Triangular meshes play a crucial role in finite element analysis, especially when modeling complex geometries with irregular boundaries and fine geometric details. However, mesh quality directly impacts analysis accuracy and efficiency. Existing mesh generation and optimization methods are still unable to meet high-quality requirements, and most mesh topology optimization approaches rely on heuristic strategies and manual expertise, making them vulnerable to local optima. To address this issue, this paper proposes a deep reinforcement learning-based topology optimization framework for triangular meshes. This framework constructs a multi-head graph attention network (GAT) that integrates vertex topological properties with edge geometric features. It designs a reward mechanism that combines irregularity improvement and geometric feature preservation, guiding the agent to independently explore optimal topological adjustment strategies through self-play without prior knowledge. This approach preserves geometric features while approximating the optimal vertex degree distribution and effectively reducing mesh irregularity. Experimental results demonstrate that the method is effective and robust in local adaptive topology optimization, providing new insights into high-quality mesh topology optimization.

In reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) systems, beamforming design based on minimizing the Cramér-Rao bound (CRB) for target direction-of-arrival (DoA) estimation is pivotal for sensing capability enhancement. However, due to practical hardware limitations, phase-shift errors (PSEs) exist at the RIS reflectors and cause performance deterioration. To reduce the adverse impact of PSEs, we develop a novel stochastic optimization (SO)-based expected CRB (ECRB) minimization framework, where the ECRB is defined as the expectation of CRB taken over random PSEs, statistical channel state information (CSI), and historical DoA estimates following known prior distributions. Specifically, we formulate an SO problem to minimize the ECRB, subject to an ergodic achievable sum-rate (EASR) constraint. To solve this non-convex problem, we propose a novel penalty-based block stochastic gradient descent (PBSGD) method. In this method, we first introduce a penalty factor to move the EASR constraint into the objective function. The optimal penalty factor is rigorously proved to be determinable via a bisection search. Then, we design a projected block stochastic gradient descent process to update transmit beamformers and RIS phase shifts alternately with guaranteed convergence. Simulation results demonstrate that our proposed method outperforms several benchmarks, including random phase-shift design, sensing-only beamforming, and state-of-the-art semidefinite relaxation (SDR)-based CRB optimization techniques, while exhibiting enhanced robustness against PSEs.

Decentralized Federated Learning (DFL) enables collaborative model training without central coordination. However, DFL faces challenges in dynamic networks, where existing methods struggle to balance consensus rate and communication efficiency, while overlooking practical issues such as topology variation. This paper presents Dynamic AirComp-enabled DFL (DA-DFL), a novel framework that integrates over-the-air computation (AirComp) with the BASE-GRAPH consensus algorithm for efficient DFL over dynamic topologies. The convergence analysis for DA-DFL under dynamic settings is conducted to reveal the influence of the consensus period and communication errors. We define communication overhead metrics, and jointly optimize transceiver beamformers and dynamic topologies. A topology matching algorithm is developed to reduce communication overhead by aligning logical and physical topologies. Experiments show significant gains of DA-DFL in communication efficiency, e.g., reducing communication links and distances by up to 42% and 50%, respectively, compared to benchmarks.

Time-of-arrival (ToA)-based user localization typically requires precise clock synchronization between base stations and user equipments, making the localization and synchronization problems tightly coupled with each other. This paper considers a distributed multi-input multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system and addresses joint multi-user localization and clock synchronization within an integrated sensing and communication (ISAC) framework. Unlike existing works that only consider clock bias, we account for the impacts of both clock bias and clock skew on MIMO-OFDM signals. Specifically, clock bias introduces a constant offset in path delays, whereas clock skew causes a mismatch in the OFDM symbol durations between the transmitter and the receiver, resulting in linearly varying delays across OFDM symbols. We formulate the joint localization and synchronization problem within a Bayesian framework. Based on variational message passing and the sum-product rule, we propose a message passing algorithm, termed Bayesian Localization and Clock Synchronization (BLACS), which jointly estimates the positions, clock parameters, and velocities of multiple users. Simulation results show that accounting for clock skew significantly improves localization accuracy compared to the baseline methods. Moreover, the proposed BLACS algorithm achieves performance close to the Bayesian Cramér–Rao Bound, demonstrating its effectiveness and near-optimality.

This paper studies the near-field localization problem under dynamic scenarios, which harnesses the non-line-of-sight (NLoS) components, in a reconfigurable intelligent surface (RIS)-aided system equipped with extremely large-scale multi-input multi-output (XL-MIMO). To reduce the complexity of the position estimation, the subarray far-field model is employed to approximate the near-field channel. A factor graph within a Bayesian framework is constructed to detail the probability transition relationship among the relevant variables. Based on the message passing in this factor graph, a near-field localization algorithm is developed to estimate the marginal probability distributions of the UE’s and scatterers’ positions in each time slot. The misspecified Cramér-Rao Lower Bound (MCRLB) is derived to evaluate the performance of the algorithm under the subarray far-field model. To explore the localization potential of the system, a closed-form solution for a low-complexity directional beamforming design and a robust beamforming design based on the gradient descent method (GDM) are further proposed. Numerical results demonstrate that the proposed algorithm outperforms the benchmark schemes, and validate the performance gain of harnessing the NLoS components.

The widespread adoption of wireless communication systems in both military and civilian applications has significantly advanced technological progress and social development across various industries. However, narrowband interference signals pose a significant challenge, severely disrupting the normal operation of wireless communication equipment. A major obstacle in existing narrowband interference detection lies in enhancing robustness under complex channel propagation conditions and diverse, dynamically changing types of interference. In view of those challenges, we propose a robustness-enhanced narrowband interference detection method by utilizing unlabeled data. The proposed detection network incorporates soft-shrink technology to isolate irrelevant signal features while adaptively extracting and fusing original and time-frequency features. The proposed method leverages the distribution characteristics of interference frequency bands to enhance model robustness in varying channel propagation environments. Additionally, we design a pseudo-label-based model tuning process to exploit the potential of unlabeled data, further enhancing the model’s robustness. Comparative experiments demonstrate the superiority of the proposed method against various baselines, as well as against configurations incorporating individual network modules.

Intelligent reflecting surface (IRS)-assisted integrated sensing and communications (ISAC) systems have been extensively studied to meet higher sensing requirements. For detection-oriented IRS-assisted ISAC problems, most studies have overlooked the detection interference caused by clutters and modeled simplified point-like targets. This paper investigates extended target detection in IRS-assisted ISAC systems within clutters. We present an optimal generalized likelihood ratio test detector and derive the corresponding probability of detection (PD) and probability of false alarm in closed form. Then, we jointly optimize the active and passive beamforming of the base station and IRS to maximize the PD under multi-user equipment (UE) communication rate constraints and the total transmit power constraint. We first simplify the complex objective function by proving the invariant property of a subspace projection matrix. We then present a novel alternating optimization (AO)-based algorithm to decouple the original problem into two subproblems, consequently convexified and solved using the semidefinite relaxation method. Simulations demonstrate the convergence of the proposed algorithm. The PD performance and the communication and sensing trade-off are significantly improved, compared to benchmarks.

The integration of global navigation satellite systems (GNSS) and fifth generation (5G) for positioning integrity monitoring has recently become a significant research focus. However, as multi-constellation GNSS and ultra-dense 5G networks are deployed, the probabilities of fault is considerably high, requiring user receivers to safeguard against numerous fault modes resulting from various simultaneous independent faults. This study introduces a fast advanced receiver autonomous integrity monitoring (FARAIM) approach utilizing GNSS/5G integration, featuring a solution separation (SS) algorithm that avoids calculating fault-tolerant position solutions, thereby reducing the number of tests traditionally required. In the FARAIM framework, we discover that adding a single 5G measurement, along with the augmentation of cellular base station clock bias states into a multi-constellation GNSS, does not completely degrade the system’s fault detection capabilities, as evidenced by the SS test’s reduced performance and the consistency of the chi-square test. We derive a tighterGershgorin boundfor the FARAIM algorithm across the overall GNSS/5G measurements. Additionally, we analyze the protection level (PL) performance for adding a single 5G measurement. Finally, autonomous ground vehicle (AGV) experimental results validate the effectiveness of the proposed FARAIM algorithm in enhancing navigation performance and evalute theoretical findings regarding the influence of 5G measurements on integrity parameters.

Unmanned aerial vehicles (UAVs) have attracted plenty of attention due to their high flexibility and enhanced communication ability. However, the limited coverage and energy of UAVs make it difficult to provide timely wireless service for large-scale sensor networks, which also exist in multiple UAVs. To this end, the advanced collaboration mechanism of UAVs urgently needs to be designed. In this paper, we propose a multi-UAV collaborative scheme for seamless data collection and transmission, where UA s are dispatched to collection points (CPs) to collect and transmit the time-critical data to the ground base station (BS) simultaneously through the cooperative backhaul link. Specifically, the mission completion time is minimized by optimizing the trajectories, task allocation, collection time scheduling, and transmission topology of UAVs while ensuring backhaul link to the BS. However, the formulated problem is non-convex and challenging to solve directly. To tackle this problem, the CP locations and transmission topology of UAVs are obtained by sensor node (SN) clustering and region division. Next, the transmission connectivity condition between UAVs is derived to facilitate the trajectory discretization and thus reduce the dimensions of variables. This simplifies the problem to optimizing the UAV hovering locations, hovering time, and CP serving sequence. Then, we propose a point-matching-based trajectory planning algorithm to solve the problem efficiently. The simulation results show that the proposed scheme achieves significant performance gains over the two benchmarks.

As a prominent and appealing paradigm, federated edge learning (FEEL) aims to orchestrate collaborative training across a multitude of distributed edge devices without the need for sensitive information transfer. However, device heterogeneity and wireless transmission distortion, which compromise training robustness and efficiency, severely hinder FEEL deployment in real-world applications. To this end, we propose in this paper a novel FEEL framework empowered by simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) with flexible aggregation. This framework harmonizes devices to train models with heterogeneous intensity in each communication round, while the deployment of STAR-RIS significantly boosts the quality of wireless aggregation. Here, we emphasize that there may exist a crucial trade-off between the heterogeneous local training intensity and the transmission quality, particularly when edge devices operate under a limited energy budget. To illuminate this perspective, we rigorously derive a novel explicit upper bound that captures the joint impact of local training accuracy and the mean square error of wireless aggregation on FEEL convergence performance. Our theoretical results indicate that a blind focus on improving the local training accuracy within a constrained energy budget may ultimately detract from overall training performance. This finding sharply contrasts with existing research, which typically aims to accelerate convergence by increasing local training intensity while neglecting the impact of wireless aggregation distortion. To strike the ideal balance, we formulate a mixed-integer nonlinear programming problem to guide the joint design of the beamforming at devices and the BS, the configuration mode of STAR-RIS, and the local training intensity. Comprehensive experiments on representative datasets demonstrate that our proposed framework achieves significant performance improvements compared with existing baselines.

Extended reality (XR) technologies and applications have grown rapidly in recent years. In addition to providing immersive ultrahigh-definition (UHD) video, XR also allows for a haptic experience where devices can be remotely manipulated to accomplish tasks. However, varying numbers of XR users accessing the communication system can strain limited spectrum resources, posing challenges in resource allocation. Therefore, this article studies resource blocks (RBs) allocation problem in a downlink transmission scenario where real-time cloud XR video and haptic contents need to be transmitted simultaneously. We also consider the random variation in the number of XR users and propose an adaptive distributed multiagent deep reinforcement learning (DRL) combined with Transformer (ADMA-DcT) for dynamic RB allocation method. This method addresses the dynamic change in network input dimensions due to user number variability using a state division module and a self-attention mechanism in the encoder module. To our knowledge, this is the first work to study the RB allocation problem in Cloud XR transmission considering simultaneous transmitting of video and haptic services with a dynamically changing user base. Our extensive simulations show that the ADMA-DcT model, end-to-end trained, outperforms other benchmarks in successfully serving a larger number of XR users under varying user number conditions, demonstrating excellent adaptivity and robustness.

With the rapid expansion of autonomous vehicles and entertainment applications, video traffic in the Internet of Vehicles (IoV) faces exponential growth. This surge in video demand presents significant challenges for effective pre-caching strategies, particularly due to high vehicle mobility and heterogeneous dwell times at edge nodes caused by varying speeds. In this paper, we propose an efficient adaptive video segment pre-caching scheme (AVSC) for the IoV, addressing varying travel durations of vehicles on road. Specifically, we develop two video evaluation models to balance the popularity of cached video segments with the fidelity of their distribution across the entire video, ensuring temporal continuity. Then, a multi-objective optimization problem is formulated to jointly maximize highlight entropy and segment distribution fidelity. By leveraging the time-frequency characteristics of the wavelet transform, initial segment candidates are identified by detecting significant changes in the time series of chunk popularity (derived from analyzing frame-level popularity). This approach reduces the search space and computation time for subsequent segment selection. Based on the initial segment candidates, the highlight-direction optimal algorithm is proposed to iteratively identify highlight candidates by improving highlight entropy. For Pareto-optimal solutions, a caching-segment adjustment algorithm based on neighborhood search is proposed to determine the final cached video segments. Theoretical guarantees are provided for the identification process. Furthermore, the adjustment algorithm is proven to detect the maximal improvement direction of distribution fidelity, enhancing convergence speed. Simulations on real-world video datasets demonstrate the effectiveness of the proposed AVSC.

This paper proposes a new Signal-to-interference-plus-noise ratio (SINR)-based Device selection, Power control, and Reconfigurable intelligent surface (RIS) configuration (SDPR) algorithm, which allows imperfect aggregation of wireless federated learning (FL) in RIS-assisted Non-Orthogonal Multiple Access (NOMA) systems. The SDPR algorithm selects the local models with SINRs within an acceptable range for global aggregations, benefiting FL from involving more local models with tolerable errors. The convergence of FL under the imperfect aggregation is analytically validated, where the influence of the local model quantization and modulation is captured through the translation of the SINR thresholds to the symbol error rates (SERs). Employing successive convex approximation and gradient descent, we jointly optimize the RIS configuration and the transmit powers of participating devices, thereby minimizing the convergence upper bound of FL under imperfect aggregation. Experimental results demonstrate that using SDPR, FL achieves superior convergence and accuracy by effectively utilizing model updates, even if they are received with errors. Moreover, more quantization bits do not necessarily offer better FL accuracy, and need to be tailored under specific SERs.

Unmanned aerial vehicles (UAVs) have received plenty of attention due to their high flexibility and enhanced communication ability, nonetheless, the limited onboard energy restricts UAVs’ application on persistent data collection missions in large areas. In this paper, we propose a rechargeable UAV-assisted periodic data collection scheme, where a UAV is dispatched to periodically collect data from sensor nodes (SNs) in the mission area and charged by a wireless charging platform. Specifically, the periodic data collection completion time is minimized by optimizing the UAV trajectory to reach the optimal balance among the collection time, flight time, and recharging time. The formulated problem is non-convex and difficult to solve directly. To tackle this problem, we divide the main problem into two sub-problems and address them by leveraging successive convex approximation (SCA), bisection search, and heuristic methods. Then, we propose a periodic trajectory optimization algorithm to iteratively solve the two sub-problems to minimize the completion time. Furthermore, to deal with the dynamics of SNs, we propose a low-complexity trajectory adjustment strategy, where the trajectory can be maintained or adjusted locally at the SNs change, which significantly mitigates the computation cost of re-optimization. The simulation results show the superiority and robustness of the proposed scheme and the completion time is on average 39% and 33% lower than the two benchmarks, respectively.

Federated learning (FL) contributes to data privacy by not disclosing raw data, but encounters challenges of privacy leakage from local gradient uploading. This paper introduces a novel over-the-air computation (AirComp)-based FL system that balances privacy and accuracy by leveraging the waveform superposition and channel propagation characteristics of AirComp. Specifically, we derive the privacy leakage metric to explicitly account for the effects of waveform aggregation and communication noise. We analyze the convergence upper bound to capture model update errors stemming from artificial and communication noise. We formulate a new joint privacy-accuracy optimization problem by incorporating privacy leakage in the model training objective, guiding the learning process towards enhanced privacy protection. We then employ convex optimization techniques to derive the optimal power scaling and artificial noise intensity. Simulations demonstrate up to 80% reduction in privacy leakage compared to baselines under stringent privacy constraints, while maintaining competitive learning performance. Our method exhibits enhanced robustness under low signal-to-noise ratios, achieving 40% lower privacy leakage under equivalent privacy budgets.

Extremely large antenna array (ELAA) not only effectively enhances system communication performance but also improves the sensing capabilities of communication systems, making it one of the key enabling technologies in 6G wireless networks. This paper investigates the multiuser localization problem in an uplink Multiple Input Multiple Output (MIMO) system, where the base station (BS) is equipped with an ELAA to receive signals from multiple single-antenna users. We exploit analog beamforming to reduce the number of radio frequency (RF) chains. We first develop a comprehensive near-field ELAA channel model that accounts for the antenna radiation pattern and free space path loss. Due to the large aperture of the ELAA, the angular resolution of the array is high, which improves user localization accuracy. However, it also makes the user localization problem highly non-convex, posing significant challenges when the number of RF chains is limited. To address this issue, we use an array partitioning strategy to divide the ELAA channel into multiple subarray channels and utilize the geometric constraints between user locations and subarrays for probabilistic modeling. To fully exploit these geometric constraints, we propose the array partitioning-based location estimation with limited measurements (APLE-LM) algorithm based on the message passing principle to achieve multiuser localization. We derive the Bayesian Cramér-Rao Bound (BCRB) as the theoretical performance lower bound for our formulated near-field multiuser localization problem. Extensive simulations under various parameter configurations validate the proposed APLE-LM algorithm. The results demonstrate that APLE-LM achieves superior localization accuracy compared to baseline algorithms and approaches the BCRB at high signal-to-noise ratio (SNR).

Channel state information (CSI) acquisition is essential for the base station (BS) to fully reap the beamforming gain in intelligent reflecting surface (IRS)-aided downlink communication systems. Recently, Wang et al. (2020) revealed a strong correlation in different users’ cascaded channels stemming from their common BS-IRS channel component, and leveraged such a correlation to significantly reduce the pilot transmission overhead in IRS-aided uplink communication. In this paper, we aim to exploit the above channel property to reduce the overhead for both pilot and feedback transmission in IRS-aided downlink communication. Note that in the downlink, the distributed users merely receive the pilot signals containing their own CSI and cannot leverage the correlation in different users’ channels, which is in sharp contrast to the uplink counterpart considered in Wang et al. (2020). To tackle this challenge, this paper proposes a novel “quantize-then-estimate” protocol in frequency division duplex (FDD) IRS-aided downlink communication. Specifically, the users quantize and feed back their received pilot signals, instead of the estimated channels, to the BS. After de-quantizing the pilot signals received by all the users, the BS estimates all the cascaded channels by leveraging their correlation, similar to the uplink scenario. Under this protocol, we manage to propose efficient user-side quantization and BS-side channel estimation methods. Moreover, we analytically quantify the pilot and feedback transmission overhead to reveal the significant performance gain of our proposed scheme over the conventional “estimate-then-quantize” scheme.

In dual-functional radar-communication (DFRC) systems, to achieve desirable performance in both direction-of-arrival (DoA) estimation for sensing targets and wireless communication for user equipments (UEs), the Cramér-Rao bound (CRB) for DoA estimation and the communication rates of UEs should be jointly optimized through beamforming design. However, the CRB function is inherently dependent on prior knowledge of DoA, which may only be obtained through existing estimators that introduce unavoidable estimation errors. Such errors inevitably degrade the optimization performance. To address this issue, we propose novel optimization methodologies for CRB minimization and communication rate guarantees, which effectively reduce the adverse impact of target DoA estimation errors. Specifically, considering a bounded DoA error model and a statistical DoA error model, two optimization problems are formulated to optimize the worst-case CRB and the statistical mean of CRB over the DoA error regions, while ensuring that the communication rates of multiple UEs exceed predefined thresholds. To tackle the first problem, we propose a semidefinite relaxation (SDR)-based iterative entropic regularization (SDR-IER) method, acquiring approximate solutions via alternating outer minimization and inner maximization. For the second problem, we develop a vectorial space analysis (VSA)-based projected stochastic gradient descent (VSA-PSGD) approach, featuring closed-form projections per iteration for single-user cases, and successive convex approximation (SCA)-based projections for multi-user cases. Simulation results demonstrate that our proposed methods exhibit enhanced robustness against target DoA estimation errors, compared with the existing benchmarks that do not take these errors into account.

Reconfigurable intelligent surface(RIS)-assisted communication systems have been extensively studied for providing high-precision location services. However, most studies have overlooked the impact ofcarrier frequency offset(CFO) andphase noise(PN) resulting from hardware impairments on localization. This paper presents a novel,alternating optimization(AO)-based algorithm to jointly estimate the CFO, PN, anduser equipment(UE) position inorthogonal frequency division multiplexing(OFDM) systems, where, provided the UE position, closed-form expressions for the CFO and PN are derived per iteration, significantly reducing the complexity and enhancing the stability of the algorithm. Another important aspect is a new RIS phase shift optimization algorithm developed to minimize the analytical lower bound of localization accuracy, hence benefiting localization. The semidefinite relaxation method and Schur complement are utilized to convexify this challenging non-convex optimization problem to a semidefinite program. Simulations demonstrate the effectiveness of the proposed algorithms, with the localization accuracy enhanced by two orders of magnitude. The localization accuracy of the proposed algorithm is close to the analytical lower bound, with a root mean square error of lower than 10−2m.

The fifth generation (5G) positioning, a breakthrough in cellular navigation, revolutionizes location services. Multistatic time-of-arrival (TOA) 5G localization is a topic of significant interest due to its exceptional performance benefits. The spatial arrangement of unmanned aerial vehicles (UAVs) and the positions of the targets play a crucial role in precisely determining the target’s location in 5G environments. This study introduces a novel approach to enhance multistatic 5G localization performance through the placement optimization of UAVs. The derivation of the Cramér-Rao lower bound (CRLB) for TOA-based multistatic 5G localization is given, using unit norm vectors instead of conventional trigonometric parameterizations. A distinctive dual iteration majorization-minimization (DIMM) algorithm is derived, grounded in the MM principle. Our method outperforms current state-of-the-art algorithms tailored for uncorrelated noise in measurements, as it effectively addresses both uncorrelated and correlated noise scenarios. Additionally, the proposed method outperforms both gradient descent and alternating directions method of multipliers (ADMM) approaches in terms of performance. A comprehensive analysis of computational complexity and convergence attests to the pragmatic viability of our methodology. Rigorous simulations affirm its effectiveness across diverse noise variances and UAV-target distances.

Relay-enabled cooperative communication has been a hot topic in the area of the Internet of Things (IoT), for its help in providing important solutions to resource limitations in IoT communication scenarios. In this article, we study the outage minimization problem in a power-limited decode-and-forward (DF) relaying network with environmental uncertainty. To reduce the outage probability and improve the quality of service, existing researches usually rely on the assumption of both exact instantaneous channel state information (CSI) and environmental uncertainty. However, it is difficult to obtain perfect instantaneous CSI immediately under practical situations where channel states change rapidly, and the uncertainty in communication environments may not be observed, which makes traditional methods not applicable. Therefore, we turn to reinforcement learning (RL) methods for solutions, which do not need any prior knowledge of underlying channels or assumptions of environmental uncertainty. The RL method is to learn from the interaction with the communication environment, optimize its action policy, and then propose relay selection and power allocation schemes. In this work, we first analyze the robustness of RL action policy by giving the lower bound of the worst case performance, when RL methods are applied to communication scenarios with environment uncertainty. Then, we propose a robust algorithm for outage probability minimization based on RL. Simulation results reveal that compared to traditional RL methods without robust design, our approach has good generalization ability and can improve the worst case performance by about 4%.

This article is dedicated to addressing the localization challenge in 5G environments, specifically utilizing time-of-arrival (TOA) measurements. The focus is on scenarios where the user equipment (UE) and base transceiver stations (BTSs) or first-generation NodeBs (gNBs) face challenges related to synchronization or inaccurate BTS or gNB positions. First, we present the range-weighted majorization minimization (RW-MM) algorithm, harnessing majorization minimization (MM) techniques for UE localization. We rigorously establish the algorithm’s monotonicity and demonstrate its convergence to a stationary point, providing a theoretical foundation for the RW-MM method. In addition, we present a novel UE localization method called robust range-weighted semidefinite relaxation (RRW-SDR). This method is specifically designed for situations involving bounded gNB position errors. The RRW-SDR algorithm optimizes the worst-case weighted least square function while uniquely addressing the individual error constraints associated with each gNB. Furthermore, we derive the Cramér-Rao lower bound (CRLB) for TOA-based UE localization. We additionally establish a more stringent lower bound on the determinant of the target estimation error covariance. This is particularly significant when dealing with scenarios that include independent measurement noise with varying variances. Moreover, we identify an optimal user-gNB geometrical configuration capable of achieving this lower bound. To validate the effectiveness and practical applicability of our contributions, we conduct a series of meticulous numerical simulations. These experiments affirm the robustness and utility of the developed methods.

This article focuses on integrated navigation systems using a combination of Global Navigation Satellite Systems (GNSS) and fifth-generation (5G) technology in urban environments. To address the challenge of accurately estimating the mobile terminal state in multipath environments, a robust evolutionary particle filter (REPF) technique is proposed. First, this article presents a modified clock compensation two-way pseudorange scheme that significantly improves pseudorange accuracy in nonideal line-of-sight/nonline-of-sight (LOS/NLOS) pseudorange environments. Then, this article derives the posterior belief conditioned on the obtained pseudorange measurements and velocity data from GNSS. Utilizing the aforementioned posterior distribution, we introduce a robust particle filter (RPF) algorithm to gauge both the sight state and localization in environments with multipath effects. To address the issue of particle degradation in the RPF algorithm, this article introduces a new evolutionary algorithm based on genetic theory to enhance the diversity of particle filtering. The proposed REPF technique is assessed in 5G ultradense networks, and simulation results demonstrate its achievement of accuracy in positioning and tracking at the meter level for moving targets in both LOS and NLOS environments.

Under numerous computation requirements in intelligent traffic, platoons comprised of several connected vehicles are expected to centralize vehicular computation resources, which can provide computing services for surrounding mobile users and thus facilitate the deployment of vehicular edge computing (VEC). Nevertheless, the computing resources in multi-platoon are distributed unevenly, making platoons’ resource allocation highly selective, especially for randomly distributed mobile users. Moreover, existing works mainly focus on single-platoon-assisted VEC, which lacks resource coordination among platoons and may result in resource imbalance. Thus, through coordinating computing resource allocation in platoons and base stations (BSs), a coordinated computing resource allocation scheme is proposed in this paper to maximize the utilities of mobile users. However, the formulated problem is a mixed integer nonlinear programming (MINLP) problem, which is NP-hard. To address this issue, the efficiency-maximized optimal computing resources allocated from platoons to users are derived in a semi-closed form. Then, to decouple the variables in allocation coordination, the efficiency maximization problem is proven to be equivalent to a low-complexity resource allocation problem oriented for single users. Based on the above results, two algorithms are proposed to convert the NP-hard problem into convex ones, 1) through efficiency-maximized task offloading and backtracking iteratively, a profit efficiency backtracking algorithm is proposed to coordinate computing resource allocation among platoons, and 2) heterogeneous profit efficiency algorithm is proposed to solve the primal problem, where a partition ratio-based efficiency maximization computing resource allocation problem is optimized in heterogeneous edges. Extensive simulation results show that our proposed scheme can improve resource allocation efficiency, task success rates, and service continuity over benchmark schemes.

With the rapid growth of cloud computing, frequent workload bursts show an increasing influence on the Quality of Service (QoS) and energy efficiency of cloud-based data centers. Existing virtual machine placement schemes are expected to optimize either QoS or energy efficiency for cloud data centers running under bursty workload conditions. To bridge this gap, we propose a burst-aware and thermal-efficient virtual machine placement technique calledBTVMP. BTVMP adopts a two-step strategy to achieve energy efficiency while assuring QoS. First, BTVMP leverages a split-and-recombine algorithm – SAR – to deal with bursty workloads. SAR prioritizes critical workloads while preventing low-priority workloads from starvation, thereby assuring QoS. Second, BTVMP utilizes an enhanced simulated annealing algorithm calledESAto offer optimal thermal-efficient virtual machine placement (VMP) solutions, aiming to minimize the energy consumption of data centers. To facilitate estimating energy consumption, we integrate into BTVMP a thermal model that takes into account heat re-circulation effects. We conduct extensive experiments with a real-world trace. We compare BTVMP with the leading-edge VMP strategies, including Genetic Algorithm (XINT-GA), Power-Aware and Performance-Guaranteed Virtual Machine Placement (PPVMP), Peak Load Scheduling Control Method (PLSC), First Come First Serve (FCFS), and GReedy based scheduling Algorithm miNImizing Total Energy (GRANITE). The experimental results unveil that BTVMP not only enhances QoS but also exhibits superb energy efficiency. In particular, BTVMP reduces PLSC's workload delay and FCFS's critical workload delay by 18$\%$and 11$\%$, respectively. Moreover, BTVMP lowers the total energy consumption of the three alternative algorithms –GRANITE, XINTGA, PPVMP, and PLSC – by anywhere between 27.8$\%$and 49.4$\%$.

As an emerging distributed learning framework, federated edge learning (FEEL) can efficaciously resolve the delay requirements and privacy concerns by enabling collaborative modeling among the edge devices under the premise of data localization. However, the communication bottlenecks, e.g., model damage and signal deviation, will critically diminish the convergence performance due to the restricted resources and the undesirable wireless fading. To overcome this challenge, one feasible way is to integrate the reconfigurable intelligent surface (RIS) into the FEEL system, to reinforce the communication quality by adaptively reconfiguring the signal propagation environment. However, the significant premise to effectively exploit the RIS in most of the prior works is the estimation of exact instantaneous channel state information (CSI), which is extremely thorny and potentially incurs additional communication overhead. To tackle this issue, we investigate in this paper the RIS-aided FEEL system under the realistic supposition where only the statistical CSI is available among devices. Specifically, considering the wireless outage caused by the uncertainty of non-line-of-sight components, we rigorously derive an explicit convergence upper bound of the RIS enabled FEEL framework with outage. Accordingly, a resource configuration problem with the goal of minimizing the sum of outage-probability is further formulated by jointly configuring the RIS configuration matrix and the bandwidth allocation. To seek the solutions, we carefully design a general Bernstein-Type inequality in this paper, and thus the probabilistic outage objective function can be effectively handled in an equivalent manner. Simulation experiments verify that our design can accomplish a significant promotion compared against state-of-the-art baselines.

In intelligent reflecting surface (IRS) assisted communication, beam search is usually time-consuming as the multiple-input multiple-output (MIMO) of IRS is usually very large. The hierarchical codebook is a widely accepted method for reducing the complexity of searching time. The performance of this method strongly depends on the design scheme of beamforming of different beamwidths. In this paper, a non-constant phase difference (NCPD) beamforming algorithm is proposed. To implement the NCPD algorithm, we first model the phase shift of IRS as a continuous function and then determine the parameters of the continuous function through the analysis of its array factor. Then, we propose a hierarchical codebook and two beam training schemes, namely the joint searching (JS) scheme and direction-wise searching (DWS) scheme by using the NCPD algorithm which can flexibly change the width, direction, and shape of the beam formed by the IRS array. Numerical results show that the NCPD algorithm is more accurate with smaller side lobes, and also more stable on IRS of different sizes compared to other wide beam algorithms. The misalignment rate of the beam formed by the NCPD method is significantly reduced. The time complexity of the NCPD algorithm is constant, thus making it more suitable for solving the beamforming design problem with practically large IRS.

The cellular network of magnetic Induction (MI) communication holds promise in long-distance underground environments. In the traditional MI communication, there is no fast-fading channel since the MI channel is treated as a quasi-static channel. However, for the vehicle (mobile) MI (VMI) communication, the unpredictable antenna vibration brings the remarkable fast-fading. As such fast-fading cannot be modeled by the central limit theorem, it differs radically from other wireless fast-fading channels. Unfortunately, few studies focus on this phenomenon. In this paper, using a novel space modeling based on the electromagnetic field theorem, we propose a 3-dimension model of the VMI antenna vibration. By proposing “conjugate pseudo-piecewise functions” and boundary$p(x)$distribution, we derive the cumulative distribution function (CDF), probability density function (PDF) and the expectation of the VMI fast-fading channel. We also theoretically analyze the effects of the VMI fast-fading on the network throughput, including the VMI outage probability which can be ignored in the traditional MI channel study. We draw several intriguing conclusions different from those in wireless fast-fading studies. For instance, the fast-fading brings more uniformly distributed channel coefficients. Finally, we propose the power control algorithm using the non-cooperative game and multiagent Q-learning methods to optimize the throughput of the cellular VMI network. Simulations validate the derivation and the proposed algorithm.

For intelligent reflecting surface (IRS) aided down-link communication in frequency division duplex (FDD) systems, the overhead for the base station (BS) to acquire channel state information (CSI) is extremely high under the conventional “estimate-then-quantize” scheme, where the users first estimate and then feed back their channels to the BS. Recently, [1] revealed a strong correlation in different users' cascaded channels stemming from their common BS-IRS channel component, and leveraged such a correlation to significantly reduce the pilot transmission overhead in IRS-aided uplink communication. In this paper, we aim to exploit the above channel property for reducing the overhead of both pilot transmission and feedback transmission in IRS-aided downlink communication. Different from the uplink counterpart where the BS possesses the pilot signals containing the CSI of all the users, in downlink communication, the distributed users merely receive the pilot signals containing their own CSI and cannot leverage the correlation in different users' channels revealed in [1]. To tackle this challenge, this paper proposes a novel “quantize-then-estimate” protocol in FDD IRS-aided downlink communication. Specifically, the users first quantize their received pilot signals, instead of the channels estimated from the pilot signals, and then transmit the quantization bits to the BS. After de-quantizing the pilot signals received by all the users, the BS estimates all the cascaded channels by leveraging the correlation embedded in them, similar to the uplink scenario. Under this protocol, we propose efficient methods for quantization at the user side and channel estimation at the BS side. Furthermore, we manage to show both analytically and numerically the great overhead reduction in pilot transmission and feedback transmission arising from our proposed “quantize-then-estimate” protocol.

In this paper, we address the key enabling technologies for collaborative global navigation satellite system/fifth generation (GNSS/5G) navigation in urban environments. First, we derive the posterior belief over the state space conditioned on the given ranging measurements in non-ideal ranging environment (e.g., line-of-sight/non-line-of-sight (LOS/NLOS)) and GNSS velocity. Then, building on the premises of non-ideal ranging in LOS/NLOS, a robust particle filter (RPF) algorithm adopted for urban environments is proposed to estimate the sight state and position of the mobile terminal (MT). Statistics on 5G measurement error in a LOS environment and in the presence of NLOS are presented. Finally, comprehensive performance evaluations are carried out in 5G ultra-dense networks. Simulation results demonstrate that meter-scale positioning and tracking accuracy can be achieved in LOS/NLOS using the proposed RPF technique.

Mobile-edge computing (MEC) technology offers computing resources for mobile devices to conduct computationally heavy activities by putting servers at the wireless mobile network’s edge. This mitigates the scarcity of computing resources in mobile devices and enhances the intelligence of the Internet of Things (IoT), which is a crucial technology for achieving industrial digitalization. Considering the time-varying channel as well as the time-varying available computing resources of MEC servers, this article formulates a hybrid optimization problem that combines task offload and resource allocation. The goal is to minimize MEC servers’ overall power consumption. Since the channel state information (CSI) stored in the MEC system is not real time, we propose a reinforcement learning (RL) algorithm for predicting current CSI from historical CSI and obtain the optimal strategy for task offloading. On the other hand, convex optimization methods are used to accomplish the dynamic resource allocation strategy. In addition, an approach based on deep RL (DRL) is put forward to overcome the dimensionality curse in RL algorithms. The simulation experiments illustrate that the proposed algorithms outperform the nonpredictive schemes by a large margin, and their performance is close to that of the optimum scheme, which utilizes simultaneous CSI.

To address the large concerns of energy consumption caused by the rapid growth of online data traffic and network services such as the applications of the Internet of Things, many technologies have been developed to help wireless communication. Mobile edge computing (MEC) can develop the efficiency and reduce the energy consumption of the network through edge-cloud benefits. Intelligent reflecting surface (IRS) can improve spectrum efficiency and decrease power costs by changing the transmission environment. Considering that IRS also has some good physical characteristics, it can be integrated into MEC as auxiliary equipment and yield marked performance improvement. In this study, we design an IRS-assisted cache-aided MEC system by optimizing the beamformer of base station (BS) and the phase-shift vector of IRS jointly. We develop two algorithms based on the block coordinate descent (BCD) method to achieve this goal. First, we propose a branch-and-bound (BB)-based algorithm. By the algorithm, an approximately optimal solution of the IRS element optimization problem can be obtained under constant modulus constraints. Then, we develop a Lagrange multiplier method-based algorithm that has less complexity. The performance of IRS-assisted cache-aided MEC with the proposed algorithms is demonstrated by simulation results.

Reconfigurable intelligent surface (RIS) has attracted enormous interest for its potential advantages in assisting both wireless communication and environmental sensing. In this paper, we study a challenging multiuser tracking problem in the multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system aided by multiple RISs. In particular, we assume that a multi-antenna base station (BS) receives the OFDM symbols from single-antenna users reflected by multiple RISs and tracks the positions of these users. Considering the users’ mobility and the blockage of light-of-sight (LoS) paths, we establish a probability transition model to characterize the tracking process, where the geometric constraints between channel parameters and multiuser positions are utilized. We further develop an online message passing algorithm, termed the Bayesian multiuser tracking (BMT) algorithm, to estimate the multiuser positions, the angles-of-arrivals (AoAs) at multiple RISs, and the time delay and the blockage of the LoS path. The Bayesian Cramér Rao bound (BCRB) is derived as the fundamental performance limit of the considered tracking problem. Based on the BCRB, we optimize the passive beamforming (PBF) of the multiple RISs to improve the tracking performance. Simulation results show that the proposed PBF design significantly outperforms the counterpart schemes, and our BMT algorithm can achieve up to centimeter-level tracking accuracy.

The intelligent reflecting surface (IRS) is promising in assisting user localization and wireless communication in the future wireless networks. In this paper, a novel IRS-aided joint localization and communication (L&C) scheme is designed in a millimeter-wave transmission system. For the proposed scheme, the user position/orientation estimation error bound (POEB) and the effective achievable data rate (EADR) are derived in closed-form as L&C performance metrics, which reveal the inherent trade-off between L&C capabilities. To achieve the joint optimal point of the POEB and EADR in consideration of the localization errors, a worst-case robust beamforming and time allocation optimization problem is formulated. To solve the original non-convex problem, a novel joint optimization approach is developed. Specifically, from an equivalent minimax problem, the local optimal solutions of the transceiver beamformers, the IRS phase-shift matrix, and the time allocation ratio between user localization stage (ULS) and effective data transmission stage (EDTS), are obtained in closed-form with respect to the localization errors. Then, the worst-case localization error is iteratively found by a dedicated majorize-minimization (MM) based algorithm. Subsequently, potential extensions to general wireless channels and discrete phase-shift models are discussed in detail. Finally, simulations are carried out to show the optimization results and the L&C performance trade-off. In comparison with the conventional non-robust method, the proposed approach is validated to be robust against the user localization uncertainty.

To exploit the potential of the reconfigurable intelligent surface (RIS) in supporting integrated sensing and communication (ISAC), this paper proposes a novel joint active and passive beamforming design for RIS-enabled ISAC system in consideration of the target size. First, the detection probability for target sensing is derived in closed-form based on the illumination power on an approximated scattering surface area of the target, and a new concept of ultimate detection resolution (UDR) is defined for the first time to measure the target detection capability. Then, an optimization problem is formulated to maximize the signal-to-noise ratio (SNR) at the user-equipment (UE) under a minimum detection probability constraint. To solve this non-convex problem, a novel alternative optimization approach is developed. In this approach, the solutions of the communication and sensing beamformers are obtained by our proposed bisection-search based method. The optimal receive combining vector is derived from an equivalent Rayleigh-quotient problem. To optimize the RIS phase shifts, the Charnes-Cooper transformation is conducted to cope with the fractional objective, and a novel convexification process is proposed to convexify the detection probability constraint with matrix operations and a real-valued first-order Taylor expansion. After the convexification, a successive convex approximation (SCA) based algorithm is designed to yield a suboptimal phase-shift solution. Finally, the overall optimization algorithm is built, followed by detailed analyses on its computational complexity, convergence behavior and problem feasibility condition. Extensive simulations are carried out to testify the analytical properties of the proposed beamforming design, and to reveal two important trade-offs, namely, communication vs. sensing trade-off and UDR vs. sensing-duration trade-off. In comparison with several existing benchmarks, our proposed approach is validated to be superior when detecting targets with practical sizes.

Millimeter-wave (mmWave) communications have been one of the promising technologies for future wireless networks that integrate a wide range of data-demanding applications. To compensate for the large channel attenuation in mmWave band and avoid high hardware cost, a lens-based beamspace massive multiple-input multiple-output (MIMO) system is considered. However, the spatial-wideband effect in wideband mmWave systems makes channel estimation very challenging, especially when the receiver is equipped with a limited number of radio-frequency (RF) chains. Furthermore, the real channel data cannot be obtained before the mmWave system is used in a new environment, which makes it impossible to train a deep learning (DL)-based channel estimator using real data set beforehand. To solve the problem, we propose a model-driven unsupervised learning network, named learned denoising-based generalized expectation consistent (LDGEC) signal recovery network. By utilizing the Stein’s unbiased risk estimator loss, the LDGEC network can be trained only with limited measurements corresponding to the pilot symbols, instead of the real channel data. Even if designed for unsupervised learning, the LDGEC network can be supervisingly trained with the real channel via the denoiser-by-denoiser way. The numerical results demonstrate that the LDGEC-based channel estimator significantly outperforms state-of-the-art compressive sensing-based algorithms when the receiver is equipped with a small number of RF chains and low-resolution ADCs.

As one of the most popular and attractive frameworks for model training, federated edge learning (FEEL) presents a new paradigm, which avoids direct data transmission by collaboratively training a global learning model across multiple distributed edge devices, thus overcoming the disadvantage of centralized machine learning in resource limitations, delay constraints, and privacy issues. However, due to the heavy cost of communicating gradient among edge devices, sharing the parameters of a large-scale neural network can still be time-intensive. To alleviate this bottleneck, an efficient scheme, called SignSGD has been recently proposed, where the one-bit gradient quantization with majority vote is featured at edge devices. Nevertheless, the performance of one-bit aggregation will inevitably deteriorate due to the undesirable propagation error introduced by wireless channels. To address this issue, we propose in this work a novel reconfigurable intelligent surface (RIS)-aided one-bit communication optimization scheme under orthogonal frequency division multiple access (OFDMA) to relieve the negative influence of communication error on the SignSGD-based FEEL. Specifically, a learning convergence analysis is firstly presented to quantitatively characterize the impact of wireless communication error measured by the union bound on pairwise bit error rate (BER) on the performance of SignSGD-based FEEL. Immediately, a unified communication-learning optimization problem is further formulated to jointly optimize the sub-band assignment strategy, the power allocation vector, and the RIS configuration matrix. Numerical experiments show that the proposed design achieves substantial performance improvement compared with the state-of-the-art approaches.

The large overhead arising from conventional channel estimations in reconfigurable intelligent surface (RIS) aided millimeter-wave communication systems, may offset the performance gain brought by the RIS. To tackle this issue, we propose a location information assisted beamforming design without the requirement of the channel training process. First, we establish the geometrical relationship between the channel model and the user location, and mathematically derive an approximate channel state information (CSI) error bound based on the user location error region. Then, for combating the negative impact of the location error on the communication performance, we formulate a worst-case robust beamforming optimization problem to optimize the beamformer at the base station (BS) and the phase-shift matrix at the RIS. To solve this non-convex problem, we develop a novel relaxed alternating optimization process (RAOP) by utilizing various optimization tools, such as the Lagrange multiplier, the matrix inversion lemma, the semidefinite relaxation (SDR), as well as the branch-and-bound (BnB). Additionally, we prove sufficient conditions for the SDR to yield rank-one solutions, and modify the BnB to acquire the phase-shift solution under an arbitrary constraint of possible phase-shift values. Finally, we analyse the convergence and complexity of the proposed RAOP, and carry out simulations for performance evaluations. Compared to the conventional non-robust beamforming, our method performs better and shows strong robustness against the location-error-related CSI uncertainty. Compared to the robust beamforming based on the S-procedure and penalty convex-concave procedure (CCP), our method with BnB shows the advantages of being able to converge faster and handle arbitrary phase-shift argument sets.

In this paper, the problem of model aggregation for the federated edge learning (FEEL) over a realistic wireless network is investigated, where multiple distributed edge devices collaboratively train a global learning model using local data. In the considered model, the one-bit gradient quantization with majority vote is adopted at edge devices, which sends the local gradient sign to the edge server instead of transmitting high-dimensional stochastic gradients directly. After aggregating the quantified signs, the edge server sends back only the majority decision to significantly minimize the transmission overhead since all communications are compressed to one bit. Nevertheless, it turns out that the quality of training will be inevitably deteriorated by the undesirable propagation error introduced by wireless channels. To address this issue, we propose in this work a reconfigurable intelligent surface (RIS) assisted one-bit communication scheme under orthogonal frequency division multiplexing (OFDM) system to reduce the signal distortion during iterative model exchange of FEEL. Specifically, a learning convergence analysis with respect to wireless communication error is first established. After that, we further formulate a unified communication-learning design problem to jointly optimize the power allocation vector and the RIS configuration matrix. Numerical results demonstrate that the proposed design achieves substantial performance improvement compared with the state-of-the-art solutions.

The intelligent reflecting surface (IRS) is an artificial metasurface making the communication environment smart and controllable. The IRS on an aerial platform (AIRS) expands the wireless network to the three-dimensional space, thus improving the degree of freedom (DoF) for the signal adjustment. Since the AIRS-enabled wireless channel is generally time-variant in practice, herein, this paper considers the time-varying characteristic of the downlink channels, and proposes a complex-valued ResNet-based deep Q-learning (DQN) algorithm to maximize the sum-rate at user equipment (UE) side, by jointly designing the transmit beamforming at base station (BS) side and the reconfigurable phase shifts at AIRS side. Our results reveal that the proposed complex-valued deep reinforcement learning (DRL) approach shows stronger generalization ability in comparison with the real-valued DRL algorithms, and is validated to be able to mitigate the problem of gradient vanishing and improve the performance over the time-varying downlink channels.

In the wireless communication, deep reinforcement learning (DRL) techniques promise performance optimizations at a low cost. Considering the time-varying property of the wireless downlink channels, this paper proposes a deep deterministic policy gradient (DDPG) approach and a hierarchical DDPG (h-DDPG) approach to optimize the sum-rate at the user equipment (UE) side, by jointly designing the power control and the beam-forming at the base station (BS). Our results demonstrate that the proposed DDPG enables continuous data representation through the deterministic policy functions, while the proposed h-DDPG is able to mitigate the sparse reward problem. Both of the two DRL algorithms are superior to the conventional deep Q-learning (DQN) algorithm, in terms of improving the communication performance over the time-varying wireless downlink channels.

Abstract For a long time, the accurate prediction of passenger flow can provide early warning information for various industries such as the public service industry, tourism industry, and industrial business, thus opportunely arranging passengers and providing homologous services to relieve the overloading of places and the accidents caused by overcrowding of people. In recent years, by using the wireless sensor network to sense the passenger data in advance, the technique of machine learning and neural networks has been utilized to assist the short‐term passenger flow prediction. In this study, building on convolutional neural network (CNN) and long short‐term memory network (LSTM), a complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) and attention‐based CNN‐LSTM network to extract both temporal and spatial characteristics of passenger flow data, is proposed. Moreover, the problem of the inaccuracy of the noise part is properly solved by adding the CEEMDAN algorithm to the input layer. With the proposed network structure, the CNN‐LSTM network is replaced with the Conv‐LSTM network to reduce the information loss and get a further performance improvement. The result shows that 39% performance improvement can be achieved than the case with a single LSTM network, and 28% performance improvement can be achieved than the CNN‐LSTM network.

Mobile augmented reality (AR), which integrates virtual objects (i.e., holographic contents) with 3-D real environments in real time, has been rapidly gaining popularity in the last five years. The delivery mechanisms of these holographic contents to mobile AR devices, however, are rarely investigated. To combat bandwidth limitations that preclude providing holographic contents to user devices on-demand, in this article, we propose the intelligent AR (Intelli-AR) preloading algorithm to improve transmission efficiency in the edge-assisted network, in which edge servers proactively transmit holographic contents to the devices. Without user devices’ future motion trajectories, the Intelli-AR preloading algorithm models the user devices’ motion trajectories as Markov decision process (MDP) and adaptively learns the optimal preloading policy. The Intelli-AR preloading is decomposed into two parts and separately deployed on the edge server and the user devices to reduce the computation complexity. The Intelli-AR solution improves the ratio of successful preloading by 11.52% compared to the best baseline in the practical data set when the users’ motion trajectories tend to be more random, and by 21.97% compared to the best baseline in the data set which is synthesized from a real-life mobile AR environment.

For Ground-penetrating radar (GPR) image and the digital panoramic image obtained from single-hole measuring, a novel and noncooperative measuring method for quantitatively calculating the relative dielectric constant of geo-bodies based on information fusion of the two types of image is proposed. Structural plane tilt angle in borehole optical image and GPR image is extracted to calculate the permittivity and quantify the geological medium properties. The vertical stratification characteristics of geo-bodies can be obtained through the analysis of changing dielectric constant. Experiment with borehole optical and GPR images in this letter reveals the continuity and discontinuity of medium change. It confirms that there are many kinds of layered structures in shallow geology, such as soil, clay, sand with different water content, crushed stone, shale, and solution cracks with varying degrees of development. On the contrary, the values of permittivity of materials in deep geology have slight variation, and it indicates that the deep geological vertical stratification characteristic is relatively simple. The method provides a new and efficient means of dielectric constant calculation and vertical stratification characteristics analysis, especially for complex shallow geology in engineering applications.

In this paper, we study the outage probability minimizing problem in a two-hop cooperative relay network. To reduce outage probability, existing studies propose many schemes for relay selection and power allocation, which are usually based on the assumption of exact channel state information (CSI). However, it is difficult to obtain perfect instantaneous CSI in practical situations where channel states change rapidly, and thus traditional methods would not perform well. Considering these factors, we turn to the emerging reinforcement learning (RL) methods for solutions. RL methods do not need any prior knowledge of CSI, but use neural network for approximation and decision after interacting with communication environment. Nevertheless, conventional RL methods, including most deep reinforcement learning (DRL) methods, cannot perform well when the search space is too large. In addition, non-stationarity is a common problem when using hierarchical reinforcement learning (HRL), which is caused by the changing behavior in different hierarchies. Therefore, we first propose a DRL framework with an outage-based reward function, which is then used as a baseline. Then, we further design an HRL framework and training algorithm. By decomposing relay selection and power allocation into two hierarchical optimization objectives, and combining on- policy and off-policy methods in the HRL framework, our method successfully address the sparse reward and non-stationary problem. Simulation results reveal that compared with traditional DRL method, the proposed HRL training algorithm can converge faster and reduce the outage probability by 8% in two-hop relay network with the same outage threshold.

Not only has information technology evolved rapidly, but the spatial cognition theory for blind and visually impaired (BVI) people has also made great strides, which has opened up a new opportunity for indoor travel assistance systems (ITASs). However, there are still some issues that have not been effectively addressed due to the lack of guidance of the spatial cognition theory. Thus, this article presents a comparative survey among ITASs proposed in the last four years in an effort to inform researchers and developers about system problems and challenges and inform BVI people about the various types and functions of the ITAS. This article will also make researchers and developers aware of the importance of the spatial cognition theory. Furthermore, we give predictions for future trends based on a detailed analysis of 17 ITASs.

Channel estimation is the main hurdle to reaping the benefits promised by the intelligent reflecting surface (IRS), due to its absence of ability to transmit/receive pilot signals as well as the huge number of channel coefficients associated with its reflecting elements. Recently, a breakthrough was made in reducing the channel estimation overhead by revealing that the IRS-BS (base station) channels are common in the cascaded user-IRS-BS channels of all the users, and if the cascaded channel of one typical user is estimated, the other users' cascaded channels can be estimated very quickly based on their correlation with the typical user's channel [1]. One limitation of this strategy, however, is the waste of user energy, because many users need to keep silent when the typical user's channel is estimated. In this paper, we reveal another correlation hidden in the cascaded user-IRS-BS channels by observing that the user-IRS channel is common in all the cascaded channels from users to each BS antenna as well. Building upon this finding, we propose a novel two-phase channel estimation protocol in the uplink communication. Specifically, in Phase I, the correlation coefficients between the channels of a typical BS antenna and those of the other antennas are estimated; while in Phase II, the cascaded channel of the typical antenna is estimated. In particular, all the users can transmit throughput Phase I and Phase II. Under this strategy, it is theoretically shown that the minimum number of time instants required for perfect channel estimation is the same as that of the aforementioned strategy in the ideal case without BS noise. Then, in the case with BS noise, we show by simulation that the channel estimation error of our proposed scheme is significantly reduced thanks to the full exploitation of the user energy.

The advantages of millimeter-wave and large antenna arrays technologies for accurate wireless localization have received extensive attentions recently. However, how to further improve the accuracy of wireless localization, even in the case with obstructed line-of-sight, is largely undiscovered. In this paper, the reconfigurable intelligent surface (RIS) is introduced into the system to make the positioning more accurate. First, we establish the three-dimensional RIS-assisted wireless localization channel model. After that, we derive the Fisher information matrix and the Cramér-Rao lower bound for evaluating the estimation of absolute mobile station position. Finally, we propose an alternative optimization method and a gradient decent method to optimize the reflect beamforming, which aims to minimize the Cramér-Rao lower bound to obtain a more accurate estimation. Our results show that the proposed methods significantly improve the accuracy of positioning, and decimeter-level or even centimeter-level positioning can be achieved by utilizing the RIS with a large number of reflecting elements.

Now indoor Location-Based Services (LBSs) have numerous applications built on indoor navigation. However, current indoor navigation can only meet the needs of finding a certain point of interest (POI), it can not help to find a certain person. Therefore, we propose Location Paging and Answering Service (LPAS) for finding out someone in an indoor environment. However, it may face serious problems of privacy disclosure of the clients' indoor location data. To ensure security, privacy and convenience, we leverage blockchain to model the indoor environment, and build Blockchain-Based Indoor LPAS (BILPAS) by embedding our three-way-handshake Diffie-Hellman Key Agreement (DHKA) procedures. Our BILPAS can automatically establish a secure and privacy communication tunnel between any two users, greatly liberating participants from unnecessary self-determining interactions. We present the proof-of-concept prototype and implement it on Hyperledger Fabric. Then, we conduct the security and privacy analysis and evaluate the time overhead. The experiment results validate the high privacy, security and convenience of our BILPAS.

Reconfigurable intelligent surface (RIS) technology can significantly improve the energy and spectrum efficiency of wireless communication systems. Most existing studies were conducted with an assumption of ideal hardware, while the impact of hardware impairments receives little attention. However, the non-negligible hardware impairments should be taken into consideration when we evaluate the system performance. In this paper, we consider an RIS assisted communication system with hardware impairments, and focus on the channel estimation study and the power scaling law analysis. First, with linear minimum mean square error estimation, we theoretically characterize the relationship between channel estimation performance and impairment level, number of reflecting elements, and pilot power. After that, we analyze the power scaling law and reveal that if the base station (BS) has perfect channel state information, the transmit power of user can be made inversely proportional to the BS antenna number and the square of the reflecting element number with no reduction in performance; If the BS has imperfectly estimated channel state information, to achieve the same performance, the transmit power of user can be made inversely proportional to the square-root of the BS antenna number and the square of the reflecting element number.

This paper investigates a dual-unmanned aerial vehicle (UAV)-enabled secure communication system, in which, a UAV moves around to send confidential messages to a mobile user while another cooperative UAV transmits artificial noise signals to confuse malicious eavesdroppers. Both UAVs have energy constraints and the location information of eavesdroppers is imperfect. We consider a worst-case secrecy rate maximization problem of the mobile user over all time slots. This optimization problem is solved by jointly designing the three-dimensional (3D) trajectory of UAVs and the time allocation (recharging and service or jamming) under practical constraints including maximum UAV speed, UAV collision avoidance, UAV positioning error, and UAV energy harvesting. Specifically, we adopt a more practical UAV-ground channel model with both large-scale and small-scale fading components. Due to the non-convex feasible region constructed by the complicated constraints, directly finding the optimal solution of the original problem is intractable. To address this issue, we decouple the original optimization problem into three subproblems and develop an iterative algorithm to find its suboptimal solution by using the block coordinate descent technique. To solve each subproblem, certain advanced optimization tools, such as integer relaxation, S-procedure, and successive convex approximation techniques, are utilized. Numerical simulation results are provided to corroborate the theoretical derivations and to evaluate the performance of the proposed algorithm. Additionally, the numerical results assist to draw new insights on the 3D UAV trajectory by comparing the performance with conventional two-dimensional (2D) schemes.

In this paper, we study a novel transmission framework based on statistical channel state information (SCSI) by incorporating edge caching and beamforming in a fog radio access network (F-RAN) architecture. By optimizing the statistical beamforming and edge caching, we formulate a comprehensive nonconvex optimization problem to minimize the backhaul cost subject to the BS transmission power, limited caching capacity, and quality-of-service (QoS) constraints. By approximating the problem using the l 0 -norm, Taylor series expansion, and other processing techniques, we provide a tailored second-order cone programming (SOCP) algorithm for the unicast transmission scenario and a successive linear approximation (SLA) algorithm for the joint unicast and multicast transmission scenario. This is the first attempt at the joint design of statistical beamforming and edge caching based on SCSI under the F-RAN architecture.

Caching high-frequency reuse contents at the edge servers in the mobile edge computing (MEC) network omits the part of backhaul transmission and further releases the pressure of data traffic. However, how to efficiently decide the caching contents for edge servers is still an open problem, which refers to the cache capacity of edge servers, the popularity of each content, and the wireless channel quality during transmission. In this paper, we discuss the influence of unknown user density and popularity of content on the cache placement solution at the edge server. Specifically, towards the implementation of the cache placement solution in the practical network, there are two problems needing to be solved. First, the estimation of unknown users’ preference needs a huge amount of records of users’ previous requests. Second, the overlapping serving regions among edge servers cause the wrong estimation of users’ preference, which hinders the individual decision of caching placement. To address the first issue, we propose a learning-based solution to adaptively optimize the cache placement policy without any previous knowledge of the user density and the popularity of the contents. We develop the extended multi-armed bandit (Extended MAB), which combines the generalized global bandit (GGB) and Standard Multi-armed bandit (MAB), to iteratively estimate both a global parameter, i.e., the user density, and individual parameters, i.e., the popularity of each content. For the second problem, a multi-agent Extended MAB based solution is presented to avoid the mis-estimation of parameters and achieve the decentralized cache placement policy. The proposed solution determines the primary time slot and secondary time slot for each edge server. The edge servers estimate expected satisfied user number of caching a content with the overlap information and determine the cache placement solution. The proposed strategies are proven to achieve the bounded regret according to the mathematical analysis. Extensive simulations verify the optimality of the proposed strategies when comparing with baselines.

Intelligent reflecting surface (IRS) is envisioned as a promising hardware solution to hardware cost and energy consumption in the fifth-generation (5G) mobile communication network. It exhibits great advantages in enhancing data transmission, but may suffer from performance degradation caused by inherent hardware impairment (HWI). For analysing the achievable rate (ACR) and optimizing the phase shifts in the IRS-aided wireless communication system with HWI, we consider that the HWI appears at both the IRS and the signal transceivers. On this foundation, first, we derive the closed-form expression of the average ACR and the IRS utility. Then, we formulate optimization problems to optimize the IRS phase shifts by maximizing the signal-to-noise ratio (SNR) at the receiver side, and obtain the solution by transforming non-convex problems into semidefinite programming (SDP) problems. Subsequently, we compare the IRS with the conventional decode-and-forward (DF) relay in terms of the ACR and the utility. Finally, we carry out simulations to verify the theoretical analysis, and evaluate the impact of the channel estimation errors and residual phase noises on the optimization performance. Our results reveal that the HWI reduces the ACR and the IRS utility, and begets more serious performance degradation with more reflecting elements. Although the HWI has an impact on the IRS, it still leaves opportunities for the IRS to surpass the conventional DF relay, when the number of reflecting elements is large enough or the transmitting power is sufficiently high.

Intelligent reflecting surface (IRS) technology has emerged as a promising way to improve the energy efficiency of wireless communication systems with less complexity and hardware cost. Most existing studies are conducted with ideal hardware, however, both physical transceiver and IRS suffer from non-negligible hardware impairments which may greatly degrade the system performance. In this paper, by considering hardware impairments, we focus on the energy efficiency analysis of IRS system. Our first contribution is to derive the optimal receive combining and transmit beamforming vectors. After that, we characterize the asymptotic channel capacity. With the derived asymptotic channel capacity and the power consumption model, the analytical upper and lower bounds on the maximal energy efficiency are provided. Our results show that an IRS system can achieve both high spectral efficiency and high energy efficiency with moderate number of antennas. This observation is encouraging for that there is no need to cost a lot on expensive high-quality antennas, which corresponds to the requirements of new communication paradigms.

With the development of precise positioning technology, a growing number of location-based services (LBSs) facilitate people's life. Most LBSs require proof of location (PoL) to prove that the user satisfies the service requirement, which exposes the user's privacy. In this paper, we propose a zero-knowledge proof of location (zk-PoL) protocol to better protect the user's privacy. With the zk-PoL protocol, the user can choose necessary information to expose to the server, so that hierarchical privacy protection can be achieved. The evaluation shows that the zk-PoL has excellent security to resist main attacks, moreover the computational efficiency is independent of input parameters and the zk-PoL is appropriate to delay-tolerant LBSs.

Ratings online are often listed alongside product recommendations, but to date, limited attention has been paid as to how credible these ratings present to end-users. For lack of an effective rating verification scheme, a cheater could fabricate some high ratings and the recommended product usually does not match the corresponding ratings. To make ratings verifiable, we encapsulate each rating into a specific structure whose core is unverified rating output (URTO) that we propose. The URTO is designed based on unspent transaction output (UTXO) in Bitcoin. On this foundation, we propose the online verifiable rating system. Our system can make ratings verifiable and the evaluated validation and mining time of a rating by the whole network peers demonstrate the high reliability of our system.

Joint pushing and caching are commonly considered an effective way to adapt to tidal effects in networks. However, the problem of how to precisely predict users' future requests and push or cache the proper content remains to be solved. In this paper, we investigate a joint pushing and caching policy in a general mobile edge computing (MEC) network with multiuser and multicast data. We formulate the joint pushing and caching problem as an infinite-horizon average-cost Markov decision process (MDP). Our aim is not only to maximize bandwidth utilization but also to decrease the total quantity of data transmitted. Then, a joint pushing and caching policy based on hierarchical reinforcement learning (HRL) is proposed, which considers both long-term file popularity and short-term temporal correlations of user requests to fully utilize bandwidth. To address the curse of dimensionality, we apply a divide-and-conquer strategy to decompose the joint base station and user cache optimization problem into two subproblems: the user cache optimization subproblem and the base station cache optimization subproblem. We apply value function approximation Q-learning and a deep Q-network (DQN) to solve these two subproblems. Furthermore, we provide some insights into the design of deep reinforcement learning in network caching. The simulation results show that the proposed policy can learn content popularity very well and predict users' future demands precisely. Our approach outperforms existing schemes on various parameters including the base station cache size, the number of users and the total number of files in multiple scenarios.

A generalized hybrid digital-analog (HDA) framework with the combination of orthogonal and nonorthogonal multiplexing is proposed, which can strike a balance between interference and resource for Internet of Things application. The optimal resource allocation for the proposed scheme is formalized as a 3-D mixed integer programming problem, which is a function of digital bandwidth, orthogonal power, and nonorthogonal power of analog signal. With divide and conquer strategy, we first search the space of digital bandwidth, which is constructed by the possible number of subcarriers in orthogonal frequency division multiplex system, then the optimization problem is reduced to a 2-D continuous optimization problem. We further decompose it into two 1-D continuous optimization problems, and prove they are convex 1-D functions unconditionally or conditionally, respectively. With their convexity, the 2-D optimization problem can be solved with iterative gradient descent algorithm. We design a resource allocation algorithm to solve the optimization problem in practical system. Our experimental results show that the proposed algorithm outperforms nonorthogonal multiplexing HDA by 1-3 dB in terms of peak signal to noise ratio.

How to strike a balance between diversity gain and spatial multiplexing gain in a soft video delivery system is an open problem. Due to the power limit, it is especially important to achieve the optimal balance during video transmission in the Internet of Things. In this paper, taking the multisimilarity feature of a soft video system into account, we design an adaptive multiple-input, multiple-output (MIMO) receiver that can utilize nearly optimally either diversity gain or multiplexing gain. At the transmitter, we arrange multisimilar video data according to the space-time coding style and transmit them through multiple antennas. At the receiver, we propose using two decoders, i.e., the multisimilar space-time block coding (Ms-STBC) decoder and the soft MIMO decoder. The decoder to be chosen is determined by the predicted performance gain. We show that the proposed Ms-STBC transmission scheme can be considered a joint source-channel design, which is proposed for a soft video multiantenna delivery system. The relationship between intracodewords similarity and the channel signal-to-noise ratio (SNR) gain is derived. The experimental results demonstrate that the proposed designs can achieve a significant improvement over either individual soft MIMO multiplexing decoding or individual space-time block coding decoding in terms of peak SNR (PSNR) under the condition of a time-varying channel and a wide range of SNR. We can obtain at most 4-dB PSNR gain compared with the soft MIMO system.

This paper proposes a novel symbol-level combining hybrid automatic repeat request (HARQ) scheme based on analog chaotic code and named HARQ-Chaotic. The transmitter of HARQ-Chaotic adopts analog chaotic code to encode Quadrature Amplitude Modulation (QAM) symbols of retransmission packets to combat fading and noise. As the analog chaotic code can only handle sources with amplitudes in the range of [-0.5, 0.5], QAM symbols must be scaled into this range. We derived the optimal scaling factor through theoretical analysis. A joint algorithm combining with novel soft chaotic decoder and novel soft QAM demapper is proposed for the receiver to enhance the performance of the whole communication system. We implemented HARQ-Chaotic with LDPC codes and 16-QAM/64-QAM to carry out simulations in both AWGN channels and multipath fading channels. Massive simulation results demonstrate that the proposed HARQ-Chaotic has 1dB-4dB gain over traditional HARQ-Chase combining (HARQ-CC) scheme in block error rate (BLER) performance.

We study the optimal degrees of freedom (DoF) region of three-user asymmetric multiple-input multiple- output (MIMO) Y channel by considering a hybrid data exchange model. In the hybrid data exchange, we consider both the pairwise data exchange and full data exchange. To derive the optimal DoF region, we analyze the DoF region from both the converse and the achievability aspects. For the converse part, the tight DoF region outer bound is derived using cut-set theorem and genie aided approach. Further, a novel and systematic approach is proposed to analyze the achievability of DoF region. In proving the optimality of achievability, we propose to design distinct patterns to pack the overall transmit data over the channel. We find that the obtained achievable DoF coincides with derived DoF region outer bound.

This paper addresses the channel uncertainty issue in the multi-input multi-output (MIMO) cloud radio access network (C-RAN) by studying the robust beamforming. Our objective is to minimize the overall network power and backhaul cost while guaranteeing the users' SINR constraints for a target proportion of users. The channel state information (CSI) is assumed to be imperfect and the additive channel state information error is modeled as Gaussian distributed variables. We model the total power by ℓ0/ℓ2-norm functions and use the semidefinite programing (SDP) and ℓ0-norm approximation to transform the original problem into tractable ones. Then, probability approach are proposed to deal with the CSI uncertainty and an alternating direction method of multipliers (ADMM) based algorithm is utilized to solve the transformed optimization problem. Simulation results verify that the proposed robust designs can significantly enhance the performance compared the non-robust case and efficiently resolve the channel uncertainty issue.

In this paper, we study the degrees of freedom (DoF) of a new network information flow model named the circular multirelay multiple-input multiple-output interference channel (CMMI). In this model, there are two clusters and each of them contains three users. Each user equipped with M antennas in one cluster intends to deliver data streams to another user in the same cluster in a circular one-way transmission via the common distributed K N-antenna relay nodes. The CMMI network model can be considered as a basic component to construct the complicated Internet of Things networks. By assuming linear processing at the users and the relays, we show that the original analysis of DoF comes down in finding solutions of some nonlinear matrix equations with rank constraints. Toward this end, by using linear precoding and post-processing techniques, we propose two different approaches to solve the nonlinear matrix equations based on different antenna configurations. We show that a √ DoF of max{min{M, (√6K/12)}, min{(M/3), (KN/2)}} is achievable for ∀(M/N) ∈ (0, +∞). In addition, to assess the optimal DoF, the cut-set approach is used for deriving the DoF upper bound by innovatively separating certain users to form two-pair two-way relay channels. We show that the DoF of CMMI is upper bounded by max{min{M, (KN/3)}, min{(2M/3), (KN/2)}}. By combining the achievable DoF and the upper bound, we finally show that the optimal DoF of CMMI can be achieved √ for (M/N)∈[0, (√6K/12)]∪[(3K/2), +∞), ∀K ≥ 1.

We study the degrees of freedom (DoFs) of a multiple-input multiple-output K-pair two-way relay channel with delayed channel state information (CSI) with J distributed relays. In the considered model, we assume that the users are equipped with M antennas and the relay with N antennas. We propose two schemes, where the signaling design can be carried out either in each individual time slot or across multiple time slots with and without knowledge of CSI. The scheme involves a joint design of user beamforming matrices, relay beamforming matrices, and user postprocessing matrices, so as to meet interference neutralization and rank conditions. We show that the optimal DoF of N2K per user can be reached for N ≥ 1 for an arbitrary number of user pairs when J = 1. This implies that delayed CSI does not compromise the DoF performance of the considered model when N ≥ 1.

In this paper, we study linear transceiver designs for indoor visible light communications (VLCs) with multiple light emitting diodes (LEDs). Specifically, we investigate VLCs including white emitting diodes and VLCs including red/green/blue (RGB) LEDs. The transmitter precoding and the offset are jointly designed by considering certain key practical lighting constraints, such as optical power, non-negativeness, and color illumination. Various non-convex transceiver design problems are formulated aiming to minimize total mean-square-error to improve transmission reliability. We show that for multi-input single-output white VLCs, the optimal precoding reduces to a simple LED selection strategy. For multi-input multi-output (MIMO) white VLCs, we prove that the optimization problem with multiple constraints can be equivalently simplified to a problem with single constraint, which enables us to propose efficient algorithms to search local optimal solutions. For MIMO RGB VLCs, by using certain useful transformations, we show that the precoding design is equivalent to covariance matrix design of transmit signals, which can be further transformed to a convex optimization problem. To develop an algorithm to find the optimal solution, we derive the optimal structure of the covariance matrix and show that the optimal solution can be obtained via a water-filling approach. Extensive simulation results are provided to verify the performance of the proposed designs.

The concept of device-to-device (D2D) communications underlaying cellular networks opens up potential benefits for improving system performance but also brings new challenges, such as interference management. In this paper, we propose a pricing framework for interference management from the D2D users to the cellular system, where the base station (BS) protects itself (or its serving cellular users) by pricing the cross-tier interference caused from the D2D users. A Stackelberg game is formulated to model the interactions between the BS and D2D users. Specifically, the BS sets prices to maximize its revenue (or any desired utility) subject to an interference temperature constraint. For given prices, the D2D users competitively adapt their power allocation strategies for individual utility maximization. We first analyze the competition among the D2D users by noncooperative game theory and an iterative-based distributed power allocation algorithm is proposed. Then, depending on how much network information the BS knows, we develop two optimal algorithms, one for uniform pricing with limited network information and the other for differentiated pricing with global network information. The uniform pricing algorithm can be implemented by a fully distributed manner and requires minimum information exchange between the BS and D2D users, and the differentiated pricing algorithm is partially distributed and requires no iteration between the BS and D2D users. Then, a suboptimal differentiated pricing scheme is proposed to reduce complexity and it can be implemented in a fully distributed fashion. Extensive simulations are conducted to verify the proposed framework and algorithms.

Magnetic induction (MI) has been proven to be an efficient wireless communication technique for overcoming the transmission challenges in some very harsh propagation environments, such as underground, underwater, etc. For a random distributed MI ad hoc network in a 3-D space composed of uniform medium, we propose a method for determining the required node density and transmitting power that creates an almost surely fully connected network. For which we involve an MI path-loss model and consider the effect of eddy currents, the effective coverage space and the expected node degree of an MI node are then calculated by a Lambert W-function-based integration. Finally, we propose optimized frequency selection methods for improving the connectivity of MI networks. In addition to an ideal frequency-switching optimization method, we provide for engineering applications a practical frequency-fixed optimization method, which is based on the gradient descent algorithm, where an improved initialization is used to reduce iterations.

We study the degrees of freedom (DoFs) of the multiple input multiple output (MIMO) multipair two-way distributed relay channel (mTDRC), where$K$pairs of users, each equipped with$M$antennas, exchange messages in a pairwise manner with the help of two geographically separated relay nodes, each with$N$antennas. We establish a general framework for the DoF analysis by combining the ideas of signal space alignment and interference neutralization. The proposed framework involves a joint design of user transmit beamformers, relay precoders, and user receive beamformers. Novel signal alignment techniques are proposed to reduce the number of linearly independent constraints for interference neutralization. Based on that, the original joint transceiver and relay design problem boils down to a problem solely on the design of the relay precoders. This problem is then solved utilizing recent development on the solvability of linear matrix equations. As a result, we derive an achievable DoF for the MIMO mTDRC with an arbitrary configuration of$(K,M,N)$. For$K=2$, the optimal DoF of the considered network is derived for$({M}/{N})\in (0, {1}/{2}) \cup (1,\infty )$by showing that the obtained achievable DoF meets a DoF upper bound. This result beats the state of the art by establishing the optimal DoF of the considered network in an extra range of$({M}/{N})\in (1,2)$. In addition, the achievable DoF obtained for$K=2$is much higher than the existing result in the range of$({M}/{N})\in ({11}/{16},1)$. Furthermore, we show that the optimal DoF of the considered network with$K\geq 3$is derived for$({M}/{N})\in (0, ({2K+\sqrt {2K}})/({4K^{2}-2K})) \cup ({3}/{2},\infty )$.

Due to its time-varying nature, oscillator phase noise can significantly degrade the performance of the channel estimation, carrier recovery, and data detection blocks in high-speed wireless communication systems. In this paper, we propose a new data-aided joint channel, carrier frequency offset (CFO) and phase noise estimator for orthogonal frequency division multiplexing (OFDM) relay systems. For the data transmission phase, we propose a new iterative receiver that tracks phase noise and detects the transmitted symbols. Additionally, we derive the hybrid Cramér-Rao lower bound for evaluating the performance of channel estimation and carrier recovery algorithms in OFDM relay networks. Extensive simulations demonstrate that the application of the proposed estimation and receiver blocks significantly improves the performance of OFDM relay networks in the presence of phase noise and CFO.

Magnetic induction (MI) is a promising technique for communications in the very harsh propagation conditions like underground or underwater, etc. In this paper we investigate the connectivity issue of MI ad hoc networks deployed in a three-dimensional space. The transmitting distance of MI covers a quasi- ellipsoid space other than a standard sphere space in a traditional wireless propagation case. We derive a closed-form expression for the effective coverage space of an MI node to evaluate the expectation of node degree. Moreover, the probability of node isolation is derived, which provides an upper bound of network connectivity probability. With these results, pairs of the critical transmitting magnetic momentum and node density can be obtained to keep the network connected with a probability close to one. Our analytic method provide a guidelines for future studies on MI networking.

We study the degrees of freedom (DoF) of a distributed multi-input multi-output (MIMO) multiway relay channel (mRC) where two pairs of users, each equipped with M antennas, exchange messages in a pairwise manner with the help of two separated relay nodes, each with N antennas. We establish a general framework to derive achievable DoF based on linear signal processing. We show that, to achieve a certain DoF, a joint design of the user transmit beamforming, relay precoding, and user receive beamforming is required. We propose novel signal alignment techniques to reduce the number of linearly independent equations required for interference neutralization. Then, the original joint transceiver and relay design problem boils down to a problem solely on the design of the relay precoders. Based on recent developments on solving linear matrix systems, we determine the solution to the relay design problem and derive the corresponding achievable DoF. Our analysis reveals that the DoF of the considered network is achieved for equation, which is broader than the existing results by covering an extra range of M over N ∈ (1, 2).

In this paper, we consider a symmetric multiple-input-multiple-output (MIMO) multiway relay channel (mRC) with L clusters and K users per cluster operating in clustered pairwise data exchange. Each user is equipped with M antennas, and the relay is equipped with N antennas. The degrees of freedom (DoF) of the MIMO mRC has recently attracted much research interest. The DoF results under certain configurations of (L,K,M,N) have been reported. However, the DoF capacity of the MIMO mRC with an arbitrary network configuration is, in general, far from being well understood. In this regard, the main contribution of this paper is to propose a systematic signal alignment approach to jointly design the beamforming matrices at the users and the relay. Based on that, an achievable DoF is derived for the MIMO mRC with an arbitrary network configuration of (L,K,M,N). Our analysis revealed that the derived achievable DoF is piecewise linear in M and N alternately. Moreover, we showed that the DoF capacity can be achieved for M/N ∈ [1/LK(K-1) + 1/2, ∞) and M/N ∈ (0, Lq/LK(Lq-1)], where q = 2 ⌈K/2⌉.We further derived the asymptotic DoF as K or L → ∞. The DoF analysis in this paper can provide insights on the practical design of efficient communication mechanisms over multiterminal MIMO relay networks.

We consider a K link multiple-input multiple-output (MIMO) interference channel, where each link consists of two full-duplex (FD) nodes exchanging information simultaneously in a bi-directional communication fashion. The nodes in each pair suffer from self-interference due to operating in FD mode, and inter-user interference from other links due to simultaneous transmission at each link. We consider the transmit and receive filter design for weighted sum-rate (WSR) maximization problem subject to sum-power constraint of the system or individual power constraints at each node of the system. Based on the relationship between WSR and weighted minimum-mean-squared-error (WMMSE) problems for FD MIMO interference channels, we propose a low complexity alternating algorithm which converges to a local WSR optimum point. Moreover, we show that the proposed algorithm is not only applicable to FD MIMO interference channels, but also applicable to FD cellular systems in which a base station (BS) operating in FD mode serves multiple uplink (UL) and downlink (DL) users operating in half-duplex (HD) mode, simultaneously. It is shown in simulations that the sum-rate achieved by FD mode is higher than the sum-rate achieved by baseline HD schemes.

We study two scenarios of full-duplex (FD) multiple-input-multiple-output cognitive radio networks: FD cognitive ad hoc networks and FD cognitive cellular networks. In FD cognitive ad hoc networks (also referred as interference channels), each pair of secondary users (SUs) operate in FD mode and communicate with each other within the service range of primary users (PUs). Each SU experiences not only self-interference but also interuser interference from all other SUs, and all SUs generate interference on PUs. We address two optimization problems: one is to minimize the sum of mean-squared errors (MSE) of all estimated symbols, and the other is to minimize the maximum per-SU MSE of estimated symbols, both of which are subject to power constraints at SUs and interference constraints projected to each PU. We show that these problems can be cast as a second-order cone programming, and joint design of transceiver matrices can be obtained through an iterative algorithm. Moreover, we show that the proposed algorithm is not only applicable to interference channels but also to FD cellular systems, in which a base station operating in FD mode simultaneously serves multiple uplink and downlink users, and it is shown to outperform HD scheme significantly.

In this paper, while considering the impact of the antenna correlation and the interference from neighboring users, we analyze channel estimation and training sequence design for multi-input multi-output (MIMO) two-way relay systems. To this end, we propose to decompose the bidirectional transmission links into two phases, i.e., the multiple access (MAC) phase and the broadcast (BC) phase. By considering the Kronecker-structured channel model, we derive the optimal linear minimum mean-square-error (LMMSE) channel estimators. The corresponding training designs for the MAC phase and the BC phase are then formulated and solved to improve channel estimation accuracy. For the general scenario of the training sequence design for both phases, two iterative training design algorithms are proposed that are verified to produce training sequences achieving near optimal channel estimation performance. Furthermore, for specific practical scenarios, where the covariance matrices of the channel or disturbances are of particular structures, the optimal training sequence design guidelines are obtained. The minimum required training lengths for channel estimation in both the MAC phase and the BC phase are also analyzed. Comprehensive simulations are carried out to demonstrate the effectiveness of the proposed training designs.

In this paper, we analyze joint channel, carrier frequency offset (CFO), and phase noise estimation in orthogonal frequency division multiplexing (OFDM) relaying networks. To achieve this goal, a detailed transmission framework involving both training and data symbols is first presented. Next, a novel algorithm that applies the training symbols to jointly estimate the channel responses, CFO, and phase noise parameters based on the maximum a posteriori criterion is proposed. Additionally, to evaluate the performance of the proposed channel estimation and carrier recovery algorithms, we analyze the ambiguities among the estimated parameters. Based on this analysis, a new Hybrid Cramér-Rao Lower Bound (HCRLB) is derived, which can effectively avoid such ambiguities. The simulation results show that the proposed estimation algorithm can achieve a performance close to the derived HCRLB.

In this paper, while considering the impact of antenna correlation and the interference from neighboring users, we study the problem of channel estimation and training sequence design in multi-input multi-output (MIMO) two-way relaying (TWR) systems. To this end, we propose to decompose the bidirectional transmission links into two phases, i.e., the multiple access (MAC) and the broadcast (BC) phases. By deriving the optimal linear minimum mean-square-error estimators, the corresponding training design problems for the MAC and BC phases are formulated and solved. Subsequently, algorithms and, in some special cases, closed-form solutions for obtaining the optimal training sequences for channel estimation in TWR systems are derived. Moreover, to further reduce channel estimation overhead, the minimum required length of the training sequences are determined. Simulation results verify the effectiveness of the proposed training designs in improving channel estimation performance in TWR systems.

We study achievable degrees of freedom (DoF) of a multi-input multi-output (MIMO) multiway relay channel (mRC) where K users, each equipped with M antennas, exchange messages in a pairwise manner with the help of a single TV-antenna relay node. A novel and systematic way of designing beamforming vectors and matrices at the user and at the relay is proposed to realize signal alignment and to implement physical-layer network coding (PNC). It is shown that, for the considered mRC with K = 3 users, the proposed beamforming design achieves the DoF capacity for any (M, N) setups. For the scenarios with K > 3, we show that the proposed scheme can be improved by disabling a portion of relay antennas so as to align signals more efficiently. Our analysis reveals that the obtained achievable DoF is always piecewise linear, and is bounded either by the number of user antennas M or by the number of relay antennas N. Asymptotic DoF as K → ∞ is also derived based on the proposed signal alignment scheme.

Idiopathic generalized epilepsy (IGE) and symptomatic generalized epilepsy (SGE) are two kinds of generalized epilepsy. In this study, we discussed the methods of automatically segmentation of MR images for patients with these two kinds of epilepsy. K-Means clustering, expectation-maximization, and fuzzy c-means algorithms were employed to perform segmentation on brain images for patients with IGE. For patients with SGE, a trimmed likelihood estimator combined with Gaussian mixture model, which we improved based on other's existing work, was employed to detect obvious brain lesions on fluid-attenuated inversion recovery images. Gray matter, white matter, and cerebrospinal fluid were then segmented from the remaining normal brain part. Similarity metrics were used to evaluate the performance of the different segmentation methods. The Dice similarity coefficient of the segmentation results exceeded 70% and satisfied the basic clinical requirement. Actually, the segmentation results were acceptable to clinicians and can provide clinicians more disease information to diagnose and treat epilepsy.

We study precoding design for cognitive two-way relay networks (C-TWRNs). In C-TWRN, the multi-antenna secondary transmitter not only transmits its own signal to the secondary receiver, it also acts as relay to help forwarding signals of two primary users via two-way relaying in the licensed frequency band. Our objective is to design linear relay transceiver or precoder such that the achievable rate of the secondary user is maximized while maintaining rate requirements of the primary users. To achieve this goal, different relay strategies, i.e., amplify-and-forward (AF), bit level XOR based decode-and-forward (DF-XOR) and symbol level superposition coding based decode-and-forward (DF-SUP), are considered. By transforming these non-convex design problems into suitable forms, efficient optimization tools are used to find the optimal solutions of all the schemes. Closed-form solutions are also obtained under certain conditions.

This paper presents an analytical study on the outage performance of amplify-and-forward (AF) two-way relay system with multi-antenna relay node (RN). Two major bidirectional protocols, i.e., two time slots multiple access broadcast (MABC) protocol and three time slots time division broadcast (TDBC) protocol, are considered. For both considerations, we first assume that instantaneous channel-state-information (CSI) is unavailable at RN, thus RN just simply uses the fixed relay gain derived from statistical CSI to scale the received signals before forwarding. We then consider the scenario where RN can obtain the instantaneous CSI to perform the zero-forcing (ZF) relay precoding. The closed-form expressions of outage probability are derived for all cases. Based on these expressions, the diversity-multiplexing tradeoff (DMT) is further obtained for the MABC protocol. The analytical results show that, for non-precoding MABC scheme, the diversity order is only 1, which is independent to the relay antenna number M. While for the ZF-precoding case, the diversity order of M - 1 can be obtained.

Two-way relaying can improve spectral efficiency in two-user cooperative communications. It also has great potential in multiuser systems. A major problem of designing a multiuser two-way relay system (MU-TWRS) is transceiver or precoding design to suppress co-channel interference. This paper aims to study linear precoding designs for a cellular MU-TWRS where a multi-antenna base station (BS) conducts bi-directional communications with multiple mobile stations (MSs) via a multi-antenna relay station (RS) with amplify-and-forward relay strategy. The design goal is to optimize uplink performance, including total mean-square error (Total-MSE) and sum rate, while maintaining individual signal-to-interference-plus-noise ratio (SINR) requirement for downlink signals. We show that the BS precoding design with the RS precoder fixed can be converted to a standard second order cone programming (SOCP) and the optimal solution is obtained efficiently. The RS precoding design with the BS precoder fixed, on the other hand, is non-convex and we present an iterative algorithm to find a local optimal solution. Then, the joint BS-RS precoding is obtained by solving the BS precoding and the RS precoding alternately. Comprehensive simulation is conducted to demonstrate the effectiveness of the proposed precoding designs.

In this paper, we consider a non-regenerative MIMO two-way relay system with imperfect channel state information (CSI). We employ a stochastic approach to model the channel uncertainties and address the robust joint source and relay optimization problem based on the minimum mean squared error (MMSE) criterion. With imperfect CSI, the self-interference (SI) cannot be completely canceled at destination nodes. Hence, both channel uncertainties and residual self-interference should be considered. We develop an optimization framework that unifies both frequency-division duplex (FDD) and time-division duplex (TDD) systems despite their different channel statistical properties. Two robust algorithms are proposed to minimize the sum mean squared error (MSE) averaged over channel uncertainties. The first algorithm adopts alternating optimization to update the source precoders, relay precoder and destination receivers iteratively with guaranteed convergence. In the second algorithm, only the relay precoder with certain structure is considered. Then the relay precoder design is reduced to the simple power allocation problem. Simulation results show that the proposed algorithms provide robustness against channel uncertainties, especially when the signal-to-noise (SNR) ratio is high.

We investigate the linear precoding designs for multiuser two-way relay system (MU-TWRS) where a multi-antenna base-station (BS) communicates with multiple single-antenna mobile stations (MSs) via a multi-antenna relay station (RS). The amplify- and-forward (AF) relay protocol is employed. The design goal is to optimize the precodings at BS, RS or both so as to minimize the total mean-square error (MSE) of the uplink messages while maintaining the individual signal-to-interference-plus-noise ratio (SINR) requirement for each downlink signal. We show that the BS precoding design problem can be converted to a standard second order cone programming (SOCP), while the RS precoding is non- convex for which a local optimal solution is obtained using an iterative algorithm. A joint BS-RS precoding is also obtained by alternating optimization of BS precoding and RS precoding with guaranteed convergence. Numerical results show that RS-precoding is superior to BS-precoding. Furthermore, the joint BS-RS precoding can significantly outperform the two individual precoding schemes. The implementation issues including complexity and feedback overhead are also discussed.

We investigate the source and relay precoding based on the minimum mean-square-error (MMSE) criterion for amplify-and-forward (AF) MIMO two-way relay systems. This joint design problem is shown to be a highly nonconvex optimization problem. In this work, we present two efficient design algorithms. The first one aims to minimize the total MSE of two users by alternatively solving three trackable sub-problems and it is iterative in nature. Since the optimal solution for each sub-problem can be obtained, the convergence is thus ensured. The second design aims to compromise computational complexity and system performance and possesses a certain precoder structure. This structure is able to parallelize channels in the Multiple Access (MAC) and Broadcast (BC) phases of the two-way relaying. Based on such structure, the joint precoding design is simply reduced to the joint source and relay power allocation problem. The efficiency of both proposed algorithms is verified through simulation.

Information theoretic criteria (ITC) based spectrum sensing is a promising blind method which can reliably detect the primary users while requiring little prior information in cognitive radio networks. In this paper, we provide an intensive treatment on the ITC sensing. We first introduce a new over-determined channel model constructed by applying multiple antennas in order to make the ITC applicable. Then, a simplified ITC sensing algorithm is introduced, which needs to compute and compare only two decision values. Compared with the original ITC (OITC) sensing algorithm, the simplified algorithm significantly reduces the computational complexity without losing any performance. Furthermore, applying the recent advances in random matrix theory, we derive closed-form expressions to tightly approximate both the probability of false alarm and probability of detection. Finally, comprehensive simulations are carried out to evaluate the performance of the proposed ITC sensing algorithms. Results show that they considerably outperform existing blind spectrum sensing methods in certain cases.