Uncrewed aerial vehicles (UAVs) play a pivotal role in ensuring seamless connectivity for Internet of Things (IoT) devices, particularly in scenarios where conventional terrestrial networks are constrained or temporarily unavailable. However, traditional coverage-hole detection approaches, such as minimizing drive tests, are costly, time-consuming, and reliant on outdated radio-environment data, making them unsuitable for real-time applications. To address these limitations, this paper proposes a UAV-assisted framework for real-time detection and recovery of coverage holes in IoT networks. In the proposed scheme, a patrol UAV is first dispatched to identify coverage holes in regions where the operational status of terrestrial base stations (BSs) is uncertain. Once a coverage hole is detected, one or more UAVs acting as aerial BSs are deployed by a satellite or nearby operational BSs to restore connectivity. The UAV swarm is organized based on Delaunay triangulation, enabling scalable deployment and tractable analytical characterization using stochastic geometry. Moreover, a collision-avoidance mechanism grounded in multi-agent system theory ensures safe and coordinated motion among multiple UAVs. Simulation results demonstrate that the proposed framework achieves high efficiency in both coverage-hole detection and on-demand connectivity restoration while significantly reducing operational cost and time.

The deep integration of 5G and the Internet of Things has spurred a pressing demand for high-precision indoor positioning in scenarios such as industrial automation and smart healthcare. However, traditional fingerprinting-based positioning techniques face significant constraints, including multipath signal degradation and insufficient adaptability, especially in challenging outdoor-to-indoor scenarios that rely on signals from external base stations. Concurrently, existing deep learning methods face challenges such as high annotation costs and limited model receptive fields. To address these limitations, this paper proposes an intelligent fingerprinting positioning system specifically designed to achieve robust indoor positioning using 5G signals from a single outdoor base station. By innovating in data preprocessing, feature matrix optimization, and an attention-enhanced model, this system aims to overcome the bottlenecks of positioning in such complex environments. Experimental results based on measured data from a real-world office building validate the superior performance of the proposed positioning system, achieving a mean absolute error (MAE) of 1.5442 m, which represents a significant improvement over state-of-the-art benchmarks.

In this paper, channel estimation (CE) for up-link hybrid-field communications involving multiple Internet of Things (IoT) devices assisted by an active intelligent reflecting surface (IRS) is investigated. Firstly, to reduce the complexity of near-field (NF) channel modeling and estimation between IoT devices and active IRS, a sub-blocking strategy for active IRS is proposed. Specifically, the entire active IRS is divided into multiple smaller sub-blocks, so that IoT devices are located in the far-field (FF) region of each sub-block, while also being located in the NF region of the entire active IRS. This strategy significantly simplifies the channel model and reduces the parameter estimation dimension by decoupling the high-dimensional NF channel parameter space into low dimensional FF sub channels. Subsequently, the relationship between channel approximation error and CE error with respect to the number of sub-blocks is derived, and the optimal number of sub-blocks is solved based on the criterion of minimizing the total error. In addition, considering that the amplification capability of active IRS requires power consumption, a closed-form expression for the optimal power allocation factor is derived. To further reduce the pilot overhead, a lightweight CE algorithm based on convolutional autoencoder (CAE) and multi-head attention mechanism, called CAEformer, is designed. The Cramér-Rao lower bound is derived to evaluate the proposed algorithm’s performance. Finally, simulation results demonstrate the proposed CAEformer network significantly outperforms the conventional least square and minimum mean square error scheme in terms of estimation accuracy.

In autonomous driving scenarios, limited sensing range of individual autonomous vehicles (AVs) and exponential growth of sensing data have drawn increasing attention. This paper focuses on an integrated system combining communication and computation assistance for sensing enhancement, exploring functional fusion and performance optimization of the autonomous driving integrated sensing, communication, and computation (ISCC) system. Specifically, we first establish a cloud-edge-terminal collaborative ISCC system tailored for autonomous driving. For this system, we model sensing, communication, and computation separately, where the sensing model incorporates task-dependent characteristics, specifically considering the sequential execution of detection and tracking as well as the parallel nature of localization. Given that the collaborative computation between AVs and edge nodes aims to maximize system performance, we formulate a mixed integer nonlinear optimization problem. To solve this problem, we design two independent agents for resource and offloading configuration based on deep reinforcement learning. The former can adaptively allocate resources in each time slot without requiring prior knowledge of task arrival times, while the latter employs a partial offloading strategy to leverage the local computing capabilities of AVs, thereby addressing the limitations of existing approaches that rely on fixed resource allocation or neglect local computation. The simulation results show that the average task completion rate of the proposed scheme is significantly improved, the system cost is notably reduced compared with traditional schemes.

Fingerprint localization has gained significant attention due to its cost-effective deployment, low complexity, and high efficacy. However, traditional methods, while effective for static data, often struggle in dynamic environments where data distributions and feature spaces evolve—a common occurrence in real-world scenarios. To address the challenges of robustness and adaptability in fingerprint localization for dynamic indoor environments, this paper proposes DF-Loc, an end-to-end dynamic fingerprint localization system based on multi-source unsupervised domain adaptation (MUDA). DF-Loc leverages historical data from multiple time scales to facilitate knowledge transfer in specific feature spaces, thereby enhancing generalization capabilities in the target domain and reducing reliance on labeled data. Extensive experiments conducted in office and classroom environments demonstrate that DF-Loc outperforms comparative methods in terms of both localization accuracy and robustness.

The Internet of Autonomous Vehicles (IoAV) faces growing challenges in user privacy and communication security, stemming from dynamic network topologies induced by highspeed vehicle mobility, resource-constrained onboard devices, and the inherent tension between identity anonymity and traceability in latency-critical applications. Given the distributed architecture of fog computing and the limited storage and computational capabilities of vehicles, conventional anonymous authentication and centralized key negotiation mechanisms prove insufficient in addressing these issues. In response, We propose a distributed authentication and key negotiation protocol that combines multifactor biometrics, zero-knowledge proof (ZKP), and physical unclonable function (PUF) without relying on a trusted third party. Specifically, we design an efficient ZKP algorithm based on Chebyshev polynomials with low overhead and strong anonymity. Our key innovation is the implementation of independent key negotiation of three untrusted entities in a single protocol cycle, enabling 23 security features and functions. The performance analysis shows that the scheme takes only 17 ms to complete the protocol flow, and it reduces vehicle memory usage by 33% to 83%, service latency by 61% to 83%, and communication overhead by 12% to 50% compared to existing schemes.

This paper investigates the covert communication network assisted by the multi-antenna relay, by taking into account two typical scenarios of eavesdropping channel state information (ECSI). In this network, the relay operates in half-duplex amplify-and-forward (AF) relaying protocol, where the relay either forwards the Alice’s confidential signal to the Bob by designing the precoding matrix to enhance the communication performance or sends artificial noise (AN) to disrupt the warden’s detection. For this covert communication system with either instantaneous ECSI (I-ECSI) or statistical ECSI (S-ECSI), an optimization problem is formulated with the aim of maximizing the received signal-to-noise ratio (SNR) at the Bob while ensuring the constraints on the transmit power and covert performance. To solve the non-convex optimization problem, an alternating optimization method is proposed through jointly optimizing the transmit power at Alice and precoding matrix at the relay. Numerical results are provided to demonstrate the effectiveness of our proposed method. In particular, our method is superior to the conventional ones up to 49.6% with I-ECSI and 57.9% with S-ECSI.

As urban rail transit evolves, the convergence of digitalization, networking and intelligence has emerged as a pivotal trend, accompanied by the surge of intensive computing tasks and real-time demand. Edge computing is a promising solution to address the local resource constraints of various application devices covered by various subsystems within Urban Rail Transit Systems (URTS). In this paper, a Digital Twin (DT) assisted 5G-enabled edge computing network in URTS is established. We consider the heterogeneous service requirements of Ultra-Reliable Low-Latency Communication (URLLC) and Enhanced Mobile Broadband (eMBB) in different intelligent applications, and accordingly, we develop a task execution queue model. Additionally, to improve the task processing performance of the system, a task offloading and resource allocation scheme based on the Multi-Agent Twin Delayed Deep Deterministic policy gradient (MATD3) algorithm is proposed. User Equipment (UEs) and Edge Servers (ESs) are treated as distinct agents, accounting for their collaborative computation capabilities and differences in decision-making. A framework is established for centralized training in a DT assisted system with decentralized execution by each agent. Simulation results demonstrate that the proposed approach ensures the stringent latency requirement for URLLC tasks, enhances the Transaction Per Time Slot (TPTS) for eMBB tasks, and significantly reduces processing delays across all tasks. In addition, the proposed scheme also achieves load balance between UEs and ESs.

Although vehicular ad hoc networks (VANETs) significantly enhance traffic convenience, the propagation of erroneous information by malicious vehicles remains a challenging issue. To maintain message reliability, it is crucial to establish a trust management model that can promptly detect malicious vehicles and identify false messages. This article presents a novel trust management model based on blockchain, machine learning, and active detection technology. In the proposed model, we designed a trust evaluation scheme to evaluate the credibility by calculating the direct and indirect trust of the vehicle. To achieve this goal, we use active detection technology to detect indirect trust in vehicles, and then store it in the blockchain. The direct trust of the vehicle is calculated using a Bayesian classifier. The use of active detection technology speeds up the process of filtering out malicious vehicles. Machine learning technology simplifies the complex iterations involved in computing the trust value. Finally, the use of blockchain ensures the consistency and tamper-proofing of the trusted data. The simulation outcomes demonstrate that our approach outperforms the present trust management models.

Mainstream lane detection methods often lack flexibility, accuracy, and efficiency in challenging scenarios, especially with occlusion and extreme lighting. To address this, we reframe lane detection as a variational inference problem. Specifically, we propose a Variational Lane Detection Network (VLD-Net) using a Conditional Variational Auto-Encoder (CVAE) as the generative network to produce multiple lane maps as candidates, supervised by the ground-truth lane map. To build a more complex, expressive probability distribution, we incorporate normalizing flows into lane map generation, enhancing realism. Additionally, we develop a Lane-Attention Fusion (LAF) module using attention mechanisms to adaptively fuse generated candidate lane maps. LAF also includes a lane local feature aggregator to enhance local lane keypoint correlation. Experimental results on TuSimple and CULane datasets show our method outperforms previous approaches in challenging scenarios.

Undesired jamming launched by malicious jammers can attack authorized communications, which is viewed as one of the critical challenges in vehicular networks. In this paper, in order to handle the problem, anti-jamming communication driven by reinforcement learning is studied in intelligent reflecting surface (IRS)-aided vehicular networks. The system sum transmission rate is optimized by joint designing the transmit beamforming at the roadside unit (RSU) and the reflection coefficients at the IRS. An anti-jamming strategy based on combining annealing bias-priority experience replay method and twin delayed deep deterministic policy gradient (TD3) technique is developed to handle the formulated challenging non-convex problem. The proposed strategy is employed to train the replay buffer in TD3, which can eliminate the deviation under the distribution change and has the advantages of fast convergence and is not easy to fall into local optima. Numerical results confirm that our proposed strategy can enhance the sum rate of multiple vehicular users and ensure radar sensing capability of RSU compared with the existing methods.

Traffic flow prediction is a difficult undertaking in transportation systems, due to the intricate periodicity and real-time dynamics for traffic data, spatial-temporal dependency for road networks, existing prediction approaches fail to yield satisfactory results. We propose a traffic flow prediction method named Extended Multi-component External Interactive Gated Recurrent Graph Convolutional Network (EMGRGCN). The extended multi-component (EMC) module is incorporated into the prediction model to address the periodic temporal diffusion problem. Then, we introduce an encoder-decoder architecture that incorporates attention mechanism to capture spatial-temporal dependencies. Specifically, an External Interactive Gated Recurrent Unit (EIGRU) is utilized to capture crucial temporal features. EIGRU and graph convolutional network are combined in the encoder to extract spatial-temporal correlation, and EIGRU and convolutional neural network based decoder transforms the spatial-temporal characteristics into a sequence to predict future traffic flows. Experiments on public transportation datasets PEMSD8 and PEMSD4 demonstrate that EMGRGCN model achieves the best performance.

In this paper, we propose a deep learning based breadth-first sphere decoding (SD) scheme to reduce the detection complexity for multiple-input multiple-output (MIMO) communication systems. Specifically, we first design the DenseNet-based deep neural network (DN-DNN) to provide the pruning threshold for SD at each layer. Then, we develop modified number-based SD (MNSD) to reduce the complexity of SD by constraining the number of visited nodes at each layer with the output of DN-DNN. We use a distance-based SD (DSD) to further reduce the complexity of MNSD by constraining the accumulated distance at each layer with the output of DN-DNN. Compared with the traditional M-best SD withM= 16, the proposed MNSD achieves similar performance but reduces about 25% complexity for QPSK modulation; the proposed DSD has better performance with up to 75% complexity reduction at the high SNR region for 16QAM.

The propagation loss of RF signals is a significant issue in simultaneous wireless information and power transfer (SWIPT) systems. Additionally, ensuring information security is crucial due to the broadcasting nature of wireless channels. To address these challenges, we exploit the potential of active intelligent reflecting surface (IRS) in a multiple-input and multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) SWIPT system. The active IRS provides better beamforming gain than the passive IRS, reducing the “double-fading” effect. Moreover, the noise introduced at the active IRS can be used as artificial noise (AN) to jam eavesdroppers. This paper formulates a secrecy sum-rate maximization problem related to precoding matrices, power splitting (PS) ratios, and the IRS matrix. Since the problem is highly non-convex, we propose a block coordinate descent (BCD)-based algorithm to find a sub-optimal solution. Moreover, we develop a heuristic algorithm based on the zero-forcing precoding scheme to reduce computational complexity. Simulation results show that the active IRS achieves a higher secrecy sum rate than the passive and non-IRS systems, especially when the transmit power is low or the direct link is blocked. Moreover, increasing the power budget at the active IRS can significantly improve the secrecy sum rate.

This paper considers a cell-free (CF) massive multiple-input multiple-output (MIMO) with network-assisted full-duplex (NAFD) system for the ultra-reliable and low-latency (URLLC) communications, jointly optimizing duplex mode selection and transceiver design. To reduce the cross-link interference (CLI) and improve the weighted URLLC sum-rate of downlink and uplink users, we propose an optimization problem to maximize the achievable sum rates for the cell-free massive MIMO with NAFD system under a finite blocklength, limited by data rates and transmission power constraints. Then, a concave-convex procedure (CCCP) algorithm is used to solve this optimization problem. Simulation results show that the proposed algorithm has better performance than the traditional co-frequency co-time full-duplex (CCFD) and half-duplex (HD) schemes.

An end-to-end learning framework is proposed to optimize each module jointly in the communication system. Recently, convolutional neural network (CNN) and conditional Generative Adversarial Network (cGAN) are used for end-to-end learning. However, deeper network layers will degrade the effect of CNN. cGAN suffers from unstable training and lacks generative diversity. In this article, we propose the end-to-end learning based on deep residual network (ResNet) and Wasserstein GAN (WGAN) for communication with unknown channels (ResNet-WGAN). First, ResNet is applied to solve the problem of network degradation to extract deeper data features. Second, for unknown channels, WGAN with conditional information is used to fit the channel effect to improve training stability and generative diversity. Finally, we present the simulation results of the ResNet-WGAN under additive white Gaussian noise (AWGN) channel, Rayleigh fading channel, and frequency selective channel. The results demonstrate that the ResNet-WGAN reduces the communication bit error rate (BER) and block error rate (BLER). In particular, this article applies ResNet-WGAN to the Internet of Vehicles (IoV) communication, and the results demonstrate that ResNet-WGAN is more effective.

As a legacy from conventional wireless services, illegal eavesdropping is regarded as one of the critical security challenges in Connected and Autonomous Vehicles (CAVs) network. Our work considers the use of Distributed Kalman Filtering (DKF) and Deep Reinforcement Learning (DRL) techniques to improve anti-eavesdropping communication capacity and mitigate jamming interference. Aiming to improve the security performance against smart eavesdropper and jammer, we first develop a DKF algorithm that is capable of tracking the attacker more accurately by sharing state estimates among adjacent nodes. Then, a design problem for controlling transmission power and selecting communication channel is established while ensuring communication quality requirements of the authorized vehicular user. Since the eavesdropping and jamming model is uncertain and dynamic, a hierarchical Deep Q-Network (DQN)-based architecture is developed to design the anti-eavesdropping power control and possibly channel selection policy. Specifically, the optimal power control scheme without prior information of the eavesdropping behavior can be quickly achieved first. Based on the system secrecy rate assessment, the channel selection process is then performed when necessary. Simulation results confirm that our jamming and eavesdropping defense technique enhances the secrecy rate as well as achievable communication rate compared with currently available techniques.

Vehicle-to-vehicle (V2V) communication applications face significant challenges to security and privacy since all types of possible breaches are common in connected and autonomous vehicles (CAVs) networks. As an inheritance from conventional wireless services, illegal eavesdropping is one of the main threats to Vehicle-to-vehicle (V2V) communications. In our work, the anti-eavesdropping scheme in CAVs networks is developed through the use of cognitive risk control (CRC)-based vehicular joint radar-communication (JRC) system. In particular, the supplement of off-board measurements acquired using V2V links to the perceptual information has presented the potential to enhance the traffic target positioning precision. Then, transmission power control is performed utilizing reinforcement learning, the result of which is determined by a task switcher. Based on the threat evaluation, a multi-armed bandit (MAB) problem is designed to implement the secret key selection procedure when it is needed. Numerical experiments have presented that the developed approach has anticipated performance in terms of some risk assessment indicators.

This article performs a unified analysis of three coordinated multipoint (CoMP) transmission strategies in the downlink of mmWave cellular networks, including the fixed-number base station (BS) cooperation (FNC), the fixed-region BS cooperation (FRC), and the interference-aware BS cooperation (IAC). We first develop a comprehensive framework for CoMP operation in cellular networks, and investigate the network performance under a Poisson point process (PPP) model together with mmWave spectrum. To show what fraction of users in the network achieve target reliability for a given signal to interference-plus-noise ratio (SINR)/signal-to-interference ratio (SIR), we derive the SINR/SIR meta distributions, and further obtain the coverage probability as well as mean local delay for the three cooperation strategies. A pivotal intermediate step to compute the performance metrics is the derivation of joint distributions of distances between a typical user and cooperative BSs. Our analysis demonstrates that parameters of blockage have a significant influence on the network performance for the three CoMP schemes. We find that the FRC scheme makes more users achieve the given link reliability for the scenario with a low density of BSs, while the IAC scheme provides better performance for the network with a high density of BSs. Moreover, the optimal CoMP scheme can be approximately selected by considering the nearest distance from the serving BS to user and the radius of the approximate line-of-sight (LoS) region in the cellular networks.

In this paper, we propose a deep learning based sphere decoding (SD) scheme to reduce the detection complexity for the multiple-input multiple-output (MIMO) communication systems. Specifically, we first design the sparsely connected deep neural network (SC-DNN) to find a moderate radius for the SD algorithm. Then, we develop the SC-SD algorithm to reduce the computational complexity by deciding the detection order from the output of the SC-DNN, the zero-forcing (ZF) detector, and the transmit power. We further reduce the complexity of the SC-SD by defining partial layers without searching. For multi-stream MIMO, where a large number of parameters in neural networks should be trained, we propose a partitioned training procedure to achieve a reasonable computational complexity. Simulation results demonstrate that the SC-SD almost achieves the performance of the maximum likelihood (ML) in MIMO system but is much faster than the classic SD algorithm.

Driven by the new era of big data and artificial intelligence (AI), as well as the increasing demands for the privacy protection, how to deployment the AI on wireless networks is drawing increasing attention. In this paper, we investigate the distributed learning mechanism of hosting AI over intelligent reflecting surface (IRS)-assisted wireless networks, where IRS is utilized to enhance communication in a cost-effective and energy-efficient manner. Firstly, a distributed learning framework is formulated based on the alternating direction method of multipliers (ADMM) to achieve the parallel processing of the objective function. Specifically, in the proposed architecture each user updates the learning model with its own data and uploads it to the global model through wireless networks. Thence, a joint passive phase shift of IRS and user scheduling scheme based on a metric of efficiency-efficacy weighted sum (EEWS) is formulated to explore both the learning efficiency and efficacy. In addition, aiming at improving the one-round learning efficiency, a grouping-based suboptimal solution about IRS’s phase is adopted to realize the max-min fair transmission. Simulation results demonstrate the relationship among the number of users involved, the scale of IRS and the learning performance.

Vehicular Edge Computing (VEC) is expected to be an effective solution to meet the ultra-low delay requirements of many emerging Internet of Vehicles (IoV) services by shifting the service caching and the computation capacities to the network edge. However, due to the constraints of the multidimensional (storage-computing-communication) resources capacities and the cost budgets of vehicles, there are two main issues need to be addressed: 1) How to collaboratively optimize the service caching decision among edge nodes to better reap the benefits of the storage resource and save the time-correlated service reconfiguration cost? 2) How to allocate resources among various vehicles and where vehicular requests are scheduled to improve the efficiency of the computing and communication resources utilization? In this paper, we formulate an edge caching and computation management problem that jointly optimizes the service caching, the request scheduling, and the resource allocation strategies. Our focus is to minimize the time-average service response delay of the random arriving service requests in a cost-efficient way. To cope with the dynamic and unpredictable challenges of IoVs, we leverage the combined power of Lyapunov optimization, matching theory, and consensus alternating direction method of multipliers to solve the problem in an online and distributed manner. Theoretical analysis shows that the developed approach achieves a close-to-optimal delay performance without relying on any prior knowledge of the future network information. Moreover, simulation results validate the theoretical analysis and demonstrate that our algorithm outperforms the baselines substantially.

This paper investigates the performance of angle-domain millimeter-wave (mmWave) multi-input multi-output (MIMO) non-orthogonal multiple access (NOMA) systems in the presence of angular estimation error. A closed-form expression for the achievable rate of the system is derived. Based on which, a simple asymptotic approximation is obtained. The findings of paper suggest that, with a large number of BS antennas, the user rate is mainly constrained by the antenna number to beam number ratio (ANTBNR) and the spatial direction distance. In particular, increasing the ANTBNR would cause a severe rate loss, and the achievable rate is an increasing function with respect to the spatial direction distance. Capitalizing on this key observation, a novel cluster grouping scheme is designed to reduce the inter-cluster interference, which shows significant performance gain over a random cluster grouping scheme. Finally, simulation results are provided to corroborate the analytical results.

With the increasing development of cellular internet of things (IoT), the upcoming fifth generation (5G) wireless network is required to support massive IoT with sporadic traffic. In order to realize massive access over limited radio spectrum, a three-phase transmission protocol which consists of device detection and channel estimation, uplink data transmission and downlink data transmission is designed for the cellular IoT. In particular, we analyze the performance of the proposed transmission protocol, and reveal the impact of system parameters on the sum rate. Finally, simulation results validate the effectiveness of the theoretical claims.

Mobile edge computing (MEC) is an innovative computing paradigm to enhance the computing capacity of mobile devices (MDs) by offloading computation‐intensive tasks to MEC servers. With the widespread deployment of wireless local area networks, each MD can offload computation task to server via multiple wireless access points (WAPs). However, computation offloading can bring a higher system cost if all users select the same access points to offload their tasks. This study proposes a computation offloading strategy and resource allocation optimisation scheme in a multiple wireless access points network with MEC, which aims to minimise the system cost by providing the optimal computation offloading strategy, transmission power allocation, bandwidth assignment, and computation resource scheduling. The proposed scheme decouples the optimisation problem into subproblems of offloading strategy and resource allocation since the problem is NP‐hard. The offloading strategy involves the optimal access point selection, which is analysed by the potential game. The resource allocation is obtained using Lagrange multiplier. The authors' analysis and simulation results verify the convergence performance of the proposed scheme, and the proposed scheme outperforms the simple resource allocation scheme and the offloading strategy optimisation scheme in terms of the system cost.

Channel estimation in time division duplexing (TDD)‐based massive multiple‐input multiple‐output (MIMO) systems is heavily hampered by the pilot contamination, which constitutes a major bottleneck on the overall system performance. This study considers the pilot contamination problem in multi‐cell TDD‐based massive MIMO systems, and analytical expressions are presented on the normalised mean square error (NMSE) of the minimum mean square error channel estimation algorithm. Based on the obtained NMSE, this study proposes an optimal pilot assignment strategy to minimise the effect of pilot contamination. In order to further improve the system performance, a pilot design‐based channel estimation scheme is proposed, where Chu sequences with perfect auto‐correlation property are employed to design the optimal pilot sequences aiming at acquiring the accurate channel state information. Simulation results show that the proposed pilot assignment strategy outperforms the random pilot assignment method, and approaches to the performance of the exhaustive search method which requires high computational complexity. Moreover, the performance gain of the pilot design‐based channel estimation scheme is verified in massive MIMO systems.

With the increasing development of the cellular Internet of Things (IoT), the upcoming fifth-generation wireless network is required to support massive access of sporadic traffic devices. In this context, we design a three-phase transmission protocol which consists of device detection and channel estimation, uplink data transmission, and downlink data transmission for the cellular IoT, so as to realize massive access over limited radio spectrum. We analyze the performance of the proposed transmission protocol and derive closed-form expressions for the uplink and downlink achievable rates in terms of channel conditions and system parameters. Moreover, to improve the overall performance, we propose a length allocation algorithm by coordinating the three-phase transmission protocol in the unified sense. Extensive simulation results show that substantial performance gain can be obtained by the proposed algorithm.

This paper presents a variable density (sinusoidal) antenna deployment scheme which is designed for mobile relay (MR) system of the high-speed train. By analyzing the large-scale fading under the high-speed railway (HSR) wireless channel environment, the instantaneous channel capacity and the total service amount of several antenna deployments are derived. Theoretical analysis and simulation results indicate that the proposed deployment can provide higher capacity for the HSR relay system. Comparing with several traditional deployments, the proposed deployment utilizes the feature of the HSR wireless environment, provides better coverage to the edge of the base stations (BS). In addition, an antenna selection scheme is proposed based on the sinusoidal deployment.

Since urban rail unmanned train is considered as one of the core technologies of Intelligent Transportation System (ITS) , how to shorten the train headway as the driven interval between unmanned trains properly is still a challenge in Communications Based Train Control (CBTC) system. In this paper, we focus on analyzing the main factors which affect the train headway in inter-station barrier tracking mode, interstation stop tracking mode and station tracking mode and present an optimization scheme for Automatic Train Control (ATC) system with mobile block technology. Simulation results show that the proposed optimization scheme can reduce train headway to improve operational efficiency and reduce costs of urban rail transit system.

To compensate the imprecise modeling method using a Poisson point process (PPP) in a cellular network, especially in urban areas, we adopt a more suitable modeling method using a Poisson cluster process (PCP) and analyze the coverage probability of millimeter-wave (mmWave) cellular networks. We apply a distribution function of the shortest distance between the typical user and its serving base station (BS) to derive the probability density function (PDF) of the distance. Then, accurate formulas for the Laplace transform of interference are derived under Rayleigh fading and lognormal fading. Furthermore, we compute the expressions of signal to interference-plus-noise ratio (SINR) and rate coverage probability under these two fading, respectively. Our analysis and simulations show that the PCP-based modeling method of mmWave networks outperforms the PPP scheme in terms of coverage probabilities in low SINR threshold, high SINR threshold and high rate threshold. The results also confirm the accuracy of the formulas derived in this paper and guide the deployment of the mmWave cellular networks.

We propose a downlink cell-edge-aware zero forcing (CEA-ZF) and block diagonalization (BD) cooperative precoding scheme to reduce the downlink interference caused by the small cell range expansion in a heterogeneous network (HetNet). The CEA-ZF precoding algorithm adopted in the downlink transmission of macro base station (MBS) exploits the spatial degrees of freedom from large antenna array to suppress the inter-cell interference, and the BD precoding algorithm is introduced in the small access point (SAP) to eliminate the multi-user interference. Simulation results demonstrate the benefits of the proposed downlink precoding scheme over the alternative approach, and verify the proposed scheme as a more effective interference mitigation scheme for the downlink massive multiple-input multiple-output (MIMO) HetNet. Moreover, the optimal range expansion bias (REB) of small cell range expansion is obtained, and we also give a lower bound for the UE sum-rate of the proposed precoding scheme.

In this paper, we derive the closed-form capacity expression as well as the capacity-achieving input distribution for an ambient backscatter system with memoryless binary input and binary output (BIBO) channel. The discrete inputs are restricted to two mass points and the outputs are binary results obtained from energy detection with certain threshold. To show the influence of the signal to noise ratio (SNR) on the capacity, a closed-form tight capacity ceiling is also derived when SNR turns relatively large. Simulations are provided to corroborate the theoretical studies. Interestingly, simulations show: (i) the detection threshold maximizing the capacity is the same to the one from the maximum likelihood detector; (ii) the capacity is achieved by a uniform distribution for the inputs.

As an effective approach to alleviate the spectrum scarcity problem, cognitive radio (CR) has recently attracted an increasing amount of attention. In this paper, an optimization algorithm that joints bandwidth and power allocation of hybrid spectrum sharing is proposed in CR. According to the location variation of cognitive user (CU) that adopt the random waypoint based mobility models, the state of CU can switch between Underlay spectrum sharing model and Overlay spectrum one. Simultaneously, this algorithm can maximize the channel capacity through jointly optimizing power and bandwidth of CU when the Primary User's (PU's) interference temperature and the CU's transmission can be satisfied. Simulation results show that the proposed algorithm can more effectively improve the channel capacity than the single Underlay system and traditional solutions that the bandwidth is evenly allocated.

Predictive green streaming have recently gained attention in wireless network literature due to its significant energy-savings and quality of experience (QoE) gains. In this paper, we investigate how predicted user rates can be exploited for mobile video streaming with the popular Hypertext Transfer Protocol (HTTP) [e.g., HTTP adaptive streaming (HAS)]. To this end, we develop a stochastic predictive HTTP Adaptive streaming (PHAS) optimization framework to achieve the following objectives: 1) an edge-cloud assisted framework for prediction based HAS and identify its key functional entities and their interactions; 2) modelling uncertainty in predicted user rates and propose a robust two-stage QoE optimization approach which dynamically allocate the risks and optimize system efficiency over a time horizon; 3) an efficient heuristic algorithm allocating time slot ratio for multi-users to improve the network efficiency, fairness and overall QoE under different prediction error variances, wireless link conditions and buffer length constraints; Simulation studies and analytical results show that our method has a better performance than traditional methods in terms of average QoE, fairness and energy efficiency.

In recent years, high‐speed railways (HSRs) are being developed rapidly all over the world because of their convenience, safety, comfort, and other advantages. However, the reliability and security of HSR wireless communication systems have faced severe challenges, such as the Doppler effect, complicated wireless channel model, frequent handovers and so on. The International Union of Railways is pushing the evolution of the global system for mobile communications for railway (GSM‐R) internationally. In this study, a high‐speed based adaptive beamforming handover scheme is proposed to improve the handover performance for HSR wireless communication systems. When the high‐speed train enters the overlapping region, the serving evolved NodeB (eNodeB) and target eNodeB are using beamforming with different gain factors to improve the received signal quality. In addition, this scheme can dynamically adjust the handover hysteresis margins of the reference signal receiving power (RSRP) and the reference signal receiving quality (RSRQ) based on the speed and position. Simulation results have demonstrated that the proposed handover scheme can improve the handover performance by increasing the handover trigger probability and success probability effectively.

In this paper, we design robust beamforming to guarantee the physical layer security for a multiuser beam division multiple access (BDMA) massive multiple-input multiple-output (MIMO) system, when the channel estimation errors are taken into consideration. With the aid of artificial noise, the proposed design are formulated as minimizing the transmit power of the base station, while providing legal users and the eavesdropper with different signal-to-interference-plus-noise ratio. It is strictly proved that, under BDMA massive MIMO scheme, the initial non-convex optimization can be equivalently converted to a convex semi-definite programming problem and the optimal rank-one beamforming solutions can be guaranteed. In stead of directly resorting to the convex tool, we make one step further by deriving the optimal beamforming direction and the optimal beamforming power allocation in closed-form, which greatly reduces the computational complexity and makes the proposed design practical for real world applications. Simulation results are then provided to verify the efficiency of the proposed algorithm.

We investigate cache technique from the spatial view and study its impact on the relaying networks. In particular, we consider a dual‐hop relaying network, where decode‐and‐forward (DF) relays can assist the data transmission from the source to the destination. In addition to the traditional dual‐hop relaying, we also consider the cache from the spatial view, where the source can prestore the data among the memories of the nodes around the destination. For the DF relaying networks without and with cache, we study the system performance by deriving the analytical expressions of outage probability and symbol error rate (SER). We also derive the asymptotic outage probability and SER in the high regime of transmit power, from which we find the system diversity order can be rapidly increased by using cache and the system performance can be significantly improved. Simulation and numerical results are demonstrated to verify the proposed studies and find that the system power resources can be efficiently saved by using cache technique.

In this paper, we propose a new channel tracking method for massive multiple-input multiple-output (MIMO) systems under both the time-varying and spatial-varying circumstance. With spatial-temporal basis expansion model (ST-BEM), the channel information is decomposed into the spatial information and gain information, where the former is determined by the central angle as well as the angular spread of the incoming signal. We first blindly track the central angle by the extended Kalman filter (EKF) and obtain the angular spread through Taylor series expansion of the steering vector. Then, the channel gain information can be estimated with only a few pilot symbols. Various numerical results are provided to demonstrate the effectiveness of the proposed method.

Exploiting predicted channel information and designing energy efficient content delivery protocols has started to draw attention, which is referred to as predictive, anticipatory, or context-aware resource allocation. In this paper, we investigate how predicted user rates can be exploited for streaming on-demand mobile video with dynamic adaptive streaming over HTTP(DASH). Specifically, we propose an edge-cloud assisted framework for prediction based DASH streaming; For optimal prediction scenario, we propose a lightweight algorithm to solve it; For imperfect prediction scenario, we model uncertainty in predicted user rates and propose a chance constraint programming method to dynamically allocate the risks, optimize QoE and system efficiency; For the multi-user scenario, we propose a quality-level-aware throughput gain maximization method to improve the network efficiency, fairness and QoE for all users under different prediction error variances; Simulation studies show that our method has a better performance than traditional methods in terms of average QoE, fairness and energy efficiency.

This paper considers the problem of tracking a mobile receiver using signals of Low Earth Orbit (LEO) satellites. Based on Time-Difference of Arrival (TDOA) and Frequency-Difference of Arrival (FDOA), we joint the time synchronization and localization together with a static reference anchor, which has unknown position. In this scenario, the satellites is asynchronous. Considering the time- and frequency- offsets as additional unknown parameters, we proposed a Maximum Likelihood estimation approach to get the reference anchor's location and offsets. Then a sequential estimator jointly track the receiver location, velocity using extended Kalman filter (EKF) after revising the TDOA and FDOA measurements. Simulations demonstrate that our measurement model has a good fit, and our proposed estimator can successfully track both the receiver location, velocity with respect to the reference anchor with good accuracy.

An algorithm of power control in two-tier heterogeneous networks is proposed in this paper. We consider femtocell base stations (FBSs) dense deployment in the macrocell base station (MBS) coverage, the MBS dynamically estimates total uplink interference of femtocell user equipments (FUEs). In order to cope with interference issues, the MBS decides the transmit power of macrocell user equipment (MUE) according to the uplink power budget. In the meanwhile, the interference pricing mechanism is introduced. We assume that the MBS protects itself by pricing the interference on each FUEs, so as to achieve the goal of controlling the interference from FUEs. Simulation results show that the proposed algorithm yields a significant performance improvement in terms of the channel capacity.

The performance of multicell massive multiple‐input multiple‐output (MIMO) systems is heavily affected by pilot contamination. This study considers the problem of pilot contamination and analytical expressions are presented on the normalised mean square error (NMSE) of the minimum mean square error channel estimation algorithm. Based on the NMSE of the massive MIMO systems, a pilot design criterion is proposed to design the optimal pilot sequences for mitigating the pilot contamination. Following this criterion, Chu sequence with perfect auto‐correction and cross‐correlation properties are employed to design the optimal pilot sequences. Then the performance of the proposed pilot design‐based scheme is investigated, and the exact NMSE expressions are presented. The excellent performance of this pilot design scheme has been confirmed in the authors’ simulations.

This paper addresses the problem of pilot contamination in multi-cell multiuser Large-Scale Multiple-Input Multiple-Output (LS-MIMO) aided orthogonal frequency division multiplexing (OFDM) systems, and the exact closed-form expression for the mean square error (MSE) of the classical least square (LS) channel estimation algorithm is derived. Then, a pilot design criterion is proposed to design the optimal pilot sequences for mitigating the pilot contamination. Following this criterion, the improved Chu sequences with perfect autocorrection property are employed. Finally, simulation results verify the effectiveness of the pilot design scheme.

Considering the vehicular networks, multi-hop broadcasting is a frequently used method to deliver messages. Connectivity of wireless multi-hop networks is a critical measure for the planning, design, and evaluation of vehicular ad hoc networks. In an urban environment, vehicles can opportunistically exploit infrastructure through open Access Points (APs) and Road Side Units (RSUs) to efficiently communicate with other vehicles. Infrastructures (i.e., Base Stations (BSs), APs) are uniformly deployed along a road, while vehicles are distributed on the road randomly according to a Poisson distribution. For infrastructure-based vehicular networks, connectivity probability is the probability that an arbitrary vehicle access to the infrastructure. This paper proposes an analytical model to improve the connectivity probability of vehicle and infrastructure through multi-hop broadcasting in the infrastructure-based vehicular networks. We also consider the following factors: propagation distance, one hop transmission range, distribution of vehicles, vehicle density, average length of vehicles, and minimum safety distance between vehicles. The analytical model is validated by simulations.

In this paper, a non-stationary geometry-based stochastic model (GBSM) for high-speed train (HST) MIMO channels is proposed. The proposed model employs geometry-based elliptical scattering model, where the received signal is a superposition of line-of-sight (LOS) and single-bounced rays. The time-varying reference system is introduced to more accurately characterize the non-stationarity of HST MIMO channels caused by the high speed factor. Based on the proposed model, the 2D space cross-correlation function (CCF) and the temporal autocorrelation function (ACF) are derived, simulated under both of isotropic and non-isotropic scattering conditions, and discussed in detail.

In this paper, a joint optimization algorithm for cognitive radio (CR) network based on centralized spectrum sharing is proposed. A more practical scenario where the primary users (PU) transmit signals with multiple levels of power is studied in CR network. To reduce the computational complexity, a low complexity suboptimal power allocation algorithm is investigated. By solving the objective function, the optimal allocation of bandwidth and power are achieved. Simulation shows that the proposed algorithm can effectively improve the system throughput, ensure the PU's performance as well as guaranteeing the QoS of the cognitive users (CU).

This paper proposes an incremental localization algorithm in wireless sensor network based on the estimation of distribution algorithms. In this algorithm, the distances between an unknown node and the anchor nodes are measured, and then samples are chosen in the area in which the unknown node is located possibly. Thus, the high accuracy samples selection is based on the calculated fitness, and the probability distribution of the location coordinates is updated. It optimizes the location results through the learning and evolution of the samples and the probability distribution. Simulation results show that the proposed algorithm achieves the comparable performance with other up-to-date complex localization algorithms at low noise level.

We study the low complexity space-time turbo detection and decoding schemes for MIMO block transmission systems such as cyclic prefix based single-carrier block transmission (CP-SCBT) system and OFDM system. Because of the circulant property of the channel matrices, the detectors can he implemented by FFT/IFFT. Simulation results show that the proposed receivers significantly outperform the traditional, non-iterative receivers.

In a CDMA system, the RAKE receiver is commonly used to attain the diversity gain by taking advantage of the good correlation properties of the spreading codes. However, at low spreading gains the good correlation properties of the spreading codes are lost and the RAKE receiver performance is severely degraded by interpath interference (IPI). In the case of multi-code CDMA system, the multi-code interference (MCI) exists in the system. In order to suppress MPI and MCI, a novel receiver based on soft-output Viterbi algorithm (SOVA) equalization is proposed in this paper. The SOVA equalization is applied to symbol sequences after RAKE combining and MCI cancellation to effectively eliminate the IPI during transmission of high rate data in wideband DS-CDMA systems. Simulation results show that the proposed receiver significantly outperform the traditional RAKE and RAKE-VA receivers.

In this paper we propose low complexity turbo detection and decoding schemes for space-time block-coded block transmission (STBC-BT) systems. Because of the circulant property of the channel matrices, the detectors can be implemented in the frequency domain. Simulation results show that the performance of the proposed receivers is better than that of the traditional, noniterative receivers.

In the broadband communication, channel impulse response usually exhibits sparse behavior (i.e.. many nearly zero taps). This paper considers the channel estimation of the sparse channel for broadband multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems. Three algorithms for MIMO-OFDM sparse channel estimation are presented and compared: least square channel estimation (LSCE), constraint least square channel estimation (CLSCE) with ideal delay estimation, matching pursuit based channel estimation (MPCE). Mean square error (MSE) performances an also analyzed. Simulation results show that MPCK has better performance than LSCE and is very close to CLSCE under the high SNR, and also MPCE does not require the a priori of channel. Using the MP-based channel estimator, the detection performance is nearly optimal.