scenarios, we first design the beamformers of both RIS-enabled and RHS-enabled radar systems to improve their performance. We then characterize the target detection and positioning performance analytically, and finally compare their performance in different scenarios. Theoretical and numerical results both reveal that: 1) in the one-dimensional linear array case, in general the performance of the RHS-enabled system is better than that of the RIS-enabled system; 2) in the two-dimensional planar array case, lower frequencies and larger physical sizes can contribute to a better performance of RIS-enabled systems than RHS-enabled systems, and vice versa.

To realize holographic communications, a potential technology for spectrum efficiency improvement in the future sixth-generation (6G) network, antenna arrays inlaid with numerous antenna elements will be deployed. However, the increase in antenna aperture size makes some users lie in the Fresnel region, leading to the hybrid near-field and far-field communication mode, where the conventional far-field channel estimation methods no longer work well. To tackle the above challenge, this paper considers channel estimation in a hybrid-field multipath environment, where each user and each scatterer can be in either the far-field or the near-field region. First, a joint angular-polar domain channel transform is designed to capture the hybrid-field channel's near-field and far-field features. We then analyze the power diffusion effect in the hybrid-field channel, which indicates that the power corresponding to one near-field (far-field) path component of the multipath channel may spread to far-field (near-field) paths and causes estimation error. We design a novel power-diffusion-based orthogonal matching pursuit channel estimation algorithm (PD-OMP). It can eliminate the prior knowledge requirement of path numbers in the far field and near field, which is a must in other OMP-based channel estimation algorithms. Simulation results show that PD-OMP outperforms current hybrid-field channel estimation methods.

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.

Recently, the rapid development of metasurface facilitates the growth of extremely large-scale antenna arrays, making the ultra-massive MIMO possible. In this paper, we study the codebook design and beam training for an intelligent omni-surface (IOS) aided multi-user system, where the IOS is a novel metasurface enabling simultaneous signal reflection and refraction. To deal with the near field expansion caused by the large-dimension of IOS, we design a near-far field codebook to serve users both in the near and far fields without prior knowledge of user distribution. Moreover, to fully exploit the dual functionality of the IOS, the coupling between the reflective and refractive signals is analyzed theoretically and utilized in the codebook design, thereby reducing the training overhead. On this basis, the multi-user beam training is adopted where each codeword covers multiple areas to enable all users to be trained simultaneously. Simulation results verify our theoretical analysis on the reflective-refractive coupling. Compared to the state-of-the-art schemes, the proposed scheme can improve the sum rate and throughput.

Multi-target detection is one of the primary tasks in radar-based localization and sensing, typically built on phased array antennas. However, the bulky hardware in the phased array restricts its potential for enhancing detection accuracy, since the cost and power of the phased array can become unaffordable as its physical aperture scales up to pursue higher beam shaping capabilities. To resolve this issue, we propose a radar system enabled by reconfigurable holographic surfaces (RHSs), a novel meta-surface antenna composed of meta-material elements with cost-effective and power-efficient hardware, which performs multi-target detection in an adaptive manner. Different from the phase-control structure in the phased array, the RHS is able to apply beamforming by controlling the radiation amplitudes of its elements. Consequently, traditional beamforming schemes designed for phased arrays cannot be directly applied to RHSs due to this structural difference. To tackle this challenge, a wave-form and amplitude optimization algorithm (WAOA) is designed to jointly optimize the radar waveform and RHS amplitudes in order to improve the detection accuracy. Simulation results reveal that the proposed RHS-enabled radar increases the probability of detection by 0.13 compared to phased array radars when six iterations of adaptive detection are performed given the same hardware cost.

Received signal strength (RSS) fingerprint map is one of the most widely-used indoor localization approaches, but it often relies on multiple access points (AP) for data collection and suffers from frequent data updates due to dynamic wireless environments. In this work, we implement a reconfigurable-intelligent-surface (RIS) assisted indoor localization system named Meta2Locate to tackle the above issues using only one AP. In the proposed system, we deploy our self-designed RIS at 5.5GHz in an indoor environment, which can customize the propagation channels between the AP and the target. For the changing propagation environment, we design a mean maximum discrepancy weighted meta-learning approach to train a model that maps the RSS fingerprint to the location of the user, and it only needs a few data for the model update.

Intelligent omni-directional surfaces (IOS), which can simultaneously reflect and refract incident signals, are considered as a promising solution for enhancing communication quality. To conduct joint beamforming of the BS and IOS, beam training is introduced such that perfect channel state information is not required anymore. However, the propagation environment is usually dynamically varying in practice, leading to frequent beam training procedures and huge training overhead. In this paper, we propose a transfer learning based beam training scheme for the IOS-assisted multi-user system to adapt to the dynamically changing propagation environment. We first build on an offline phase to train a beam prediction model that outputs the optimal beam with the highest data rate given only the received power of a small number of beams as the input. Then a transfer learning based method is developed such that the above beam prediction model can be updated to adapt to the dynamic environment rapidly. Simulation results demonstrate that the proposed scheme outperforms the existing beam training schemes in dynamic environments in terms of the convergence speed and the sum rate.

Capable of reflecting and refracting the incident signals on both sides simultaneously, the intelligent omnidirectional surface (IOS) has recently been proposed as a promising solution to enhance the capacity of wireless networks. However, the large number of IOS elements brings a heavy burden to the beamforming scheme design, especially for applications that require a fast response to varying environments. In this paper, aiming to maximize the sum rate of an IOS-aided multi-user system via IOS-enabled beamforming design that can rapidly adapt to dynamic channel states and user mobility, we develop a novel meta-critic reinforcement learning framework where a meta-critic network recognizes the environment change and automatically re-trains of the learning model. A stochastic Explore and Reload procedure is tailored to reduce the high-dimensional action space problem. Simulation results show the proposed scheme can converge to a higher sum rate more rapidly compared to the benchmark methods in dynamic settings. The robustness of our scheme against different IOS sizes is also verified.

Edge computing has been viewed as a powerful technology to realize the vision of network services. However, due to the limited capabilities and insufficient security systems, edge computing is vulnerable to distributed denial of service (DDoS) attacks which may exhaust the resources of edge servers with excessive requests and degrade their service capabilities. Setting detection thresholds for DDoS detection indicators can effectively prevent DDoS attacks, but existing thresholding methods fail to update detection thresholds in time to guarantee the detection performance whenever the system settings vary. In this paper, we propose a multi-dimensional thresholding method against DDoS attacks in edge computing. We design three detection indicators based on the behavior features of DDoS attackers. By solving a threshold optimization problem, we obtain closed-form solutions and numerical solutions of the optimal detection thresholds, which adapt to dynamic system settings. Simulations show that the proposed thresholding method has a superior detection performance in terms of both the accuracy and robustness.

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.

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%.

In this paper, we consider an integrated terrestrial-aerial access network where multiple access points (APs) such as cellular base stations (BSs), unmanned aerial vehicles (UAVs), and high altitude platforms (HAPs) coordinate to serve various ground users. Advanced forms of typical data services for 5G and beyond such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC), are provided via network slicing (NS) to cope with the heterogeneity brought by the terrestrial-aerial integration. To satisfy various requirements of users, network slices consist of different virtual network functions (VNFs), each of which consumes different amounts of compute resource at APs. To achieve a tradeoff between data rate and consumed compute resource of VNFs, we aim to maximize the compute resource utilization efficiency by jointly optimizing the VNF deployment, power and spectrum resource allocation. The mutual relation between VNF deployment and user scheduling is modelled, based on which an iterative algorithm is proposed to solve the problem. Simulation results show that the proposed algorithm achieves a better tradeoff than benchmark ones. Influence of the VNF deployment and size on the system performance is revealed as well.

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.

Visible Light Backscatter Communication (VLBC) presents an emerging ultra-low-power IoT connectivity solution with high spatial-spectral efficiency and intrinsic human-perceivable privacy advantages. However, research progress on enhanced data rate and sophisticated device coordination of state-of-the-art VLBC systems still cannot meet the low-latency requirement (sub-second level for an IoT network) for massive connections.

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.

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.

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.

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.

In this paper, we study the cellular communications over unlicensed millimeter-wave (mmWave) bands to satisfy the extra-high transmission rate demands. To guarantee the harmonious coexistence of cellular and WiGig users over 60 GHz bands, we design a frame structure for the unlicensed mmWave spectrum sharing to reduce the interference caused by cellular data transmission to WiGig networks. By joint frequency and spatial resource allocation, an iterative channel allocation and hybrid beamforming algorithm is designed to maximize the sum rate of all cellular users while minimizing the interference to WiGig networks. Simulation results show that our proposed scheme outperforms the SVD-based hybrid beamforming method. The fairness between the cellular and the WiGig users is achieved by setting an optimal transmit power of the gNB for unlicensed mmWave spectrum sharing. The influence of the beamwidth and beam direction of the gNB is evaluated in terms of the system performances of both the unlicensed cellular users and WiGig users.

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 this paper, we investigate the traffic-sensitive multi-layer low Earth orbit (LEO) satellite-terrestrial network. Massive terrestrial user access to the core network is realized via the backhaul supported by multi-layer LEO satellites. The ultra-dense satellite topology enables a promising solution for the high-capacity backhaul data transmission for terrestrial users. Jointly considering the backhaul capacity requirement and traffic dynamics of terrestrial satellite terminals, we analyze their average backhaul capacity using both stochastic geometry and queueing theory. Aiming to minimize the total required satellite number for fulfilling the backhaul capacity and seamless global coverage requirements, we propose a multi-layer LEO satellite constellation deployment scheme considering the satellite mobility. Simulation results verify the backhaul capacity analysis and the advantage of multi-layer constellation for saving satellites. The optimized multi-layer LEO satellite constellation with any coverage requirement and traffic rate is presented.

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.

The federated learning scheme enhances the privacy preservation through avoiding the private data uploading in cloud-edge computing. However, the attacks against the uploaded model updates still cause private data leakage which demotivates the privacy-sensitive participating edge devices. Facing this issue, we aim to design a privacy-preserving incentive mechanism for the federated cloud-edge learning (PFCEL) system such that 1) the edge devices are motivated to actively contribute to the updated model uploading, 2) a trade-off between the private data leakage and the model accuracy is achieved. We formulate the incentive design problem as a three-layer Stackelberg game, where the server-device interaction is further formulated as a contract design problem. Extensive numerical evaluations demonstrate the effectiveness of our designed mechanism in terms of privacy preservation and system utility.

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.

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.

In this paper, we investigate the close contact detection for COVID-19 patients based on the heterogeneous mobile edge computing (MEC) framework. Collecting the spatial-temporal data of a large number of mobile users, the base stations equipped with MEC servers organize these data via the R-tree structure. The cloud center (CC) aggregates the spatial-temporal data from all MEC servers. Considering the mobility of users as well as various positions of MEC servers, the CC then partitions and assigns the close contact detection tasks to different servers for faster processing. Aiming to minimize the system latency, we propose a Deep Deterministic Policy Gradient-based task and resource allocation scheme, where the computing loads are balanced among different servers. Simulation results show that a minimum system latency is reached while maintaining the load balance among all servers. Up to 37% detection accuracy enhancement is achieved compared with an existing task allocation scheme without load balance.

In this paper we propose a cluster-based platoon handoff protocol (CPHP), in which the platoon is divided into clusters to minimize its handoff delay between two adjacent cells. Via the vehicle-to-vehicle (V2V) communications, multiple clusters are constructed within one platoon and only the cluster head (CH) communicates with the base station (BS) via the vehicle-to-infrastructure (V2I) communications. In this way, the number of V2I links can be greatly saved and the signaling overhead is reduced, thereby shortening the handoff delay. Specifically, we formulate a delay minimization problem based on a discrete-time Markov chain and then optimize the number of clusters and spectrum resource allocation. Simulation results demonstrate a significant decrease in the handoff delay of the platoon. The optimal expected delay under a platoon with 50 vehicles by utilizing the proposed CPHP is 15% smaller than that of the traditional handoff scheme.

Abstract Reconfigurable intelligent surface (RIS) has drawn great attention as a promising technique that triggers a revolution in multi‐antenna systems. It can intelligently reconstruct the propagation environments passively without extra hardware or power consumption. In this paper, the multi‐RIS aided downlink multi‐cell communication systems are considered. Adjacent base stations (BSs) are allowed to share and jointly control the same RISs to mitigate the influence brought by the inter‐cell interference. For the sum‐rate maximisation, a distributed negotiation mechanism is designed where each BS only communicates with its neighbours to reach a consensus on the RIS‐based analogue beamforming. Meanwhile, given the incomplete knowledge of other cells, each BS independently optimises its digital beamformer based on its iteratively updated estimates over the other cells without revealing any location and channel information of its own serving users. Simulation results show that the proposed scheme achieves a close performance compared to the centralised scheme, and much better than the traditional no‐RIS system. The influence of the discrete phase shifts and the RIS size on the system performance are also evaluated.

In this article, we consider a downlink ultra-dense LEO-based multi-terminal satellite system where multiple satellites fly in formation to serve a number of ground terminal stations for data transmission. Benefited from the dense satellite constellation, high channel capacity can be achieved via the massive virtual antenna array formed by multiple satellites. We aim at exploring how the satellite distribution and formation size influence the channel capacity in this case. We first derive the upper bound of the channel capacity in the general multi-antenna satellite networks. The single-antenna satellite case is considered where we present the specific forms of the derived capacity bounds to prove that the capacity first increases linearly then increases more and more slowly with the satellite formation size, and there exists an optimal LEO satellite distribution to achieve the maximum capacity of the system. Simulation results verify our theoretical analysis and show that such statements also hold for the general multi-antenna satellite case.

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.

In this paper, we consider a three-layer distributed multi-access edge computing (MEC) network where multiple clouds, MEC servers, and edge devices (EDs) are deployed at the top layer, middle layer, and bottom layer, respectively. Each cloud center (CC) is associated with an independent service provider and publishes an application-driven computing task. To deliver the tasks, CCs rely on EDs to generate the raw data and offload part of the computing tasks to both EDs and MEC servers such that their computing and transmission resources can be fully utilized to reduce the system latency. However, in such a three-layer network, the distributed deployment of tasks leads to inevitable resource competition among CCs. To address this issue, we propose a distributed scheme based on multi-agent reinforcement learning, where each CC jointly determines the task offloading and resource allocation strategy based on its inference of other CCs' decisions. Simulation results indicate that a lower system latency is achieved via our proposed scheme compared with the existing schemes. In addition, the influence of the number of CCs, MEC servers, and EDs on latency performance is also discussed.

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 study the harmonious coexistence of cellular and WiGig users around 60 GHz unlicensed millimeter-wave bands. To guarantee the fairness of two types of users, we propose a sensing based adaptive unlicensed channel sharing protocol. Jointly considering the frequency and spatial resource allocation, an iterative channel allocation and hybrid beamforming algorithm is designed to maximize the sum rate of all cellular users while minimizing the interference to WiGig networks. Simulation results show that our proposed scheme achieves a significant sum rate enhancement compared with that when utilizing only the licensed band.

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 this paper, we consider the distributed energy saving for heterogeneous multi-layer mobile edge computing (HetMEC), where the edge devices (EDs) upload computing tasks to the mobile edge computing (MEC) servers and the cloud center (CC) for processing. To reduce the energy consumption, task offloading and resource allocation are performed by each device independently to distribute the computation load. However, due to the partial information, the offloading strategies of devices on different layers can hardly be aligned, which may lead to network congestion. To address this problem, we propose a smart pricing mechanism to coordinate the strategies of multi-layer devices, where the CC charges the MEC servers and EDs for computing services and network congestion. The pricing based distributed task offloading and resource allocation algorithm is designed to minimize the total energy consumption subject to latency requirements. Simulation results indicate that our algorithm achieves lower energy consumption and congestion probability compared with the traditional distributed method.

In this paper, we consider an uplink ultra-dense LEO-based multi-terminal satellite system where each ground terminal station connects to multiple satellites for data transmission. Benefited from the dense satellite constellation, high channel capacity can be achieved via the spatial diversity of multiple satellites. To evaluate the multi-satellite channel capacity performance, we first derive the lower bound and upper bound of the channel capacity in uplink LEO-based multi-terminal systems with multiple single-antenna satellites. Based on the capacity bounds, we theoretically prove that the capacity grows almost linearly with the number of satellites, and there exists an optimal LEO satellite distribution to achieve the maximum capacity of the system. We then illustrate that such statements also hold for the multi-antenna satellite case where the upper bound of the channel capacity and the lower bound of the achievable rate after receive beamforming are derived separately. Simulation results verify our theoretical analysis.

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.

Recently, the ultra-dense low earth orbit (LEO) satellite constellation over high-frequency band has served as a potential solution for terrestrial data offloading owing to its seamless coverage and high-capacity backhaul. In this paper, we consider an integrated ultra-dense LEO-based satellite-terrestrial network where the terrestrial operator (TO) can offload its subscribed users to the LEO satellite network owned by the satellite operator (SO) for satellite-backhauled network access. However, data offloading consumes extra resources of the SO and degrades the quality-of-service of the SO's original users. Therefore, we aim to design a pricing mechanism based on the Stackelberg game to motivate both operators for data offloading, and the Stackelberg equilibrium is achieved by jointly optimizing the C-band user association, Ka-band spectrum allocation, and data service pricing. Simulation results show that our proposed pricing mechanism can motivate two operators for offloading efficiently. The influence of available frequency resources, data service prices, and the number of LEO satellites on the system performance are also discussed.

Driven by great demands on low-latency services of the edge devices (EDs), mobile edge computing~(MEC) has been proposed to enable the computing capacities at the edge of the radio access network. However, conventional MEC servers suffer some disadvantages of limited computing capacity, preventing computation-intensive tasks to be processed on time. To relief this issue, we propose a heterogeneous multi-layer MEC (HetMEC) network where data that cannot be timely processed at the edge are allowed to be offloaded to the upper-layer MEC servers, and finally to the cloud center (CC) with more powerful computing capacity. We design the latency minimization algorithm by jointly coordinating the task assignment, computing and transmission resources among the EDs, multi-layer MEC servers, and the CC. Simulation results indicate that our proposed algorithm can achieve a lower latency and stronger robustness than the conventional MEC schemes.

In this paper, we consider an ultra-dense LEO-based satellite-terrestrial network where the traditional operator (TO) cannot satisfy the increasing data demand of its subscribed users due to the limited backhaul capacity of traditional small cells. Therefore, the TO offloads its users to LEO-based small cells owned by the satellite operator (SO) for satellite-backhauled network access. To motivate both operators for data offloading and maximize their revenues, we propose a pricing mechanism for data offloading based on the Stackelberg game. An iterative optimization algorithm framework is developed by jointly considering user association, Ka-band spectrum allocation and pricing to achieve the Stackelberg equilibrium. The user association scheme and pricing scheme to maximize the TO and the SO's revenues are designed separately. The closed-form optimal solution for user association problem is derived, and the iterative pricing mechanism is also designed. Simulation results show that our proposed pricing scheme can motivate two operators for offloading efficiently. The influence of frequency resources and the number of LEO satellites is also discussed.

In this work, we consider a heterogeneous multi-cloud mobile edge computing (Het-MEC) network, where multiple independent cloud centers (CCs) publish tasks to edge devices (EDs) and MEC servers, and compete for their computing and transmission resources. To minimize the system latency, we propose a distributed scheme for each CC to determine its data offloading and resource allocation strategy independently. Competition among multiple CCs in this distributed scheme is depicted by our designed multi-agent reinforcement learning (MARL) based algorithm, where each CC is self-motivated to learn the explicit models of other CCs and adjusts their behaviors. Simulation results indicate that multiple clouds are self-organized to take full advantage of computing and transmission resources to minimize their own task latency, while a lower system latency can be achieved compared with the cloud computing and local computing schemes.

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.

Due to the limited backhaul capacity of traditional small cells (TSC), it is non-trivial for the traditional operator (TO) to satisfy the increasing data demand of users. To relieve this issue, we propose a data offloading scheme, where the TO offloads TSC users to LEO-based small cells owned by the satellite operator for satellite-backhauled network access. An iterative optimal algorithm based on the Stackelberg game is developed to maximize both operators' revenues. Simulation results show the effectiveness of our scheme.

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.

Driven by great demands on low-latency services of the edge devices (EDs), mobile edge computing (MEC) has been proposed to enable the computing capacities at the edge of the radio access network. However, conventional MEC servers suffer some disadvantages such as limited computing capacity, preventing and computation-intensive tasks to be processed on time. To relief this issue, we propose the heterogeneous multi-layer MEC (HetMEC) where data that cannot be timely processed at the edge are allowed to be offloaded to the upper-layer MEC servers, and finally to the cloud center (CC) with more powerful computing capacity. We aim to minimize the system latency, i.e., the total computing and transmission time on all layers for the data generated by the EDs. We design the latency minimization algorithm by jointly coordinating the task assignment, computing, and transmission resources among the EDs, multi-layer MEC servers, and the CC. The simulation results indicate that our proposed algorithm can achieve a lower latency and higher processing rate than the conventional MEC scheme.

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 consider a cooperative autonomous driving system where a vehicle overtakes the one in front based on collective perception. To avoid collisions with vehicles on the other lane, we propose a V2X-based cooperative collision avoidance scheme. The overtaking vehicle estimates its distance with the neighbors via V2V communications and decides whether to overtake or not. Two cases where the distance information is obtained independently and cooperatively are taken into account. We derive the probability of collision avoidance, and analyze the influence of different factors such as speed and density of vehicles on the system performance. Simulation results verify our analysis and show the improvement brought by the cooperative case compared to the independent case.

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.

In this paper, we consider a three-level hierarchical structure for resource allocation in the radio access network (RAN) slicing. The infrastructure provider (InP) allocates the RAN slices to the mobile virtual network operators (MVNOs), and the MVNOs then allocate the radio resources to the users. It is challenging for the InP to determine resource allocation strategy efficiently due to the selfish strategic responses of both the MVNOs and the users. To handle this issue, we propose a tri-level Stackelberg game to jointly solve the frequency and power allocation and payment negotiation problem among the three levels. Simulation results verify a general market principle that the more the MVNOs focus on revenue collecting, the lower payoff the InP and the users will obtain.

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.

In this paper, we propose a trellis coded modulation (TCM) based non-orthogonal multiple access (NOMA) scheme. Different from those in the traditional code-domain NOMA, the incoming bit streams of multiple layers are jointly coded and mapped to the codewords so as to improve the coding gain of the system. Based on the multi- dimensional TCM techniques, additional coding gain from the error control coding can be achieved without any bandwidth extension. New design criteria are provided and a novel set partitioning algorithm is proposed for multi-dimensional signal set labeling. To achieve the trade-off between the BER performance and complexity, a suboptimal two- layer Viterbi algorithm is proposed for joint decoding. Simulation results show that our proposed TCM-based NOMA scheme performs significantly better than the traditional code- domain NOMA in terms of the BER performance.

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.

Recently, owing to the advances in the self-interference cancellation technology, the in-band full-duplex (FD) capability has been demonstrated at Wi-Fi range. However, the simultaneous uplink (UL) and downlink (DL) transmission may lead to inter-user interference (IUI) and result in decoding failure. Spectrum efficiency should also be considered in the construction process of the FD transmission. In this paper, we propose a hybrid half-duplex/FD MAC protocol based on a two-fold RTS/CTS contention resolution mechanism, in order to fully exploit the channel access opportunities provided by the simultaneous UL and DL transmissions. The noteworthy features of the proposed protocol lie in the following two aspects. First, the protocol provides the flexibility for the AP to decide the probability of constructing FD transmission, and then it adopts a second-fold of RTS/CTS mechanism to prevent the constructed transmission from being affected by the IUI. The second-fold contention and the probability of constructing FD transmission are optimized separately to maximize the spectrum efficiency given different transmission demands. Simulation results show that the proposed MAC protocol achieves higher capacity compared with previous works, and the hybrid characteristic enables the FD Wi-Fi networks to meet with different system requirements.

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.

In this paper, we consider a dense vehicular communication network where each vehicle broadcasts its safety information to its neighborhood in each transmission period. Such applications require low latency and high reliability, and thus, we exploit non-orthogonal multiple access to reduce the latency and to improve the packet reception probability. In the proposed scheme, the BS performs semi-persistent scheduling and allocates time-frequency resources in a non-orthogonal manner while the vehicles autonomously perform distributed power control. We formulate the centralized scheduling and resource allocation problem as a multi-dimensional stable roommate matching problem and develop a novel rotation matching algorithm to solve it. Simulation results show that the proposed scheme outperforms the traditional orthogonal multiple access scheme in terms of the latency and reliability.

Recently, the in-band full-duplex (FD) capability has been demonstrated at Wi-Fi range. However, the simultaneous uplink (UL) and downlink (DL) transmission may lead to inter-user interference (IUI). In this paper, we propose a hybrid MAC protocol, in which the AP decides the probability of constructing FD transmission. The protocol also adopts a second fold of RTS/CTS mechanism to prevent the constructed transmission from being affected by the IUI. The second fold RTS/CTS mechanism and the probability for FD transmission are optimized respectively to maximize the expected spectrum efficiency. Simulation results show that the proposed MAC protocol achieves higher capacity compared to a half-duplex counterpart in terms of both UL and DL throughput.

In this paper, we consider a dense vehicular communication network where each vehicle broadcasts its safety information to its neighborhood in each transmission period. Such applications require low latency and high reliability, and thus, we exploit non-orthogonal multiple access to reduce the access latency and to improve the packet reception probability. In the proposed two-fold scheme, the BS performs semi-persistent scheduling and allocates time-frequency resources in a nonorthogonal manner while the vehicles autonomously perform distributed power control with iterative signaling control. We formulate the centralized scheduling and resource allocation problem as equivalent to a multi-dimensional stable roommate matching problem, in which the users and time/frequency resources are considered as disjoint sets of objects to be matched with each other. We then develop a novel rotation matching algorithm, which converges to an L-rotation stable matching after a limited number of iterations. Simulation results show that the proposed scheme outperforms the traditional orthogonal multiple access scheme in terms of the access latency and reliability.

With the rapid growth of sensor technology, smartphone sensing has become an effective approach to improve the quality of smartphone applications. However, due to time-varying wireless channels and lack of incentives for the users to participate, the quality and quantity of the data uploaded by the smartphone users are not always satisfying. In this paper, we consider a smartphone sensing system in which a platform publicizes multiple tasks, and the smartphone users choose a set of tasks to participate in. In the traditional non-cooperative approach with incentives, each smartphone user gets rewards from the platform as an independent individual and the limit of the wireless channel resources is often omitted. To tackle this problem, we introduce a novel cooperative approach with an overlapping coalition formation game (OCF-game) model, in which the smartphone users can cooperate with each other to form the overlapping coalitions for different sensing tasks. We also utilize a centralized case to describe the upper bound of the system sensing performance. Simulation results show that the cooperative approach achieves a better performance than the non-cooperative one in various situations.

In this paper, we study the resource allocation problem for a single-cell non-orthogonal multiple access (NOMA) relay network where an OFDM amplify-and-forward relay allocates the spectrum and power resources to the source-destination (SD) pairs. We aim to optimize the resource allocation to maximize the average sum-rate. The optimal approach requires an exhaustive search, leading to an NP-hard problem. To solve this problem, we propose two efficient many-to-many two-sided SD pair-subchannel matching algorithms, in which the SD pairs and sub-channels are considered as two sets of players chasing their own interests. The proposed algorithms can provide a sub-optimal solution to this resource allocation problem in affordable time. Both the static matching algorithm and the dynamic matching algorithm converge to a pair-wise stable matching after a limited number of iterations. Simulation results show that the capacity of both proposed algorithms in the NOMA scheme significantly outperforms the conventional orthogonal multiple access scheme. The proposed matching algorithms in NOMA scheme also achieve a better user-fairness performance than the conventional orthogonal multiple access.

In this paper, we study the codebook-based resource allocation problem for an uplink sparse code multiple access (SCMA) network. The base station (BS) assigns to each user a set of subcarriers corresponding to a specific codebook, and each user performs power control over multiple subcarriers. We aim to optimize the subcarrier assignment and power allocation to maximize the total sum-rate. To solve the above problem, we formulate it as a many-to-many two-sided matching problem with externalities. A novel swap-matching algorithm is then proposed in which the users and the subcarriers are considered as two sets of players, and every two users can cooperate to swap their matches so as to improve each other's profits. The algorithm converges to a pair-wise stable matching after a limited number of iterations. Simulation results show that the proposed algorithm greatly outperforms the orthogonal multiple access scheme and a random allocation scheme.

In this paper, we study the resource allocation problem for a single-cell non-orthogonal multiple access (NOMA) relay network where an OFDM amplify-and-forward (AF) relay allocates the spectrum and power resources to the source-destination (SD) pairs. We aim to optimize the spectrum and power resource allocation to maximize the total sum-rate. This is a very complicated problem and the optimal approach requires an exhaustive search, leading to a NP hard problem. To solve this problem, we propose an efficient many-to-many two sided SD pair-subchannel matching algorithm in which the SD pairs and sub-channels are considered as two sets of rational and selfish players chasing their own interests. The algorithm converges to a pair-wise stable matching after a limited number of iterations with a low complexity compared with the optimal solution. Simulation results show that the sum-rate of the proposed algorithm approaches the performance of the optimal exhaustive search and significantly outperforms the conventional orthogonal multiple access scheme, in terms of the total sum-rate and number of accessed SD pairs.

In this paper, the resource allocation and scheduling problem for a full-duplex (FD) orthogonal frequency-division multiple-access network is studied where an FD base station simultaneously communicates with multiple pairs of uplink (UL) and downlink (DL) half-duplex (HD) users bidirectionally. In this paper, we aim to maximize the network sum-rate through joint UL and DL user pairing, OFDM subchannel assignment, and power allocation. We formulate the problem as a non-convex optimization problem. The optimal algorithm requires an exhaustive search, which will become prohibitively complicated as the numbers of users and subchannels increase. To tackle this complex problem more efficiently, we formulate the user-pairing and subchannel allocation problem as a three-sided matching problem, and propose a novel low-complexity near-optimal matching algorithm. The algorithm is analyzed, and we prove that it converges to a stable matching. Simulation results show that the FD scheme can significantly improve the spectrum efficiency compared with the HD scheme. The proposed algorithm performs very close to the optimal algorithm, and significantly outperforms other resource allocation schemes.

In this paper, we study the resource allocation and user scheduling problem for a downlink non-orthogonal multiple access network where the base station allocates spectrum and power resources to a set of users. We aim to jointly optimize the sub-channel assignment and power allocation to maximize the weighted total sum-rate while taking into account user fairness. We formulate the sub-channel allocation problem as equivalent to a many-to-many two-sided user-subchannel matching game in which the set of users and sub-channels are considered as two sets of players pursuing their own interests. We then propose a matching algorithm, which converges to a two-side exchange stable matching after a limited number of iterations. A joint solution is thus provided to solve the sub-channel assignment and power allocation problems iteratively. Simulation results show that the proposed algorithm greatly outperforms the orthogonal multiple access scheme and a previous non-orthogonal multiple access scheme.

In this paper, we study the resource allocation and scheduling problem for a downlink non- orthogonal multiple access (NOMA) network where the base station (BS) allocates the spectrum resources and power to the set of users. We aim to optimize the sub-channel assignment and power allocation to achieve a balance between the number of scheduled users and total sum-rate maximization. To solve the above problem, we propose a many-to-many two-sided user-subchannel matching algorithm in which the set of users and sub-channels are considered as two sets of players pursuing their own interests. The algorithm converges to a pair-wise stable matching after a limited number of iterations. Simulation results show that the proposed algorithm can approach the performance of the upper bound and greatly outperforms the OFDMA scheme.

The idea of in-band full-duplex (FD) communications revives in recent years owing to the significant progress in the self-interference cancellation and hardware design techniques, which offers the potential to double spectral efficiency. However, the adaptations from lower to upper layers are highly demanded in the design of FD communication systems. In this paper, we first propose a novel medium access control (MAC) protocol on a single channel using FD techniques that allows transmitters to monitor the channel usage while transmitting, and backoff when collision happens. Specifically, imperfect sensing brought by residual self-interference (RSI) in the physical layer is taken into account in the design of the protocol, and throughput is analytical derived. Then, we extend the protocol to multichannel scenario and propose a multichannel selection mechanism based on the rate and occupancy history of each channel for FD users. Simulation results show the effectiveness of the channel selection protocol and indicate that the total throughput of the proposed FD-MAC protocol can significantly outperforms the greedy channel selection strategy based on the CSMA access mechanism.

In this paper, we study the resource allocation problem in a small cell Orthogonal Frequency Division Multiple Access (OFDMA) network consisting of multiple access points (APs) and subscribed users. We formulate the subchannel allocation and the user assignment as a joint optimization problem, and solve it utilizing a novel three-sided matching algorithm. In the proposed algorithm, a unified cooperative framework is constructed in which we discuss both the non-overlapping and the overlapping coalitions formed by the APs, as well as the traditional non-cooperative case.

With the rapid growth of sensor technology, smartphone sensing has become an effective approach to help improve the quality of applications in smartphones. However, the quality and quantity of the sensed data uploaded by the users are not always satisfying due to the lack of incentives for users to participate. In this paper, we design an incentive mechanism in which the users can get satisfying rewards from the platform by efficiently allocating their resources, and meanwhile, the platform can achieve a relatively high social welfare. Specifically, to solve the resource allocation problem in the incentive mechanism, we consider a cooperative game model with overlapping coalitions, in which the smartphone users can self-organize into the overlapping coalitions for different sensing tasks. Then, we propose a distributed algorithm that converges to a stable outcome in which no user has the motivation to change its current resource allocation so as to increase its individual payoff unilaterally. Simulation results show that the proposed scheme achieves a better performance than the average distribution case and the single-task distribution case in various conditions.