In this paper, we study a multi-source wireless power transfer (MS-WPT) enabled Internet of Nano Things (IoNT), where massive nanonodes wirelessly transmit packets to the same destination via clustered data collection and multi-hop relaying with the aid of nanonodes. A fairness-aware reliability-oriented design is provided aiming at minimizing the maximum transmission error probability among all the nanonodes. In particular, we formulate a joint resource allocation problem that optimizes MS-WPT dynamic transmit power and the blocklength for both WPT and wireless information transfer (WIT) phases. However, the problem is non-convex and intractable due to the mutual effects of multi-source, the nonlinear EH model, the complex finite blocklength (FBL) reliability model, and the infinite optimization variables regarding time-varying MS-WPT power. To tackle these difficulties, we first characterize the optimal frame structure for MS-WPT and prove that an equivalent optimal performance can be achieved by limited WPT decisions corresponding to a finite number of sub-slots. Following this frame structure reconstruction, an optimization problem with finite number of variables is formulated, nevertheless, remaining nonconvex. To cope with it, variable substitution, nonconvex relationship decoupling, relax variable introduction and successive convex approximation (SCA) are utilized, to further transfer the problem into local convex ones. A sub-optimal solution is finally achieved by the proposed iteration-based algorithm. Via numerical simulation, it is validated that a significant performance improvement is achieved by reasonable joint resource allocation while maintaining an appropriate compromise among massive nanonodes.

With the growing demands for marine environmental monitoring, collaborative operations, and marine resource development, high-speed and reliable Internet of Things (IoT) communication has become essential for maritime activities. This paper presents a wideband channel measurement campaign at 3.3 GHz in complex nearshore environments to investigate ship-to-ship (S2S) channel characteristics. The study systematically examines time-frequency stationarity, large-scale fading (LSF), dispersion, and small-scale fading (SSF). Results show that the mean stationarity distance reaches 22.48 m, with an average stationarity bandwidth exceeding 39.95 MHz. The statistical close-in (CI) model provides superior accuracy for path loss modeling, while the shadow fading autocorrelation exhibits a periodic oscillatory pattern, captured by a proposed improved model. Time–frequency–angular analysis indicates weak overall dispersion, with stronger dispersion in complex nearshore environments compared to open sea. Furthermore, RMS delay spread, Doppler spread, and angular spread are accurately modeled by Lognormal, Weibull, and Lognormal distributions, respectively. The Rician distribution dominates SSF envelope modeling, with the K-factor following a Normal distribution (mean 14.28–16.03 dB). These findings provide valuable insights for the design and evaluation of maritime IoT communication systems in nearshore environments.

The advent of artificial intelligence (AI)-native wireless communication is fundamentally reshaping the design paradigm of next-generation (NextG) systems, where intelligent air interfaces are expected to operate adaptively and efficiently in highly dynamic environments. Conventional orthogonal frequency division multiplexing (OFDM) systems rely heavily on pilots and the cyclic prefix (CP), resulting in significant overhead and reduced spectral efficiency. To address these limitations, we propose an adaptive end-to-end (E2E) transceiver architecture tailored for pilot-free and CP-free wireless systems. The architecture combines AI-driven constellation shaping and a neural receiver through joint training. To enhance robustness against mismatched or time-varying channel conditions, we introduce a lightweight channel adapter (CA) module, which enables rapid adaptation with minimal computational overhead by updating only the CA parameters. Additionally, we present a framework that is scalable to multiple modulation orders within a unified model, significantly reducing model storage requirements. Moreover, to tackle the high peak-to-average power ratio (PAPR) inherent to OFDM, we incorporate constrained E2E training, achieving compliance with PAPR targets without additional transmission overhead. Extensive simulations demonstrate that the proposed framework delivers superior bit error rate (BER), throughput, and resilience across diverse channel scenarios, highlighting its potential for AI-native NextG.

Large artificial intelligence (AI) models for wireless communications have demonstrated remarkable success across a range of wireless downstream tasks. However, their high computational overhead, low training efficiency, and limited privacy protection pose significant challenges for deployment on resource-constrained terminal devices. To address this issue, we propose a novel distributed framework that utilizes a three-layer cooperative paradigm to effectively achieve cooperation among agents, namely Multi-agent cooperative Large AI Models for Wireless Communications: MaLAM4Com. However, two key challenges in MaLAM4Com are how to effectively extract knowledge from shared information and how to alleviate the significant complexity arising from high-dimensional information sharing. To address these bottlenecks, we introduce federated distillation and Lyapunov cooperation to achieve robust knowledge transfer and consistent dynamic evolution, enabling the agents to capture the intrinsic structure of wireless channels. Subsequently, we innovatively utilize low-dimensional embeddings to facilitate information sharing among agents, significantly reducing cooperation complexity by up to 94% while enhancing privacy protection. This breaks traditional cooperative paradigms that rely on wireless channels. Moreover, we further introduce dataset distillation to enhance training efficiency by synthesizing elite data instead of directly utilizing raw datasets. Numerical results demonstrate that MaLAM4Com significantly outperforms existing baselines, with gains exceeding 45% under low sampling ratios. Remarkably, low-dimensional embeddings have also shown significant advantages in downstream tasks, reducing inference complexity by over 96%.

Low-altitude economy (LAE) is an emerging business model, which heavily relies on integrated sensing and communications (ISAC), mobile edge computing (MEC), and covert communications. This paper investigates the covert transmission design in MEC-based networked ISAC systems towards LAE, where an MEC server coordinates multiple access points to simultaneously receive computation tasks from multiple unmanned aerial vehicles (UAVs), locate a target in a sensing area, and maintain the UAVs’ covert transmission against multiple wardens. We first derive closed-form expressions for the detection error probability (DEP) at the wardens. Then, we formulate a total energy consumption minimization problem by optimizing communication, sensing, and computation resources as well as UAV trajectories, subject to the requirements on the quality of MEC services, DEP, and the radar signal-to-interference-and-noise ratio, and the causality constraints of UAV trajectories. An alternating optimization-based algorithm is proposed to handle the considered problem, which decomposes it into two subproblems: joint optimization of communication, sensing, and computation resources, and UAV trajectory optimization. The former is addressed by a successive convex approximation-based algorithm, while the latter is solved via a trust-region-based algorithm. Simulations validate the effectiveness of the proposed algorithm compared with various benchmarks, and reveal the trade-offs among communication, sensing, and computation in LAE systems.

This paper investigates the joint transmit and receive beamforming design for a bistatic integrated sensing and communication (ISAC) system, where a transmit base station (BS) and a receive BS are coordinated to simultaneously serve multiple downlink and uplink users as well as estimate target positions. The closed-form expression for the Cramér-Rao bound (CRB) for target positions and reflection coefficients is derived and minimized subject to constraints of the transmit power budget and communication requirements. To address the considered problem, the closed-form expression for the optimal receive beamforming vectors is derived, facilitating the development of a successive convex approximation (SCA)-based algorithm for optimizing the transmit information beamforming vectors and sensing covariance matrix. In addition, a learning-based approach named ISACNet, trained in an unsupervised manner, is proposed to handle the considered problem. The ISACNet incorporates multi-head self-attention and cross-attention mechanisms to significantly enhance its expressive capability. Simulations validate the effectiveness of the proposed SCA-based algorithm and ISACNet. It is observed that the sensing performance is predominantly affected by the downlink communication more than the uplink communication. Furthermore, our ISACNet generates an effective solution in millisecond-level response times with only a marginal performance degradation compared to the SCA-based algorithm.

In vehicle-to-everything (V2X) scenarios, the high dynamic characteristics of V2X environments impose significant challenges on communication channel estimation, where the emerging integrated sensing and communication technology could serve as a vital tool for achieving accurate channel estimation. This paper leverages radar-sensed angle information to assist in communication channel estimation and proposes a deep unfolding-based radar-assisted channel estimation network (Radar-CEnet). Specifically, for the radar module, to address the challenges posed by insufficient data in imperfect arrays, we employ a model-agnostic meta-learning with a convolutional neural network (MAML-CNN) approach to achieve high-precision direction-of-arrival (DOA) estimation. Then, the angle information obtained by the radar module, as prior knowledge, is used for channel estimation. Building on this, we design a novel soft-thresholding shrinkage function and propose the Radar-CEnet algorithm to efficiently estimate the sparse channel. Finally, we rigorously prove the convergence of the Radar-CEnet algorithm and demonstrate that it achieves a lower estimation error. Experimental results show that the proposed Radar-CEnet outperforms existing traditional methods and deep learning-based approaches in channel estimation performance. At an SNR of 20dB, the proposed Radar-CEnet method reduces the NMSE from –23.75dB to –27.15dB compared to the learning-based iterative soft-thresholding method, achieving an estimation accuracy improvement of approximately 54%.

Extensive channel measurement campaigns have shown that multipath components (MPCs) generally exhibit clustered distributions, making cluster-based models a cornerstone of wireless channel modeling. Developing time-varying cluster-based channel models requires not only clustering MPCs in the delay, angle, and power domains, but also tracking their temporal evolution. However, existing multipath clustering algorithms typically rely on fixed hyperparameters and therefore are often poorly suited to dynamically evolving clusters, while tracking algorithms based solely on distance metrics are prone to ambiguous associations among spatially proximate clusters. Moreover, treating clustering and tracking as separate processes often prevents temporal evolution information from being fully exploited during clustering. This paper proposes a structure-aware unified framework that integrates Adaptive Neighborhood Robust Mean Shift (AN-RMS) clustering with BoxKF-Cluster tracking. Within the proposed framework, AN-RMS, built upon kernel density estimation, adaptively determines the effective number of nearest neighborsKby detecting abrupt changes in the second-order gradient of the neighborhood-distance sequence, thereby identifying structural cluster boundaries and providing locally adaptive guidance for density-mode iteration. BoxKF-Cluster introduces oriented bounding boxes derived from root-mean-square statistics to characterize the evolving morphology of clusters, and combines Kalman filtering with a successive-interference-cancellation (SIC)-like strategy, in which the influence of existing clusters is first removed to facilitate the detection of newly emerging ones. As a result, clustering and tracking are jointly accomplished within a unified framework. The proposed method is validated in complex scattering scenarios using both ray-tracing simulations and real vehicle-to-vehicle millimeter-wave measurement data, demonstrating its effectiveness for time-varying channel modeling.

In this paper, we investigate the channel estimation problem in near-field extremely large-scale multiple-input multiple-output (XL-MIMO) systems, explicitly accounting for both spherical-wave propagation characteristics and spatial non-stationary effects. Building on these properties, we propose a novel model-based deep learning framework that delivers high-accuracy channel estimation with low computational complexity by tightly integrating domain knowledge and data-driven learning. Specifically, the proposed framework comprises three key unfolding networks: a sparse channel recovery network, a codebook update network, and an error cancellation network. The first network, referred to as variational Bayesian inference (VBI)-Net, is derived by unfolding the inverse-free VBI (IF-VBI) algorithm. It enables high-precision sparse channel reconstruction without requiring explicit prior assumptions, by learning the underlying precision distribution directly from data. The second network, gradient (Grad)-Net, is developed by unfolding the gradient ascent procedure, where learnable step sizes are introduced to adaptively refine the parameters of the polar-domain grids. Moreover, Grad-Net captures spatial non-stationary characteristics associated with the polar-domain representation by jointly exploiting gradient information and estimated path parameters. The third network, termed projected gradient descent (PGD)-Net, is constructed by unfolding the PGD algorithm. It iteratively refines the channel estimates and effectively suppresses residual estimation errors induced by spherical-wave propagation and spatial non-stationarity. Extensive numerical simulations demonstrate that the proposed framework significantly outperforms existing methods in both estimation accuracy and computational efficiency. Furthermore, the proposed framework achieves a superior accuracy-complexity tradeoff for practical XL-MIMO systems, delivering enhanced performance while maintaining very low computational complexity.

Grant-free (GF) random access has emerged as a promising solution for massive machine-type communications (mMTC). However, the non-uniform distribution of user equipment (UE) and real-world limitations on base station (BS) placement lead to random access imbalance among BSs and heavy access collisions in some cells. To this end, an unsupervised learning-based location-aware random access (ULL-RA) scheme is proposed in this paper to select the accessing BSs and channels simultaneously. Specifically, ULL-RA adopts a deep learning model named ULL-RA-Net, comprising parameter-shared feedforward neural network (FFNN) layers that enable each active UE to select a BS and an access channel to maximize the achievable rate. The model is trained in an unsupervised manner to maximize a designed differentiable objective, mapping UE locations to channel access probabilities. Notably, we propose a rate-collision loss tailored to the model architecture, which combines a collision-free channel capacity term and a sparsity-inducing collision penalty term to reduce access collisions and enhance the achievable rate. Experimental results using the Deep-MIMO dataset indicate that ULL-RA outperforms conventional GF access in both the average achievable rate and the access success rate.

The intelligent development of high-speed railways (HSRs) necessitates support for various services to ensure safe and reliable train operations while providing high-quality travel experiences for passengers. Network slicing presents a promising solution via isolated and service-specific radio resource management. However, meeting heterogeneous quality of service (QoS) requirements in HSR communications is particularly challenging due to imperfect channel state information (CSI) caused by high-speed mobility. In this work, we investigate a slicing puncture strategy in a moving network with an imperfect train-to-ground downlink, supporting passenger entertainment and safety-related services. The system includes two transmission links: outboard and inboard. Given the impact of high-speed mobility, we characterize the statistical probability distribution of the actual CSI and model the average transmission rates in the outboard link. We aim to minimize the system Resource Block (RB) and power in the above two links while satisfying the diverse QoS requirements of services. Since the resource minimization problem is a mixed-integer nonlinear programming, we decompose it into three subproblems: RB allocation, power allocation, and slicing puncture optimization. A Speed-Aware Resource allocation and Slicing puncture (SA-RS) algorithm is proposed. Specifically, analytical expressions are derived for RB allocation, and a bisection-based algorithm is designed for power allocation. Moreover, the slicing puncture strategy is obtained using a genetic-based algorithm. Simulation results demonstrate that the proposed strategy can improve the system performance compared with other baseline schemes under imperfect CSI.

A novel near-field wideband integrated sensing and communication framework is proposed to address the prohibitively high pilot overhead challenge in extremely large-scale MIMO systems. Unlike conventional approaches that rely on exhaustive two-dimensional codebook search, a unified architecture leveraging true-time-delay-based rainbow beamforming with controllable distance-dependent beam squint is proposed to extend spatial coverage. Furthermore, the inter-antenna phase ambiguity is harnessed to introduce beam split phenomena, enabling simultaneous multi-angle and multi-distance sensing within a single pilot transmission. Based on this architecture, a two-stage low-complexity sensing protocol is carried out, where distance-ring identification via beam-split-enhanced rainbow beams is performed in the first stage using sub-array structures, followed by angle refinement in the second stage. To mitigate frequency-dependent beamwidth variations, a frequency-compensated joint reconstruction algorithm based on virtual grid mapping and sparse optimization is proposed. Additionally, an echo-aided velocity estimation method exploiting intra-symbol Doppler diversity across subcarriers is developed, eliminating the need for multiple pulse transmissions. Simulation results demonstrate that: 1) complete spatial coverage is achieved with only two OFDM symbols, representing over 98% overhead reduction compared to exhaustive search methods; 2) the proposed scheme achieves superior localization accuracy with root-mean-square errors below 0.001 in normalized angle domain and 0.01 in distance-ring domain at moderate SNR; 3) communication rates are improved by 7% to 15% compared to conventional near-field beam training approaches under identical pilot budgets.

High-speed railway (HSR) communication is subject to unauthorized eavesdropping, and the acquisition of perfect channel state information (CSI) is difficult, which increases the secrecy outage probability of the system and poses a threat to secure data transmission. In addition, the train’s operating environment is complex. Deviations in train speed can increase Doppler shift compensation errors, further increases the secrecy outage probability. This paper constructs a train speed prediction model based on the L-Ns-Transformer. By compensating for Doppler shift, the model achieves more accurate channel modeling. Furthermore, an intelligent reflecting surface (IRS)-assisted beamforming method for HSR secure communication is proposed when the eavesdropper’s (Eve) CSI is unknown. We propose a two-stage deep learning (TS-DL)-based approach to design transmitter beamforming and IRS jointly, where the precoding vector and phase shift matrix are designed to minimize the secrecy outage probability. Simulation results demonstrate that the proposed TS-DL approach has lower computational complexity and can effectively reduce the system secrecy outage probability, thereby enhancing the security of HSR wireless communication.

By integrating the Internet of Things and sensing technologies, transportation systems can achieve higher management accuracy and efficiency. This paper explores a Reconfigurable Intelligent Surface (RIS) assisted integrated wireless sensing and power transfer (IWSPT) system in traffic scenarios with channel estimation errors and obstacles. Specifically, a transmitter deployed within transportation infrastructure optimizes the beamforming vector and RIS phase shifts cooperatively. The objective is to maximize the energy received by multiple energy harvesting devices (EHDs) under the constraint of beampattern thresholds for sensing in multiple directions. The coupled optimization variables in the proposed problem yield a non-convex result, so we propose a semi-infinite relaxation-based method for solving this optimization problem. We then introduce a low-complexity optimization algorithm to address the high computational complexity of the semi-infinite relaxation approach. The proposed algorithm significantly reduces the computational burden by leveraging Taylor expansion and successive convex approximation (SCA) techniques. Simulation results validate the effectiveness and robustness of the algorithm, highlighting its practical applicability in intelligent transportation systems.

Low Earth Orbit (LEO) satellites are playing an important role in earth observation, providing valuable images for training machine learning (ML) models used in tasks such as environmental monitoring and pattern recognition. However, due to unstable communication links and limited downlink bandwidth, it is economically impractical to transmit all raw images to ground stations (GSs) for model training. Federated learning (FL), a privacy-preserving distributed machine learning method, can reduce the communication overhead by exchanging model parameters. Generally, FL needs a fixed central server to aggregate the global model, which is challenging in LEO satellite networks, given the dynamic nature of satellite orbits. To overcome this, we propose a decentralized federated learning (DFL) framework that enables efficient model aggregation through satellite collaboration. Specifically, the proposed framework, named FedHRA, is based on model-agnostic meta-learning (MAML), which jointly optimizes hyperparameters and resource allocation to mitigate straggler effect and address statistical heterogeneity. Extensive numerical results on MNIST and CIFAR-10 datasets demonstrate that FedHRA achieves shorter learning time and higher model accuracy compared to the benchmark frameworks.

This paper proposes a graph neural network (GNN)-enabled coordinated beamforming design, termed HSTGNN, for high speed railway (HSR) communication systems, where a high-speed train (HST) and low-speed users (LUs) coexist in multi-cell scenarios. Two transmission schemes with the goal of maximizing quality-of-service (QoS)-constrained sum rate and rate of the HST are formulated and then reformulated using a hybrid maximum ratio transmission and zero-forcing strategy. The HSR communication system is abstracted into a heterogeneous graph, and HSTGNN consists of complex heterogeneous graph attention layers and fully-connected layers. To meet QoS requirements and power budget constraints, we employ constraint-based penalty terms and a numerical scaling operation. HSTGNN is trained via unsupervised learning to solve the two schemes in a unified framework. Numerical results demonstrate that HSTGNN achieves millisecond-level inference speed with an average optimality gap of only 4% relative to traditional optimization algorithm across various scenarios. Moreover, HSTGNN exhibits strong scalability to unseen configurations of both cells and LUs.

Privacy disclosure from model parameters and malicious attacks are critical issues in federated learning (FL). Existing research has yet to effectively address the simultaneous need for efficient communication design, privacy protection, and attack detection, which impedes the widespread adoption of FL in mobile edge networks over 6G wireless communication. In this paper, we propose a trustworthy FL framework that can ensure privacy, robustness, accountability, fairness, and explainability in mobile edge networks. Specifically, we integrate authenticated zero-knowledge proofs (ZKPs) and Pedersen commitments into the FL process. Despite the lack of direct access between servers and mobile devices, the servers can still identify trustworthy clients for specific tasks. Clients can verify the authenticity of the received global model based on the provided proofs and commitments. Furthermore, we leverage Ethereum to act as the verifier and authenticator of models. This verification and authentication process enables the servers to detect abnormal local models and perform trust-based aggregations. Numerical results demonstrate that the proposed trustworthy FL framework significantly improves the global model's in terms of accuracy, convergence rate, and security.

This paper investigates the active reconfigurable intelligent surface (RIS)-assisted integrated sensing, communication, and power transfer (ISCPT) networks, where rate-splitting multiple access (RSMA) scheme is employed to serve multiple downlink communication users. To promote the energy efficiency (EE) of such a system, we formulate an EE maximization problem by jointly optimizing the beamforming matrix, the sensing matrix, the active RIS matrix, the power splitting (PS) ratio vector, and the common rate allocation vector. Due to the non-convexity of the problem, we first design a successive convex approximation scheme with alternating optimization method (named SCA-AO) to solve it. As SCA-AO operates in an iterative manner, which is with relatively high computational complexity, we then design a mixture of experts (MoE)-based deep reinforcement learning (DRL) scheme with smooth clipping function (named MoE-SCF). In comparison, SCA-AO is able to achieve higher solution accuracy, while MOE-SCF has a shorter online execution response time. In order to integrate the advantages of both presented SCA-AO and MoE-SCF simultaneously, we further propose a hybrid MoE (H-MoE) scheme, where both the SCA-AO and the MoE-SCF are employed as expert strategies, and an opportunistic activator (OPA) is designed to dynamically select the best strategy generated by all expert combinations according to the performance evaluation function. Simulation results demonstrate that the proposed H-MoE promotes the system's EE by about 18.14% compared to traditional MoE, with similar response time. Additionally, compared to the SCA-AO, H-MoE significantly decreases the response time by approximately 56.17%, while only marginally compromising the EE performance by less than 3.1%.

The technological landscape is rapidly evolving toward large-scale systems. Networks supporting massive connectivity through numerous Internet of Things (IoT) devices are at the forefront of this advancement. In this paper, we examine Wireless Power Transfer (WPT)-enabled networks, where a server requires to collect data from these IoT devices to compute a task with massive Ultra-Reliable and Low-Latency Communication (mURLLC) services. We focus on information freshness, using Age-of-Information (AoI) as the key performance metric. Specifically, we aim to minimize the maximum AoI among IoT devices by optimizing the scheduling policy. Our analytical findings demonstrate the convexity of the problem, enabling efficient solutions. We introduce the concept of AoI-oriented cluster capacity and analyze the relationship between the number of supported devices and network AoI performance. Numerical simulations validate our proposed approach's effectiveness in enhancing AoI performance, highlighting its potential for guiding the design of future IoT systems requiring mURLLC services.

The problem of uplink transmissions in massive connectivity is commonly dealt with using schemes for grant-free random access. When a large number of devices transmit almost synchronously, the receiver may not be able to resolve the collision. This could be addressed by assigning dedicated pilots to each user, leading to a contention-free random access (CFRA), which suffers from low scalability and efficiency. This paper explores contention-based random access (CBRA) schemes for asynchronous access in massive multiple-input multiple-output (MIMO) systems. The symmetry across the accessing users with the same pilots is broken by leveraging the delay information inherent to asynchronous systems and the angle information from massive MIMO to enhance activity detection (AD) and channel estimation (CE). The problem is formulated as a sparse recovery in the delay-angle domain. The challenge is that the recovery signal exhibits both row-sparse and cluster-sparse structure, with unknown cluster sizes and locations. We address this by a cluster-extended sparse Bayesian learning (CE-SBL) algorithm that introduces a new weighted prior to capture the signal structure and extends the expectation maximization (EM) algorithm for hyperparameter estimation. Simulation results demonstrate the superiority of the proposed method in joint AD and CE.

Cell-free (CF) massive multiple-input multiple-output (MIMO) has recently emerged as a highly promising technology for supporting future six-generation (6G) networks, owing to its unique ability to provide high data rates and reliable connectivity. However, a primary challenge in CF massive MIMO is severe inter-user interference, which is caused by densely located user equipments (UEs) and the presence of imperfect channel state information (CSI). Fortunately, the rate-splitting (RS) strategy offers significant benefits by enabling partially interference decoding, thereby greatly enhancing overall system performance. In this paper, we investigate the performance of uplink RS in CF massive MIMO systems. Considering the inevitable channel estimation errors caused by pilot contamination, we first derive a novel closed-form expression for characterizing spectral efficiency (SE). Moreover, we propose two innovative decoding strategies tailored to the 6G scenario, highlighting their role in enhancing the interference management capabilities of RS, while balancing decoding performance with computational complexity. To ensure successful decoding of each sub-message to the greatest extent possible, we devise an optimization-based power control scheme to maximize the minimum SE of the sub-messages, and propose a low-complexity scheme for comparative analysis. Additionally, we investigate the total energy efficiency (EE) of the system and propose a power control scheme for maximizing EE by exploiting fractional programming (FP) theory. Simulation results corroborate our theoretical expressions and demonstrate that both RS and the proposed power control schemes can significantly improve both SE and EE.

Cell-free (CF) massive multiple-input multiple-output (MIMO) has emerged as a promising technique to deliver uniform signal coverage and high data rates. However, employing low-precision hardware in user equipment introduces susceptibility to hardware impairments (HI), resulting in significantly degraded channel state information (CSI) accuracy. Fortunately, rate-splitting multiple access (RSMA) has been proposed as a robust solution to mitigate the adverse effects of imperfect CSI by performing message splitting at the transmitter and successive interference cancellation (SIC) at the receiver. In this paper, we incorporate RSMA into CF massive MIMO systems to tackle the problem posed by imperfect CSI. Taking into account inevitable pilot contamination, we first derive a novel and closed-form expression for the spectral efficiency (SE) to analytically characterize the performance of RSMA-enabled CF massive MIMO systems under spatially correlated Rician fading channels. Subsequently, we focus on optimizing the decoding order, power allocation, and fronthaul weights to maximize the system’s sum SE. To address this mixed-integer nonlinear programming (MINLP) problem, we initially propose an alternating optimization (AO)-based optimization method that decomposes the original intractable problem into three manageable subproblems, which are iteratively handled until convergence. Considering the significant computational complexity associated with the AO-based approach, we further propose a proximal policy optimization (PPO)-based method to establish an effective and low-complexity optimization framework. Simulation results unveil the detrimental impact of HI on both CSI accuracy and the overall sum SE performance. In particular, the presence of HI introduces residual interference that limits the performance gains achievable through additional RSMA layers, especially in strong line-of-sight scenarios, highlighting the trade-off between these gains and the SIC-related costs in terms of computational complexity and decoding latency.

In this paper, we investigate task offloading and computing in an urban mobile edge computing (MEC) system assisted by an aerial intelligent reflecting surface (AIRS). To enhance information freshness while saving energy, we formulate a joint optimization problem to minimize the weighted sum of average age of information (AoI) and total energy consumption by jointly optimizing task offloading decisions, resource allocation, and AIRS configuration including its deployment position, phase shifts, and panel size, subject to offloading quality and computing deadline constraints. To tackle the resulting mixed-integer and nonconvex problem, we develop a hierarchical optimization framework based on the objective priority and variable coupling relations. Under this framework, the problem is solved in two stages using quadratic penalty, numerical analysis, and convex optimization techniques. Specifically, an AoI-aware task offloading policy is first designed to maximize information freshness; subsequently, given the obtained offloading policy, an AIRS configuration and resource allocation scheme is proposed to minimize energy consumption. Simulation results demonstrate that the proposed approach significantly outperforms benchmark schemes in reducing both AoI and energy consumption, while exhibiting superior convergence performance.

The distributed deployment of communication infrastructure is a promising evolutionary trend in the next-generation wireless communication systems, as exemplified by the novel cell-free massive multiple-input multiple-output (CF mMIMO) technology. In user-centric CF mMIMO systems, synchronization among access points (APs) is a critical challenge that significantly impacts the effectiveness of coherent joint processing gains. In this paper, we investigate a CF mMIMO system featuring distributed AP deployments and low-resolution analog-to-digital converters (ADCs). To guarantee precise phase synchronization, we first propose two double-layer AP clustering approaches for rapid synchronization using the Leader-Follower paradigm: one based on the K-means algorithm and the other utilizing classical graph theory with geographical distance metrics in AP deployment. Specifically, in the first layer, a designated Leader AP keeps synchronization with its serving secondary Follower-1 APs, while in the second layer, each Follower-1 AP communicates with its neighboring Follower-2 APs. Next, we propose novel phase synchronization and carrier frequency synchronization strategies among APs based on an over-the-air synchronization signal transmission mechanism, which enables mutual calibration without transmitting any measurements to the central processing unit via fronthaul links. Furthermore, we consider the effect of quantization accuracy of radio frequency hardware on synchronization performance, thereby facilitating the adoption of low-cost components. Finally, simulation results demonstrate that synchronization precision can be significantly improved, reaching values on the order of$10^{-5}$. Additionally, even with moderately coarse ADC quantization, near-optimal performance can be achieved in practical scenarios.

Extremely large-scale multiple-input multiple-output (XL-MIMO) systems are pivotal to next-generation wireless communications, where dynamic RF chain architectures offer enhanced performance. However, efficient precoding in such systems requires accurate channel state information (CSI) obtained with low complexity. To address this challenge, spatial-domain channel extrapolation has attracted growing interest. Existing methods often overlook near-field spherical wavefronts or rely heavily on sparsity priors, leading to performance degradation. In this paper, we propose an adaptive near-field channel extrapolation framework for multi-subcarrier XL-MIMO systems, leveraging a strategically selected subset of antennas. Subsequently, we develop both on-grid and off-grid algorithms, where the latter refines the former’s estimates for improved accuracy. To further reduce complexity, a cross-validation (CV)-based scheme is introduced. Additionally, we analytically formulate the mutual coherence of the sensing matrix and propose a coherence-minimizing-based random pattern to ensure robust extrapolation. Numerical results validate that the proposed algorithms significantly outperform existing methods in both extrapolation accuracy and achievable rate, while maintaining low computational complexity. In particular, our proposed CV ratio offers a flexible trade-off between accuracy and efficiency, and the corresponding off-grid algorithm achieves high accuracy with complexity comparable to conventional on-grid methods.

The integration of reconfigurable intelligent surfaces (RIS) with cell-free massive MIMO (CF mMIMO) represents a compelling paradigm for satisfying the stringent reliability and latency demands of ultra-reliable low-latency communication (URLLC). In this framework, distributed access points (APs) provide substantial macro-diversity gains, while dynamically controllable RIS elements facilitate enhanced signal propagation. This paper investigates a practical RIS-aided CF mMIMO system designed for URLLC applications, where communications occur through RIS-reflected links under realistic spatially correlated Rayleigh fading channels, with practical impairments such as RIS phase estimation errors and electromagnetic interference explicitly considered. To evaluate reliability in the short-packet regime, we adopt the decoding error probability (DEP) as the performance metric and derive its analytical expression based on user-side SINR. We formulate a non-convex optimization problem to minimize the maximum DEP among users by jointly optimizing AP beamforming, RIS phase shifts, and blocklength allocation. A hybrid solution framework is proposed, combining deep reinforcement learning for continuous variables with a differential evolution (DE) algorithm for discrete blocklength optimization. Simulation results demonstrate the superior performance of the proposed method over alternating optimization and genetic algorithm (GA) baselines. Notably, increasing the number of AP antennas and transmission blocklength improves network availability, although gains saturate due to inter-user interference and diminishing returns. Moreover, the proposed DE-based algorithm for blocklength optimization consistently outperforms the GA method in terms of both solution quality and computational efficiency.

Reconfigurable intelligent surfaces (RIS) are emerging as a crucial technology to address the energy consumption challenges posed by the widespread deployment of access points (APs) in cell-free massive multiple-input multiple-output (CF mMIMO) systems within future sixth-generation (6G) networks. However, most existing studies on RIS-aided CF mMIMO systems assume ideal hardware and static channel conditions, which deviate from practical deployment scenarios. This work analyzes the performance of a RIS-aided CF mMIMO system by incorporating the combined effects of hardware impairments from non-ideal transceivers and channel aging caused by user mobility. We first characterize both direct and cascaded channels between APs and user equipment, modeling them using correlated Rician fading to capture realistic propagation effects. The overall channel is then estimated via the minimum mean square error method under perfect and imperfect line-of-sight phase knowledge, and we derive an analytical expression for the instantaneous spectral efficiency (SE). We also derive the closed-form expressions of the use-and-then-forget bound with the maximum-ratio transmission precoding method. Building on these insights, we establish an efficient joint optimization framework for beamforming in the AP and phase-shift adaptations in the RIS, exploring an alternating optimization method and a deep-reinforcement learning (DRL)-based algorithm. The numerical results validate our theoretical analysis, illustrating the impact of hardware impairments and channel aging on SE. Although the DRL-based method is scalable and adapts well to dynamic environments, its high computational and memory demands pose challenges for real-time deployment, highlighting a trade-off between performance and feasibility.

Recently, an emerging flexible-antenna technology, termed pinching antennas, has attracted growing academic interest. By inserting discrete dielectric materials, pinching antennas can be activated at arbitrary points along waveguides, allowing for flexible customization of channel conditions. This paper investigates a multi-waveguide pinching-antenna integrated sensing and communications (ISAC) system, where transmit pinching antennas (TPAs) and receive pinching antennas (RPAs) coordinate to simultaneously detect one potential target and serve one downlink user. We formulate a communication rate maximization problem subject to radar signal-to-noise ratio (SNR) requirement, transmit power budget, and the allowable movement region of the TPAs, by jointly optimizing TPA locations and transmit beamforming design. To address the non-convexity of the problem, we propose a novel fine-tuning approximation method to reformulate it into a tractable form, followed by a successive convex approximation (SCA)-based algorithm to obtain the solution efficiently. Furthermore, we derive the closed-form optimal solution for a special multi-waveguide case involving a single TPA. Extensive simulations validate both the system design and the proposed algorithm. Results show that the proposed method achieves near-optimal performance compared with the computational-intensive exhaustive search-based benchmark, and pinching-antenna ISAC systems exhibit a distinct communication-sensing trade-off compared with conventional systems.

The cell-free massive multi-input multi-output (CF-mMIMO) is a promising technology for the six generation (6G) communication systems. Channel prediction will play an important role in obtaining the accurate CSI to improve the performance of CF-mMIMO systems. This paper studies a deep learning (DL) based joint space-time-frequency domain channel prediction for CF-mMIMO. Firstly, the prediction problems are formulated, which can output the multi-step prediction results in parallel without error propagation. Then, a novel channel prediction model is proposed, which adds frequency convolution (FreqConv) and space convolution (SpaceConv) layers to Transformer-encoder. It is able to utilize the space-time-frequency correlations and extract the space correlation in the irregular AP deployment. Next, simulated datasets with different sizes of service areas, UE velocities and scenarios are generated, and correlation analysis and cross-validation are used to determine the optimal hyper-parameters. According to the optimized hyper-parameters, the prediction accuracy and computational complexity are evaluated based on simulated datasets. It is indicated that the prediction accuracy of the proposed model is higher than traditional model, and its computational complexity is lower than traditional Transformer model. After that, the impacts of space-time-frequency correlations on prediction accuracy are studied. Finally, realistic datasets in a high-speed train (HST) long-term evolution (LTE) network are collected to verify the prediction accuracy. The verification results demonstrate that it also achieves higher prediction accuracy compared with traditional models in the HST LTE network.

Cell-free (CF) massive multiple-input multiple-output (mMIMO) and reconfigurable intelligent surface (RIS) are two advanced transceiver technologies for realizing future sixth-generation (6G) networks. In this paper, we investigate the joint precoding and access point (AP) selection for an energy-efficient RIS-aided CF mMIMO system. To address the associated computational complexity and communication power consumption, we advocate for user-centric dynamic networks in which each user is served by a subset of APs rather than by all of them. Based on the user-centric network, we formulate a joint precoding and AP selection problem to maximize the energy efficiency (EE) of the considered system. To solve this complex nonconvex problem, we propose an innovative double-layer multi-agent reinforcement learning (MARL)-based scheme. Moreover, we propose an adaptive power threshold-based AP selection scheme to further enhance the EE of the considered system. To reduce the computational complexity of the RIS-aided CF mMIMO system, we introduce a fuzzy logic (FuZ) strategy into the MARL scheme to accelerate convergence. The simulation results show that the proposed FuZ-based MARL cooperative architecture effectively improves EE performance, offering a 85% enhancement over the zero-forcing (ZF) method, and achieves faster convergence speed compared with MARL. It is important to note that increasing the transmission power of the APs or the number of RIS elements can effectively enhance the spectral efficiency (SE) performance, which also leads to an increase in power consumption, resulting in a non-trivial trade-off between the quality of service and EE performance.

Integrated Sensing and Communication (ISAC) technology plays a critical role in future intelligent transportation systems, by enabling vehicles to perceive and reconstruct the surrounding environment through reuse of wireless signals, thereby reducing or even eliminating the need for additional sensors such as LiDAR or radar. However, existing ISAC-based reconstruction methods often lack the ability to track dynamic scenes with sufficient accuracy and temporal consistency, limiting the real-world applicability. To address this limitation, we propose a deep learning based framework for vehicular environment reconstruction by using ISAC channels. We first establish a joint channel–environment dataset based on multi-modal measurements from real-world urban street scenarios. Then, a multi-stage deep learning network is developed to reconstruct the environment. Specifically, a scene decoder identifies the environmental semantic context such as buildings, trees, and so on; a semantic center decoder predicts coarse spatial layouts by localizing dominant object centers; a point cloud decoder recovers fine-grained geometry and structure of surrounding environments. Experimental results demonstrate that the proposed method achieves high-quality global reconstruction of dynamic environments, with a Chamfer Distance of 0.29 and [email protected] of 0.87. In addition, complexity analysis demonstrates the efficiency and practical applicability of the method in real-time scenarios. This work provides a pathway toward low-cost environment reconstruction based on ISAC for future intelligent transportation.

In recent years, satellite internet has been widely recognized as a key component of future integrated space-air-ground networks. With advancements in satellite miniaturization and launch technologies, mega-constellations have become a growing trend. The increasing number of satellites in constellations presents challenges for interference analysis. This paper proposes a novel interference analysis method based on time-elevation interference spectrum. The proposed method can provide a more comprehensive analysis for NGSO mega-constellations by considering the aggregated dynamic interference under the time-elevation domain. The interference characteristics under different orbital inclination, orbital plane numbers, orbital height, and ground station latitude are analyzed. Furthermore, the probability distributions of interference are derived based on the joint distribution of satellites and ground stations. The outage probabilities and throughput are also analyzed to measure the system’s availability. Through the validation of STK and the Monte Carlo method, our method has high accuracy.

While spatial, temporal, and frequency domains are explored to enhance spectral efficiency (SE) in wireless communications, utilizing the polarization of electromagnetic (EM) waves also contributes to achieving this goal. Unlike conventional reconfigurable intelligent surface (RIS)-aided communications, polarization-transforming RIS (PTRIS) is first applied in this work to assist the line-of-sight (LoS) communication system. We introduce a novel framework for accurately modeling the direct and cascaded channels in the PTRIS-aided LoS communication system. This framework considers the spatial positions of the transmitter and receiver, the radiation patterns, antenna rotations, and the physical propagation mechanisms of EM waves based on antenna theory. The aperture field method is used to model the physical reflection of EM waves by PTRIS. Additionally, we aim to maximize SE by investigating four cases regarding the polarization-transforming capability of the PTRIS. Optimal closed-form solutions are derived for Case 1 and Case 2, while a best-effort approximation-based alternative optimization (BEA-AO) method is proposed for Case 3 to obtain sub-optimal solutions. Case 4 can be solved optimally with the barnch-and-cut algorithm within a reasonable computation time. Numerical results demonstrate that PTRIS can provide a robust and enhanced SE in LoS communications, even with arbitrary antenna rotations, compared to the scenarios without PTRIS.

Integrating dense channel fingerprints into deep learning (DL) becomes a promising way to realize precise three-dimensional (3D) indoor localization. However, most existing methods are frequency-dependent, which limits the localization precision when operating in different frequency bands. To address this challenge, this paper proposes a masked Transformer encoder (MTE) model capable of using the channel state information (CSI) data of an arbitrary number of sub-channels (frequency bands) as input. The proposed MTE model can locate a UE using frequency-scalable CSI data, to realize resilient localization. We first introduce how to transform CSI data into sequential data suitable for Transformer-based models, with length of the sequence determined by the number of sub-channels. Based on this, an MTE model is designed to achieve resilient FP localization with frequency-scalability, i.e., capable of processing the CSI data of an arbitrary number of sub-channels. Next, we construct a 3D CSI FP dataset using ray-tracing (RT) simulations based on real-world indoor scenarios and versatile electromagnetic (EM) coefficients. The reliability of the dataset is verified by measurement data. Extensive experiments demonstrate that the MTE model outperforms many state-of-the-art baselines, classical time-series models, and alternative Transformer-based methods, especially under arbitrary sub-channel CSI data. Moreover, we demonstrate that the MTE model also offers many advantages in terms of training and storage costs through comparisons with conventional models.

In this paper, we investigate the low-complexity distributed combining scheme design for near-field cell-free extremely large-scale multiple-input-multiple-output (CF XL-MIMO) systems. Firstly, we construct the uplink spectral efficiency (SE) performance analysis framework for CF XL-MIMO systems over centralized and distributed processing schemes. Notably, we derive the centralized minimum mean-square error (CMMSE) and local minimum mean-square error (LMMSE) combining schemes over arbitrary channel estimators. Then, focusing on the CMMSE and LMMSE combining schemes, we propose five low-complexity distributed combining schemes based on the matrix approximation methodology or the symmetric successive over relaxation (SSOR) algorithm. More specifically, we propose two matrix approximation methodology-aided combining schemes: Global Statistics & Local Instantaneous information-based MMSE (GSLI-MMSE) and Statistics matrix Inversion-based LMMSE (SI-LMMSE). These two schemes are derived by approximating the global instantaneous information in the CMMSE combining and the local instantaneous information in the LMMSE combining with the global and local statistics information by asymptotic analysis and matrix expectation approximation, respectively. Moreover, by applying the low-complexity SSOR algorithm to iteratively solve the matrix inversion in the LMMSE combining, we derive three distributed SSOR-based LMMSE combining schemes, distinguished from the applied information and initial values.

Movable antenna (MA) technology has recently attracted significant research attention as a promising solution for enhancing wireless network performance. However, conventional MAs can only effectively serve users in close proximity, resulting in restricted coverage. To overcome this limitation, in this paper, we explore a beyond-diagonal reconfigurable intelligent surface (BD-RIS)-aided MA system. First, we propose a penalty-based block coordinate descent optimization algorithm tailored to the new constraints imposed by BD-RIS-aided MA systems. Specifically, our method decouples the inherently non-convex and coupled antenna distance constraints by introducing auxiliary optimization variables. Subsequently, the resulting problem is efficiently addressed via alternating optimization, with closed-form updates for the auxiliary variables. Furthermore, recognizing the challenges posed by large-scale BD-RIS deployments, which have the potential for serving a substantial number of users, traditional centralized optimization frameworks encounter considerable difficulties, including high computational complexity, excessive communication overheads, as well as limited scalability with increasing system size. To address these limitations, we propose an efficient asynchronous alternating direction method of multipliers (AS-ADMM) scheme aimed at maximizing the sum rate. Our numerical results demonstrate that the BD-RIS-aided MA system achieves superior performance compared to both conventional fixed position antenna and BD-RIS-aided systems. Furthermore, the proposed AS-ADMM framework can achieve a trade-off between performance and computational overhead, highlighting its potential for practical implementation in large-scale wireless communication networks.

In this paper, a 3D geometrical cluster-based channel model is developed for reconfigurable intelligent surface (RIS)-assisted multi-user communication systems. Herein, spatial consistency of multi-user channels is considered to guarantee smooth evolution of large-scale (i.e. Ricean K-factor, shadow fading, and number of clusters) and small-scale (i.e. distance and angle of multipath components) channel parameters for base station (BS)-receiver (Rx) and BS-RIS-Rx links of different users. Two different optimization objectives for multi-user channels are presented, namely, maximizing sum of channel gains (O1) and maximizing channel capacity (O2). Then, projected gradient ascent algorithm is applied to solve RIS phase shift matrix under both optimization objectives. Furthermore, some channel statistical characteristics, namely, correlation coefficient, singular value spread, channel capacity, and root mean square (RMS) delay spread are derived and analyzed. At the same time, impacts of solved RIS phase matrices under different optimization objectives, as well as locations of RIS and users, on channel characteristics are compared and investigated. Simulation results demonstrate that the role of RIS under O1 is to align path phases of BS-RIS-Rx link with those of BS-Rx link as much as possible, whereas under O2, the RIS aims to enhance power while reducing user channel correlation. In addition, solved RIS phase under O2 can increase channel capacity and reduce channel correlation between different BS antennas, whereas O1 leads to a reduction in channel capacity. Location of RIS is also found to affect channel response impulse amplitude of different users. Furthermore, accuracy of the proposed model is verified by comparing RMS delay spread with measured data. These observations will provide a foundation for developing reconstructed wireless propagation environment of future multi-user communication systems.

In this paper, a novel vision transformer (ViT) network is proposed to adaptively construct radio multi-user channels via reconfigurable intelligent surface (RIS), with objective of enhancing received power at the required users’ locations. Different from deep learning scheme that solves RIS phase by learning relationship between the multi-user channel matrix and the RIS phase, channel impulse response (CIR) image is used as input for ViT network, which unifies representation of channels with arbitrary numbers and positions of users. Thus, the network can adaptively solve RIS phase for arbitrary channel scenarios with random user positions and different numbers of users, realizing to reconstruct channels with the required characteristics. Technically, in train stage, local CIR image sets are used as input and RIS phase sets are used as output to train ViT network. Herein, RIS phase sets are determined by closed-form solution and deep neural network for single- and multi-user scenarios, respectively. The CIR image sets are generated by a geometry-based channel model and solved RIS phase. In prediction stage, the desired CIR images, generated based on users’ locations and a mask model, serve as inputs for the trained ViT network to predict RIS phase. Functional verification of ViT network is conducted by comparing differences between CIR image generated by predicted RIS phase and original CIR images input into network. At the same time, different optimization schemes, including convolutional neural network, deep neural network, projected gradient ascent algorithm, and coordinate descent algorithm are compared with ViT network to evaluate prediction performance. The results show that the solved RIS phase by ViT network can achieve the largest coverage probability and magnitude of CIR among the other compared algorithms. At the same time, generalization capability of the above optimization schemes is evaluated under different initial user numbers. The Furthermore, impacts of some parameters related to the desired CIR image generation, such as noise range and bound, on ViT network performance are investigated. These observations can present a reference for intelligent design of communication environment via RIS.

As a novel technology in the sixth-generation (6G) wireless communication systems, integrated sensing and communication (ISAC) enables intelligent agents to perceive, predict, and interact with the environment. It is crucial for ISAC to acquire environmental information based on electromagnetic propagation, referred to as channel semantics, to facilitate tasks such as decision-making and beamforming. However, channel models that focus on physical characteristics face challenges in representing the semantics embedded in the channel, thereby limiting the performance evaluation of ISAC systems. To tackle this, we present a novel unified framework for channel modeling from the conceptual event perspective. By leveraging a multi-level semantic structure and characterized knowledge libraries, the framework decomposes complex channel characteristics into composable and extensible high-level semantic characterization, thereby better capturing the relationship between the environment and channel, and enabling more flexible adjustments of channel models for different events without requiring a complete reset. Specifically, we define channel semantics from three levels: status semantics, behavior semantics, and event semantics, corresponding to channel transient multipaths, channel time-varying trajectories, and channel topology, respectively. Taking a realistic vehicular ISAC scenario as an example, we perform semantic clustering through depth estimation and semantic segmentation of environmental images, and further characterize the channel status semantics by fitting multipath statistical distributions; behavior semantics are modeled using Markov chains to capture time-varying characteristics; event semantics are characterized by employing a co-occurrence matrix. The results indicate that the proposed model can generate accurate channels whereas representing rich semantic information. Additionally, generalization of the model for customized semantics is demonstrated.

Integrated sensing and communication (ISAC) is a key technology for improving spectrum efficiency and enabling intelligent wireless networks, yet its deployment in short-packet transmission scenarios faces significant challenges such as finite block-length (FBL) effects, channel estimation uncertainty, and limited coverage. To address these issues, this paper investigates a short-packet ISAC system assisted by a hybrid simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and proposes a two-stage ISAC transmission paradigm. In Stage I, the hybrid STAR-RIS performs target direction-of-arrival estimation, and a closed-form expression of Cramér–Rao Bound (CRB) is derived to establish channel state information (CSI) uncertainty model based on CRB. Meanwhile, each user performs channel estimation locally and feeds results back to DFBS. In Stage II, the estimated CSI is utilized to jointly design resource allocation, and an optimization problem is formulated to maximize target illumination power under FBL and CSI uncertainty constraints. To tackle this strongly coupled non-convex problem, we develop a hierarchical solution strategy: the sensing duration is first determined via one-dimensional search, and then, an alternating optimization framework is employed to decouple the problem into DFBS beamforming and hybrid STAR-RIS coefficient optimization, where iterative algorithms based on semi-definite relaxation, semi-definite programming, and singular value decomposition are proposed to ultimately obtain a convergent optimal solution. Simulation results validate the fast convergence and superior performance of our proposed algorithm, reveal the inherent trade-off between the two stages under constrained resources, and demonstrate the importance of joint two-stage resource design assisted by hybrid STAR-RIS in enhancing short-packet ISAC system performance.

In this paper, we design a novel two-stage transmission framework in millimeter wave-ISAC systems with multiple communication users (CUs) and multiple target scenarios. In stage I, the dual-functional base station (DFBS) performs beam scanning with pilot signals, estimating target direction of arrival angles (DoAs) through the maximum likelihood estimation and multiple signal classification techniques, while the CU estimate DoAs via minimum mean square error and MUSIC techniques. Further, we derive the closed-form Cramér-Rao Bound (CRB) expressions for estimated CU/target DoAs and establish the relationship between channel station information (CSI) error and CRB. In stage II, the DFBS transmits ISAC signals and maximizes the minimum effective signal-to-interference-plus-noise ratio (SINR) of CU by jointly optimizing two stage resources, while meeting sensing performance requirements and accounting for the impact of imperfect CSI. Since the complex interactions and strong coupling among variables, the formulated problem is non-convex and difficult to be solved directly. To address this issue, we begin by employing one-dimensional search to determine the sensing duration of Stage I. Then, based on this result, the DFBS beamforming optimization design is carried out with S-procedure method, penalty-based and successive convex approximation algorithms to convert the original problem into a tractable convex optimization problem. Finally, simulations are executed to confirm the advantages and effectiveness of our developed scheme.

As a key technology of future communication systems, reconfigurable intelligent surfaces (RISs) can intelligently control wireless signal reflections, thereby optimizing propagation channels and enhancing communication performance. However, the indoor channel characteristics and communication performance of RISs in real-world deployment environments remain insufficiently studied. In this paper, we develop a RIS-based measurement platform for simultaneous channel measurement and link-level performance analysis, followed by verification and validation in a laboratory environment. Utilizing this platform, we conduct wideband channel measurement and performance analysis work in indoor scenarios at 2.6 GHz band. Multiple RIS phase shift codebooks are employed, including the 1-bit discrete Fourier transform (DFT) codebook, the Ring-type codebook, and the conditional sample mean (CSM) codebook. Based on these measurements, two empirical path loss (PL) models, namely the floating-intercept (FI) model and the close-in (CI) model, are fitted to the measured data. Furthermore, we comprehensively analyze and compare the channel characteristics of RIS-assisted communications, including shadow fading (SF), multipath components (MPCs) statistics, the Rician K-factor (KF), and root mean square (RMS) delay spread. Finally, we evaluate and compare the wireless coverage and link-level performance of various RIS reflection schemes. The findings on propagation characteristics and communication performance provide essential insights that can support the future application and deployment of RIS-assisted communication systems.

As next-generation wireless communication systems need to be able to operate in high-frequency bands and high-mobility scenarios, delay-Doppler (DD) domain multicarrier (DDMC) modulation schemes, such as orthogonal time frequency space (OTFS), demonstrate superior reliability over orthogonal frequency division multiplexing (OFDM). Accurate DD domain channel modeling is essential for DDMC system design. However, since traditional channel modeling approaches are mainly confined to time, frequency, and space domains, the principles of DD domain channel modeling remain poorly studied. To address this issue, we propose a systematic DD domain channel measurement and modeling methodology in high-speed railway (HSR) scenarios. First, we design a DD domain channel measurement method based on the long-term evolution for railway (LTE-R) system. Second, for DD domain channel modeling, we investigate quasi-stationary interval, statistical power modeling of multipath components, and particularly, the quasi-invariant intervals of DD domain channel fading coefficients. Third, via LTE-R measurements at 371 km/h, taking the quasi-stationary interval as the decision criterion, we establish DD domain channel models under different channel time-varying conditions in HSR scenarios. Fourth, the accuracy of proposed DD domain channel models is validated via bit error rate comparison of OTFS transmission. In addition, simulation verifies that in HSR scenario, the quasi-invariant interval of DD domain channel fading coefficient is on millisecond (ms) order of magnitude, which is much smaller than the quasi-stationary interval length on 100 ms order of magnitude. This study could provide theoretical guidance for DD domain modeling in high-mobility environments, supporting future DDMC and integrated sensing and communication designs for 6G and beyond.

Emerging wireless applications pursue a paradigm shift towards the integrated system that is capable of secure data transmission and high-resolution sensing in near-field environments. Conventional far-field use-cases suffer from the fundamental limitations in security, spatial precision, and spectral coexistence. Against this backdrop, this paper investigates an integrated covert communications and sensing (ICCS) system operating in the near-field environment. Specifically, the transmitter (Alice) aims to covertly convey messages to legitimate receivers (Bobs), while circumventing the detection by the eavesdropper (Willie) as well as improving the sensing performance at the target. To elevate communication performance, we aim to maximize the achievable sum rate to jointly optimize the communication and sensing beamforming matrices at Alice. The optimization problem is subject to multiple constraints with coupled variables: the transmit power budget at Alice, the minimum communication rate requirements for Bob, the Cram$\acute {e}$r-Rao bound (CRB) constraint to ensure accurate parameter estimation in sensing, and the covertness constraint against Willie’s detection. Given the non-convex nature of the formulated problem, an efficient successive convex approximation and semidefinite relaxation algorithms are proposed. In addition, we provide a theoretical analysis to confirm the convergence behaviour of the proposed algorithm, which can achieve the near-optimal solution. Finally, the numerical results are presented to highlight the superiority of the proposed ICCS system over existing counterparts. These results numerically verify the effectiveness of the proposed approach in enhancing communication rates while maintaining sensing performance and covertness in the near-field regime.

5G-for-Railway (5G-R), the next-generation railway dedicated mobile communication system, has a leap-forward improvement compared with the previous system. Accompanied by the differences in frequency, bandwidth and scenario, it brings new challenges to channel characterization and modeling. This paper presents our latest research in this field. Specifically, the 5G-R professional channel measurement of a real railway scenario is carried out. Based on the measured data, the multidimensional channel characteristics such as large-scale fading, delay spread, stationarity, and spatial distribution are evaluated. Furthermore, key cluster parameters such as lifetime, cluster power, delay, and their correlation are analyzed to support the mainstream cluster-based channel model. These pioneering measurement and modeling work in this field can provide a basis for forming reliable and accurate 5G-R channel models.

We consider the problem of high-dimensional channel estimation in fast time-varying millimeter-wave MIMO systems with a hybrid architecture. By exploiting the low-rank and sparsity properties of the channel matrix, we propose a two-phase compressed sensing framework consisting of observation matrix completion and channel matrix sparse recovery, respectively. First, we formulate the observation matrix completion problem as a low-rank matrix completion (LRMC) problem and develop a robust rank-one matrix completion (R1MC) algorithm that enables the matrix and its rank to iteratively update. This approach achieves high-precision completion of the observation matrix and explicit rank estimation without prior knowledge. Second, we devise a rank-aware batch orthogonal matching pursuit (OMP) method for achieving low-latency sparse channel recovery. To handle abrupt rank changes caused by user mobility, we establish a discrete-time autoregressive (AR) model that leverages the temporal rank correlation between continuous-time instances to obtain a complete observation matrix capable of perceiving rank changes for more accurate channel estimates. Simulation results confirm the effectiveness of the proposed channel estimation frame and demonstrate that our algorithms achieve state-of-the-art performance in low-rank matrix recovery with theoretical guarantees.

The deep integration of the vehicular computing power network (VCPN) and artificial intelligence offers the potential to meet the computation-intensive and low-latency demands of emerging vehicular applications. However, due to the dynamic VCPN scenarios, task heterogeneity gives rise to differentiated and time-varying task requirements, while node mobility and wireless channel fluctuations further exacerbate scheduling complexity, which jointly hinder efficient and on-demand task scheduling. In this paper, we propose a metric termed the age of task (AoT), which characterizes the differentiated service requirements of tasks in VCPN. A utility-driven scheduling model is developed that jointly considers AoT reduction and computing cost of concurrent tasks, and a task utility maximization problem is formulated. Due to the complexity of directly solving the problem, the original problem is decomposed into a joint multi-task scheduling and matching subproblem and a resource allocation subproblem. To address the dynamic scheduling challenges in VCPN, including task heterogeneity, vehicle mobility, and fluctuating wireless channels, we model the joint task scheduling and matching subproblem as a Markov decision process. An age-aware, multi-dimensional double-deep Q-learning algorithm is designed to handle discrete action spaces and mitigate the overestimation bias in traditional DQN methods. Additionally, we develop an improved interior point-based resource allocation algorithm to obtain the optimal solution. Numerical results show that the proposed algorithm effectively adjusts learning strategies to maximize the task utility.

With the development of 6G and beyond, space-air-ground integrated networks (SAGINs) have become a key factor in promoting high-speed seamless connectivity for users. In this paper, we studied a heterogeneous SAGIN in the millimeter wave (mmWave) band, which comprises unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), and low earth orbit satellite as aerial base stations (ABSs) in a scenario featuring partial damage to ground base stations (GBSs). Under the constraint of limited system power resources, we optimize the deployment of ABSs and the association and scheduling of users. We formulate a mixed-integer nonlinear programming (MINLP) problem aimed at maximizing the scheduling of users with quality of service (QoS) requirements and propose a block coordinate descent (BCD) based sequential quadratic programming-minimum rate ratio (SQP-MQR) algorithm to solve this problem. Simulation results demonstrate the superiority of our proposed algorithm in scheduling users with QoS requirements compared to other selected schemes in the heterogeneous SAGIN.

The intelligent development of high-speed railways (HSRs) necessitates the support of multiple services to ensure the safe and reliable operations of trains, and high-quality travel experiences for passengers. Network slicing offers a promising solution through isolated and service-specific radio resource management. However, meeting heterogeneous quality of service (QoS) requirements in HSR communications with imperfect channel state information (CSI) presents a great challenge. In this paper, we propose a bandwidth allocation and slicing puncture strategy in a train-to-ground moving network to support safetyrelated driver assistance services (DAS) and high-throughput video-on-demand services (VDS) for passengers. We formulate the resource slicing problem to minimize system resource block allocation, considering the throughput constraint of VDS and the latency and jitter constraints of VDS. The original problem is divided into two subproblems: VDS RB allocation and DAS puncturing. For VDS RB allocation, we transform the subproblem into a convex form and derive a closed-form expression. For DAS puncturing, a Genetic-based mini-slots puncturing (GMP) algorithm is proposed to address the non-convexity. Simulation results demonstrate the effectiveness of the proposed block coordinate descent-based RB allocation and puncturing (BCDAP) algorithm under imperfect CSI conditions, outperforming the baseline schemes.

The train-to-ground communication system is of vital importance to the safe and reliable operation of the vacuum tube high-speed train (vactrain). To better design the communication system, a full understanding of the channel characterization is essential and the accurate channel model needs to be investigated. In this paper, we propose a hybrid model of ray-tracing (RT) method and the propagation graph (PG) method based on directive scattering model and diffraction model. The line-of-sight (LoS) component, reflection, scattering, and diffraction components are considered. Based on the hybrid model, the expression of the channel transfer function (CTF) is derived and the power delay profile (PDP) and delay spread are obtained and analyzed. The simulation results show the wireless channel characteristics in vactrain scenarios and provide useful insights for future vactrain communication systems.

Reconfigurable intelligent surface (RIS) holds significant potential to enhance wireless communications by offering good performance, high security, and great efficiency. In particular, RIS provides notable benefits in improving wireless location accuracy for user equipments (UEs), which are often overlooked. In this paper, we utilize RIS for received signal strength (RSS)-based indoor fingerprinting localization. To improve positioning accuracy, we propose a two-stage RIS configuration selection approach. Specifically, in the first stage, we select the RIS configuration that contributes significantly to positioning. In the second stage, a supervised learning approach is used to select features, effectively reducing the large state space of the RIS. The effectiveness of the proposed RIS-assisted RSS fingerprinting localization technique is validated through simulation and field test results.

Using Clustered Vehicular Federated Learning (CVFL) in vehicular networks can enhance model intelligence through distributed collaboration while preserving data privacy. CVFL groups clients with similar data distributions to reduce the impact of non-independent and identically distributed (non-iid) data on federated learning efficiency, thereby supporting realtime and dynamic traffic decision-making. However, in practical applications, the high mobility of vehicles and the intermittent connectivity of communication links make it challenging to flexibly determine the number of clusters and the vehicles within each cluster. To address these issues, we propose a Graph Neural Network(GNN)-based clustering scheme called GNN-CVFL. Our proposed scheme includes two modules: clustering and resource allocation. In the clustering module, we treat the clustering problem of vehicle clients as a node classification problem in GNN, using a GNN-based method to cluster clients accurately based on their data distribution. The proposed method can automatically determine the number of clusters and the vehicles within each cluster in real-time. Moreover, based on the clustering results, We use the Lagrangian relaxation method to dynamically allocate available bandwidth resources and CPU frequencies to minimize system latency, ensuring efficient and flexible service for all vehicles. Numerical results demonstrate the feasibility and efficiency of our proposed scheme.

Ad Hoc networks, as a flexible type of wireless sensor network, find wide applications in disaster relief, and industrial scenarios. In industrial applications, there is a growing demand for deterministic communication to support time-critical business flows. However, previous research mainly focused on aspects like routing and re-routing, and few studies have addressed the issue of ensuring determinism. This paper proposes a novel Time-Triggered Ad Hoc (TTA) network framework. It uses a Time Division Multiple Access (TDMA)-based time-triggered transmission mechanism to achieve self-organized, deterministic communication. The framework includes time synchronization and offline scheduling to optimize transmission performance. Experimental results show that the TTA framework outperforms traditional methods in terms of network capacity, latency, and jitter, demonstrating its effectiveness in solving the determinism problem in Ad Hoc networks.

In the high-speed railway (HSR) scenario, handover (HO) reliability is constrained by the high-speed movement and weak coverage at cell edges, posing a threat to the "always online" transmission requirement. To address this, we propose a dynamic beamforming scheme to mitigate HO failures and improve data rates. First, a beamforming-based HO model is established, quantifying the impact of beam direction on data rate and HO reliability. Based on this, an optimization problem is formulated, aiming to enhance data rate, reduce HO failure probability, and minimize beam adjustment overhead. Subsequently, a dynamic beam adjustment algorithm based on deep reinforcement learning is designed, leveraging its real-time decision-making capability to adaptively optimize the beam direction under rapidly changing channel conditions. Simulation results demonstrate that the proposed scheme requires only 23% of the beam adjustment overhead of the ideal real-time precise beamforming, while achieves nearly identical HO performance and 98.9% of its data rate.

This paper presents a measurement of dynamic wireless channels between ultra-low altitude unmanned aerial vehicle (UAV) and vehicle in a semi-urban scenario, with the UAV acting as the transmitter and the vehicle as the receiver. Through quantitative analysis of the delay and doppler domains, we observe significant dynamic changes in the channel state as the vehicle passes through a T-shaped intersection. We compare the root-mean-square delay spread (RMS DS) and root mean square doppler spread (RMS DPS) of the vehicle as it approaches, passes through, and moves away from the intersection, and conduct modeling analysis based on the time-frequency domain dispersion. To describe the channel characteristics at the intersection, we develop a tap delay line (TDL) channel model based on markov birth-death process. This study aims to deepen the understanding of UAV-to-vehicle (U2V) channel characteristics, with a particular focus on the intersection scenario. The findings provide important theoretical foundations and practical guidance for the development, optimization, performance evaluation, and resource allocation strategies of low-altitude communication systems.

Semantic communication is emerging as a promising paradigm for the future of wireless communication, with recent progress in deep learning-based joint source-channel coding (JSCC) achieving notable success. However, the performance of wireless communication systems is often constrained by the variability and dynamics of channel conditions, which presents significant challenges for system robustness and adaptability. In real-world applications, such as in railway environments where multiple diverse scenarios are encountered, including rural, viaduct, tunnel and hilly terrain scenarios, the variability of the channel conditions can be particularly pronounced. Existing deep learning-based JSCC approaches typically train models in fixed channel models, limiting their ability to generalize across different channel scenarios. As a result, a model trained for one specific channel type is ineffective in others, necessitating the deployment of distinct models for different channel models. To address this issue, we propose a novel channel scenario adaptive deep joint source-channel coding (SA-DJSCC) method that integrates the channel type label into both the training and inference phases. By incorporating channel scenario information into the JSCC model, our approach allows a single model to effectively generalize across multiple channel models. Experimental results show that our method improves scenario adaptability and outperforms existing approaches across different channel models and signal-to-noise ratio conditions. This work represents a key advancement towards developing more resilient and efficient semantic communication systems capable of operating in dynamic wireless environments.

Dynamic wireless channel modeling is essential for future communication system design. However, existing methods struggle to capture the long-term non-stationary behavior of real-world channels driven by high mobility, dense deployment, and complex environments in the sixth generation (6G) scenarios. As system-level design depends on channel statistics rather than exact realizations, statistically consistent channel generation offers a more appropriate modeling strategy. In this work, We propose a deep learning-based hybrid framework that shifts the modeling goal from precise point-wise prediction to generating synthetic channel sequences that statistically replicate long-term non-stationary dynamics. Instead of forecasting exact future states, the model reproduces key statistical features such as the evolution of power delay profiles (PDPs), root mean square (RMS) delay spread and wide-sense stationary (WSS) regions. Experimental results demonstrate that the generated sequences closely match reference data, supporting scalable data generation for digital twins and system-level simulations under realistic dynamic conditions.

LEO satellite communication systems experience significant space-to-ground propagation delays, which lead to outdated channel state information (CSI). As a consequence, the CSI available at a given time instance reflects the channel conditions from several time instances earlier, a phenomenon known as channel aging. Therefore, accurate channel prediction is crucial. Existing channel prediction methods often rely on sequential models that predict the CSI at each future time instance based on the CSI of only the previous time instance. However, this approach tends to suffer from error accumulation over time, resulting in degraded prediction accuracy as the prediction horizon extends. To address this challenge, we propose a Transformer-Long Short-Term Memory (T-LSTM) channel predictor that combines the Transformer and LSTM architectures for future channel prediction. The model predicts the CSI at the current time instance by utilizing a sequence of outdated CSI from several instances earlier. Simulation experiments conducted on a practical doubly selective satellite channel model demonstrate that the proposed T-LSTM model effectively mitigates the impact of channel aging. Moreover, it improves the prediction accuracy of satellite communication channels affected by long propagation delays, specifically compared to LSTM and Transformer methods operating individually.

In this paper, a measurement campaign is conducted to investigate unmanned surface vehicle (USV) communication channels in complex nearshore environments. The experiment setup includes a fixed offshore platform as the transmitter and a mobile vessel as the receiver. Based on the collected data, this study analyzes and models the stationarity and fading characteristics of the channel. The results show that the values for the 1st percentile, 50th percentile, and 90th percentile of the channel’s stationary distance (threshold=0.8) are 0.412 m, 5.99 m, and 23.19 m, respectively. Path loss analysis indicates that the close-in free-space reference path loss with a path loss exponent of 2.407 provides an accurate representation, while shadow fading follows a normal distribution with a mean of zero and a standard deviation of 4.55 dB. To better capture the autocorrelation of shadow fading, a novel model is proposed, improving accuracy by 35% compared to the conventional exponential model. Additionally, small-scale fading envelope analysis using the Akaike information criterion (AIC) confirms that the Rician distribution is the most suitable model. The K-factor of the Rician distribution can be statistically modeled as a bimodal Gaussian distribution, with a goodness-of-fit (GoF) value of 0.026.

Accurate path loss prediction is crucial to evaluation of wireless coverage, and deep learning based path loss prediction has recently received a lot of attention in complex scenarios. Previous studies based on deep learning methods often overlook impacts of radio propagation. Therefore, this paper proposes a deep learning framework enhanced by empirical propagation models, which guides the network learning process through physical constraints to enhance the prediction model. The model uses a convolutional neural network to extract environmental features and analyzes impacts of different empirical models at various network locations on path loss prediction performance. Experimental results show that the deep learning network using the close-in free space reference distance path loss model achieves fairly high prediction accuracy on a 5.9 GHz urban scenario dataset, with a root mean square error as low as 5.63 dB.

In intelligent communication networks, efficient and timely data delivery is critical, particularly with the rise of applications like IoT, autonomous driving, and Industry 4.0. Age of Information (AoI) has become a vital metric for assessing data freshness, especially in high-frequency millimeter-wave (mmWave) bands, which are prone to signal blockages. Recent advancements in Reconfigurable Intelligent Surfaces (RIS) offer promising solutions for enhancing signal propagation in these environments. This paper addresses the AoI optimization problem in RIS-assisted multi-cell mmWave communication systems. We propose a joint optimization strategy involving base station scheduling, user equipment (UE) selection, and RIS phase configuration to minimize system-wide AoI. Through problem decomposition, we derive two sub-problems focused on reducing power consumption via RIS phase adjustments and minimizing AoI through a joint scheduling algorithm. Simulation results reveal that the proposed scheme significantly reduces weighted AoI across various configurations, demonstrating the advantages of RIS-assisted setups over conventional mmWave communication systems without RIS or with randomly configured RIS elements.

Reconfigurable intelligent surface (RIS) has emerged as a promising key technology for sixth-generation (6G) wireless communication systems, primarily due to its capability to intelligently manipulate the propagation environment. In this paper, we conduct comprehensive measurements of the wireless channel and link-level performance of an RIS-assisted communication system in indoor corridor scenarios. We analyze the time dispersion characteristics and channel capacity under both RIS intelligent reflection (RIS-IR) and without RIS (WR) cases for comparative evaluation. The results demonstrate that wideband channel characteristics have a notable impact on the performance of RIS-assisted communications. Specifically, we model the fading distribution of the visual-line-of-sight (VLoS) path introduced by the RIS and establish a corresponding tapped-delay-line (TDL) channel model. The insights derived from this study offer theoretical foundations and practical guidelines for the deployment and performance evaluation of RIS-assisted communication systems.

In satellite communication systems, the Doppler effect caused by the high-speed motion of satellites significantly impacts the stability and performance of communication links. This is especially evident in pseudo-random code modulation systems, where the Doppler effect induces a frequency offset in the pseudo-code rate, thereby degrading signal demodulation performance. Focusing on the code Doppler effect in satellite communication scenarios, this paper investigates a channel simulation method adapted to the code Doppler effect and verifies its effectiveness.

ABSTRACT With the evolution of wireless communication technologies towards the sixth generation (6G) mobile communication system, the space‐air‐ground‐sea integrated network architecture has emerged as a critical development direction for achieving global seamless coverage. Focusing on the unmanned aerial vehicle (UAV)‐to‐ship maritime communication scenario within this network framework, a three‐dimensional (3D) geometry‐based stochastic model is proposed. The model adopts a combined structure of elliptical and cylindrical components to comprehensively characterize multipath propagation mechanisms, including line‐of‐sight, sea surface reflection, as well as single‐bounced and double‐bounced components. By introducing the wave equation of sea surface to establish the 3D motion trajectory model of the ship and integrating it with the 3D rotational motion model of the UAV, the time‐varying propagation distance‐induced channel non‐stationarity is accurately captured. Based on this model, key statistical characteristics such as the space‐time‐frequency correlation function (STF‐CF) and Doppler power spectral density are derived. Furthermore, the impacts of sea surface wind speed, UAV rotation, ship oscillation, and ship size on channel statistical properties and space‐time non‐stationarity are thoroughly analysed. These numerical results provide theoretical foundations for the design and performance optimization of UAV‐assisted communication systems in complex maritime environments.

Enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) are two critical service types in industrial automation. In a closed-loop control system, device-to-device (D2D) communication is typically employed for direct transmission due to its low-latency requirements. However, this approach does not leverage the large-scale antenna gains of massive MIMO cellular systems. To address this limitation, we introduce a multi-connectivity network that integrates both cellular and D2D links to serve URLLC sensors while accommodating the transmission needs of general eMBB traffic. Since the D2D link serves as the primary link for URLLC transmission in a multi-connectivity setup, we first analyze the packet loss probability components for single-D2D URLLC link. Then, we formulate an optimization problem to maximize the sum channel capacity of eMBB sensors while satisfying URLLC QoS requirements. A sub-optimal power and spectrum allocation scheme is proposed to solve this coexistence problem of single-D2D URLLC and cellular eMBB transmission. For multi-connectivity, we examine the packet loss probability and present two transmission frameworks based on selection combining (SC) and maximal ratio combining (MRC). Simulation results validate the properties of the optimal solution for the relaxed problem and demonstrate the performance gains of multi-connectivity over single-D2D links.

With the rapid advancement of intelligent railway systems, a high-quality train-ground communication system is crucial. However, ensuring reliable wireless communication in ultra-high-speed environments remains a significant challenge due to severe Doppler effects, frequent inter-cell handovers, and diverse QoS demands. Existing solutions, such as soft/hard handover schemes, lack the flexibility to adapt to dynamic conditions, leading to service interruptions and suboptimal performance. In this paper, we propose a dynamic resource allocation strategy based on dual base station coordination, utilizing millimeter-wave (mmWave) technology and real-time train position prediction. This approach dynamically optimizes user association and spectrum allocation to accommodate the rapid movement of trains, reducing service interruptions and ensuring continuous high-quality communication. Simulation results show that our algorithm improves system QoS satisfaction by 37.5%-68.8% and achieves spectrum utilization rates between 76% and 90%, outperforming the comparison schemes. These results validate the effectiveness of our approach in addressing high-speed mobility challenges.

The deployment of space–air–ground integrated networks (SAGIN) is critical for addressing signal coverage challenges in high-speed railway (HSR) tunnel scenarios. However, when aerial access networks and ground base stations (BSs) simultaneously serve trains, co-frequency interference can severely degrade system performance, preventing the satisfaction of Quality of Service (QoS) requirements for flows. To address this issue, this article formulates an optimization problem aimed at maximizing the number of scheduled flows through transmission optimization for HSR tunnel communications in SAGIN. Subsequently, a link selection algorithm is proposed to identify valid transmitter-receiver associations by filtering potential link combinations based on the signal-to-interference-plus-noise ratio (SINR) threshold, ensuring that only feasible associations are selected to meet each flow’s QoS requirement. To improve the number of successfully scheduled flows, a graph theory-based transmission optimization (GTTO) algorithm is developed. This approach effectively avoids the simultaneous transmission of highly interfering links by introducing an interference factor and reorders the scheduling sequence of flows to prioritize those associated with links that have higher SINR. The proposed method is evaluated through simulations, demonstrating its ability to substantially improve the number of scheduled flows and system throughput under varying conditions, such as different train speeds, QoS requirements, airship altitudes, tunnel lengths, and the number of mobile relays (MRs). The results also demonstrate the superiority of the proposed method over conventional approaches, achieving reliable communication and enhanced performance across HSR tunnel scenarios.

With the rapid development of 6G communication and the wide deployment of high-speed rail (HSR), it becomes essential to enhance the utilization of HSR communication resources while ensuring the requirements of communication-sensitive users for high reliability and low latency. Meanwhile, the development of integrated sensing and communication (ISAC), brings more inspiration for smart HSR. In this background, we model an ISAC and hyper-reliable low-latency communication (HRLLC) system for UAV-assisted HSR. We formulate a mixed integer nonlinear programming problem (MINLP) with the objective of maximizing the fair sum rate while satisfying the minimum radar sensing requirement. To solve this problem of nonconvex and high coupling, we propose a hybrid particle swarm optimization-genetic algorithm (PSO-GA) that combines the fast convergence of PSO-only (PSO) and the strong global search ability of GA, with parameter-free penalty functions. Through careful design, PSO-GA dynamically balances the exploration and development capabilities. It achieves the best overall performance with a faster convergence speed than existing algorithms. An average improvement of 29%, 57%, and 42% has been achieved with different numbers of passengers, total transmission power, and number of resource blocks. This article supports the future development of intelligent HSR communication.

Low-Earth orbit (LEO) satellites have been considered essential to future air-space-ground integrated networks. Wireless channels significantly impact the performance of communication systems, especially in terms of large-scale fading characteristics. In this article, we propose a novel geometry-based stochastic channel model (GBSM) for LEO satellite-ground large-scale channels. Propagation probabilities of Line-of-Sight (LoS) links, ground specular links, and building specular links for suburban, urban, dense urban, and high-rise urban in different elevations are computed. The Fresnel zone is utilized to determine whether the signals can arrive at the receiver. The impact of the radio coverage and receiver height on propagation probabilities are considered for each scenario. Based on the derived propagation probabilities, the average path loss is computed. In the simulation section, the results of our model are validated by the Monte Carlo method, and the average path loss is compared with the standard model in 3GPP TR 38.811. The comparison results have good consistency with the standard model. Moreover, our model can be applied in multiple scenarios by adjusting the environment parameters compared with the standard model.

Ray tracing (RT) is a key tool for establishing accurate mappings between physical environments and propagation channels. However, due to the complexity of indoor scatterers and the difficulty of fully capturing modeling details, there is no unified specification for scenario modeling, leaving the impact of modeling detail on RT simulations unclear. This work proposes an indoor modeling method based on LiDAR point clouds, which achieves automated mesh reconstruction via minimum bounding box estimation and generates scenario models at different levels of detail (LOD). Comparative analysis shows that modeling detail significantly affects multipath components (MPCs) accuracy. A simple-detail model fails to accurately capture MPCs, whereas a medium-detail model exhibits higher simulation accuracy, and the full-detail model achieves the closest agreement with the ground truth model, although further accuracy gains diminish as complexity increases. Furthermore, indoor scatterers detail has a greater influence on RT results than room boundaries. To balance simulation accuracy and computational complexity, indoor scatterers should include at least external contour features, with opening structures preferred. In contrast, RB modeling can be simplified to a medium-detail model with only external contours. The sensitivity of different channel parameters to LOD also depends on the propagation scenario: in LOS scenarios, DS and PL are highly sensitive to LOD, whereas AS is relatively insensitive; in NLOS scenarios, DS and AS are sensitive, while PL sensitivity significantly decreases.

Based on emerging artificial intelligence (AI) tasks, cloud-edge–terminal architecture can provide powerful computing, intelligent interconnection, and real-time response, which can also be regarded as AI-driven network. Unfortunately, multiple network layers in the AI-driven network usually face various types of network threats, such as malicious network reconnaissance, side-channel attacks, and distributed denial of service (DDoS). Traditional security solutions respond to network threats after the occurrence of attacks. To solve this problem, the concept of moving target defense (MTD) has been proposed as a proactive defense mechanism that aims to defend against cyber attacks before they occur. In this article, we first provide a thorough analysis of the threats in the cloud-edge–terminal network. Then, we conduct a comprehensive survey to discuss the concept, design principles, and main classifications of MTD. Next, we further introduce the development potential in terms of AI-powered MTD on each network layer. Meanwhile, we also explore how MTD improves the security of AI algorithms. Lastly, we describe the existing challenges and research directions of MTD. The aim of this article is to provide an in-depth understanding for the readers on how to realize the integration between MTD and AI-driven network.

This paper investigates a near-field integrated sensing and symbiotic radio (SR) communication system supported by a reconfigurable intelligent surface (RIS). In the near-field region, the base station (BS) leverages the RIS to realize symbiotic communication performance while simultaneously performing target sensing by analyzing echo signals. The BS antenna architecture encompasses both fully-digital and dynamic metasurface antenna (DMA) configurations. An optimization problem is developed to maximize the symbiotic transmission rate for the IoT devices, subject to constraints imposed by the Cram4er-Rao bound (CRB), the signal-to-noise ratio (SNR), the RIS phase shifts, the antenna parameters and system power. An alternating optimization (AO) framework with a semidefinite relaxation (SDR) is proposed to solve the problem, while for the Lorentz-constrained phase matrix of the frequency response of DMA surface elements, we propose to apply the Riemannian conjugate gradient (RCG) algorithm to solve it. Numerical results validate the efficiency of the proposed framework, demonstrating that the near-field approach enables accurate target localization. Furthermore, where the DMA configuration achieves higher symbiotic transmission rates with lower power consumption compared to fully-digital antennas.

Although semantic communication (SC) has shown its potential in efficiently transmitting multimodal data such as texts, speeches and images, SC for videos has focused primarily on pixel-level reconstruction. However, these SC systems may be suboptimal for downstream intelligent tasks. Moreover, SC systems without pixel-level video reconstruction present advantages by achieving higher bandwidth efficiency and real-time performance of various intelligent tasks. The difficulty in such system design lies in the extraction of task-related compact semantic representations and their accurate delivery over noisy channels. In this paper, we propose an end-to-end SC system, named VideoQA-SC for video question answering (VideoQA) tasks. Our goal is to accomplish VideoQA tasks directly based on video semantics over noisy or fading wireless channels, bypassing the need for video reconstruction at the receiver. To this end, we develop a spatiotemporal semantic encoder for effective video semantic extraction, and a learning-based bandwidth-adaptive deep joint source-channel coding (DJSCC) scheme for efficient and robust video semantic transmission. Experiments demonstrate that VideoQA-SC outperforms traditional and advanced DJSCC-based SC systems that rely on video reconstruction at the receiver under a wide range of channel conditions and bandwidth constraints. In particular, when the signal-to-noise ratio is low, VideoQA-SC can improve the answer accuracy by 5.17% while saving almost 99.5% of the bandwidth at the same time, compared with the advanced DJSCC-based SC system. Our results show the great potential of SC system design for video applications.

Both the space-air-ground integrated networks (SAGIN) and the integrated sensing and communication (ISAC) are promising technologies in future communication systems. This paper investigates the mobile user (MU) tracking and robust beamforming design by the unmanned aerial vehicle (UAV) in an SAGIN-ISAC system. Two schemes for acquiring the location information of MUs at the UAV are proposed, namely the space-assisted and ISAC-assisted schemes. The former requires the precise location information from the satellite by the space-air transmission, while the latter estimates the location information of MUs via a proposed extended Kalman filter based algorithm. The obtained location information is then utilized to predict the channel distribution of MUs, which can be used to formulate an outage-constrained energy efficiency (EE) maximization problem. The considered problem is first reformulated based on the Bernstein-type inequality to derive computationally tractable forms of the outage probability constraints. Then, the reformulated problem is solved via the semi-definite relaxation (SDR) and successive convex approximation methods, where the tightness of employing SDR is theoretically proved. Numerical results illustrate the trajectories of the UAV for tracking MUs under the space-assisted and ISAC-assisted schemes, and discuss the impact of the space-air transmission on the EE performance. It is observed that there exists a trade-off between space-air transmission overhead and location prediction precision of MUs. By integrating the ISAC in SAGIN, the information demand from the space is reduced compared with traditional SAGIN.

Emerging industrial applications involving robotic collaborative operations and mobile robots require a more reliable and precise wireless network for deterministic data transmission. To meet this demand, the 3rd Generation Partnership Project (3GPP) is promoting the integration of 5th Generation Mobile Communication Technology (5G) and Time-Sensitive Networking (TSN). Time synchronization is essential for deterministic data transmission. Based on the 3GPP’s vision of the 5G and TSN integrated networking with interoperability, we improve the time synchronization of TSN to conquer the multi-gNB competition, re-transmission, and mobility problems for the integrated 5G time synchronization. We implemented the improvement mechanisms and systematically validated the performance of 5G+TSN time synchronization. Based on the simulation in 500m x 500m industrial environments, the improved time synchronization achieved a precision of 1 microsecond with interoperability between 5G nodes and TSN nodes.

Smart railways integrate advanced information technologies into railway operating systems to improve efficiency and reliability. Although the development of the fifth generation (5G) has enhanced railway services, future smart railways require ultra-high speeds, ultra-low latency, ultra-high security, full coverage, and ultra-high positioning accuracy, which 5G cannot fully meet. Therefore, the sixth generation (6G) is envisioned to provide green and efficient all-day operations, strong information security, fully automatic driving, and low-cost intelligent maintenance. To achieve these requirements, we propose an integrated network architecture leveraging communications, computing, edge intelligence, and caching in railway systems. We have conducted in-depth investigations on key enabling technologies for reliable transmissions and wireless coverage. For high-speed mobile scenarios, we propose an artificial intelligence (AI)-enabled cross-domain channel modeling and orthogonal time–frequency space–time spread multiple access mechanism to alleviate the conflict between limited spectrum availability and massive user access. The roles of blockchain, edge intelligence, and privacy technologies in endogenously secure rail communications are also evaluated. We further explore the application of emerging paradigms such as integrated sensing and communications (SACs), AI-assisted Internet of Things (IoT), semantic communications (SCs), and digital twin (DT) networks for railway maintenance, monitoring, prediction, and accident warning. Finally, possible future research and development directions are discussed. © 2026 IEEE

CANDECOMP/PARAFAC (CP) decomposition is the mostly used model to formulate the received tensor signal in a massive MIMO system, as the receiver generally sums the components from different paths or users. To achieve accurate and low-latency channel estimation, good and fast CP decomposition (CPD) algorithms are desired. The CP alternating least squares (CPALS) is the workhorse algorithm for calculating the CPD. However, its performance depends on the initializations, and good starting values can lead to more efficient solutions. Existing initialization strategies are decoupled from the CPALS and are not necessarily favorable for solving the CPD. This paper proposes a deep-learning-aided CPALS (DL-CPALS) method that uses a deep neural network (DNN) to generate favorable initializations. The proposed DL-CPALS integrates the DNN and CPALS to a model-based deep learning paradigm, where it trains the DNN to generate an initialization that facilitates fast and accurate CPD. Moreover, benefiting from the CP low-rankness, the proposed method is trained using noisy data and does not require paired clean data. The proposed DL-CPALS is applied to millimeter wave MIMO-OFDM channel estimation. Experimental results demonstrate the significant improvements of the proposed method in terms of both speed and accuracy for CPD and channel estimation.

In high-speed railway (HSR) scenarios, it is essential to ensure reliable handover for sustaining always-online communications of trains. However, this reliability is challenged by limited wireless coverage at cell edges and frequent handovers. To tackle these challenges, this paper explores the potential of dynamic beamforming to simultaneously improve the probability of successful handover and mitigate communication disruptions. First, we establish a beamforming-based transmission model for trains during handovers. Based on this model, we derive the impact of the beam directions of the serving and target cells on communication performance. Second, we formulate an optimization problem aiming at maximizing the conditional data rate of the train within handover regions, where the impacts of handover failure, rapid mobility, and beamforming overhead are considered. Third, to solve this optimization problem, we propose a dynamic beam direction adjustment algorithm by leveraging the property of deep reinforcement learning. The algorithm efficiently determines the optimal beam direction adjustment strategy based on the dynamic channel conditions. Finally, compared to state-of-the-art deep learning methods and beamforming strategies, simulation results demonstrate that the proposed method achieves superiority in communication quality at cell edges and handover performance, providing an efficient and reliable technical solution for HSR communications.

In the space-air-ground integrated network (SAGIN), high-speed railway (HSR) communication is expected to be enhanced by simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS). However, when both aerial platforms and ground base stations (BSs) provide services to HSR user equipments (UEs), severe signal interference can arise, leading to the system failing to meet the quality of service (QoS) requirements of the flows. In this paper, we introduce the optimization problem of transmission scheduling for STAR-RIS assisted train-to-ground communications in SAGIN. To address this issue, a phase optimization algorithm of passive STAR-RIS is proposed to improve the channel quality of HSR UEs. Furthermore, a coalition game algorithm is proposed to associate HSR UEs with the optimal links that minimize inter-flow interference. Finally, a QoS-aware flow scheduling algorithm is proposed to optimize the order of the flows for the selected links. Simulation results confirm that the proposed scheduling scheme effectively increases the number of completed flows and total transmitted bits for STAR-RIS assisted train-to-ground communications in SAGIN, outperforming traditional methods.

In this paper, we investigate the downlink distributed transmit beamforming problem in partially connected cell-free massive multiple-input multiple-output (CF mMIMO) systems, specifically designed to satisfy the stringent requirements of ultra-reliable and low-latency communication (URLLC) services. First, we propose a scalable framework that incorporates partial access points (APs) to serve active user equipment (UE), with a reduced energy consumption and computational complexity. To this end, a min-max optimization problem is formulated for minimizing the decoding error probability (DEP) among URLLC services. Then, a graph neural network (GNN)-based strategy called G4PCF is proposed for partially connected CF mMIMO, which takes into account the underlying characteristics of the problem. Furthermore, by leveraging the temporal correlation in channel state information acquired from the previous frame, we develop a parallel G4PCF (P-G4PCF) scheme that significantly reduces both the signaling overhead and computation delay for minimizing DEP of the worst UE. Simulation results demonstrate that the proposed G4PCF and P-G4PCF architectures exhibit excellent scalability for CF mMIMO networks, offering superior performance over existing methods in terms of quality of service outage probability. Notably, P-G4PCF excels in supporting URLLC services with short frame durations and highly correlated channels, while G4PCF performs better under lower channel correlation. Moreover, the proposed algorithms can significantly enhance the application of GNNs into CF mMIMO systems with a reduced complexity compared with the classical weighted minimum mean-squared error algorithm, especially with delay sensitive services.

The orthogonal delay-Doppler (DD) division multiplexing (ODDM) modulation has been recently proposed as a promising modulation scheme for next-generation communication systems with high mobility. Despite its benefits, ODDM modulation and other DD domain modulation schemes face the challenge of excessive equalization complexity. To address this challenge, we propose time domain Tomlinson-Harashima precoding (THP) for the ODDM transmitter, to make the DD domain single-tap equalizer feasible, thereby reducing the equalization complexity. In our design, we first pre-cancel the inter-symbol-interference (ISI) using the linear time-varying (LTV) channel information. Second, different from classical THP designs, we introduce a modified modulo operation with an adaptive modulus, by which the joint DD domain data multiplexing and time-domain ISI pre-cancellation can be realized without excessively increasing the bit errors. We then analytically study the losses encountered in this design, namely the power loss, the modulo noise loss, and the modulo signal loss. Based on this analysis, BER lower bounds of the ODDM system with time domain THP are derived when 4-QAM or 16-QAM modulations are adopted for symbol mapping in the DD domain. Finally, through numerical results, we validate our analysis and then demonstrate that the ODDM system with time domain THP is a promising solution to realize better BER performance over LTV channels compared to orthogonal frequency division multiplexing systems with single-tap equalizer and ODDM systems with maximum ratio combining.

With the arrival of 6G, the Internet of Things (IoT) traffic is becoming more and more complex and diverse. To meet the diverse service requirements of IoT devices, massive machine-type communications (mMTC) becomes a typical scenario, and more recently, grant-free random access (GF-RA) presents a promising direction due to its low signaling overhead. However, existing GF-RA research primarily focuses on improving the accuracy of user detection and data recovery, without considering the heterogeneity of traffic. In this paper, we investigate a non-orthogonal GF-RA scenario where two distinct types of traffic coexist: event-triggered traffic with alarm devices (ADs), and status update traffic with monitor devices (MDs). The goal is to simultaneously achieve high detection success rates for ADs and high information timeliness for MDs. First, we analyze the age-based random access scheme and optimize the access parameters to minimize the average age of information (AoI) of MDs. Then, we design an age-based prior information aided autoencoder (A-PIAAE) to jointly detect active devices, together with learned pilots used in GF-RA to reduce interference between non-orthogonal pilots. In the decoder, an Age-based Learned Iterative Shrinkage Thresholding Algorithm (LISTA-AGE) utilizing the AoI of MDs as the prior information is proposed to enhance active user detection. Theoretical analysis is provided to demonstrate the proposed A-PIAAE has better convergence performance. Experiments demonstrate the advantage of the proposed method in reducing the average AoI of MDs and improving the successful detection rate of ADs.

With the number of users growing rapidly, the federated learning (FL) algorithm gradually moves towards a multi-layer framework to ensure learning performance. In this article, considering the non-independent and identically distributed scenario, a new hierarchical federated meta-learning (HFML) framework is studied. The Hessian-free Model-Agnostic Meta-Learning is introduced into our model to personalize the local models of edge users (EUs), which is more computationally efficient than the traditional meta-learning. To alleviate the learning performance reduction due to the scarce available bandwidth resources, a multilayer perceptron model prediction scheme based on the attention mechanism is deployed at the side of edge nodes (ENs). To achieve the tradeoff between learning time and model accuracy, the semi-synchronous cloud aggregation mechanism based on the learning states and parameter freshness is proposed. The convergence analysis of the proposed HFML algorithm is also provided to prove that the upper bound of the loss decay exists. To solve the complex nonconvex optimization problem whose target is to maximize the learning efficiency of HFML, considering device selection and communication resource allocation, a decentralized algorithm based on Jacobi-Proximal ADMM (JP-ADMM) is proposed. Extensive simulations are performed to demonstrate the effectiveness of the proposed method. Particularly, compared with the traditional hierarchical federated learning algorithm, the proposed HFML achieves better learning performance while reducing the latency.

Rate Splitting (RS) is a versatile and powerful technique for multi-antenna transmission. In this paper, we study the precoding optimization for RS, which is critically important for improving the system performance but often challenging to address. We first investigate the non-convex sum rate maximization problem under perfect Channel State Information at the Transmitter (CSIT). By constructing a separable structure for the sum-of-functions-of-ratios problem and jointly leveraging the Convex Concave Procedure (CCP) and the Alternating Direction Method of Multipliers (ADMM), we obtain a fast algorithm that substantially reduces the overall computational time through the parallel computation and explicit closed-form solutions. We then investigate the average sum rate maximization problem under imperfect CSIT, which is known as a more challenging non-convex stochastic problem. To obtain a fast algorithm for practical use, we carefully approximate the non-convex stochastic problem to a non-convex deterministic one with acceptable performance loss and tailor the fast algorithm derived from perfect CSIT for imperfect CSIT with modest changes. Numerical results show that compared to the state-of-the-art algorithms, the proposed algorithms achieve comparable sum rates or average sum rates but short computation times for large problem sizes, owing to the unique parallel computation structures and few matrix inverse operations.

Integrated Sensing and Communication (ISAC) is a promising technology for 6G, with the goal of providing end-to-end information processing and inherent perception capabilities for future communication systems. Within ISAC emerging application scenarios, vehicular ISAC technologies have the potential to enhance traffic efficiency and safety through integration of communication and synchronized perception abilities. To establish a foundational theoretical support for vehicular ISAC system design and standardization, it is necessary to conduct channel measurements, and model to obtain a deep understanding of the radio propagation. In this paper, a dynamic statistical channel model is proposed for vehicular ISAC scenarios, incorporating Sensing Multi-Path Components (S-MPCs) and Clutter Multi-Path Components (C-MPCs), which are identified by the proposed tracking algorithm. Based on actual vehicular ISAC channel measurements at 28 GHz, time-varying sensing characteristics in front, left, and right directions are investigated. To model the dynamic evolution process of channel, number of new S-MPCs, lifetimes, initial power and delay positions, dynamic variations within their lifetimes, clustering, power decay, and fading of C-MPCs are statistically characterized. Finally, the paper provides implementation of dynamic vehicular ISAC model and validates it by comparing key simulation statistics between measurements and simulations.

Cell-free (CF) massive multiple-input multiple-output (MIMO) provides a ubiquitous coverage to user equipments (UEs) but it is also susceptible to interference. Rate-splitting (RS) effectively extracts data by decoding interference, yet its effectiveness is limited by the weakest UE. In this paper, we investigate an RS-based CF massive MIMO system, which combines strengths and mitigates weaknesses of both approaches. Considering imperfect channel state information (CSI) resulting from both pilot contamination and noise, we derive a closed-form expression for the sum spectral efficiency (SE) of the RS-based CF massive MIMO system under a spatially correlated Rician channel. Moreover, we propose low-complexity heuristic algorithms based on statistical CSI for power-splitting of common messages and power-control of private messages, and genetic algorithm is adopted as a solution for upper bound performance. Furthermore, we formulate a joint optimization problem, aiming to maximize the sum SE of the RS-based CF massive MIMO system by optimizing the power-splitting factor and power-control coefficient. Importantly, we improve a generative AI (GAI) algorithm to address this complex and non-convexity problem by using a diffusion model to obtain solutions. Simulation results demonstrate its effectiveness and practicality in mitigating interference, especially in dynamic environments.

In remote industrial Internet of Things (IIoT) monitoring systems, the uncrewed aerial vehicle (UAV) serves as supplementary infrastructure to aggregate data from a large number of distributed sensors, and achieve industrial operation intelligence. In the wireless data aggregation process, using conventional orthogonal multiple access techniques face challenges such as scarce bandwidth, high communication latency and energy consumption. To tackle these issues, the over-the-air computation (AirComp) technique has emerged. It allows concurrent data transmissions from sensors, as well as integrates communication and computation processes, ultimately enabling fast data aggregation. However, the energy consumption issue remains unresolved. In this paper, we exploit spatial correlations among sensor measurements, and design an energy-efficient AirComp in UAV-assisted IIoT networks, where only a subset of sensors transmit data instead of all sensors. Then, we derive a closed-form expression for the mean square error (MSE) of each combination under a specific number of sensor transmissions. By jointly optimizing the UAV deployment and pre-coding coefficients of sensors, we formulate the problem of minimizing MSE for each combination of transmitted sensors. Furthermore, the MSE optimization algorithm is developed to output the average MSE of all combinations. Finally, we evaluate the average MSE and network lifetime performance of proposed scheme.

This paper investigates the deep learning based approaches for simultaneous wireless information and power transfer (SWIPT). The quality-of-service (QoS) constrained sumrate maximization problems are, respectively, formulated for power-splitting (PS) receivers and time-switching (TS) receivers and solved by a unified graph neural network (GNN) based model termed SWIPT net (SWIPTNet). To improve the performance of SWIPTNet, we first propose a single-type output method to reduce the learning complexity and facilitate the satisfaction of QoS constraints, and then, utilize the Laplace transform to enhance input features with the structural information. Besides, we adopt the multi-head attention and layer connection to enhance feature extracting. Furthermore, we present the implementation of transfer learning to the SWIPTNet between PS and TS receivers. Ablation studies show the effectiveness of key components in the SWIPTNet. Numerical results also demonstrate the capability of SWIPTNet in achieving nearoptimal performance with millisecond-level inference speed which is much faster than the traditional optimization algorithms. We also show the effectiveness of transfer learning via fast convergence and expressive capability improvement.

This paper investigates the graph neural network (GNN)-enabled beamforming design for interference channels. We propose a model termed interference channel GNN (ICGNN) to solve a quality-of-service constrained energy efficiency maximization problem. The ICGNN is two-stage, where the direction and power parts of beamforming vectors are learned separately but trained jointly via unsupervised learning. By formulating the dimensionality of features independent of the transceiver pairs, the ICGNN is scalable with the number of transceiver pairs. Besides, to improve the performance of the ICGNN, the hybrid maximum ratio transmission and zero-forcing scheme reduces the output ports, the feature enhancement module unifies the two types of links into one type, the subgraph representation enhances the message passing efficiency, and the multi-head attention and residual connection facilitate the feature extracting. Furthermore, we present the over-the-air distributed implementation of the ICGNN. Ablation studies validate the effectiveness of key components in the ICGNN. Numerical results also demonstrate the capability of ICGNN in achieving near-optimal performance with an average inference time less than 0.1 ms. The scalability of ICGNN for unseen problem sizes is evaluated and enhanced by transfer learning with limited fine-tuning cost. The results of the centralized and distributed implementations of ICGNN are illustrated.

Diffusion models have been extensively utilized in AI-generated content (AIGC) in recent years, thanks to the superior generation capabilities. Combining with semantic communications, diffusion models are used for tasks such as denoising, data reconstruction, and content generation. However, existing diffusion-based generative models do not consider the stringent bandwidth limitation, which limits its application in wireless communication. This paper introduces a diffusion-driven semantic communication framework with advanced VAE-based compression for bandwidth-constrained generative model. Our designed architecture utilizes the diffusion model, where the signal transmission process through the wireless channel acts as the forward process in diffusion. To reduce bandwidth requirements, we incorporate a downsampling module and a paired upsampling module based on a variational auto-encoder with reparameterization at the receiver to ensure that the recovered features conform to the Gaussian distribution. Furthermore, we derive the loss function for our proposed system and evaluate its performance through comprehensive experiments. Our experimental results demonstrate significant improvements in pixel-level metrics such as peak signal to noise ratio (PSNR) and semantic metrics like learned perceptual image patch similarity (LPIPS). These enhancements are more profound regarding the compression rates and SNR compared to deep joint source-channel coding (DJSCC).

Extremely large-scale multiple-input multiple-output (XL-MIMO) is gaining attention as a prominent technology for enabling the sixth-generation (6G) wireless networks. However, the vast antenna array and the huge bandwidth introduce a non-negligible beam squint effect, causing beams of different frequencies to focus at different locations. One approach to cope with this is to employ true-time-delay lines (TTDs)-based beamforming to control the range and trajectory of near-field beam squint, known as the near-field controllable beam squint (CBS) effect. In this paper, we investigate the user localization in near-field wideband XL-MIMO systems under the beam squint effect and spatial non-stationary properties. Firstly, we derive the expressions for Cramér-Rao Bounds (CRBs) for characterizing the performance of estimating both angle and distance. This analysis aims to assess the potential of leveraging CBS for precise user localization. Secondly, a user localization scheme combining CBS and beam training is proposed. Specifically, we organize multiple subcarriers into groups, directing beams from different groups to distinct angles or distances through the CBS to obtain the estimates of users’ angles and distances. Furthermore, we design a user localization scheme based on a convolutional neural network model, namely ConvNeXt. This scheme utilizes the inputs and outputs of the CBS-based scheme to generate high-precision estimates of angle and distance. The numerical results derived from CRBs reveal that the inherent spatial non-stationary characteristics notably increase the CRB for angle, but have an insignificant impact on the CRB for distance estimation. In addition, the CRBs for both angle and distance decrease with increasing bandwidth and number of subcarriers. More importantly, our proposed ConvNeXt-based user localization scheme achieves centimeter-level accuracy in localization estimates.

Hybrid analog and digital beamforming (HBF) is a cost-efficient technique to achieve high data rates in millimeterwave (mmWave) communication systems. This paper applies the emerging graph neural networks (GNNs) to HBF by leveraging the topological information in wireless networks for better adaptation to dynamic environments. To address the issue of limited feature extraction capability of the existing single-type GNNs, such as node-GNN or edge-GNN, we model the mmWave communication systems as both homogeneous and heterogeneous graphs, and separate the HBF design into node- and edge-level subtasks. Then, the two graphs are presented by two novel models based on homogeneous graph attention network (GAT) and heterogeneous GAT (HGAT), respectively, and mapped to the desired power allocation, radio frequency precoder and baseband precoder. Both the proposed GAT and HGAT are generalizable in the user variation scenarios, while the HGAT is also generalizable in antenna variation scenarios through the incorporation of a complex embedding layer. Furthermore, we introduce a constraint adaptive layer in the GAT and HGAT to ensure feasible outputs. Extensive numerical results based on the public dataset DeepMIMO are provided to evaluate the GAT and HGAT. The proposed approaches generally outperform existing baselines in terms of adaptability to system settings and generalizability to (unseen) problem parameters/sizes, while the HGAT can even achieve faster and better inference than traditional optimization approaches.

To enhance the capacity and reliability of next-generation wireless communication systems, the novel cell-free massive multiple-input multiple-output (mMIMO) has emerged as a pivotal technology in satisfying the stringent quality of service requirements of massive network-connected devices. In this paper, we propose a partially coherent joint transmission (PCJT) approach that draws insights from both coherent and non-coherent joint transmission (NCJT) strategies. Specifically, we design the downlink transmit beamformers to maximize the weighted sum rate (WSR) and compare the performance in three distinct joint transmission modes, ranging from coherent and partially coherent, to non-coherent joint transmission. Specifically, a non-convex optimization problem is formulated that incorporates multiple data stream transmission and transmit power constraints. Given the intractability of the problem, the weighted minimum mean square error (WMMSE) approach is introduced to transform it into an equivalent form, which facilitates the development of a low-complexity and low-interaction reduced WMMSE (R-WMMSE) beamforming algorithm design to acquire an effective solution. For further reducing communication overhead and improving convergence rates, we propose a novel graph convolution network-based unfolding technique for R-WMMSE algorithm. It significantly reduces the number of iterations required while achieving similar performance to the original WMMSE algorithm, thus alleviating the signaling overhead burdens in distributive implementation. Simulation results demonstrate the significant performance gains achieved by the proposed algorithm in terms of superior WSR and rapid convergence performance. Furthermore, it is evident that the performance of PCJT can promote the performance achieved by NCJT, positioning it as an alternative between the existing two joint transmission strategies.

To enhance global data transmission, uncrewed aerial vehicle (UAV)-aided space-air-ground integrated networks (SAGIN) represent a pivotal direction for future advancements. In this paper, we focus on the trajectory optimization problem with the goal of maximizing the energy efficiency (EE), thereby balancing the system capacity with energy expenditure. To this end, we first introduce a cell-free SAGIN network where UAVs function as flying access points to serve ground user equipment (GUE). Given that the transmission power of satellite direct-to-cell devices typically exceeds that of GUEs, we investigate the interference effect and derive exact closed-form expressions for the uplink spectral efficiency. In order to improve the service access efficiency, a GUE grouping scheme based on density distribution is proposed. Then, an effective EE analysis model is established considering the power consumption of fixed-wing UAVs. To solve the UAV trajectory optimization problem, two algorithms over two timescales are proposed: a successive convex approximation strategy and a multi-agent reinforcement learning (MARL)-based algorithm. In particular, to reduce the algorithmic complexity, we employ a shared Critic network in the proposed MARL algorithm to reduce the training parameters. Importantly, our approach comprehensively optimizes the UAV trajectory, acceleration, and velocity parameters. The results show that the proposed GUE grouping algorithm and the MARL-based optimization algorithm demonstrate adaptability in dynamic time-varying environments.

Cell-free massive multiple-input multiple-output (mMIMO) offers significant advantages in mobility scenarios, mainly due to the elimination of cell boundaries and strong macro diversity. In this paper, we examine the downlink performance of cell-free mMIMO systems equipped with mobile-APs utilizing the concept of unmanned aerial vehicles, where mobility and power control are jointly considered to effectively enhance coverage and suppress interference. However, the high computational complexity, poor collaboration, limited scalability, and uneven reward distribution of conventional optimization schemes lead to serious performance degradation and instability. These factors complicate the provision of consistent and high-quality service across all user equipments in downlink cell-free mMIMO systems. Consequently, we propose a novel scalable framework enhanced by multi-agent reinforcement learning (MARL) to tackle these challenges. The established framework incorporates a graph neural network (GNN)-aided communication mechanism to facilitate effective collaboration among agents, a permutation architecture to improve scalability, and a directional decoupling architecture to accurately distinguish contributions. In the numerical results, we present comparisons of different optimization schemes and network architectures, which reveal that the proposed scheme can effectively enhance system performance compared to conventional schemes due to the adoption of advanced technologies. In particular, appropriately compressing the observation space of agents is beneficial for achieving a better balance between performance and convergence.

This paper investigates the rate outage constrained (ROC) energy efficiency (EE) under multiple-input single-output (MISO) interference channels, where only channel distribution information (CDI) is available at base stations (BSs). An EE maximization problem is formulated under the constraints of tolerable rate outage probability of each user and the power budget of each BS. Due to the computationally intractable form of the considered problem, two equivalent formulations are derived and four algorithms are proposed, where two are based on the block successive upper bound minimization (BSUM) method and the other two are based on the successive convex approximation (SCA) method. Further, the philosophies behind the BSUM-based algorithms and the SCA-based algorithms are analyzed. Numerical results demonstrate the efficacy of the proposed algorithms, while the BSUM-based algorithms are much more computationally efficient than all the state-of-the-art algorithms, as long as all the coupling requirements on quality of service (like ROC transmission) can be fused into EE. Besides, the impacts of power budget, circuit power and tolerable outage probability on the ROC EE are revealed and discussed.

Wireless train communication network (WLTCN) is an emerging technology for enabling intelligent rail vehicles. It is responsible for providing train control services (TCS), passenger information services (PIS), and train sensing services (TSS). These services within WLTCN have notably different quality of service (QoS) requirements from traditional telecommunication services. In this paper, to incorporate multiple services in a single WLTCN, we propose a radio access network (RAN) slicing architecture empowered WLTCN to satisfy the demands of services and save bandwidth resource. In particular, the service and slicing models of TCS, PIS, and TSS are investigated. By analyzing the heterogeneous characteristics and QoS requirements of the above services within WLTCN, we exploit the orthogonal multiple access scheme for TCS and PIS and the non-orthogonal multiple access scheme for TSS, respectively. The system bandwidth minimization problem is formulated with slicing resource allocation for TCS, PIS, and TSS and non-orthogonal access grouping for TSS terminals as a mixed-integer nonlinear programming (MINLP). To solve the intractable MINLP, the original problem is transformed and decoupled into the two subproblems. Then, we propose a joint bandwidth optimization and terminal clustering (JBOTC) algorithm to tackle the bandwidth allocation problem with optimal terminal grouping strategy for TSS effectively. The closed-form expressions of the optimal bandwidth allocation strategy for three services are derived. The simulation results illustrate the performance superiority for saving bandwidth of the JBOTC algorithm to the benchmark schemes. Our proposed slicing strategy enables WLTCN to support heterogeneous services co-existence with minimal bandwidth consumption.

Cell-free (CF) massive multiple-input multiple-output (mMIMO) systems are emerging as promising alternatives to cellular networks, especially in ultra-dense environments. However, further capacity enhancement requires the deployment of more access points (APs), which will lead to high costs and high energy consumption. To address this issue, in this paper, we explore the integration of low-power, low-cost stacked intelligent metasurfaces (SIM) into CF mMIMO systems to enhance AP capabilities. The key point is that SIM performs precoding-related matrix operations in the wave domain. As a consequence, each AP antenna only needs to transmit data streams for a single user equipment (UE), eliminating the need for complex baseband digital precoding. Then, we formulate the problem of joint AP-UE association and precoding at APs and SIMs to maximize the system sum rate. Due to the non-convexity and high complexity of the formulated problem, we propose a two-stage signal processing framework to solve it. In particular, in the first stage, we propose an AP antenna greedy association (AGA) algorithm to minimize UE interference. In the second stage, we introduce an alternating optimization (AO)-based algorithm that separates the joint power and wave-based precoding optimization problem into two distinct sub-problems: the complex quadratic transform method is used for AP antenna power control, and the projection gradient ascent (PGA) algorithm is employed to find suboptimal solutions for the SIM wave-based precoding. Finally, the numerical results validate the effectiveness of the proposed framework and assess the performance enhancement achieved by the algorithm in comparison to various benchmark schemes. The results show that, with the same number of SIM meta-atoms, the proposed algorithm improves the sum rate by approximately 275% compared to the benchmark scheme.

In this paper, we explore the integration of low-power, low-cost stacked intelligent metasurfaces (SIM) into cell-free (CF) massive multiple-input multiple-output (mMIMO) systems to enhance access point (AP) capabilities and address high power consumption and cost challenges. Specifically, we investigate the uplink performance of a SIM-enhanced CF mMIMO system and propose a novel system framework. First, the closed-form expressions of the spectral efficiency (SE) are obtained using the unique two-layer signal processing framework of CF mMIMO systems. Second, to mitigate inter-user interference, an interference-based greedy algorithm for pilot allocation is introduced. Third, a wave-based beamforming algorithm for SIM is proposed, based only on statistical channel state information, which effectively reduces the fronthaul costs. Finally, two different power control algorithms are proposed to improve the performance of UE with inferior channel conditions. The results indicate that increasing the number of SIM layers and meta-atoms leads to significant performance improvements and allows for a reduction in the number of APs and AP antennas, thus lowering the costs. In particular, the best SE performance is achieved with the deployment of 20 APs plus 1200 SIM meta-atoms. Finally, the proposed wave-based beamforming algorithm can enhance the SE performance of SIM-enhanced CF-mMIMO systems by 57%, significantly outperforming traditional CF mMIMO systems.

Reconfigurable intelligent surface (RIS)-assisted terahertz (THz) communication is emerging as a key technology to support the ultra-high data rates in future sixth-generation networks. However, the acquisition of accurate channel state information (CSI) in such systems is challenging due to the passive nature of RIS and the hybrid beamforming architecture typically employed in THz systems. To address these challenges, we propose a novel low-complexity two-phase channel estimation scheme for RIS-assisted THz systems with beam split effect. In the proposed scheme, we first estimate the full CSI over a small subset of subcarriers (SCs), then extract angular information at both the base station and RIS. Subsequently, we recover the full CSI across remaining SCs by determining the corresponding spatial directions and angle-excluded coefficients. Theoretical analysis and simulation results demonstrate that the proposed method achieves superior performance in terms of normalized mean-square error while significantly reducing computational complexity compared to existing algorithms.

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is an emerging transceiver technology for enabling next-generation communication systems, due to its potential for substantial enhancement in both the spectral efficiency and spatial resolution. However, the achievable performance limits of various promising XL-MIMO configurations have yet to be fully evaluated, compared, and discussed. In this paper, we develop an effective degrees of freedom (EDoF) performance analysis framework specifically tailored for near-field XL-MIMO systems. We explore five representative distinct XL-MIMO hardware designs, including uniform planar array (UPA)-based with infinitely thin dipoles, two-dimensional (2D) continuous aperture (CAP) plane-based, UPA-based with patch antennas, uniform linear array (ULA)-based, and one-dimensional (1D) CAP line segment-based XL-MIMO systems. Our analysis encompasses two near-field channel models: the scalar and dyadic Green’s function-based channel models. More importantly, when applying the scalar Green’s function-based channel, we derive EDoF expressions in the closed-form, characterizing the impacts of the physical size of the transceiver, the transmitting distance, and the carrier frequency. In our numerical results, we evaluate and compare the EDoF performance across all examined XL-MIMO designs, confirming the accuracy of our proposed closed-form expressions. Furthermore, we observe that with an increasing number of antennas, the EDoF performance for both UPA-based and ULA-based systems approaches that of 2D CAP plane and 1D CAP line segment-based systems, respectively. Moreover, we unveil that the EDoF performance for near-field XL-MIMO systems is predominantly determined by the array aperture size rather than the sheer number of antennas.

The key technologies of sixth generation (6G), such as ultra-massive multiple-input multiple-output (MIMO), enable intricate interactions between antennas and wireless propagation environments. As a result, it becomes necessary to develop joint models that encompass both antennas and wireless propagation channels. To achieve this, we utilize the multi-port communication theory, which considers impedance matching among the source, transmission medium, and load to facilitate efficient power transfer. Specifically, we first investigate the impact of insertion loss, mutual coupling, and other factors on the performance of multi-port matching networks. Next, to further improve system performance, we explore two important deep unfolding designs for the multi-port matching networks: beamforming and power control, respectively. For the hybrid beamforming, we develop a deep unfolding framework, i.e., projected gradient descent (PGD)-Net based on unfolding projected gradient descent. For the power control, we design a deep unfolding network, graph neural network (GNN) aided alternating optimization (AO)-Net, which considers the interaction between different ports in optimizing power allocation. Numerical results verify the necessity of considering insertion loss in the dynamic metasurface antenna (DMA) performance analysis. Besides, the proposed PGD-Net based hybrid beamforming approaches approximate the conventional model-based algorithm with very low complexity. Moreover, our proposed power control scheme has a fast run time compared to the traditional weighted minimum mean squared error (WMMSE) method.

This paper proposes a novel deep learning enabled beamforming design for ultra-dense wireless networks by integrating prior knowledge and graph neural network (GNN), termed model-based GNN. An energy efficiency (EE) maximization problem is first subject to the power budget and quality of service (QoS) requirements, and then reformulated based on the minimum mean square error scheme and the hybrid zero-forcing and maximum ratio transmission scheme. The model-based GNN is designed to realize the mapping from channel state information to beamforming vectors to address the reformulated problems. Particularly, the multi-head attention mechanism and the residual connection are adopted to enhance the feature extracting, and a scheme selection module is designed to improve the adaptability to channel conditions. The unsupervised learning is adopted, and a various-input training strategy is proposed to enhance the stability of the model-based GNN. Numerical results demonstrate that the proposed model-based GNN can realize a millisecond-level inference with limited performance loss, the scalability to different numbers of users and the adaptability to various channel conditions and QoS requirements in ultra-dense wireless networks.

Communication objectives with high mobility bring severe Doppler shifts, causing the inter-carrier interference of orthogonal frequency division multiplexing (OFDM) system, which raises the requirements of novel modem schemes. However, existing modem schemes for high-mobility communications face challenges in adapting to diverse channel environments, involving complex channel estimation and etc. Fortunately, the potential of deep learning (DL) has been exploited in various communication applications. In order to design the consistent and robust modem scheme for different channel environments, we propose the DL-based architecture termed the closer twins (CTs) model, which borrows the idea from the Siamese structure in contrastive learning. In specific, two identical network backbones like twins can simultaneously process different channel inputs and make outputs consistent. We design a convlutional neural network called modem network (ModNet) as the backbone for the design of consistent and robust modem scheme. Moreover, to make traditional channel estimation and interpolation methods applicable to the designed modem scheme, a training-aided strategy called random-pilot (R-P) is proposed. In R-P strategy, we simulate the process of conventional channel estimation to modify the objective function of the modem scheme design. Furthermore, the performance of traditional channel estimation can be further improved by DL-based methods. We utilize the CTs model and design the backbone called estimation matrix network (EMNet) to optimize a linear channel estimation method, who outperforms the traditional methods with a similar complexity. Simulation results demonstrate that the proposed modem scheme outperforms OFDM, especially with high Doppler spread. The channel estimation strategy, supported by the R-P strategy and EMNet, achieves lower normalized mean square error compared with traditional methods, contributing to more reliable transmission.

The use of predictor antennas (PAs) has significant potential to enhance wireless channel prediction performance in high-speed railway (HSR) communications. The PA system features two sets of antennas installed on the roof of a vehicle. The PA is located at the front of the vehicle and is used to predict the channel observed by the receive antenna (RA), which is located behind the PA. The PAs can be integrated with dense pilots spatially, but the prediction performance decreases when channel estimations are sparse. Therefore, this paper first proposes a multiple PAs (mPAs) system combined with interpolation for sparse channel estimations. Subsequently, recognizing the need for PAs to measure all antenna channels in the estimation interval, we propose an informer-based mPAs system. This system predicts future RA channels in parallel, effectively solving the problem of error propagation in sequential prediction methods. Simulation results demonstrate that as the prediction horizon extends, the proposed informer-based mPAs system outperforms others. Finally, we investigate how varying velocities impact prediction accuracy. It was found that the prediction accuracy of a single PA system performs well at low speeds but drops rapidly at high speeds. Moreover, our proposed informer-based mPAs system achieves higher prediction horizons and maintains efficiency at high speeds.

This paper explores the potential of employing two-hop full-duplex (FD) relaying systems to alleviate transmission latency. The approach involves dividing a message into smaller packets and transmitting them sequentially with possible retransmissions. Notably, we characterize the expected transmission latency of multiple packet transmissions for the first time. By introducing a novel error probability propagation method, the expected number of time slots needed for successfully transmitting all packets is recursively derived. The article tackles the minimization of transmission latency by jointly considering packet division, blocklength per packet, and power allocation. To cope with the complex nonconvex nature of this optimization problem, a subproblem is extracted, and a reformulation utilizing variable substitution is proposed. Furthermore, a tight convex approximation at any feasible point is developed to facilitate the design of an iterative algorithm to gradually converge towards a suboptimal solution. Simulation results validate the efficacy of the proposed solution, demonstrating its convergence and latency advantages over both half-duplex (HD) and FD relaying systems lacking power control.

Orthogonal delay-Doppler (DD) division multiplexing (ODDM) modulation has recently emerged as a promising candidate for ensuring reliable communications over high mobility channels. One of the key challenges faced by systems based on ODDM modulation and other DD domain modulation schemes (e.g., orthogonal time frequency space modulation), is the excessive receiver complexity. To address this challenge, we propose time domain Tomlinson-Harashima precoding (THP) for the ODDM systems to make the single-tap equalizer feasible, thereby significantly reducing the receiver complexity. Different from previous THP designs, we first propose intersymbol-interference (ISI) pre-cancellation based on the linear time-varying (LTV) channel information. Second, we propose a modified modulo operation with an adaptive modulus to realize DD domain data modulation and time domain ISI precancellation. We analytically investigate the bit error rate (BER) performance of our proposed ODDM system with time domain THP. Particularly, we investigate three types of losses that can degrade the performance, namely the modulo signal loss, the power loss, and the modulo noise loss. Based on these, a lower bound for the BER of our proposed ODDM system with time domain THP under the DD domain single-tap equalizer is derived. Through numerical simulations, our analysis is first validated and finally, we show the significance of our design to attain a low complex receiver and superior BER performance over LTV channels.

The stacked intelligent metasurface (SIM) emerges as an innovative technology with the ability to directly manipulate electromagnetic (EM) wave signals, drawing parallels to the operational principles of artificial neural networks (ANN). Leveraging its structure for direct EM signal processing alongside its low-power consumption, SIM holds promise for enhancing system performance within wireless communication systems. In this paper, we focus on SIM-assisted multi-user multi-input and single-output (MU-MISO) system downlink scenarios in the transmitter. We proposed a joint optimization method for SIM phase shift configuration and antenna power allocation based on the twin delayed deep deterministic policy gradient (TD3) algorithm to efficiently improve the sum rate. The results show that the proposed algorithm outperforms both deep deterministic policy gradient (DDPG) and alternating optimization (AO) algorithms. Furthermore, increasing the number of meta-atoms per layer of the SIM is always beneficial. However, continuously increasing the number of layers of SIM does not lead to sustained performance improvement.

Semantic communication (SemCom) holds promise for reducing network resource consumption while achieving the communications goal. However, the computational overheads in jointly training semantic encoders and decoders—and the subsequent deployment in network devices—are overlooked. Recent advances in Generative artificial intelligence (GAI) offer a potential solution. The robust learning abilities of GAI models indicate that semantic decoders can reconstruct source messages using a limited amount of semantic information, e.g., prompts, without joint training with the semantic encoder. A notable challenge, however, is the instability introduced by GAI’s diverse generation ability. This instability, evident in outputs like text-generated images, limits the direct application of GAI in scenarios demanding accurate message recovery, such as face image transmission. To solve the above problems, this paper proposes a GAI-aided SemCom system with multi-model prompts for accurate content decoding. Moreover, in response to security concerns, we introduce the application of covert communications aided by a friendly jammer. The system jointly optimizes the diffusion step, jamming, and transmitting power with the aid of the generative diffusion models, enabling successful and secure transmission of the source messages.

A channel measurement system for accurate modeling of the millimeter-wave (mmWave) band air-to-ground (A2G) wireless channel is presented. The measurement system consists of a ground station on a small cart and an air station installed on a custom-made octocopter unmanned aerial vehicle (UAV). Channel sounders at 26 GHz mmWave band with a bandwidth of 1 GHz are mounted on the air station and the ground station. In contrast to the on-the-shelf drones, the custom-made UAV offers the advantages of greater payload capacity and extended flight endurance. The measurement campaign was conducted in a university campus scenario, providing valuable measurement data for further analyses of the A2G channel. To analyze multipath effects in the A2G channel, simulation result of a ray tracing (RT) model is used to identify the principal propagation effects in the actual measurements. Subsequently, the path-loss exponent and the standard deviation of shadowing are calculated based on the measurement. Furthermore, sparsity of the A2G mmWave channel is evaluated through analyses of the Gini index and the Rician$K$factor. By comparing with the measurement results in the different propagation scenarios presented in the related work, e.g., industrial Internet-of-Things (IIoT), vehicular urban, and laboratory room, unique characteristics of the A2G mmWave channel are discussed.

Cell-free (CF) massive multiple-input multiple-output (MIMO) system has emerged as a highly promising technology, primarily due to its ability to improve coverage and performance. However, one of the key challenges is their reliance on perfect channel state information (CSI). To address this issue, we propose the incorporation of a rate-splitting (RS) strategy, which has been proven to effectively mitigate the negative impact of imperfect CSI. In this paper, we investigate CF massive MIMO systems that utilize the RS strategy. We derive a closed-form expression for the RS-assisted CF massive MIMO system in the uplink, while accounting for pilot contamination. We also present four decoding schemes that can be implemented in practical systems. Our extensive simulations reveal that CF massive MIMO systems utilizing the RS strategy outperform those that do not in terms of sum spectral efficiency (SE). These findings emphasize the effectiveness of RS technology in mitigating the negative effects of imperfect CSI in CF massive MIMO systems. The insights gained from this research can serve as a basis for the design and optimization of future CF massive MIMO systems, ultimately improving their performance and expanding their applicability in diverse scenarios.

We consider a reconfigurable intelligent surface (RIS)-aided wireless network supporting local gradient upload for federated learning (FL). For the first time, the impact of wireless uploads with finite blocklength (FBL) on FL performance is investigated and provides a corresponding performance enhancement design. More specifically, we characterize the im-pact of wireless transmissions/uploads on the convergence and the optimality gap of FL, and formulate a resource allocation problem to minimize such impact accordingly. To tackle the formulated non-convex problem, we first conduct a convex approximation to the problem, then propose a block coordinate descent (BCD) based algorithm alternately optimizing the power allocation and RIS phase shifts via addressing two sub-problems. Specifically, we prove the convexity for the pure power allocation sub-problem, while for RIS phase design one, a closed-form expression of the optimal solution is derived by applying the path-following (PF) method. Numerical results demonstrate that the proposed design significantly improves the FL performance compared to baseline schemes.

Emerging as an important enabling technology for smart factories, ultra-reliable low latency communications (URLLC) have attracted extensive attention from academia and industry. In this paper, we aim to improve the performance of downlink URLLC in a smart factory. We first construct the system model based on the 5G New Radio (NR) standard, which specifies the modulation scheme, resource block structure and achievable data rates under finite blocklength codes (FBC). Next, since it is challenging to fulfill all transmission requests with limited radio and power resources, we formulate the problem to maximize the network throughput while considering delay and reliability constraints. This is a mixed integer non-convex nonlinear problem that is difficult to solve directly. To be tractable, we decompose it into two sub-problems, and apply the alternating optimization to obtain a sub-optimal solution. Specifically, the flow scheduling sub-problem is transformed into a matching game (MG) and solved by a delayed acceptance-based algorithm. Also a local water-filling algorithm is utilized to solve the power allocation sub-problem. Simulation results reveal that our proposed scheme outperforms other benchmark schemes.

Millimeter wave (mmWave) communications are highly affected by blockage, whereas the emerging reconfigurable intelligent surface (RIS) has the potential to overcome this issue. This paper proposes a Neyman-Pearson (N-P) criterion-based blockage-aware algorithm to improve resilience to blockage in mobile mmWave multiple input multiple output (MIMO) systems. By virtue of this pragmatic blockage-aware technique, we further propose an outage-constrained beamforming design for RIS-assisted mmWave MIMO transmission to achieve outage probability minimization and achievable rate maximization. Specifically, we propose an accelerated projected gradient descent (PGD) algorithm to solve the computational challenge of high-dimensional RIS phase-shift matrix (PSM) optimization. Particularly, we formulate a new Nesterov momentum acceleration scheme to speed up the convergence rate. Extensive experiments confirm the effectiveness of the proposed blockage-aware approach and the proposed accelerated PGD algorithm outperforms a number of representative baseline algorithms in terms of the achievable rate performance.

In this paper, we propose two deep joint source and channel coding (DJSCC) structures with attention modules for the multi-input multi-output (MIMO) channel, including a serial structure and a parallel structure. With singular value decomposition (SVD)-based precoding scheme, the MIMO channel can be decomposed into various sub-channels, and the feature outputs will experience sub-channels with different channel qualities. In the serial structure, one single network is used at both the transmitter and the receiver to jointly process data streams of all MIMO subchannels, while data steams of different MIMO sub-channels are processed independently via multiple sub-networks in the parallel structure. The attention modules in both serial and parallel architectures enable the system to adapt to varying channel qualities and adjust the quantity of information outputs with the channel qualities. Experimental results demonstrate the proposed DJSCC structures have improved image transmission performance, and reveal the phenomenon via non-parameter entropy estimation that the learned DJSCC transceivers tend to transmit more information over better sub-channels.

In this paper, we investigate a reconfigurable intelligent surface (RIS)-aided multiple-input single-output (MISO) system in the presence of electromagnetic interference (EMI) and channel aging with a Rician fading channel model between the base station (BS) and user equipment (UE). Specifically, we derive the closed-form expression for downlink spectral efficiency (SE) with maximum ratio transmission (MRT) precoding. The Monte-Carlo simulation supports the theoretical results, demonstrating that amplifying the weight of the line-of-sight (LoS) component in Rician fading channels can boost SE, while EMI has a detrimental impact. Furthermore, continuously increasing the number of RIS elements is not an optimal choice when EMI exists. Nonetheless, RIS can be deployed to compensate for SE degradation caused by channel aging effects. Finally, enlarging the RIS elements size can significantly improve system performance.

Reconfigurable intelligent surface (RIS)-aided tera-hertz (THz) communication is considered as a promising technique for the sixth-generation network. However, employment of hybrid beamforming in THz systems introduces beam split effect, resulting in severe achievable data rate loss. To realize the full potential of RIS-aided THz systems, it becomes crucial to acquire accurate channel state information. Therefore, in this paper, we propose a novel cyclic beam split generalized approximating message passing (CBS-GAMP) channel estimation scheme without requiring the knowledge of number of propagation paths. Specifically, we expand cascaded channels into sparse representations by designing a CBS dictionary, and then we propose the CBS-GAMP algorithm based on statistical inference framework. Numerical simulations demonstrate effectiveness of the proposed CBS-GAMP scheme against the existing solutions.

Reconfigurable intelligent surfaces (RISs) and cell-free (CF) massive multiple-input multiple-output (MIMO) are two promising technologies for sixth-generation (6G) networks. This paper investigates the achievable uplink sum rate of a RIS-aided CF massive MIMO system considering electromagnetic interference (EMI) at the RISs and imperfect channel state information (CSI). Our focus is on proposing an integrated approach that optimizes the beamforming at the access points (APs) and the RIS phase shift alternately using successive convex approximation and penalty convex-concave procedures to maximize the uplink sum rate. The results demonstrate that the proposed algorithm significantly improves the performance of the RIS-aided CF massive MIMO system and effectively mitigates the interference caused by EMI and imperfect CSI. Additionally, we find that the negative impact of EMI becomes more pronounced as the channel uncertainty increases. Moreover, increasing the number of RIS reflecting elements proves beneficial, but the returns diminish as the number of RIS elements becomes sufficiently large. Furthermore, deploying RIS beyond a certain limit of EMI power leads to degradation in system performance.

In this paper, a 3D geometry-based stochastic channel model (GBSM) is proposed for RIS-assisted multi-user communications. The proposed GBSM is divided into two sub-channels, that is, BS-RIS and RIS-Rx links, and propagation distances and angles of multipath components are derived to describe multi-user channels. In addition, optimization objective for multi-user channel is proposed, and deep reinforcement learning is introduced to solve high-dimensional RIS phase problem. Based on the proposed model and solved RIS phase, channel capacity and root mean square delay spread are derived. The simulation results show that RIS optimization parameters and channel parameters have major impact on channel characteristics. The conclusions can provide a reference for designing and developing of RIS-assisted multi-user systems.

Recently, deep learning enabled semantic communications have been developed to understand transmission content from semantic level, which realize effective and accurate information transfer. Aiming to the vision of sixth generation (6G) networks, wireless devices are expected to have native perception and intelligent capabilities, which associate wireless channel with surrounding environments from physical propagation dimension to semantic information dimension. Inspired by these, we aim to provide a new paradigm on wireless channel from semantic level. A channel semantic model and its characterization framework are proposed in this paper. Specifically, a channel semantic model composes of status semantics, behavior semantics and event semantics. Based on actual channel measurement at 28 GHz, as well as multi-mode data, example results of channel semantic characterization are provided and analyzed, which exhibits reasonable and interpretable semantic information.

In order to satisfy the increasingly massive connection in mMTC system, multiple access technology is a key enabler in the future 6G mMTC. Recently, an emerging multiple access scheme named resource hopping based multiple access (RHMA) has been proposed to achieve reliable user identification and data detection in grant-free random access for mMTC. However, the collision resolution of RHMA is still limited for the future 6G mMTC requirements. Therefore, an improved design is proposed in this paper to enhance the collision resolution capability of RHMA. Specifically, successive interference cancellation (SIC) is combined with user identification and segment decoding at the receiver of RHMA. Also, the user identification of RHMA is improved to eliminate the false alarm user caused by collision and blind channel estimation is considered to recover the signal on the colliding segments. The simulation results show that the improved design is able to achieve a higher collision resolution capability of RHMA.

Backscatter communication (BC) has emerged as a promising paradigm for enabling energy-efficient and low-cost Internet of Things (IoT) systems. One practical challenge for BC implementation is phase-locked loop (PLL) non-ideality, which mainly includes phase noise and spurs. These non-ideal factors can introduce time-varying interference, yielding wavy backscatter signals and striped-shape constellation clusters that can substantially degrade the system performance. In this paper, we investigate the PLL non-ideality of the BC system, and propose a method to suppress its resulted time-varying interference. Specifically, we first establish the BC system model with phase noise and spurs, mathematically showing how these factors can generate time-varying interference and result in wavy signals and striped-shape constellations. We then introduce Dynamic Self-Interference Cancellation (DSIC) algorithms to mitigate the time-varying interference and implement it on a practical backscatter platform. Finally, our experimental measurements show that DSIC can reduce the bit error rates (BER) by half, demonstrating its effectiveness in mitigating the effects of PLL non-ideality and improving the system performance.

The strict block error rate (BLER) requirement under the time-varying nature of wireless channels in Ultra-reliable low-latency communication (URLLC) systems pose sig-nificant challenges for link adaptation (LA). To tackle these challenges, we propose a novel LA method that adaptively selects the modulation and coding scheme (MCS) without requiring perfect channel knowledge which is unrealistic to obtain in URLLC. The goal is to maximize the coding rate while ensuring strict BLER constraints in URLLC systems. To achieve this, we utilize the Deep Q-Network (DQN) algorithm to select the MCS dynamically. Furthermore, we enhance the MCS selection process by using the Outer Loop Link Adaptation algorithm for transmission reliability improvement. Given the nature of URLLC, the samples of ACK and NACK are highly imbalanced, which can cause issues in the training process. To address it, we propose a novel training mechanism that improves the performance of DQN model and convergence speed during the training stage. Through extensive simulations, we demonstrate that our proposed algorithm outperforms existing methods regarding coding rate and imposing strict BLER constraints.

In this paper, we study a multi-source wireless power transfer (MS-WPT) enabled Internet of Nano Things (IoNT) supporting multi-hop ultra-reliable low-latency communications (URLLC), i.e., nanosensors wirelessly transmit short packets to the same destination in a multi-hop collecting-then-relaying manner. For such MS-WPT enabled nanoscale relaying network, we for the first time characterize the fairness-aware reliability and propose a joint blocklength and dynamic MS-WPT power allocation design for maximum transmission error probability minimization. However, the mutual effects between multi-source, the infinite MS-WPT schemes, the nonlinear EH model, and the complex finite blocklength (FBL) reliability model make the problem nonconvex and intractable. To tackle these difficulties, we first characterize the optimal frame structure for MS-WPT and prove that an equivalent optimal performance can be achieved by limited WPT decisions corresponding to a finite number of sub-slots. Following that, an optimization problem with finite number of variables is formulated, nevertheless, remaining nonconvex. To cope with it, variable substitution, nonconvex relationship decoupling, relax variable introduction as well as successive convex approximation (SCA) are utilized to further reformulate the problem into local convex ones. A sub-optimal solution is finally achieved by the proposed iteration-based algorithm. Via numerical simulation, it is validated that a significant performance improvement is achieved by our proposed design.

Abstract Backscatter communications, which originated from World War II, have been widely applied in the logistics domain, and recently attract emerging interest from both academic and industrial circles. Here, the backscatter communication systems equipped with retrodirective arrays that can re‐transmit the impinging signals back toward the direction of incidence are studied so as to reduce the power loss of the signals. Specifically, the authors consider the tag is equipped with retrodirective arrays to improve reliability and enhance communication range. The probability density function of channel coefficients is then derived. Next, a channel estimator based on Bayesian theory is proposed to acquire the modulus values of channel parameters and calculate its Bayesian Cramer–Rao Lower Bound. Finally, simulation results are provided to corroborate these theoretical studies.

Timely and energy-efficient task offloading and computing can be challenging in mobile edge computing (MEC) networks when the communication links between devices and edge servers are unreliable. In this paper, we apply multiple aerial intelligent reflective surfaces (AIRSs) to assist devices in offloading computing tasks to the edge server in a timely and reliable manner in the MEC network with poor offloading environments. To evaluate the timeliness of offloading and computing, we derive the evolution process of age-of-information (AoI) under the random arrival of the computing tasks. The association between devices and AIRSs, offloading order of computing tasks, design of IRS phase shift, and allocation of communication and computing resources are jointly optimized to minimize the average AoI and system energy consumption given computing requirements. To solve the formulated minimization problem, we propose an efficient problem-solving framework to cope with the challenge of variable coupling. Firstly, we derive a closed-form optimal IRS phase shift to provide a reliable offloading environment. Then, we optimize the association between devices and AIRSs while reducing the offloading complexity and balancing the number of devices associated with each AIRS. Finally, we develop a low-complexity task offloading and resource allocation algorithm based on convex optimization to attain a good enough solution. Simulation results indicate the proposed solution outperforms benchmarks in timeliness and energy saving.

With the increasing demands from passengers for data-intensive services, millimeter-wave (mmWave) communication is considered as an effective technique to release the transmission pressure on high speed train (HST) networks. However, mmWave signals encounter severe losses when passing through the carriage, which decreases the quality of services on board. In this paper, we investigate an intelligent refracting surface (IRS)-assisted HST communication system. Herein, an IRS is deployed on the train window to dynamically reconfigure the propagation environment, and a hybrid time division multiple access-nonorthogonal multiple access scheme is leveraged for interference mitigation. We aim to maximize the overall throughput while taking into account the constraints imposed by base station beamforming, IRS discrete phase shifts and transmit power. To obtain a practical solution, we employ an alternating optimization method and propose a two-stage algorithm. In the first stage, the successive convex approximation method and branch and bound algorithm are leveraged for IRS phase shift design. In the second stage, the Lagrangian multiplier method is utilized for power allocation. Simulation results demonstrate the benefits of IRS adoption and power allocation for throughput improvement in mmWave HST networks.

As research on integrated sensing and communication (ISAC) progresses, it has been discovered that ISAC can be effectively utilized to enhance the security of wireless communications. Its sensing function can assist in both eavesdropping detection and physical-layer security techniques. In this article, our focus lies on addressing the security challenges associated with high-speed train-to-ground communication using ISAC technology. We explore a novel secure communication scheme. Specifically, we exploit the sensing capabilities of ISAC to detect eavesdropping at the receiving end and subsequently establish a signal blind zone at the location where eavesdropping occurs through beamforming and waveform optimization techniques. This approach ensures the achievement of secure wireless communication. Mathematically modeling the problem as an optimization problem, we derive a lower bound for simplification purposes. Subsequently, we employ an alternating optimization algorithm to iteratively find suboptimal solutions for the optimization variables. Through extensive simulation experiments and comparative analysis, we demonstrate that our proposed algorithm not only guarantees communication security but also outperforms existing algorithms in terms of efficiency.

Reconfigurable intelligent surface (RIS)-aided terahertz (THz) cell-free massive multiple-input-multiple-output (mMIMO) networks have been envisioned as a prospective technology for future 6G networks. However, due to the ultrawide bandwidth and the frequency-dependent characteristics of RISs, beam-split effect has become an unavoidable obstacle. To compensate the severe performance degradation caused by beam split effect, we introduce additional time delay (TD) layers at both access points (APs) and RISs. Accordingly, we propose a joint precoding framework at APs and RISs to fully unleash the potential of the considered network. Specifically, we first formulate the joint precoding as a nonconvex optimization problem. Then, given the location of unchanged RISs, we adjust the TDs of APs to align the generated beams toward RISs. After that, with knowledge of the optimal TDs of APs, we decouple the optimization problem into three subproblems of optimizing the baseband beamformers, RISs and TDs of RISs, respectively. Exploiting multidimensional complex quadratic transform, we transform the subproblems into convex forms and solve them under alternate optimizing framework. Numerical results verify that the proposed method can effectively mitigate beam split effect and significantly improve the achievable rate compared with conventional cell-free mMIMO networks.

Deploying reconfigurable intelligent surfaces (RIS) near source and destination is of practical interest for improving the link quality of vehicle-to-vehicle (V2V) wireless systems. However, the accurate channel modeling and RIS tile deployment constitute a pair of challenges to evaluate the system performance such as error performance, capacity, etc. In this paper, we investigate the double-RIS channel characteristics and propose a geometry-based triple-cylinder model, where the RIS sub-surface/tile is enabled to assist V2V systems and other elements are turned off. To determine tile locations on RIS surface, we formulate an optimization problem by maximizing the end-to-end channel gain and solve it using gradient ascent (GA) method. Following this, channel correlation function, channel capacity, and outage probability are derived according to the proposed model. Five typical mobile scenarios are discussed to validate the convergence of proposed GA algorithm, where the results show that channel gain can converge to its maximum with optimized tile locations. In addition, channel correlation under different parameters and conditions are explored. Simulation results validate the enhanced channel capacity and outage probability obtained by optimizing the tile locations.

Introducing free-space optical (FSO) communication into space-air-ground integrated networks (SAGINs) can enable the realization of high data rates to achieve next-generation wireless communication. However, investigating the performance of the unmanned-aerial-vehicle (UAV)-assisted dual-hop radio-frequency (RF)/FSO systems for SAGINs remains challenging owing to severe channel fading. This article proposes a UAV-assisted RF/FSO relay system with a decode-and-forward relay protocol. A unified statistical channel model that considers the influences of attenuation loss, atmospheric turbulence, pointing errors, and angle-of-arrival fluctuations on the FSO link is developed, and the Málaga distribution is employed to characterize atmospheric turbulence. Closed-form expressions of the outage probability and the average bit error rate are derived for the pure FSO link and overall RF/FSO relay system. We also derive expressions of the system metrics under atmospheric turbulence characterized by Gamma-Gamma and Log-normal distributions, leveraging the broader coverage of our proposed channel model. The effects of the system and channel parameters on the performance of the pure FSO link and the overall RF/FSO relay system are investigated. Finally, our analytical expressions agree well with the Monte Carlo simulation results, demonstrating the validity of the expressions.

The Industrial Internet of Things (IIoT) enables machines to communicate robustly. High reliability, high throughput, and low latency are the critical capabilities of IIoT, which have posed great challenges to existing wireless solutions for industrial applications. Due to the vast available bandwidth, the emerging millimeter-wave (mmWave) technology is promising to address this bottleneck. However, the propagation behaviors at such high frequencies in the harsh industrial environment have yet been well understood. In this work, extensive measurements have been conducted in a representative industrial application scenario using a 2-GHz wideband directional channel sounder in the 28-GHz mmWave band. By exploiting the measurement with excellent resolution, the multipath components’ (MPCs) delay-angular space is transformed onto the scatter points (SPs) in the propagation environment. An effective clustering algorithm is then proposed to cluster the SPs without prior knowledge and iterations. Through a geometrical optics analysis, the SP clusters are classified corresponding to the reflectors. By doing this, the cluster-generating reflectors are reduced to a quasi-deterministic (QD) channel model that ensures spatial consistency and MPCs’ stochastic dispersion. Finally, it is shown that the measurement data agrees well with the proposed QD model, indicating the high fidelity of the proposed model.

Utilizing unmanned aerial vehicles (UAVs) to provide reliable and effective wireless communication services for ground users has become a promising solution in emergency scenarios. However, energy consumption limitations lead to new challenges to timeliness of UAV communications. In this article, we propose a service time optimization strategy using UAV as an aerial base station. Total service time of UAV, including hover time and flight time, is minimized while satisfying user load demand. First, the K-means algorithm is used to complete area division. Under the constraints of UAV coverage and transmit power, hover positions of UAV in each partition are optimized to obtain the shortest hover time. Subsequently, the shortest flight time is obtained by solving the traveling salesman problem. Finally, the above process is integrated into a complete service time optimization problem. Simulation results show that the proposed strategy successfully achieves full-area coverage with the shortest service time, which significantly outperforms other existing algorithms, and there exists an optimal altitude to minimize the total service time.

Fifth generation new radio vehicle-to-everything (5G-V2X) communication is an emerging technology to support advanced use cases and higher automation levels in Internet of Vehicles. A comprehensive and accurate knowledge of narrow-beam channels plays a crucial role in the utilization of the 5G-V2X technique. This paper focuses on measurement and characterization of vehicle-to-infrastructure (V2I) narrow-beam channel. A flexible narrow-beam channel measurement system using a phased-array antenna is designed, and is employed to perform a series of 3.35 GHz channel measurements with different beam widths in highway primary road and auxiliary road. Based on the collected data, the fading characteristics of V2I narrow-beam channel including path loss, shadow fading and K-factor are extracted, analyzed and then modeled. Then, the V2I narrow-beam channel dispersion in time-frequency-space domain is characterized, and the statistical models of root-mean-square (RMS) delay spread, RMS Doppler spread and RMS angular spread are proposed. In addition, the V2I narrow-beam channel nonstationarity is discussed in terms of the stationarity interval and birth-death process of multipath components, and results of the Markov chain model with parameters like state transition probability matrix and steady-state probability are reported. The results can contribute to the design and evaluation of 5G-V2X technology.

Line-of-sight (LOS) or non-line-of-sight (NLOS) identification is of vital significance to the localization of mobile sensors in intelligent substations for power Internet of Things. This article investigates the LOS/NLOS identification in substation scenarios, based on deep learning networks and feature fusion methods. Channel measurement data in high-voltage substation environments with LOS and NLOS cases are collected, and both original and manually extracted channel features are obtained to generate data sets. A novel LOS/NLOS identification model is proposed, which employs a deep neural network and a self-attention network to separately learn the information contained in the manually extracted channel features and the original channel feature. This model also applies a hybrid fusion method to capture correlation between the channel features and mitigate data inundation risk caused by the dimension difference of input features. The results of performance evaluation show that the proposed model not only has the identification accuracy as high as 98.95%, but also possesses good noise robustness and acceptable computational complexity.

The cellular-vehicle-to-everything (C-V2X) communication network is constantly evolving and changing the way people travel. To realize connected automated vehicles, both precise positioning and reliable communications of vehicles and associated terminals are demanding. Since the orthogonal frequency division multiplexing (OFDM) scheme is vulnerable to Doppler spread under high-mobility, the orthogonal time frequency space (OTFS) modulation is proposed recently to tackle this challenge based on the sparsity and stability of the channel spreading function. To this end, this article proposes a waveform design for V2X based on OTFS modulation, named orthogonal time frequency code space modulated waveform (OTFCSMW). The waveform design is able to realize random access and positioning simultaneously. In detail, the transceiver design of OTFCSMW is introduced, where orthogonal spreading sequences are utilized to provide spreading gain and represent terminal identifications based on the proposed orthogonal spreading combinations. Then a joint time-of-arrival (ToA) estimation and channel estimation strategy is proposed. The ToAs of terminals are estimated based on the sparsity of taps in the channel spreading function, and remaining unknown channel parameters are estimated based on the minimum-mean-square-error (MMSE) principle. Finally, the equalization scheme for OTFCSMW based on MMSE principle is proposed. Simulation results demonstrate that OTFCSMW can realize similar positioning performance to OFDM and outperforms the orthogonal-spreading-based-OFDM-waveform (S-OFDMW) scheme on bit error rate (BER) in different V2X channel environments.

Groundbreaking applications such as ChatGPT have heightened research interest in generative artificial intelligence (GAI). Essentially, GAI excels not only in content generation but also signal processing, offering support for wireless sensing. Hence, we introduce a novel GAI-assisted human flow detection system (G-HFD). Rigorously, G-HFD first uses the channel state information (CSI) to estimate the velocity and acceleration of propagation path length change of the human induced reflection (HIR). Then, given the strong inference ability of the diffusion model, we propose a unified weighted conditional diffusion model (UW-CDM) to denoise the estimation results, enabling detection of the number of targets. Next, we use the CSI obtained by a uniform linear array with wavelength spacing to estimate the HIR’s time of flight and direction of arrival (DoA). In this process, UW-CDM solves the problem of ambiguous DoA spectrum, ensuring accurate DoA estimation. Finally, through clustering, G-HFD determines the number of subflows and the number of targets in each subflow, i.e., the subflow size. The evaluation based on practical downlink communication signals shows G-HFD’s accuracy of subflow size detection can reach 91%. This validates its effectiveness and underscores the significant potential of GAI in the context of wireless sensing.

In this paper we study the effective degree of freedom (EDoF) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. We consider two XL-MIMO hardware designs, uniform planar array (UPA) based and continuous aperture (CAP) based XL-MIMO, as well as two representative near-field channel models: scalar Green function based and dyadic Green function with triple polarization based models. First, for UPA-based XL-MIMO with a discrete array aperture, we evaluate the EDoF performance by applying discrete channel matrices generated by the scalar or dyadic Green channel model. Then, for CAP-based XLMIMO, a tailored EDoF performance evaluation framework for a two-dimensional (2D) CAP plane based system is constructed by leveraging asymptotic analysis and extending the analysis approaches for a one-dimensional (1D) CAP line segment based system. This framework incorporates the triplepolarized auto-correlation kernel function, which can efficiently capture the impact of multiple polarization on the EDoF performance. Numerical results show that, with an increase in the number of antennas, the UPA-based XL-MIMO system can achieve an EDoF performance close to the EDoF performance for the CAP plane based XL-MIMO system. Moreover, the EDoF performance can be enhanced by the multiple polarization in channels and increased physical size of the transceiver.

Combining different heterogeneous components into a full HPC system results in combinatorial effects in their complexity.It is a huge challenge to design systems such that they can be used efficiently by the expected workloads, particularly, when the workload is very heterogeneous.Modular systems can help, deciding according to the user portfolio how much weight a particular module should get, and what connectivity is required within and between modules.To deal with these challenges, integrated projects that cover all levels of the HPC ecosystem are needed.Also, interoperability and exchangeability of components, both hardware and software, should be easier to give system designers and users, alike, more flexibility.This special issue calls for recent research which focused on the heterogeneous HPC for IDA, such as memory management, workload management for heterogeneous systems and so as the heterogeneity in storage technologies.

An introduction of intelligent interconnectivity for people and things has posed higher demands and more challenges for sixth-generation (6G) networks, such as high spectral efficiency and energy efficiency (EE), ultralow latency, and ultrahigh reliability. Cell-free (CF) massive multiple-input-multiple-output (mMIMO) and reconfigurable intelligent surface (RIS), also called intelligent reflecting surface (IRS), are two promising technologies for coping with these unprecedented demands. Given their distinct capabilities, integrating the two technologies to further enhance wireless network performances has received great research and development attention. In this article, we provide a comprehensive survey of research on RIS-aided CF mMIMO wireless communication systems. We first introduce system models focusing on system architecture and application scenarios, channel models, and communication protocols. Subsequently, we summarize the relevant studies on system operation and resource allocation, providing in-depth analyses and discussions. Following this, we present practical challenges faced by RIS-aided CF mMIMO systems, particularly those introduced by RIS, such as hardware impairments (HIs) and electromagnetic interference (EMI). We summarize the corresponding analyses and solutions to further facilitate the implementation of RIS-aided CF mMIMO systems. Furthermore, we explore an interplay between RIS-aided CF mMIMO and other emerging 6G technologies, such as millimeter wave (mmWave) and terahertz (THz), simultaneous wireless information and power transfer (SWIPT), next-generation multiple access (NGMA), and unmanned aerial vehicle (UAV). Finally, we outline several research directions for future RIS-aided CF mMIMO systems.

In this paper, we investigate the amalgamation of cell-free (CF) and extremely large-scale multiple-input multiple-output (XL-MIMO) technologies, referred to as a CF XL-MIMO, as a promising advancement for enabling future mobile networks. To address the computational complexity and communication power consumption associated with conventional centralized optimization, we focus on user-centric dynamic networks in which each user is served by an adaptive subset of access points (AP) rather than all of them. We begin our research by analyzing a joint resource allocation problem for energy-efficient CF XL-MIMO systems, encompassing cooperative clustering and power control design, where all clusters are adaptively adjustable. Then, we propose an innovative double-layer multi-agent reinforcement learning (MARL)-based scheme, which offers an effective strategy to tackle the challenges of high-dimensional signal processing. In the section of numerical results, we compare various algorithms with different network architectures. These comparisons reveal that the proposed MARL-based cooperative architecture can effectively strike a balance between system performance and communication overhead, thereby improving energy efficiency performance. It is important to note that increasing the number of user equipments participating in information sharing can effectively enhance SE performance, which also leads to an increase in power consumption, resulting in a non-trivial trade-off between the number of participants and EE performance.

The reconfigurable intelligent surface (RIS) technology holds great promise for the advancement of future sixth-generation networks. However, existing research on RIS-assisted communication systems often relies on ideal hardware and static channel conditions, which are impractical in real-world scenarios. In this study, we assess the performance of a RIS-assisted communication system, considering the combined effects of hardware impairments caused by imperfect transceivers and channel aging resulting from user mobility. To achieve this, we analyze the direct and cascade channels between the base station and the user, assuming correlated Rician distributions. We employ the linear minimum mean square estimation method to estimate the overall channel and derive a closed-form expression for the uplink spectral efficiency (SE). By formulating an optimization problem for RIS phase shift, we maximize SE using the projected gradient ascent algorithm. Monte Carlo simulations reveal the impact of channel aging and hardware impairments on system performance. While practical RIS implementations may introduce phase estimation error in the reflected signal, these errors can be mitigated through phase shift optimization. Overall, our results highlight the significant potential of RIS technology in addressing challenges posed by imperfect hardware and users’ mobility.

Scalability is a major concern in implementing deep learning (DL) based methods in wireless communication systems. Given various channel reconstruction tasks, applying one DL model for one specific task is costly in both model training and model storage. In this paper, we propose a novel unsupervised deep plug-and-play prior method for three channel reconstruction tasks in the downlink of massive multiple-input multiple-output (MIMO) systems, including channel estimation, antenna extrapolation and channel state information (CSI) feedback. The proposed method corresponding to these three channel reconstruction tasks employs a common DL model, which greatly reduces the overhead of model training and storage. Unlike general multi-task learning, the DL model of the proposed method does not require further fine-tuning for specific channel reconstruction tasks. Extensive experiments are conducted on the DeepMIMO dataset to demonstrate the convergence, performance, and storage overhead of the proposed method for the three channel reconstruction tasks.

Mobile edge computing (MEC) has been regarded as a promising paradigm to support the compute-intensive and delay-sensitive industrial Internet of things (IIoT) applications. However, the nature of broadcasting in wireless communications may cause that the task offloading security is easy to be threatened from eavesdroppers. Aiming at improving the task offloading security, this article studies the benefit of deploying the emerging reconfigurable intelligent surface (RIS) in MEC-enabled IIoT networks with eavesdroppers, and forms the RIS-aided secure MEC system with time-division multiple access. In addition, we formulate a joint RIS phase shift, power control, local computation rate, and time-slot allocation optimization problem to maximize the weighted sum secrecy computation efficiency (WSSCE) among IIoT devices. To address this intractable problem, we propose a deep reinforcement learning (DRL)-based algorithm, where a deep deterministic policy gradient (DDPG) agent is adopted. Numerical results demonstrate that 1) deploying the RIS can improve the WSSCE performance; 2) the proposed DDPG-based algorithm can obtain higher WSSCE than other baseline methods.

Compared with the traditional transportation data, the transportation big data (TBD) is under the background of “Internet + traffic.” It is a great challenge for analyzing and processing TBD because of its complex and unstructured characteristics, such as sequence, strong relevance, accuracy, and closed loop. This Special Issue provides high-quality and up-to-date technology related to the application of SP and AI into TBD and their applications in the COVID-19 scenario and beyond and serves as a forum for researchers all over the world to discuss their works and recent advancements in the field, especially for defensing COVID-19 in public transportation.

This paper studies the high-speed railway mobile networks (HSRMN), where multiple railway-side sensors (RSs) are deployed along the track to sense environmental data, and multiple train-mounted sensors (TSs) are deployed on the train to collect train data. Both RSs and TSs are scheduled to transmit their sensed data respectively to the ground base station (BS) in a time division multiple access (TDMA) mode. To keep the data received at the BS from the RSs as fresh as possible and also ensure that the TSs complete the given uploading tasks, an optimization problem is established to minimize the average age of information (AoI) of the data gathered from RSs by jointly optimizing sensors’ scheduling and transmission power control constrained by the maximum transmission power budget of RSs and TSs. Since the problem is non-convex and lacks an explicit expression of the objective function and the prior information about future channel state, we present a deep Q-learning network (DQN)-based method to solve it. Particularly, the BS is viewed as the agent, and the action space is constructed by scheduling policy and power control. To further accelerate the convergence speed of the presented DQN-based solution framework, an action space-reduced (ASR) version of the DQN-based method, i.e., the ASR-DQN-based method, is designed by deriving a closed-form solution to the optimal transmission power for a given sensors’ scheduling policy. Numerical simulations show that, compared to the DQN-based method, the ASR-DQN-based method decreases the number of episodes required for convergence by about 23% and reduces the running time by about 41%. Moreover, compared with three baselines, i.e., the random method, the round-robin method, and the deep-Sarsa method, our presented ASR-DQN-based method achieves the lowest average AoI and has the best robustness among these compared methods.

To alleviate the pressure on the ground base station (BS) from intensive video requests, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has become a promising and flexible solution. The UAV carries a MEC server to provide caching and transcoding services for adaptive bitrate video streaming, which can reduce duplicate transmissions of the BS and the content acquisition latency of users, while improving the flexibility of video delivery. However, considering the uncertainty of user requests and content popularity distribution, improving the robustness of video caching is a challenge to promote practical applications. Thus, by integrating caching and transcoding on the UAV, as well as backhaul retrieving, we study the bitrate-aware video caching and processing with uncertain popularity distribution. Then, the problem of joint cache placement and video delivery scheduling under the worst-case distribution is formulated to minimize the total expected system latency with energy consumption constrained. Specifically, we use$\zeta$-structure probability metrics to characterize the uncertainty and construct confidence sets of arrival distribution. Furthermore, a distributionally robust latency optimization algorithm based on convex optimization theory is designed to obtain a robust solution. Finally, we conduct extensive simulations using real-world datasets to evaluate the effectiveness and robustness of the proposed scheme.

Grant-free non-orthogonal multiple access has been regarded as a viable approach to accommodate access for a massive number of machine-type devices with small data packets. The sporadic activation of the devices creates a multiuser setup where it is suitable to use compressed sensing in order to detect the active devices and decode their data. We consider asynchronous access of machine-type devices that send data packets of different frame sizes, leading todata length diversity. We address the composite problem of activity detection, channel estimation, and data recovery by posing it as a structured sparse recovery, having three-level sparsity caused by sporadic activity, symbol delay, and data length diversity. We approach the problem through approximate message passing with a backward propagation algorithm (AMP-BP), tailored to exploit the sparsity, and in particular the data length diversity. Moreover, we unfold the proposed AMP-BP into a network, termed learned AMP-BP (LAMP-BP), which enhances detection performance. The results show that the proposed LAMP-BP outperforms existing methods in activity detection and data recovery accuracy.

To reduce multiuser interference and maximize the spectrum efficiency in orthogonal frequency division duplexing massive multiple-input multiple-output (MIMO) systems, the downlink channel state information (CSI) estimated at the user equipment (UE) is required at the base station (BS). This paper presents a novel method for massive MIMO CSI feedback via a one-sided one-for-all deep learning framework. The CSI is compressed via linear projections at the UE, and is recovered at the BS using deep learning (DL) with plug-and-play priors (PPP). Instead of using handcrafted regularizers for the wireless channel responses, the proposed approach, namely CSI-PPPNet, exploits a DL based denoisor in place of the proximal operator of the prior in an alternating optimization scheme. In this way, a DL model trained once for denoising can be repurposed for CSI recovery tasks with arbitrary compression ratio. The one-sided one-for-all framework reduces model storage space, relieves the burden of joint model training and model delivery, and could be applied at UEs with limited device memories and computation power. Extensive experiments over the open indoor and urban macro scenarios show the effectiveness and advantages of the proposed method.

Backscatter communications (BCs), allowing passive devices to transmit information by reflecting incident RF signals, have emerged as an attractive solution for the green Internet of Things (IoT). In the practical implementation of BC systems, we observe two common and interesting phenomena: wavy backscatter signals and striped-shape constellation clusters. These phenomena differ significantly from the traditional point-to-point communication and the theoretical BC systems, substantially degrading the system performance. Unfortunately, their causes and potential solutions remain unexplored. Motivated by this, this paper investigates the origins and designs of the corresponding solving methods. Specifically, we first reveal the causes of these phenomena: the time-varying interference stemming from the phase-locked loop (PLL) non-ideality. Then, we introduce our solutions: the dynamic self-interference cancellation (DSIC) and the data-aided decision boundary (DDB) algorithms. Finally, we implement and evaluate our solutions on a practical BC platform. Experimental results show that our solutions can reduce the bit error rate (BER) by up to two orders of magnitude, extend the communication range by over three times, and maintain linear runtime complexity, demonstrating their effectiveness and applicability in practical BC systems.

The advantages of graph neural networks (GNNs) in leveraging the graph topology of wireless networks have drawn increasing attentions. This paper studies the GNN-based learning approach for the sum-rate maximization in multiple-user multiple-input single-output (MU-MISO) networks subject to the users’ individual data rate requirements and the power budget of the base station (BS). By modeling the MU-MISO network as a graph, a GNN-based architecture named complex residual graph attention network (CRGAT) is proposed to directly map channel state information to beamforming vectors. The attention-enabled aggregation and the residual-assisted combination are adopted to enhance the learning capability and mitigate the oversmoothing issue. Furthermore, a novel activation function is proposed for the constraint due to the limited power budget at the BS. The CRGAT is trained via unsupervised learning with two proposed loss functions. An evaluation method is proposed for the learning-based approaches, based on which the effectiveness of the proposed CRGAT is validated in comparison with several convex optimization and learning based approaches. Numerical results are provided to reveal the advantages of the CRGAT including the millisecond-level response with limited optimality performance loss, the scalability to different number of users and power budgets, and the adaptability to different system settings.

Cell-free (CF) extremely large-scale multiple-input multiple-output (XL-MIMO) is regarded as a promising technology for enabling future wireless communication systems. Significant attention has been generated by its considerable advantages in augmenting degrees of freedom. In this paper, we first investigate a CF XL-MIMO system with base stations equipped with XL-MIMO panels under a dynamic environment. Then, we propose an innovative multi-agent reinforcement learning (MARL)-based power control algorithm that incorporates predictive management and distributed optimization architecture, which provides a dynamic strategy for addressing high-dimension signal processing problems. Specifically, we compare various MARL-based algorithms, which shows that the proposed MARL-based algorithm effectively strikes a balance between spectral efficiency (SE) performance and convergence time. Moreover, we consider a double-layer power control architecture based on the large-scale fading coefficients between antennas to suppress interference within dynamic systems. Compared to the single-layer architecture, the results obtained unveil that the proposed double-layer architecture has a nearly 24% SE performance improvement, especially with massive antennas and smaller antenna spacing.

Cell-free massive multiple-input multiple-output (mMIMO) is a promising technology to empower next-generation mobile communication networks. In this paper, to address the computational complexity associated with conventional fingerprint positioning, we consider a novel cooperative positioning architecture that involves certain relevant access points (APs) to establish positioning similarity coefficients. Then, we propose an innovative joint positioning and correction framework employing multi-agent reinforcement learning (MARL) to tackle the challenges of high-dimensional sophisticated signal processing, which mainly leverages on the received signal strength information for preliminary positioning, supplemented by the angle of arrival information to refine the initial position estimation. Moreover, to mitigate the bias effects originating from remote APs, we design a cooperative weighted K-nearest neighbor (Co-WKNN)-based estimation scheme to select APs with a high correlation to participate in user positioning. In the numerical results, we present comparisons of various user positioning schemes, which reveal that the proposed MARL-based positioning scheme with Co-WKNN can effectively improve positioning performance. It is important to note that the cooperative positioning architecture is a critical element in striking a balance between positioning performance and computational complexity.

Reconfigurable intelligent surface (RIS) has been proposed as a wireless coverage enhancement enabler. However, due to the passive feature of the RIS, it is challenging to acquire the instantaneous channel state information for RIS-user links. This paper investigates the outage-constrained transmission design for RIS-assisted multi-user multiple-input-single-output (MISO) systems under interference channel based on channel distribution information. The transmission design problem is formulated to maximize the sum transmission rate under constraints of the tolerable outage probability of each user, the power budget of each transmitter and the phase shift coefficient of each reflecting element. To solve the computational intractable problem, a block successive upper bound minimization (BSUM)-based algorithm is proposed where the feasible set is separated w.r.t. variables into several blocks, and for each block, a computationally efficient surrogate subproblem is formulated and solved. Furthermore, the non-decreasing behavior and optimality performance of the proposed algorithms are theoretically analyzed. Numerical results show that the proposed algorithm is more computational efficient than traditional alternative optimization based algorithm, and the proposed the outage-constrained transmission design is able to suppress the average outage rate to a required level as well as maximizing the sum transmission rate.

With the recent advancements in edge artificial intelligence (AI), future sixth-generation (6G) networks need to support new AI tasks such as classification and clustering apart from data recovery. Motivated by the success of deep learning, the semantic-aware and task-oriented communications with deep joint source and channel coding (JSCC) have emerged as new paradigm shifts in 6G from the conventional data-oriented communications with separate source and channel coding (SSCC). However, most existing works focused on the deep JSCC designs for one task of data recovery or AI task execution independently, which cannot be transferred to other unintended tasks. Differently, this paper investigates the JSCC semantic communications to support multi-task services, by performing the image data recovery and classification task execution simultaneously. First, we propose a new end-to-end deep JSCC framework by unifying the coding rate reduction maximization and the mean square error (MSE) minimization in the loss function. Here, the coding rate reduction maximization facilitates the learning of discriminative features for enabling to perform classification tasks directly in the feature space, and the MSE minimization helps the learning of informative features for high-quality image data recovery. Next, to further improve the robustness against variational wireless channels, we propose a new gated deep JSCC design, in which a gated net is incorporated for adaptively pruning the output features to adjust their dimensions based on channel conditions. Finally, we present extensive numerical experiments to validate the performance of our proposed deep JSCC designs as compared to various benchmark schemes. It is shown that our proposed designs simultaneously provide efficient multi-task services, and the proposed gated deep JSCC framework efficiently reduces the communication overhead with only marginal performance loss. It is also shown that performing the classification task on the feature space via coding rate reduction maximization is able to better defend the label corruption than the traditional label-fitting methods.

Orthogonal time frequency space (OTFS) modulation is a promising technology for communications under high mobility in the sixth-generation (6G) communications system. To enable machine-type communications (MTC) with high mobility in 6G, researchers have considered designing multiple access (MA) technologies based on OTFS modulation. The reliability and connectivity of OTFS-MA are highly correlated with the characteristics of the channel spreading function. In the spectrum-limited MA system where the channel spreading function is not sparse and compact enough, the system device capacity of OTFS-MA schemes is limited. To this end, a grant-free MA scheme, named orthogonal time frequency code space modulation enabled multiple access (OTFCSMA) is proposed in this article. In general, orthogonal code domain resources are introduced into OTFCSMA to enhance device connectivity and transmission reliability of MA systems based on OTFS modulation. In detail, firstly, the characteristic of the realistic channel spreading function is described, especially the reduced sparsity and compactness of the channel in the spectrum-limited system. Secondly, the principles for designing OTFCSMA are described, including orthogonal spreading/despreading, data interleaving/deinterleaving, device identification, two-stage channel estimation, and data recovery strategies. Thirdly, the system device capacity and the system complexity of OTFCSMA are analyzed. Fourthly, a date-block-wise device connectivity scaling-up scheme for OTFCSMA is proposed, based on which the exponential system user capacity growth is realized. Finally, the performances of OTFCSMA on transmission reliability and device connectivity are demonstrated, and the gain brought by orthogonal spreading is analyzed.

This paper investigates the system model and the transmit beamforming design for the Cell-Free massive multi-input multi-output (MIMO) integrated sensing and communication (ISAC) system. The impact of the uncertainty of the target locations on the propagation of wireless signals is considered during both uplink and downlink phases, and especially, the main statistics of the MIMO channel estimation error are theoretically derived in the closed-form fashion. A fundamental performance metric, termed communication-sensing (C-S) region, is defined for the considered system via three cases, i.e., the sensing-only case, the communication-only case and the ISAC case. The transmit beamforming design problems for the three cases are respectively carried out through different reformulations, e.g., the Lagrangian dual transform and the quadratic fractional transform, and some combinations of the block coordinate descent method and the successive convex approximation method. Numerical results present a 3-dimensional C-S region with a dynamic number of access points to illustrate the trade-off between communication and radar sensing. The advantage for radar sensing of the Cell-Free massive MIMO system is also studied via a comparison with the traditional cellular system. Finally, the efficacy of the proposed beamforming schemes is validated in comparison with zero-forcing and maximum ratio transmission schemes.

As a promising approach to increase spectrum efficiency and user fairness, non-orthogonal multiple access (NOMA) technique, with strong potential for applications in integrated satellite-terrestrial relay networks (ISTRNs), has been considered as a vital part of the future wireless network architecture. However, studies on the symbol error performance of NOMA-based ISTRNs with multiple relays and multiple users are still in their infancy. In this study, we propose a dual-user NOMA-based ISTRN architecture with hardware impairments to all nodes. This study uses the opportunistic scheduling scheme to select the optimal relay for the relaying system with a decode-and-forward protocol and maximum ratio combination technique to improve the signal quality. We also use a shadowed Rician distribution to model fading in the satellite channels, while the terrestrial channels are assumed to follow a Nakagami-mfading distribution. In addition, the impacts of the path loss and beam pattern on the system are considered. Closed-form expressions are derived for the average symbol error rate (SER) for near and far users. We verify that the numerical results agree with the theoretical calculations and demonstrate the superiority of the proposed architecture with decode-and-forward protocol compared with the case where line-of-sight links are used to transmit signals alone and with the case using amplify-and-forward protocol. Finally, we analyze the effect of some critical parameters on the average SER of the considered system and present some helpful insights in relation to engineering design.

Millimeter wave (mmWave) communications are sensitive to blockage over radio propagation paths. The emerging paradigm of reconfigurable intelligent surface (RIS) has the potential to overcome this issue by its ability to arbitrarily reflect the incident signals toward desired directions. This paper proposes a Neyman-Pearson (NP) criterion-based blockage-aware algorithm to improve communication resilience against blockage in mobile mmWave multiple input multiple output (MIMO) systems. By virtue of this pragmatic blockage-aware technique, we further propose an outage-constrained beamforming design for downlink mmWave MIMO transmission to achieve outage probability minimization and achievable rate maximization. To minimize the outage probability, a robust RIS beamformer with variant beamwidth is designed to combat uncertain channel state information (CSI). For the rate maximization problem, an accelerated projected gradient descent (PGD) algorithm is developed to solve the computational challenge of high-dimensional RIS phase-shift matrix (PSM) optimization. Particularly, we leverage a subspace constraint to reduce the scope of the projection operation and formulate a new Nesterov momentum acceleration scheme to speed up the convergence process of PGD. Extensive experiments confirm the effectiveness of the proposed blockage-aware approach, and the proposed accelerated PGD algorithm outperforms a number of representative baseline algorithms in terms of the achievable rate.

In this paper, a 3D geometry-based reconfigurable intelligent surface (RIS)-aided channel model is proposed with directional antenna at base station (BS) side. Different from traditional RIS-assisted channel model, visible area (i.e., range of observed scatters) and radiated power of directional antenna with uniform, cos and Gaussian patterns are considered. The time-varying large- and small-scale parameters of complete communication links including BS-receiver, BS-RIS, and RIS-receiver links are modeled considering intensively line-of-sight (LoS) and non-LoS (NLoS) paths. Three transmission cases at BS antenna side, that is, LoS Case, Mixed Case, and RIS Case are defined by directivity and number of beams. The principle of case selection is to maximize absolute value of CIR as optimization objective. Based on the proposed channel model and directional transmission cases, some second-order statistical characteristics, such as space-time correlation function and Doppler power spectrum density are derived, and conversion between different cases causes the simulation results to vary discontinuously in time or space domains. Furthermore, impacts of different cases and switching of direction antenna, radiated patterns of directional beam, RIS optimization phase, and environment conditions on channel characteristics are investigated. The results show that the proposed case selection method can well enhance correlation and lead to correlation taking on a sawtooth shape. In addition, it is also able to reduce the Doppler power spectrum curve. Furthermore, the proposed model is validated by comparing root mean square delay spread with measurement. These observations can be used to present a reference for designing and evaluating directional RIS-assisted communication systems.

To support abundant mission-critical applications, the next-generation ultra-reliable low latency communication (URLLC) is expected to meet more stringent requirements. In this work, to promote the advancement of URLLC, we target at exploring the fundamental trade-offs in finite blocklength (FBL) regime. Taking the short blocklength impacts into account, we integrate broadcasting into time-division multiple access (TDMA) strategy and adopt a hybrid broadcasting-TDMA (BC-TDMA) strategy for the multiple access URLLC services. Within hybrid BC-TDMA, user grouping has been implemented, such that grouped users can be served over the shared large blocklength and thus get rid of the performance hindrance from short blocklengths. We formulate a problem for fairly minimizing the error probability for all users via optimizing the user grouping decision together with the joint power and blocklength allocation. For given grouping, we characterize four necessary optimality conditions for the joint resource allocation and accordingly construct the optimal closed-form resource allocation solution. The analytical characterizations have also enabled two criteria for efficiently filtering out the optimal grouping in an iterative manner. Finally, via simulations, we examine our proposed algorithms for both obtaining optimal resource allocation and filtering the optimal grouping. The extremely low complexity and significant reliability advantages of our proposed hybrid BC-TDMA solution are also highlighted in comparison to benchmarks.

The emerging 6G network envisions integrated sensing and communication (ISAC) as a promising solution to meet growing demand for native perception ability. To optimize and evaluate ISAC systems and techniques, it is crucial to have an accurate and realistic wireless channel model. However, some important features of ISAC channels have not been well characterized, for example, most existing ISAC channel models consider communication channels and sensing channels independently, whereas ignoring correlation under the consistent environment. Moreover, sensing channels have not been well modeled in the existing standard-level channel models. Therefore, in order to better model ISAC channel, a cluster-based statistical channel model is proposed in this paper, which is based on measurements conducted at 28 GHz. In the proposed model, a new framework based on 3GPP standard is proposed, which includes communication clusters and sensing clusters. Clustering and tracking algorithms are used to extract and analyze ISAC channel characteristics. Furthermore, some special sensing cluster structures such as shared sensing cluster, newborn sensing cluster, etc., are defined to model correlation and difference between communication and sensing channels. Finally, accuracy of the proposed model is validated based on measurements and simulations.

In this paper, we study a robust beamforming design in a downlink reconfigurable intelligent surface (RIS)-aided multiple-input single-output (MISO) communication system with consideration of bounded channel uncertainty. The goal of the design is to minimize the transmit power by employing the symbol-level precoding (SLP) at the base station (BS), while satisfying the quality-of-service requirements of the primary users (PU) and secondary user (SU). Unlike most existing works where RIS is only a signal reflector, in this paper, RIS also operates as a transmitter and delivers the secondary information to SU by switching the reflecting beamformers. In single-PU scenario, the secondary information recovery (SIR) scheme is proposed and a power minimization problem is formulated. To tackle the non-convex problem, we decompose the problem into two sub-problems to alternately optimize the transmit and reflecting beamformers. Then, two algorithms namely the penalty-based algorithm and the monotone accelerated projected gradient-based algorithm are proposed to address the unit-modulus constraint on the reflecting beamformer. The problem is further extended to the multi-PU scenario and the extended SIR scheme is provided. Finally, the simulation results demonstrate the effectiveness of our proposed algorithms and exhibit the advantages of the SIR scheme.

Vehicle-to-infrastructure (V2I) communications have attracted much attention in recent years due to its application in intelligent transportation systems. To apply the dynamic beamforming technology in V2I communications, an accurate V2I narrow-beam channel model is required. This paper investigates cluster-based dynamic narrow-beam channel modeling for V2I scenarios. Firstly, a phased-array antenna based channel measurement system is designed and used to perform a series of narrow-beam channel measurements at 3.35 GHz in V2I highway scenarios. Data processing methods such as beam synthesis, angle estimation based on single-scattering assumption, and variational Bayesian Gaussian mixture model clustering are applied to extract parameters of multipath components and clusters. Then, we propose a cluster-based dynamic narrow-beam channel model, which considers the directional attenuation of narrow-beam and non-stationarity of clusters. In this model, the parameters of spatial-temporal characteristics are divided into global-cluster parameters and scatterer-cluster parameters, and the statistical distribution of these parameters are studied and modeled. Finally, a simulation method for the proposed channel model is provided, and model validation results show a good match between measurements and simulations in terms of channel characteristics such as delay spread and angle spread. This model provides a better characterization of the V2I narrow-beam channel and will be useful for designing and evaluating V2I communication systems.

With the development of artificial intelligence (AI), wireless channel prediction based on deep learning (DL) has become a hot research issue. Channel prediction plays an important role in channel state information (CSI) feedback enhancement in AI-native air interface. To better predict the CSI, this paper investigates the Transformer network based channel prediction. Firstly, real channel data are obtained in Beijing-Tianjin railway line, and the channel prediction datasets are constructed through preprocessing. After formulating the channel prediction problem, a channel prediction model based on the Transformer network is newly proposed. The unique multi-head attention mechanism and position encoding of the Transformer network enable the proposed model to have more powerful parallel computation capability and better global information capture capability. Then, the hyper-parameters of the model are determined by autocorrelation analysis and cross-validation. Finally, the performance of the proposed model is evaluated in terms of prediction accuracy and space and time computational complexity using several evaluation metrics, and is compared with classical DL models. It is shown that the proposed model possesses higher prediction performance in the appropriate range of computational complexity.

At present, in order to make use of sufficient spectrum resources to provide even better quality of service and with the development of millimeter wave (mm-wave) communications technology, high speed railway (HSR) communication systems have also taken mm-wave frequency band into consideration. However, since the train runs with high speed as well as the operating environment is complex and dynamic, which may cause the communication link blockage issue for a while. To solve the problem, we adopt the emerging innovative intelligent reflecting surface (IRS) technology to enhance the robustness of mm-wave HSR communication system by introducing a reflection link. Therefore, when the direct communication link is blocked in some case, the reflection link can ensure that the communication is not interrupted. In this paper, we focus on maximizing the number of flows meeting their QoS requirements. A robust IRS-assisted scheduling scheme is proposed under the constraints of half duplex transmission, transmit power, IRS phase shift and limited time slots. As the formulated problem is non-convex, it is difficult to solve directly. Therefore, we divide it into four subproblems, and utilize alternative optimization method to get a sub-optimal solution. The simulation results show that compared with the other three baseline schemes, the IRS-assisted transmission scheduling algorithm proposed in this paper can improve the system performance effectively.

Massive machine-type communication (mMTC) is an important scenario for 5G and future 6G networks, as it can provide massive connectivity for internet of things (IoT) devices. However, the large number of supported devices raises challenges to random access with limited spectrum resources. In this paper, we propose a dictionary learning based method for active user detection (AUD) in massive MIMO systems, which leverages the potential spatial channel characteristics of users. Our approach separates users into clusters and reuses the same pilot pool among different clusters, which greatly saves the pilot resource. To resolve collisions caused by the reuse of pilots, we propose a cluster-specific dictionary to differentiate multiple active users of different clusters. Numerical experiments demonstrate the improved performance of the proposed AUD algorithm in comparison to the existing methods.

This paper investigates the beamforming design in the Cell-Free massive multi-input multi-output (MIMO) integrated sensing and communication (ISAC) system, termed as the CF-ISAC system, in presence of the channel state information (CSI) estimation error. The beamforming design is formulated into a sensing beampattern matching mean square error minimization problem under the constraints of the power budgets of the access points (APs) and the ergodic rate requirements of the users. A computationally tractable lower bound of the ergodic rate over the imperfect CSI is derived based on the Jensen's Inequality, and then a successive convex approximation based algorithm is proposed to solve the considered problem. Numerical results illustrate the beampatterns for different direction of arrival estimations of the targets. The advantage of the CF-ISAC system for radar sensing is revealed based on the relative location between the AP and the target.

Wireless train communication network (WLTCN) is a promising technology for intelligent rail vehicles. It is responsible for bearer of train control services (TCS) and passenger information services (PIS), with the latter mainly referring to multimedia services. The two services have notably different quality of service (QoS) requirements from traditional telecommunication services. To realize multiple services co-existence bearer with different quality of service (QoS) requirements in a single network, we propose a radio access network (RAN) slicing framework to fully utilize the bandwidth resource within a WLTCN in this paper. Based on the characteristics of TCS and PIS, the communications models for the two services are proposed. Next, the slicing strategy problem is formulated as the system bandwidth minimization problem, and then it is transformed into an equivalent problem as the non-convexity. We proposed a dual-decomposition based bandwidth allocation (DBA) algorithm to derive the closed-form expressions for the optimal resource allocation. Simulation results show that the proposed slicing strategy enables WLTCN to meet the QoS requirements for TCS and PIS with minimal bandwidth consumption.

This paper investigates the energy-efficient beamforming design in a simultaneous transmission and reflection-reconfigurable intelligent surface (STAR-RIS) assisted wireless communication system, where the antenna selection scheme is adopted. An energy efficiency (EE) maximization problem is formulated by optimizing the transmit beamformers and the phase shift vectors subject to the power budget constraint of the base station (BS), the maximum transmit power constraint per antenna and the users' data rate requirements. An alternating optimization-based algorithm is proposed to tackle the coupled variables, and the quadratic transform is used to deal with the fractional formulations. Simulation results demonstrate that the antenna selection scheme can significantly improve the EE performance by suppressing the energy consumption due to massive antennas. With the assistance of the STAR-RIS, the EE performance is further enhanced.

Device activity detection in the emerging cell-free massive multiple-input multiple-output systems has been recognized as a crucial task in machine-type communications, in which multiple access points jointly identify the active devices from a large number of potential devices based on the received signals. Most of the existing works addressing this problem rely on the impractical assumption that different active devices transmit signals synchronously. However, in practice, synchronization cannot be guaranteed due to the low-cost oscillators, which brings additional discontinuous and nonconvex constraints to the detection problem. To address this challenge, this paper reveals an equivalent reformulation to the asynchronous activity detection problem, which facilitates the development of a distributed algorithm that satisfies the highly nonconvex constraints in a gentle fashion as the iteration number increases. To reduce the capacity requirements of the fronthauls, we further design a communication-efficient accelerated distributed algorithm. Simulation results demonstrate that the proposed two distributed algorithms outperform state-of-the-art approaches. Moreover, the accelerated distributed algorithm requires a very small number of quantization bits to approach the ideal detection performance.

The millimeter-wave (mmWave) train-to-ground (T2G) communications is an essential enabling technology for future intelligent railways, where the acquisition of beam alignment information is one of the most challenging and significant issues. Hence, in this paper, we investigate the optimal beamformers and performance bounds for the mmWave T2G beam alignment with the aid of train location information. We first propose a mmWave T2G system model, which can be used by arbitrary array geometry and identifies a clear relationship between the T2G scenario and the wireless channel. Then, based on the Cramer Rao bound (CRB), we provide two bounds characterizing the average and worst minimum mean square error (MMSE) of beam alignment with the bounded error model of train location. Next, two non-convex optimization problems are formulated aiming to find the transmitting beamformers that can minimize the bounds, which is solved optimally by relaxation and recovery techniques. Finally, numerical simulations are conducted to validate the proposed beamformers and bounds for the mmWave T2G beam alignment. In particular, the results show that the MSE performance of optimal beamformers converges to the bounds by applying the maximum likelihood estimator (MLE) in the high SNR regime.

This paper investigates the transmission design of an active reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system. A sensing beampattern matching mean squared error (MSE) minimization problem is formulated under constraints of the power budgets at the base station and the RIS, the amplification factor of the RIS and the information rate requirements of users, by jointly optimizing the transmit beamforming vectors, the covariance matrix of the sensing signal and the reflection coefficients of the RIS. The considered problem is solved in an alternative optimization manner by decomposing the original problem into two sub-problems, where each sub-problem is solved via semi-definite relaxation (SDR) and successive convex approximation (SCA). The tightness of applying SDR is theoretically proved. Simulation results verify the convergence behavior and effectiveness of the proposed algorithm. It is also shown that the active RIS is able to improve the sensing beampattern matching performance by enhancing the information transmission.

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is a promising technology for the future sixth-generation (6G) networks to achieve higher performance. In practice, various linear precoding schemes, such as zero-forcing (ZF) and regularized zero-forcing (RZF) precoding, are capable of achieving both large spectral efficiency (SE) and low bit error rate (BER) in traditional massive MIMO (mMIMO) systems. However, these methods are not efficient in extremely large-scale regimes due to the inherent spatial non-stationarity and high computational complexity. To address this problem, we investigate a low-complexity precoding algorithm, e.g., randomized Kaczmarz (rKA), taking into account the spatial non-stationary properties in XL-MIMO systems. Furthermore, we propose a novel mode of randomization, i.e., sampling without replacement rKA (SwoR-rKA), which enjoys a faster convergence speed than the rKA algorithm. Besides, the closed-form expression of SE considering the interference between subarrays in downlink XL-MIMO systems is derived. Numerical results show that the complexity given by both rKA and SwoR-rKA algorithms has 51.3% reduction than the traditional RZF algorithm with similar SE performance. More importantly, our algorithms can effectively reduce the BER when the transmitter has imperfect channel estimation.

Due to the inherent high-mobility features in the Vehicles-to-Everything (V2X) scenarios, accurate channel estimation is essential to ensure the quality of communication services. Recently, multiple-input multiple-output (MIMO) radar has shown the potential to aid channel estimation. In this paper, we consider the MIMO radar aided V2X communication systems and propose a prior information aided deep learning method for channel estimation. Specifically, we use the MIMO radar to measure the angle information of moving vehicles. Based on the estimated angles, we obtain the non-zero position information of sparse angle-frequency channel. Then, by formulating the channel estimation as solving a group row sparse recovery problem, we propose a new shrinkage function and derive a priori assisted deep unfolding method. Experimental results show that the proposed method achieves the highest channel estimation accuracy compared with existing compressive sensing algorithms and deep-learning-based baseline methods.

With the emergence of various IoT applications, a large-scale IoT system is set to revolutionize the various industry. Massive machine type communication will play a crucial role in providing robust support to this system. However, in mMTC, implementing user access control to block malicious users remains a critical issue that needs to be addressed. To this end, this paper proposes a user access control scheme based on resource hopping multiple access (RHMA). This scheme utilizes the unique resource hopping patterns of different users to control user access. The controllability of these resource hopping patterns effectively prevents malicious users from intruding into the system. Moreover, the proposed user access control scheme is implemented in practice with USRP platform. The experimental results confirm the reliability and effectiveness of the proposed access control system.

The explosive growth of data results in more scarce spectrum resources. It is important to optimize the system performance under limited resources. In this paper, we investigate the weighted throughput (WPT) maximization for cell-free (CF) multiuser (MU) MIMO multicarrier (MC) systems through resource allocation (RA) in finite blocklength regime (FBL) while ensuring the quality of service (QoS) of each user under the constraints of total power consumption. Since the channels vary in different subcarriers and inter-user interference strengths, the WPT can be maximized by scheduling the best users in each time-frequency (TF) resource and advanced beamforming design (BF). With this motivation, we propose a joint user scheduling (US) and BF algorithm to address an mixed integer nonlinear programming (MINLP) problem. Numerical results demonstrate that the proposed RA scheme outperforms the comparison schemes. And the CF system in our scenario is capable of achieving higher spectral efficiency (SE) than the centralized antenna systems (CAS).

Powered by deep learning, semantic communication is an intelligent communication paradigm, aiming to transmit useful information in the semantic domain. In most existing work, robust semantic features can be learned against wireless channel degradation, and directly transmitted in an analog fashion. However, analog semantic communication raises various challenges to the existing system from hardware/protocol to encryption issues. In this paper, we propose a novel non-linear quantization module to efficiently quantify semantic features. A sparse scaling vector is further incorporated to reduce the dimension of transmitted semantic features. Experimental results demonstrate that the proposed nonlinear quantization achieves better performance than the linear quantization method, and the performance of the digital system achieves better performance.

To meet increasing applications of Internet of Things (IoT), the 3rd generation partnership project has specified Narrow Band Internet of Things (NB-IoT) standard. However, collisions in NB-IoT physical random access channel (NPRACH) can be severe due to mismatch between frequent random access requests of a huge number of devices and limited available preambles. In this paper, we propose to use non-orthogonal frequency hopping between preambles and introduce controllable partial collisions to effectively avoid full preamble collisions. This will increase available preambles and thus reduce preamble collision significantly. The proposed hopping pattern maintains compatibility with the current standard NB-IoT system, by keeping NPRACH structure in standard with minimal change. Simulation results show that the proposed frequency hopping pattern can greatly increase accessing devices due to collision avoidance while slightly reducing detection probability.

Intelligent reflecting surface (IRSs) have recently played a crucial role for numerous application associated with unmanned aerial vehicle (UAV) communications. To provide good quality of service (QoS) for the users, it is necessary to study the IRS-aided UAV communication system with performance analysis. Thus, in this paper, a three-dimensional (3D) geometry-based stochastic multiple-input multiple-output (MIMO) channel model is developed to characterize the IRS-aided air-to-ground (A2G) propagation environments, which takes into account the multipath fading and height-dependent path loss effects. Based on the proposed channel model, the impact of some important system parameters on the outage probability (OP) is numerically investigated. One key insight from our analysis is the AIRS operating on mmWave frequencies is more suitable for high mobility scenarios. The obtained results can provide guidance for achieving better system performance optimization.

With the rapid development of wireless communications, the industries and academia acknowledge that the millimeter-wave (mm-wave) frequency band is rich in spectral resources. This paper considers the mm-wave train-ground communication system with multiple mobile relays (MRs) in a high-speed rail (HSR) scenario. We use the deep reinforcement learning (DRL) method to solve the beam management problem to maximize the system throughput. First, the inter-beam interference in the MRs scene is modeled due to the effect of the inter-beam angle on the system performance. Second, the maximization problem of system throughput is constructed for the beam management at both the transmitter and receiver. And we proposed the dynamic joint beam management scheme for the base station and the MRs located on the train’s roof based on DRL. Finally, the performance evaluation shows that the proposed scheme displays low computational complexity in the online phase. Additionally, the system throughput performance is close to the ideal optimal beam tracking algorithm, which combines high performance and low complexity, proving the feasibility of using DRL theory for beam management in train-ground communication.

Wireless channel modeling is widely considered as foundation of wireless communication system design. Sufficient and diverse channel data provides strong support for wireless channel characterization and modeling. However, channel data from real measurement is usually limited considering complexity of channel measurements for different scenarios and frequency bands. In this work, a deep learning-based cross-frequency channel generation and modeling framework is proposed. Without requiring a traditional parametric channel model, the proposed framework can generate realistic cross-frequency channels by employing generative adversarial networks. Based on vehicular channel measurement data, cross-frequency reconstruction performance of the proposed framework is validated by comparing characteristics of measured and reconstructed channels. It is also found that channel non-stationary characteristics can be well embodied in the reconstructed channels.

Reconfigurable intelligent surface (RIS) has received increasing attention due to its capability of extending cell coverage by reflecting signals toward receivers. This paper considers a RIS-assisted high-speed train (HST) communication system to improve coverage probability. We derive the closed-form expression of coverage probability. Moreover, we analyze impacts of some key system parameters, including transmission power, signal-to-noise ratio threshold, and horizontal distance between base station and RIS. Simulation results verify the efficiency of RIS-assisted HST communications in terms of coverage probability.

The Internet of Things (IoT) will be widely used in all areas of life and transportation as the 5th Generation (5G) communication technology matures and becomes commercially available. Especially in the field of railway transportation, the IoT technology can alleviate the challenge caused by insufficient wireless spectrum resources and improve the railway communication performance. However, the existing IoT is made up of a large heterogeneous network. In such a super-dense heterogeneous network scenario, how to allocate the most appropriate access point (AP) according to the needs of users has become a problem demanding prompt solution, which also brings additional challenges for the intelligent transportation system (ITS) to develop green and efficient network communication technology. Therefore, focusing on the selection and access of heterogeneous networks in the Railway IoT, this article studies the spatial characteristics of the intelligent spectrum situation of the Internet of Mobile Things in the railway scenario, and establishes the opportunistic access situation of Railway IoT based on the graph convolutional neural (GCN) network. Furthermore, we utilize the GCN network to mine the spatial correlation between different APs, and propose a railway communication AP decision algorithm based on the GCN network combined with the traditional heterogeneous network multiattribute decision algorithm. Our experimental results prove that the proposed algorithm can effectively reduce transmission delay and improve the throughput of the communication system.

With the massive deployment of 5G commercial applications, the Internet of Everything promotes the transformation and upgrading of the society production mode. The Internet of Things (IoT) is supported by the massive Machine-Type Communications (mMTCs), which is one of the three major application scenarios of 5G. Recently, a novel spreading-based nonorthogonal multiple access (NOMA) scheme named tandem spreading multiple access (TSMA) has been proposed for grant-free random access in mMTC. However, TSMA only considers the case of single antenna, the connectivity expansion on spatial domain has not been considered. In this article, a multiantenna system scheme of TSMA (MIMO-TSMA) is proposed to scale up user connections. In this scheme, spectrum efficiency can be promoted by sharing the time–frequency resources in different beams. In the meantime, scheme against channel deep fading is considered in this work. The simulation results show that MIMO-TSMA can effectively take advantage of multiple-input–multiple-output and TSMA to enhance the mMTC system performance.

High-speed train (HST) communications are envisioned to provide diversified broadband services by integrating with 5G while the high mobility induces fast-fading channels and potentially degrades the system performance. To address this issue, we investigate an HST communication network empowered by intelligent reflecting surfaces (IRSs) with the multiple-input–multiple-output (MIMO) technology. Statistical channel state information (CSI) is exploited to mitigate the impact of the fast time-varying fading. The transceiver beamforming vectors and the IRS phase shift matrix are optimized to improve the system performance in terms of the outage probability and the ergodic capacity considering the channel uncertainty. First, we derive the analytical expression of the outage probability with a generalized Marcum$Q $-function. Then, we develop an alternating optimization algorithm to minimize the outage probability by capitalizing on the generalized eigenvalue–eigenvectors. Moreover, the ergodic capacity is deduced with statistical CSI and then optimized by analyzing the upper bound with Jensen’s approximation. Extensive simulations show that simulation results are consistent with the theoretical analysis, and the IRS-assisted system significantly outperforms the system without IRS in terms of the outage probability and the ergodic capacity.

Aerial intelligent reconfigurable surface (IRS) is a promising technology to enhance channel quality in data delivery. In this article, we study an aerial IRS deployment problem to enable timely and reliable data delivery in a remote Internet of Things (IoT) scenario, in which an IRS mounted on an unmanned aerial vehicle (UAV) is adopted as a mobile relay to assist devices in uploading data to the base station (BS). The objective is to minimize the average Age of Information (AoI) of the data received by the BS over time by jointly determining the aerial IRS deployment position and phase shift, transmit power of devices, and data uploading time. Under the requirements of peak AoI (PAoI) and communication reliability, we formulate an average AoI minimization problem. Since the nonlinear relations among optimization variables make the formulated problem nonconvex and intractable to solve, we propose a block coordinate descent (BCD)-based iterative algorithm which decomposes the formulated problem into several subproblems. The variables are optimized in each subproblem individually in an alternately iterative manner to attain a near-optimal solution. Simulation results demonstrate the superiority of the proposed algorithm in improving the information freshness compared with the benchmark schemes.

The sixth generation (6G) wireless communication aims to enable ubiquitous intelligent connectivity in future space–air–ground–ocean-integrated networks, with extremely low latency and enhanced global coverage. However, the explosive growth in Internet of Things devices poses new challenges for smart devices to process the generated tremendous data with limited resources. In 6G networks, conventional mobile edge computing (MEC) systems encounter serious problems to satisfy the requirements of ubiquitous computing and intelligence, with extremely high mobility, resource limitation, and time variability. In this article, we propose the model of wireless computing power networks (WCPNs), by jointly unifying the computing resources from both end devices and MEC servers. Furthermore, we formulate the new problem of task transfer, to optimize the allocation of computation and communication resources in WCPN. The main objective of task transfer is to minimize the execution latency and energy consumption with respect to resource limitations and task requirements. To solve the formulated problem, we propose a multiagent deep reinforcement learning (DRL) algorithm to find the optimal task transfer and resource allocation strategies. The DRL agents collaborate with others to train a global strategy model through the proposed asynchronous federated aggregation scheme. Numerical results show that the proposed scheme can improve computation efficiency, speed up convergence rate, and enhance utility performance.

Recently, grant-free orthogonal time–frequency space-based tandem spreading multiple access (OTFS-TSMA) is proposed for machine-type communications (mMTCs) in smart railways environmental sensing. To achieve massive connections with scarce radio resources, OTFS-TSMA combines the advantages of OTFS and TSMA. It shows high connectivity and reliability under time–frequency-selective fading channels. Meanwhile, smart railways require over-horizon and all-weather environmental sensing based on mMTC, and the implementation of both would cost a lot in terrestrial networks. With the development of low-Earth orbit (LEO) satellites, enabling smart railways mMTC over LEO satellite is a potential diagram. However, in this scenario, due to the larger transmission delay and Doppler frequency shift, the time–frequency resource requirements of the OTFS modulation-based system increase significantly and are difficult to meet. To this end, OTFS-TSMA based on differential Doppler shift is proposed in this article. Specifically, in this article, the satellite-to-ground communication system model consisting of three sections is introduced, and the Doppler shift and differential Doppler shift characteristics of access points (APs) are investigated. Next, it is proven that designing OTFS-based multiple access schemes over the LEO satellite based on differential Doppler shift is not only resource-friendly but also has the advantages of service continuity and controllable multiuser interference. Then, the transceiver of differential-Doppler-shift-based OTFS-TSMA and its improved designs are proposed. Finally, the simulation results demonstrate that the proposed transceiver realizes high resource efficiency, collision resolution capability, and reliability for smart railways mMTC over the LEO satellite.

Wireless communication technologies play a key role to support the implementation of Power grid Internet of Things (PIoT). An in-depth knowledge of the radio channel is vital to the application of wireless communication technologies in PIoT. This article investigates the radio channel measurements and characterization for PIoT substation scenarios. A novel channel sounder is designed for achieving omnidirectional measurements and phased array antennas-based directional measurements, and a directional multipath components extraction algorithm is proposed. The channel sounding system is verified and is used to perform a series of 3.35-GHz omnidirectional and directional channel measurements in four substation scenarios, involving a 10-kV switch room, a 110-kV GIS room, a semi-indoor 110-kV substation, and an outdoor 220-kV substation. Based on the measured channel impulse response data, both large-scale and small-scale fading characteristics of substation channels are extracted and analyzed. Empirical models of path loss, shadow fading, Rician$K$-factor and root-mean-square (RMS) delay spread are proposed. In addition, results of RMS angle spread (AS) of departure and RMS AS of arrival are presented. These results will provide useful reference for the deployment and optimization of wireless communication networks in PIoT substation scenarios.

Emerging intelligent transportation services are latency-sensitive with heavy demand for computing resources, which can be supported by a multi-tier computing system composed of vehicular edge computing (VEC) servers along the roads and micro servers on vehicles. In this work, we investigate the dual Uu/PC5 interface offloading and resource allocation strategy in Cellular Vehicle-to-Everything (C-V2X) enabled multi-tier VEC system. The successful transmission probability is characterized to obtain the normalized transmission rate of PC5 interface. We aim to minimize the system latency of task processing while satisfying the resource requirements of Uu and PC5 interfaces. Due to the non-convex and variables coupling, we decompose the original problem into two subproblems, i.e., resource allocation and offloading strategy subproblems. Specifically, we derive the closed-form expressions of packet transmit frequency of PC5 interface, transmission power of Uu interface, and CPU computation frequency in the resource allocation subproblem. Moreover, for the offloading strategy subproblem, the offloading ratio matrix is obtained by proposing the PC5 interface based greedy offloading (PC5-GO) algorithm, which concludes offloading decision and ratio. Simulation results are provided that the proposed PC5-GO algorithm can significantly improve the system performance compared with other baseline schemes by 13.7% at least.

Cell-free (CF) massive multiple-input multiple-output (MIMO) and reconfigurable intelligent surface (RIS) are two promising technologies for realizing future beyond-fifth generation (B5G) networks. In this paper, we consider a practical spatially correlated RIS-aided CF massive MIMO system with multi-antenna access points (APs) over spatially correlated fading channels. Different from previous work, the electromagnetic interference (EMI) at RIS is considered to further characterize the system performance of the actual environment. Then, we derive the closed-form expression for the system spectral efficiency (SE) with the maximum ratio (MR) combining at the APs and the large-scale fading decoding (LSFD) at the central processing unit (CPU). Moreover, to counteract the near-far effect and EMI, we propose practical fractional power control (FPC) and max-min power control algorithms to further improve the system performance. We unveil the impact of EMI, channel correlations, and different signal processing methods on the uplink SE of user equipments (UEs). The accuracy of our derived analytical results is verified by extensive Monte-Carlo simulations. Our results show that the EMI can substantially degrade the SE, especially for those UEs with unsatisfactory channel conditions. Besides, increasing the number of RIS elements is always beneficial in terms of the SE, but with diminishing returns when the number of RIS elements is sufficiently large. Furthermore, the existence of spatial correlations among RIS elements can deteriorate the system performance when RIS is impaired by EMI.

The increased throughput brought by MIMO technology relies on the knowledge of channel state information (CSI) acquired in the base station (BS). To make the CSI feedback overhead affordable for the evolution of MIMO technology (e.g., massive MIMO and ultra-massive MIMO), deep learning (DL) is introduced to deal with the CSI compression task. In traditional communication systems, the compressed CSI bits is treated equally and expected to be transmitted accurately over the noisy channel. While the errors occur due to the limited bandwidth or low signal-to-noise ratios (SNRs), the reconstruction performance of the CSI degrades drastically. As a branch of semantic communications, deep joint source-channel coding (DJSCC) scheme performs better than the separate source-channel coding (SSCC) scheme—the cornerstone of traditional communication systems—in the limited bandwidth and low SNRs. In this paper, we propose a DJSCC based framework for the CSI feedback task. In particular, the proposed method can simultaneously learn from the CSI source and the wireless channel. Instead of truncating CSI via Fourier transform in the delay domain in existing methods, we apply non-linear transform networks to compress the CSI. Furthermore, we adopt an SNR adaption mechanism to deal with wireless channel variations. The extensive experiments demonstrate the validity, adaptability, and generality of the proposed framework.

In practical cell-free (CF) massive multiple-input multiple-output (MIMO) networks with distributed and low-cost access points, the asynchronous arrival of signals at the user equipments increases multi-user interference that degrades the system performance. Meanwhile, rate-splitting (RS), exploiting the transmission of both common and private messages, has demonstrated to offer considerable spectral efficiency (SE) improvements and its robustness against channel state information (CSI) imperfection. The signal performance of a CF massive MIMO system is first analyzed for asynchronous reception capturing the joint effects of propagation delays and oscillator phases of transceivers. Taking into account the imperfect CSI caused by asynchronous phases and pilot contamination, we derive novel and closed-form downlink SE expressions for characterizing the performance of both the RS-assisted and conventional non-RS-based systems adopting coherent and non-coherent data transmission schemes, respectively. Moreover, we formulate the design of robust precoding for the common messages as an optimization problem that maximizes the minimum individual SE of the common message. To address the non-convexity of the design problem, a bisection method is proposed to solve the problem optimally. Simulation results show that asynchronous reception indeed destroys both the orthogonality of the pilots and the coherent data transmission resulting in poor system performance. Besides, thanks to the uniform coverage properties of CF massive MIMO systems, RS with a simple low-complexity precoding for the common message obtained by the equal ratio sum of the private precoding is able to achieve substantial downlink sum SE gains, while the application of robust precoding to the common message is shown to be useful in some extreme cases, e.g., serious oscillator mismatch and unknown delay phase.

The emerging intelligent transparent surface (ITS), unlike the intelligent reflection surface (IRS), allows incident signals to penetrate it instead of being reflected, which enables the ITS to combat the severe signal penetration loss for high-speed railway (HSR) wireless communications. This paper thus investigates the channel estimation problem where the ITS is attached to the HSR carriage window. We first propose a new transmission scheme with two pilot blocks for each frame. Second, we formulate the channels as functions of physical parameters and thus transform the problem into a parameter recovery problem. Third, we develop a successive closed-form, maximum likelihood (ML) channel estimation algorithm. Specifically, each estimate is expressed as the sum of its perfectly known value and the estimation error. By leveraging the relationship between channels for the two pilot blocks, we eliminate the unknown parameters besides Doppler shifts, which can be thereby recovered. With the reconstructed Doppler-induced phase shifts, we acquire other channel parameters. Moreover, the Cramér-Rao lower bound (CRLB) for each parameter is derived as a performance benchmark. Finally, we provide numerical results to establish the effectiveness of our proposed estimators.

In this paper, we investigate a cell-free massive multiple-input multiple-output system with both access points and user equipments equipped with multiple antennas over the Weichselberger Rayleigh fading channel. We study the uplink spectral efficiency (SE) for the fully centralized processing scheme and large-scale fading decoding (LSFD) scheme. To further improve the SE performance, we design the uplink precoding schemes based on the weighted sum SE maximization. Since the weighted sum SE maximization problem is not jointly over all optimization variables, two efficient uplink precoding schemes based on Iteratively Weighted sum-Minimum Mean Square Error (I-WMMSE) algorithms, which rely on the iterative minimization of weighted Mean Square Error (MSE), are proposed for two processing schemes investigated. Furthermore, with maximum ratio combining applied in the LSFD scheme, we derive novel closed-form achievable SE expressions and optimal precoding schemes. Numerical results validate the proposed results and show that the I-WMMSE precoding schemes can achieve excellent sum SE performance with a large number of UE antennas.

Accurate knowledge of non-stationary narrow-beam channel characteristics is the prerequisite of applying dynamic beamforming technology in high-mobility communication scenarios. This paper proposes a three-dimensional geometry-based stochastic model (GBSM) for non-stationary narrow-beam channels in high-mobility communication scenarios. In the proposed GBSM, the impact of antenna pattern on angular distribution and the birth-death process of clusters in space-time-frequency (STF) domain are considered. Statistical properties of the proposed model are derived, including STF correlation function and multi-link spatial cross-correlation function (CCF), and the corresponding numerical results are analyzed. Moreover, the proposed model is verified by channel measurements in two high-mobility communication scenarios, such as vehicle-to-infrastructure scenario and high-speed railway scenario, in terms of temporal autocorrelation function, frequency correlation function and multi-link spatial CCF. It shows a good agreement between the measurement and model results, which confirms the reliability of the proposed model.

Millimeter-wave automotive radar is essential for realizing autonomous driving. Realistic channel model and simulation are important for system and sensing algorithm design. This work introduces channel measurements and ray-tracing (RT) channel simulations for frequency modulated continuous wave (FMCW) automotive mmWave radar. The 77 GHz channel measurements in an urban crossroads environment are presented. The dominant echoes (multi-path components) of the measurement are detected and matched with corresponding objects for each frame. A measurement-based electromagnetic (EM) parameter estimation method is proposed to find the optimal parameter set that minimizes the error of simulated radar cross section (RCS). As a result, the issue of lacking reliable EM material parameters for RT simulation at 77 GHz frequency band is tackled. Simulation results are validated in power, range, velocity, and angle domains with the provided EM parameter set. Extending validated RT simulations in similar environments with various configurations allows more reliable channels for rigorous testing without the limitation of channel measurement.

With the development of wireless communication, people have put forward higher requirements for train-ground communications in the high-speed railway (HSR) scenarios. With the help of mobile relays (MRs) installed on the roof of the train, the application of Millimeter-Wave (mm-wave) communication which has rich spectrum resources to the train-ground communication system can realize high data rate, so as to meet users’ increasing demand for broad-band multimedia access. Also, full-duplex (FD) technology can theoretically double the spectral efficiency. In this paper, we formulate the user association and transmission scheduling problem in the mm-wave train-ground communication system with MR operating in the FD mode as a nonlinear programming problem. In order to maximize the system throughput and the number of users meeting quality of service (QoS) requirements, we propose an algorithm based on coalition game to solve the challenging NP-hard problem, and also prove the convergence and Nash-stable structure of the proposed algorithm. Extensive simulation results demonstrate that the proposed coalition game based algorithm can effectively improve the system throughput and meet the QoS requirements of as many users as possible, so that the communication system has a certain QoS awareness.

Cell-free massive multiple-input multiple-output (CF mMIMO) systems serve the user equipments (UEs) by geographically distributed access points (APs) by means of joint transmission and reception. To limit the power consumption due to fronthaul signaling and processing, each UE should only be served by a subset of the APs, but it is hard to identify that subset. Previous works have tackled this combinatorial problem heuristically. In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-term signal processing coefficients are jointly optimized. We formulate two sparsity-inducing mean-squared error (MSE) minimization problems and solve them by using efficient proximal approaches with block-coordinate descent. For the downlink, more specifically, we develop a virtually optimized large-scale fading precoding (V-LSFP) scheme using uplink-downlink duality. The numerical results show that the proposed sparse processing schemes work well in both uplink and downlink. In particular, they achieve almost the same spectral efficiency as if all APs would serve all UEs, while the energy efficiency is 2–4 times higher thanks to the reduced processing and signaling.

To support modern machine-type communications, a crucial task during the random access phase is device activity detection, which is to identify the active devices from a large number of potential devices based on the received signal at the access point. By utilizing the statistical properties of the channel, state-of-the-art covariance based methods have been demonstrated to achieve better activity detection performance than compressed sensing based methods. However, covariance based methods require to solve a high dimensional nonconvex optimization problem by updating the estimate of the activity status of each device sequentially. Since the number of updates is proportional to the device number, the computational complexity and delay make the iterative updates difficult for real-time implementation especially when the device number scales up. Inspired by the success of deep learning for real-time inference, this paper proposes a learning based method with a customized heterogeneous transformer architecture for device activity detection. By adopting an attention mechanism in the architecture design, the proposed method is able to extract features reflecting relevance among device pilots and received signal, permutation equivariant with respect to devices, and its training parameter number is independent of the device number. Simulation results demonstrate that the proposed method achieves better activity detection performance with much shorter computation time than state-of-the-art covariance approach, and generalizes well to different numbers of devices and BS-antennas, different pilot lengths, transmit powers, and cell radii.

Device activity detection in the emerging cell-free massive multiple-input multiple-output (MIMO) systems has been recognized as a crucial task in machine-type communications, in which multiple access points (APs) jointly identify the active devices from a large number of potential devices based on the received signals. Most of the existing works addressing this problem rely on the impractical assumption that different active devices transmit signals synchronously. However, in practice, synchronization cannot be guaranteed due to the low-cost oscillators, which brings additional discontinuous and nonconvex constraints to the detection problem. To address this challenge, this paper reveals an equivalent reformulation to the asynchronous activity detection problem, which facilitates the development of a centralized algorithm and a distributed algorithm that satisfy the highly nonconvex constraints in a gentle fashion as the iteration number increases, so that the sequence generated by the proposed algorithms can get around bad stationary points. To reduce the capacity requirements of the fronthauls, we further design a communication-efficient accelerated distributed algorithm. Simulation results demonstrate that the proposed centralized and distributed algorithms outperform state-of-the-art approaches, and the proposed accelerated distributed algorithm achieves close detection performance to that of the centralized algorithm but with a much smaller number of bits to be transmitted on the fronthaul links.

Orthogonal time frequency space (OTFS) modulation is an emerging technology to tackle time-frequency (TF) selective channel in high mobility scenarios. In OTFS, resource is multiplexed in the delay-Doppler (DD) domain. Based on the potential sparsity, separability, stability and compactness of the channel spreading function, OTFS is able to realize lower complexity of channel estimation, higher diversity and higher reliability compared with orthogonal frequency division multiplexing (OFDM). However, the channel spreading function for practical communication systems is rarely considered in the current OTFS-related literature. High-speed railway (HSR) is a typical high mobility scenario with trains travelling at over 200km/h, which has the potential to employ OTFS. To this end, the HSR channel spreading function is characterized and the performance of OTFS in HSR is evaluated based on the realistic channel measurement in this article. Firstly, the HSR channel in TF domain is measured based on the long term evolution railway (LTE-R) network. Then, the characteristics of the channel spreading function are analyzed. In particular, the impact of time domain channel fading on the spreading function is investigated. The characteristics of the measured spreading function are analyzed with the proposed metrics in railway viaduct and tunnel scenarios. Based on the above analysis, an algorithm for generating the channel spreading function is proposed. Feasibility of the proposed DD domain channel generation algorithm is verified through comparing metrics of which to those of the measured channel. By simulating the bit error rate (BER) and mean square channel estimation error performances of OTFS modulation in the practical band-limited systems, it is shown that the impacts of Doppler shift, delay, SFFT and time domain channel fading need be considered for the application of OTFS modulation, in contrast to the state-of-art DD domain channel generation scheme based on tap delay link (TDL) model. For example, compared to OTFS modulation under the ideal channel spreading function, OTFS modulation requires a signal gain greater than 5 dB under the practical channel spreading function affected by above factors, to achieve the same BER less than 10−2 under parameters defined in simulation.

Providing satisfactory quality of service (QoS) in high-speed railway (HSR) network is being strangled by external interference as well as jamming. To address this issue, we study the reconfigurable intelligent surface (RIS)-aided HSR network, where one RIS is deployed nearby the onboard mobile relay (MR) to suppress the interference as well as jamming in HSR system. Aiming at enhancing the HSR network capacity against the interference, we formulate an optimization problem for designing the phase shifts at the RIS. Since the HSR environment is time-varying and complicated, the optimization problem is challenging to settle. Inspired by the recent advances of deep reinforcement learning (DRL), we propose a deep deterministic policy gradient (DDPG)-based scheme to settle the problem through designing the action space, the state space as well as the reward function. Simulation results present that 1) deploying the RIS nearby the onboard MR is strongly facilitative of suppressing the interference; 2) the proposed DDPG scheme can achieve better capacity than the baseline schemes, and be gradually close to the upper boundary with the number of RIS elements increasing.

In this paper, we consider the design of a wireless sensor network (WSN) that aims at monitoring the environment and collecting data periodically. In view of the limited energy and computational capability of the sensor nodes, a mobile edge computing (MEC) server is deployed in the WSN as a data processing unit. The goal of the design is to maintain the freshness of the data, which is characterized by the criterion of the age of information (AoI). Therefore, we analyze the long-term average AoI of the considered network. Then, the energy and time constraints for the WSN are modeled with consideration of transmission and computation. Next, a non-convex average AoI minimization problem is formulated subject to the energy and time constraints by jointly optimizing the sampling rate, computing scheduling, and transmit power. To tackle the challenging problem, the geometric programming and successive convex approximation (SCA) technique are applied to develop an algorithm with convergence guarantee. Moreover, to exhibit the benefits of the MEC server, a joint design is investigated for the WSN without the MEC server. Finally, the numerical results demonstrate the efficiency of our proposed SCA-based algorithm and show the impact of the sampling rate on the AoI performance.

Cell-free massive MIMO (CF mMIMO) is a promising next generation wireless architecture to realize federated learning (FL). However, sensitive information of user equipments (UEs) may be exposed to the involved access points or the central processing unit in practice. To guarantee data privacy, effective privacy-preserving mechanisms are defined in this paper. In particular, we demonstrate and characterize the possibility in exploiting the inherent quantization error, caused by low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs), for privacy-preserving in a FL CF mMIMO system. Furthermore, to reduce the required uplink training time in such a system, a stochastic non-convex design problem that jointly optimizing the transmit power and the data rate is formulated. To address the problem at hand, we propose a novel power control method by utilizing the successive convex approximation approach to obtain a suboptimal solution. Besides, an asynchronous protocol is established for mitigating the straggler effect to facilitate FL. Numerical results show that compared with the conventional full power transmission, adopting the proposed power control method can effectively reduce the uplink training time under various practical system settings. Also, our results unveil that our proposed asynchronous approach can reduce the waiting time at the central processing unit for receiving all user information, as there are no stragglers that requires a long time to report their local updates.

Many emerging intelligent transportation services are latency-sensitive with heavy demand for computing resources, which can be handled by a multi-tier computing system composed of vehicular edge computing (VEC) servers in the roadside and micro servers carried by vehicles. In multi-tier VEC system, the offloading of vehicle-to-vehicle (V2V) can be supported using the Cellular Vehicle-to-Everything (C- V2X) links, through Uu or PC5 interfaces. In this work, we investigate the offloading and resource strategy in C- V2X enabled multi-tier VEC system. The successful transmission probability of PC5 interface is modeled to characterize the normalized transmission rate of C- V2X link. We aim to minimize the total system latency of the task processing to optimize the offloading ratio matrix and packet transmit frequency of the PC5 interface, and computation resource allocation of vehicles and VEC server. Due to the non-convex and variables coupling, the latency minimization problem is decomposed into two subproblems, i.e., resource allocation and offloading strategy subproblems, and propose a PC5 interface based greedy offloading (PC5-GO) algorithm. Specifically, for the resource allocation subproblem, we derive the closed expressions of packet transmit frequency of PC5 interface and CPU computation frequency at vehicle and VEC server. For the offloading strategy subproblem, the offloading ratio matrix is obtained by the proposed PC5-GO algorithm. Simulation results are provided that the proposed PC5-GO algorithm can significantly enhance the system performance compared with other benchmark schemes by 5.88% at least.

In this paper, we investigate a cell-free massive multiple-input multiple-output (CF mMIMO) system with both multi-antenna user equipments (UEs) and access points (APs) over spatially correlated Rayleigh fading channels. In practi-cal CF mMIMO systems, the in-phase and quadrature-phase imbalance (IQI) and low-resolution analog-to-digital converters (ADCs) at the APs are critical for the system performance. Taking these factors into account, the achievable uplink spectral efficiency (SE) is analyzed based on a two-layer decoding scheme. In particular, the maximum ratio (MR) and local minimum mean-square error (L-MMSE) combining are adopted at the APs while the large-scale fading decoding (LSFD) is implemented at the central processing unit (CPU). Furthermore, we derive novel closed-form SE expressions with the MR combining and investigate the SE performance for different combining schemes, quantization bits, and IQI parameters. Numerical results reveal the performance degradations caused by both the low-resolution ADCs and IQI. Additionally, increasing the number of APs is an effective means to promote the system performance.

We consider a practical spatially correlated recon-figurable intelligent surface (RIS)-aided cell-free (CF) massive multiple-input-multiple-output (mMIMO) system with multi-antenna access points (APs) over spatially correlated Rician fading channels. The minimum mean square error (MMSE) channel estimator is adopted to estimate the aggregated RIS channels. Then, we investigate the uplink spectral efficiency (SE) with the maximum ratio (MR) and the local minimum mean squared error (L-MMSE) combining at the APs and obtain the closed-form expression for characterizing the performance of the former. The accuracy of our derived analytical results has been verified by extensive Monte-Carlo simulations. Our results show that increasing the number of RIS elements is always beneficial, but with diminishing returns when the number of RIS elements is sufficiently large. Furthermore, the effect of the number of AP antennas on system performance is more pronounced under a small number of RIS elements, while the spatial correlation of RIS elements imposes a more severe negative impact on the system performance than that of the AP antennas.

Wireless random access (RA) faces huge challenges under the explosive growth of the Internet of Things devices to be connected to the base station. This leads to inevitable RA collisions. In this paper, we consider the RA in massive multiple-input multiple-output (MIMO) systems, and propose an activity detection and channel estimation algorithm based on dictionary learning. By exploiting the sporadic feature of massive connected devices that a small fraction of them being active, we exploit compressive sensing for simultaneous channel estimation and activity detection. More importantly, the proposed algorithm utilizes dictionary learning to abstract the sparse characteristics of the spatial channel in the massive MIMO system. A dictionary is learned by using historical channel information of each cell, and thus is appropriate for the specific cell. Simulation results demonstrate the superiority of the proposed method compared with existing methods.

This paper investigates the cache-assisted power allocation in time domain for high-speed railway communications (HSRC), where train users are divided into real-time users (RUs) and non-real-time users (NRUs) from the time-sensitive perspective. RU's real-time data rate demand is ensured by power allocation, and NRU's data amount requirement is guaranteed by releasing cached content. In order to maximize the mobile service amount (MSA) of HSRC, an optimization problem is formulated to find the optimal cache switching time and power distribution under the constraints of total available energy, maximum power, caching and releasing causality, RU's minimal data rate requirement and NRU's minimal data amount requirement. Since the formulated problem is non-convex, a two-stage algorithm is proposed. In the first stage, we fix the cache switching time and transform the problem to be convex, and then use Karush-Kuhn-Tucker (KKT) condition to determine the optimal power distribution. In the second stage, one-dimensional search is employed to find the optimal cache switching time. Simulation results show that our proposed method is able to guarantee the RU's data rate threshold all the time by sacrificing some MSA. Moreover, the increase of data rate threshold and speed lead to a decrease of MSA, while the cache usage rate has relatively weak influence on MSA. © 2022 IEEE.

How to manage the interference introduced by the enormous wireless devices is a crucial issue to address in the prospective sixth-generation (6G) communications. The treating interference as noise (TIN) optimality conditions are commonly used for interference management and thus attract significant interest in existing wireless systems. Cell-free massive multiple-input multiple-output (CF mMIMO) is a promising technology in 6G that exhibits high system throughput and excellent interference management by exploiting a large number of access points (APs) to serve the users collaboratively. In this paper, we take the first step on studying TIN in CF mMIMO systems from a stochastic geometry perspective by investigating the probability that the TIN conditions hold with spatially distributed network nodes. We propose a novel analytical framework for TIN in a CF mMIMO system with both Binomial Point Process (BPP) and Poisson Point Process (PPP) approximations. We derive the probability that the TIN conditions hold in close form using the PPP approximation. Numerical results validate our derived expressions and illustrate the impact of various system parameters on the probability that the TIN conditions hold.

In the next-generation wireless communication systems, learning-based dynamic spectrum access strategy at the medium access control layer and physical layer shows its powerful capability of achieving optimal resources allocation, and it has become a hot research topic for the harmonious coexistence of heterogeneous wireless networks. In this paper, we propose a multiple access control method to achieve high network throughput by combining deep reinforcement learning and memory module. In specific, we introduce the bidirectional gated recurrent unit (Bi-GRU) in deep Q-learning (DQL) to utilize the information of varying environment observation at each time-step. Furthermore, we apply the method in a freeway scenario with real-world datasets, where the DQL node contends the same wireless channel with other nodes. Evaluated results demonstrate that the proposed approach learns an optimal policy without using complex mechanism or prior. Moreover, we consider realistic cases involving saturated or unsaturated uplink traffic flows of nodes on a freeway segment, and the on-line training strategies of the DQL node near the roadside facilities. The experimental results show that the proposed scheme leads to the highest throughput in all cases compared with the competing approaches.

In this paper, we investigate a cell-free massive MIMO system with both access points and user equipments equipped with multiple antennas over the Weichselberger Rayleigh fading channel. We study the uplink spectral efficiency (SE) based on a two-layer decoding structure with maximum ratio (MR) or local minimum mean-square error (MMSE) combining applied in the first layer and optimal large-scale fading decoding method implemented in the second layer, respectively. To maximize the weighted sum SE, an uplink precoding structure based on an Iteratively Weighted sum-MMSE (I-WMMSE) algorithm using only channel statistics is proposed. Furthermore, with MR combining applied in the first layer, we derive novel achievable SE expressions and optimal precoding structures in closed-form. Numerical results validate our proposed results and show that the I-WMMSE precoding can achieve excellent sum SE performance.

In this paper, we apply reconfigurable intelligent surface (RIS) technique to aid the computation offloading of mobile edge computing (MEC) network and investigate how it can be exploited to reduce computation offloading delay. In order to minimize the long-term computation offloading delay, we formulate an optimization problem, which jointly optimizes the power control, computation offloading volume, the edge computing resource assigned to each user equipment (UE), as well as the RIS phase shift. To tackle this problem, we first convert it into a Markov decision process (MDP), then propose an efficient algorithm based on deep reinforcement learning (DRL), namely deep deterministic policy gradient (DDPG). Numerical results demonstrate that 1) compared to the MEC network without RIS, the RIS aid MEC network can achieve lower delay; 2) the proposed DDPG-based scheme can learn from the RIS aided environment to effectively reduce the computation offloading delay.

Cell-free (CF) massive multiple-input multiple-output (MIMO) systems are expected to implement advanced cooperative communication techniques to let geographically distributed access points jointly serve user equipments. Building on the Team Theory, we design the uplink team minimum mean-squared error (TMMSE) combining under limited data and flexible channel state information (CSI) sharing. Taking into account the effect of both channel estimation errors and pilot contamination, a minimum MSE problem is formulated to derive unidirectional TMMSE, centralized TMMSE and statistical TMMSE combining functions, where CF massive MIMO systems operate in unidirectional CSI, centralized CSI and statistical CSI sharing schemes, respectively. We then derive the uplink spectral efficiency (SE) of the considered system. The results show that, compared to centralized TMMSE, the unidirectional TMMSE only needs nearly half the cost of CSI sharing burden with neglectable SE performance loss. Moreover, the performance gap between unidirectional and centralized TMMSE combining schemes can be effectively reduced by increasing the number of APs and antennas per AP.

High-speed train (HST) communications with orthogonal frequency division multiplexing (OFDM) techniques have received significant attention in recent years. Besides, cell-free (CF) massive multiple-input multiple-output (MIMO) is considered a promising technology to achieve the ultimate performance limit. In this paper, we focus on the performance of CF massive MIMO-OFDM systems with both matched filter and large-scale fading decoding (LSFD) receivers in HST communications. HST communications with small cell and cellular massive MIMO-OFDM systems are also analyzed for comparison. Considering the bad effect of Doppler frequency offset (DFO) on system performance, exact closed-form expressions for uplink spectral efficiency (SE) of all systems are derived. According to the simulation results, we find that the CF massive MIMO-OFDM system with LSFD achieves both larger SE and lower SE drop percentages than other systems. In addition, increasing the number of access points (APs) and antennas per AP can effectively compensate for the performance loss from the DFO. Moreover, there is an optimal vertical distance between APs and HST to achieve the maximum SE.

With the massive deployment of 5G commercial, the interconnection of all things promotes the transformation and upgrading of the social production mode. The Internet of Things (IoT) is supported by the massive machine-type communications (mMTC), which is one of the three major application scenarios of 5G. Recently, a novel spreading based non-orthogonal multiple access (NOMA) scheme named tandem spreading multiple access (TSMA) has been proposed for grant-free random access in mMTC. However, TSMA only considers the case of single antenna. In this article, a multi-antenna system scheme of TSMA with MIMO (MIMO-TSMA) is proposed to scale up user connections. In this scheme, spectrum efficiency can be promoted by sharing the non-orthogonal resources in different beams. The simulation results show that MIMO-TSMA can effectively take advantage of MIMO and TSMA to enhance the mMTC system performance.

Benefiting from the advantage of joint source-channel coding in the finite block length regime and the advancement of AI technologies, deep joint source channel coding (DJSCC) has been extensively investigated for various sources (e.g., text source, image source, and video source) and achieved remarkable performance in limited bandwidth and low signal-to-noise ratios (SNRs). However, the performance gain brought by DJSCC methods is all observed via simulations in literature, where synchronization, channel estimation and the power amplifier are assumed to be perfect. In this paper, we design a software-defined radio (SDR) based platform to validate the DJSCC method for image transmission in real wireless scenarios.

The fast moving characteristics of high-speed trains pose a challenge to the beam alignment of high-speed railway millimeter wave communication systems. With powerful learning capabilities, machine learning-based methods can help improve the beam alignment performance, such as greatly reducing the delay. In this paper, a non-convex optimization problem is formulated aiming at maximizing the received power of downlink transmission, and deep reinforcement learning is used to assist beam alignment. Particularly, an adaptive beam alignment algorithm based on prioritized experience replay double deep Q-Learning is proposed to adjust beam direction dynamically. The algorithm observes the position of the train and the beam direction of the train roof mobile relay to guide the beam adjustment. To reduce the adjustment frequency and the requirements for train position accuracy, the service range of remote radio head is divided into multiple location bins. The beam direction adjustment is only executed when the train enters the next location bin. Simulation results verify that compared with other baseline schemes, the proposed algorithm can effectively improve the received power and reduce the beam alignment delay.

In this paper, we investigate a robust beamforming design in a millimeter wave (mmWave) communication network with consideration of the random blockages. The network with multiple remote radio units (RRUs) is taken into account, where the joint transmission coordinated multi-point (JT-CoMP) scheme is adopted to improve the spectrum efficiency. To further enhance the communication performance and maintain the reliability of the network, an active intelligent reflecting surface (IRS) panel is deployed. We formulate the robust beamforming design of the mmWave communication network as an optimization problem aiming at minimizing the total transmit power at the RRUs subject to the average signal-to-interference-plus-noise ratio (SINR) constraints and power constraint over the active IRS. To deal with the challenge caused by the variables coupling, an algorithm based on the alternating optimization (AO) and semidefinite programming (SDP) is proposed. Numerical results illustrate that: i) the transmit power of the network can be reduced by deploying both the passive and active IRSs; ii) the integration of the active IRS and CoMP scheme can obtain a significant performance gain compared to that of the conventional passive IRS and CoMP.

Abstract This paper investigates an advanced effective signal processing technique to suppress noise, addressing a modern high‐performance detection in the field of radar sensing. To achieve a higher accuracy, the frequency modulated continuous wave radar is taken as a case study to derive the algorithm based on Karhunen ‐ Loève transform (KLT) before detection. KLT defines a linear projection of the signal statistics on the eigenfunctions domain, which makes the input‐dependent signals orthogonal to each other under new eigen‐basis and eigenvalues. The highest energy along slow time dimension of each range bin is concentrated in the transformed domain corresponding to the largest N eigenvalues. The performance of the algorithm is evaluated by different eigenvalue selection strategies. Numerical experiments are employed to obtain the relationship between signal‐to‐noise ratio and different eigenvalue selection strategies. Pertaining to the detection performance, constant false alarm ratio detector is applied to demonstrate the detection ability as a result of the processor by use of probability of detection ( P d ).

In this work, we investigate the transmission and offloading strategy in the nonorthogonal multiple access (NOMA)-enabled fog computing system for the Internet of Things (IoT). We aim to minimize the total energy consumption of the IoT system while satisfying the latency requirements. Due to the energy minimization problem is a mixed-integer nonlinear programming, we decompose the problem into two subproblems for different optimizing variables, i.e., fog node selection and resource allocation subproblems, and propose a multinode collaboration transmission and computation (MCTC) algorithm. Specifically, the fog node selection subproblem can be transformed into the assignment problem, which is constructed as a bipartite graph to obtain the node selection strategy. For the resource allocation subproblem, we propose an iterative algorithm to obtain the offloading workload, duration allocation, and computation resource. Simulation results are provided, which demonstrate that the proposed algorithm outperforms the other strategies by 56.88% at least.

This article studies the coverage performance of unmanned aerial vehicle (UAV)-assisted simultaneous wireless information and power transfer (SWIPT) networks under the nonlinear and linear energy harvesting (EH) models in the rich scattering scenarios, including smart farming and smart ranching, where the None-Line-of-Sight (NLoS) links are the dominate component of the wireless channel. Multiple UAVs are equipped with directional antennas to transfer information and energy to ground users (GUs). Power splitting (PS) or time switching (TS) architecture is employed at GUs. In order to evaluate the system performance in fading channels, the information and energy coverage probabilities of the system are discussed, and by using the stochastic geometry approach and the approximate scaling method, the general and lower bound explicit expressions of the coverage probabilities are derived. Numerical results show that the coverage performance of PS-based systems is superior to that of TS-based systems. Moreover, although the linear EH model yields better results than the nonlinear one, as the linear EH model is too ideal, its yielded results may mismatch practical EH circuits, and the ones yielded by the nonlinear EH model is much closer to practice, as the nonlinear EH model is based on real data measurement. Additionally, the nonlinear EH model has a relatively small effect on the harvested energy coverage probability, and the linear EH model introduces greater bias for the TS-based system than that for PS-based one.

Recently, unmanned aerial vehicle (UAV)-assisted ground targets localization is widely used in search and rescue (SAR) scenes. In this article, we propose a UAV search and localization strategy, which can search and locate unknown number of victims. The proposed strategy uses the UAV as a mobile anchor to measure time of arrival (TOA) and can effectively mitigate localization errors caused by non-line-of-sight (NLOS) propagation. Generally speaking, the strategy is divided into two parts: 1) trajectory planning and 2) localization. By selecting waypoints and planning a suitable search trajectory, UAV can obtain all the measurement information in deployment area and ensure that each target has at least three anchors for localization even in the NLOS propagation environment. In the localization stage, a new estimator is proposed based on maximum-likelihood estimation (MLE), which estimates average NLOS bias together with the coordinates of target and can be well solved by the particle swarm algorithm. The simulation results show that the proposed strategy outperforms for mitigating NLOS error compared with other methods, and can effectively ensure high localization accuracy in the NLOS propagation environment.

This article investigates the performance of nonorthogonal multiple access (NOMA)-based cell-free massive multiple-input–multiple-output (mMIMO) for the Internet of Things (IoT) considering spatially correlated Rician fading channels. The exact closed form of downlink spectral efficiency (SE) and energy efficiency expressions is derived with three estimators and the maximum ratio transmission by taking the impacts of imperfect successive interference cancellation and pilot contamination into account. Subsequently, the performance of a local-MMSE precoder with the three aforementioned estimators is analyzed. Then, a large-scale fading-based user pairing scheme is proposed to further analyze the system SE. Besides, we formulate the optimum power control design as a max–min problem and a computational efficient suboptimal algorithm is proposed based on the successive convex approximation. Furthermore, our results reveal that the magnitude of the spatial correlation negligibly effects the SE in spatially correlated Rician fading channels. Then, numerical results confirm the positive effect of the proposed power control scheme. Also, our results further illustrate that NOMA-based cell-free mMIMO for IoT provides significant performance gain compared with its counterpart deploying conventional orthogonal multiple-access schemes.

In massive machine-type communications (mMTC), the conflict between millions of potential access devices and limited channel freedom leads to a sharp decrease in spectrum efficiency. The nature of sporadic activity in mMTC provides a solution to enhance spectrum efficiency by employing compressive sensing (CS) to perform multiuser detection (MUD). However, CS-MUD suffers from high computation complexity and fails to meet the strict latency requirement in some critical applications. To address this problem, in this paper, we propose a novel deep learning (DL) based framework for grant-free non-orthogonal multiple access (GF-NOMA), where we utilize the information distilled from the initial data recovery phase to further enhance channel estimation, which in turn improves data recovery performance. Besides, we design an interpretable and structured Model-driven Prior Information Aided Network (M-PIAN) and provide theoretical analysis that demonstrates the proposed M-PIAN can converge faster and support more users. Experiments show that the proposed method outperforms existing CS algorithms and DL methods in both computation complexity and reconstruction accuracy.

Recently, intelligent reflecting surface (IRS) has become a research focus for its capability of controlling the radio propagation environments. Compared to the conventional terrestrial IRS, aerial IRS (AIRS) exploiting unmanned aerial vehicle (UAV)/high-altitude platform (HAP) can provide better deployment flexibility. To this end, a three-dimensional (3D) one-cylinder model is first developed for AIRS-assisted multiple-input multiple-output (MIMO) narrowband channels. In order to change the wireless channel with AIRS and create a favorable propagation environment, we propose a novel method of designing the phase-shifts for the IRS elements. Based on the model, channel impulse response (CIR), space-time correlation function, and channel capacity are derived and thoroughly investigated. A key observation in this paper is that multipath and Doppler effects in radio propagation environments can be effectively mitigated via adjusting the phase-shifts of IRS. More specifically, for the special propagation environments in the absence of any scatterers, it is found that the effects of multipath fading can be completely eliminated by IRSs. While for the general propagation environments with multiple scatterers, a small number of IRS elements can also significantly reduce the Doppler spread and the deep fades of the channels. Based on the numerical investigation of channel correlations, it is shown that channel non-stationarity is not introduced into the time domain when the phase shift of IRS is linear related to the time. Moreover, the channel capacity can also be improved by the proposed methods. Finally, the model with non-ideal IRSs is considered and it is found that using non-ideal IRSs results in poor performances compared with using ideal IRSs. These conclusions will provide a fundamental support for developing intelligent and controllable propagation environments of the future sixth-generation (6G) wireless networks.

Cell-free (CF) massive multiple-input multiple-output (MIMO) systems show great potentials in low-mobility scenarios, due to cell boundary disappearance and strong macro diversity. However, the great Doppler frequency offset (DFO) leads to serious inter-carrier interference in orthogonal frequency division multiplexing (OFDM) technology, which makes it difficult to provide high-quality transmissions for both high-speed train (HST) operation control systems and passengers. In this paper, we focus on the performance of CF massive MIMO-OFDM systems with both fully centralized and local minimum mean square error (MMSE) combining in HST communications. Considering the local maximum ratio (MR) combining, the large-scale fading decoding (LSFD) cooperation and the practical effect of DFO on system performance, exact closed-form expressions for uplink spectral efficiency (SE) expressions are derived. We observe that cooperative MMSE combining achieves better SE performance than uncooperative MR combining. In addition, HST communications with small cell and cellular massive MIMO-OFDM systems are compared in terms of SE. Numerical results reveal that the CF massive MIMO-OFDM system achieves a larger and more uniform SE than the other systems. Finally, the train antenna centric (TA-centric) CF massive MIMO-OFDM system is designed for practical implementation in HST communications, and three power control schemes are adopted to optimize the propagation of TAs for reducing the impact of the DFO.

In this paper, we investigate the performance of an RIS-aided wireless communication system subject to outdated channel state information that may operate in both the near- and far-field regions. In particular, we take two RIS deployment strategies into consideration: (i) the centralized deployment, where all the reflecting elements are installed on a single RIS and (ii) the distributed deployment, where the same number of reflecting elements are placed on multiple RISs. For both deployment strategies, we derive accurate closed-form approximations for the ergodic capacity, and we introduce tight upper and lower bounds for the ergodic capacity to obtain useful design insights. From this analysis, we unveil that an increase of the transmit power, the Rician-$K$factor, the accuracy of the channel state information and the number of reflecting elements help improve the system performance. Moreover, we prove that the centralized RIS-aided deployment may achieve a higher ergodic capacity as compared with the distributed RIS-aided deployment when the RIS is located near the base station or near the user. In different setups, on the other hand, we prove that the distributed deployment outperforms the centralized deployment. Finally, the analytical results are verified by using Monte Carlo simulations.

Recent research on joint source channel coding (JSCC) for wireless communications has achieved great success owing to the employment of deep learning (DL). However, the existing work on DL based JSCC usually trains the designed network to operate under a specific signal-to-noise ratio (SNR) regime, without taking into account that the SNR level during the deployment stage may differ from that during the training stage. A number of networks are required to cover the scenario with a broad range of SNRs, which is computational inefficiency (in the training stage) and requires large storage. To overcome these drawbacks our paper proposes a novel method called Attention DL based JSCC (ADJSCC) that can successfully operate with different SNR levels during transmission. This design is inspired by the resource assignment strategy in traditional JSCC, which dynamically adjusts the compression ratio in source coding and the channel coding rate according to the channel SNR. This is achieved by resorting to attention mechanisms because these are able to allocate computing resources to more critical tasks. Instead of applying the resource allocation strategy in traditional JSCC, the ADJSCC uses the channel-wise soft attention to scaling features according to SNR conditions. We compare the ADJSCC method with the state-of-the-art DL based JSCC method through extensive experiments to demonstrate its adaptability, robustness and versatility. Compared with the existing methods, the proposed method takes less storage and is more robust in the presence of channel mismatch.

The Long Term Evolution for Railway (LTE-R) communication system is providing a reliable data link for High-Speed Railway (HSR) communication. However, when the train passes through different railway environments, the channel capacity of the base station and the number of users are always in highly dynamic changes. Therefore, accurate predicting the changing law of wireless spectrum resources can make more efficient use of wireless spectrum resources. The purpose of this paper is to use the Long Short-Term Memory network ($LST\!M$) to predict the channel occupancy changes of wireless spectrum resources. Under the premise of ensuring the safe and reliable service for primary users (PU), it provides a feasible method for the secondary user’s (SU) opportunistic access to the authorized channels, thereby improving theLTE-Rsystem Utilization rate of spectrum resources. Based on the “occupied/idle” status of the authorized channel at the previous$n$historical moments, we infer the status of the authorized channel at the current moment, build the spectrum situation of the authorized channels, and guide the SU to conduct Dynamic opportunistic Spectrum Access (DSA) to the authorized channels. The simulation results show that when SU uses the channel situation constructed by the$LST\!M$network to access the authorized channels, it has fewer handovers and lower collision rates, and can obtain higher throughput.

To support the operational response for ships in distress and oil spills, a space-air-sea-ground integrated monitoring network-based forecasting system was constructed. A three-layered structure of the framework was designed, including sensing layer, network layer and application layer. The observations are collected by space-air-sea-ground sensors, followed with the transmission by the network layer. These observations are received and processed, and are then combined with the forecasting. The coverage, availability and reliability of various observations are discussed. The uncertainty in forecasting and the approach to improve its performance are studied. The system was applied to the uncontrolled ship and the following oil spill from Sanchi accident occurred in East China sea in 2018. The space-bore synthetic aperture radar (SAR) observations for ship and oil spill are verified by air-borne SAR observations and on-site observations. The accuracy of the forecasting trajectory is improved by integrating more observations. The uncertainty in the forecasting is accounted for by an ensemble of the available wind and current forcing, in order to improve the reliability of the conclusions. The monitoring and forecasting play a complementary role, which have improved the support to maritime transportation emergency response.

In order to realize the reliable and safe operation of the smart railways, and provide high quality information transmission service for passengers, the railway system needs to develop innovative communication network and advanced communication technology to meet the gradually increasing service demand of multi-dimensional comprehensive information resources. The Space-Air-Ground Integrated Network (SAGIN) can provide seamless information services for land, sea, air and space users, and is an effective solution to the challenge posed by the future smart railways to the all-time, all-domain, all-air, high-reliability and high-throughput communication. This paper aims to comprehensively discuss the technical development and application examples of High-Speed Railways (HSRs) based onSAGIN. Firstly, we analysis the development of theSAGINand the mobile communication network of theHSRs, and comprehensively discuss the single network architecture of the space-based, air-based and ground-based networks, as well as the integrated network, and discuss the application scenario and network structure of the combination of the integrated networks. At the same time, the communication services, existing problems and key technologies of the space-based, air-based and ground-based networks are discussed, and the application trend of theSAGINinHSRsis presented. Furthermore, the application scenarios of Artificial Intelligence (AI) technologies in solving the efficient resource utilization of smart railways communication and theSAGINare studied. Based on these technologies, we point out the research direction for the future development of AI technologies inSAGINinHSRscommunications.

In the evolution of railway mobile communications from Long Term Evolution for Railway (LTE-R) to the future 5th Generation Wireless System (5G), the rapid increase in the number of low-power base station nodes along the railway has brought more frequent handovers. The current handover parameter selection mechanism often relies on the on-site measured results in a limited number of discrete scenarios. It cannot deal with the continuous changing characteristics of the high-speed railway mobile communication environment, which leads to a serious lack of accuracy, adaptability and intelligence. This article hopes to construct a parameter-adaptive handover mechanism suitable for5Gin the high-speed railway dedicatedLTE-Rcommunication system. The mechanism first uses the interaction of Temporal-Difference(TD)-learning-based reinforced agents to obtain high-speed railway handover performance and network performance in different combinations of speeds and handover parameters, and continuously updates the accumulated rewards used to target optimization, obtaining a Discrete TD value cube with closely related handover performance. Further, based on the Discrete TD value cube, we use the approximation function method for the completion of “continuous” situation of handover parameter selection, and construct a continuous TD value cube and the corresponding performance cubes. Our experimental results prove that TD learning agents with function approximation can accurately estimate and predict the handover performance and network performance of state combinations with different speeds and handover parameters, and further show that the handover parameter adaptation mechanism based on the Inference ability can find the optimal handover parameters to improve the handover performance and network performance.

Railway is stepping into the field of smart railway. Unfortunately, the challenge of obtaining accurate instantaneous channel state information in high-speed railway (HSR) scenario makes it difficult to apply conventional power allocation schemes. In this paper, to respond to the challenge, we propose an innovative experience-driven power allocation algorithm in the millimeter-wave (mmWave) HSR systems with hybrid beamforming, which is capable of learning power decisions from the past experience instead of the accurate mathematical model, just like one person learns one new skill, such as driving. To be specific, with the purpose of maximizing the achievable sum rate, we first characterize the power allocation problem of the mmWave HSR systems as a multi-agent deep reinforcement learning problem and then solve it by using emerging multi-agent deep deterministic policy gradient (MADDPG) approach, which enables the agent, i.e., the mobile relay onboard the train to learn the power decisions from the past experience in a distributed manner. The simulation results indicate that the spectral efficiency of proposed MADDPG algorithm significantly outperforms existing state-of-the-art schemes.

Multi-access mobile edge computing (MEC) is envisioned as a key enabling technology to support compute-intensive and delay-sensitive applications in railway Internet of Things (RIoT) networks. However, the time-varying channel variations in RIoT scenarios make it challenging to achieve efficient resource allocation. The emerging deep reinforcement learning (DRL) is able to respond to the above-mentioned challenge. In this paper, with the aim of reducing the total computational cost (weighted sum of consumed energy and delay), we investigate the dynamic resource management issue of joint subcarrier assignment, offloading ratio, power allocation and computation resource allocation in multi-access MEC assisted RIoT networks. To address this intractable mixed integer nonlinear programming issue, we put forward a hybrid DRL (HDRL) scheme, which is an integration of deep double Q-learning (DDQN) and deep deterministic policy gradient (DDPG). The HDRL algorithm is capable of learning the advisable strategies for actions including discrete-continuous hybrid variables. In HDRL algorithm, DDQN plays the role of making subcarrier assignment decision, and DDPG plays the role of making offloading ratio, power allocation as well as computation resource allocation decisions. Numerical results demonstrate that HDRL scheme can yield much less computational cost than the existing baselines for multi-access MEC assisted RIoT networks. In addition, the HDRL scheme is close to the near-optimal performance with comparatively low execution time.

Fifth-Generation mobile networks for Railway (5G-R) is a promising train-ground communication solution to deal with the design challenges on ubiquitous connections and high reliability transmission for smart railways. Considering the linear coverage scenario along the railway lines, deep overlapping coverage can enhance the coverage reliability, but the capacity of cell edge users is plagued by severe inter-cell interference. In this paper, the fundamental performance of 5G-R systems with linear redundant coverage is investigated. We quantitatively analyze the impact of inter-cell interference on the capacity of cell edge users, in which the exponential effective signal to interference plus noise (SINR) mapping method is utilized to analyze SINR distribution of 5G-R users. Then, we investigate the coverage reliability considering the base station (BS) failure. Finally, taking cell edge user capacity as the optimization objective, we analyze the cell edge user capacity-coverage reliability tradeoff for 5G-R systems, and then provide useful insights into BS deployment for 5G-R systems through performance simulations and numerical results.

Propagation scenario identification is of vital significance for boosting the performance of future smart high-speed railway (HSR) communication networks. This paper investigates the HSR propagation scenario identification model, based on deep learning networks and feature fusion methods. With the assistance of railway long-term evolution (LTE) networks, we collected the channel impulse responses in four typical HSR scenarios including unobstructed viaduct, obstructed viaduct, station and suburban. Four channel characteristics involving power delay profile, root mean square (RMS) delay spread, RMS angular spread and Ricean K-factor form the datasets used for model training and testing. Then, a novel propagation scenario identification model is proposed by merging a weighted score based feature fusion method into the long short-term memory (LSTM) neural network. The hyper-parameters of the proposed model such as time window length and numbers of hidden units and layers are determined by autocorrelation analysis and cross-validation. Finally, the model performance is evaluated by focusing on the impact of feature selection, comparison of different feature fusion methods, and computational complexity. The evaluation results show that the proposed model has high identification accuracy but acceptable computational complexity.

Intelligent channel prediction plays a key role in artificial intelligence (AI)-optimized or AI-native communication networks for smart high-speed railways (HSRs). This paper investigates the spatial-temporal prediction of channel state information (CSI) and channel statistical characteristics (CSCs) based on deep-learning (DL) for the future smart HSR communication network. A propagation-graph simulation method is used to generate datasets of CSI and CSCs for massive multiple-input multiple-output (mMIMO) channels in a HSR cutting scenario, and realistic channel measurements are used to validate the datasets. Then, single-step ahead and multi-step ahead prediction problems are formulated with the consideration of both spatial and temporal information hidden in the datasets. By exploiting the temporal and spatial correlations of the HSR mMIMO channel, a novel spatial-temporal channel prediction model that combines the convolutional neural network (CNN) and convolutional long short-term memory (CLSTM) is proposed and called as Conv-CLSTM. Moreover, the hyper-parameters of the Conv-CLSTM model are determined by autocorrelation and similarity analysis and cross-validation. Finally, the performance of the Conv-CLSTM model is evaluated in terms of prediction accuracy and space and time computational complexity, and is compared with classical DL models. The evaluation results show that the proposed model has high prediction accuracy but acceptable computational complexity.

To support massive connectivity for sporadically active devices is a challenging task, as the randomness of the channel and the large number of users lead to enormous increase of communication overhead. Different to the existing methods that differentiate users in resources including time, frequency and code, we propose a new joint activity detection and channel estimation framework for massive multiple-input multiple-output (MIMO) systems, where angular domain information of active users is exploited to enhance activity detection and channel estimation. By exploiting the sporadic activity of users and the angular spread of the wireless signals, the activity detection and channel estimation is formulated as a compressive sensing problem with multiple measurement vectors, which has a simultaneously row-sparse and clustered sparse structure. The sizes and positions of the nonzero clusters are arbitrary, which brings new challenges for algorithm derivation. To this end, we develop new algorithms based on sparse Bayesian learning, where novel hyper-priors are proposed to capture the structural signal characteristics, and appropriate approximations are employed to facilitate algorithm derivations. Numerical experiments demonstrate the improved activity detection and channel estimation performance of the proposed approach in comparison to the existing methods.

For the ultra-reliable low-latency communication (URLLC), the existing joint design algorithms of channel training and data transmission are not applicable due to the stringent reliability requirement and limited blocklength. To address this issue, we develop a low-complexity joint design framework for MISO communication based on the finite blocklength code (FBC). Specifically, an approximate bound of the packet error probability (PEP) is first derived and validated by practical modulation and coding schemes. It reveals the inherent tension between reliability, latency, and information bit number. Then, we formulate the joint design into a nonconvex optimization problem with the objective to maximize the information bit number. By exploiting the monotonicity of the PEP approximate bound, we provide closed-form solutions of power and blocklength allocation. Thereby, we develop a low-complexity algorithm to support the URLLC services aiming at the information bit number maximization. Furthermore, we investigate the joint designs to optimize the reliability, latency, and total energy, respectively, to fully meet the diverse demands of URLLC services. Finally, numerical results are provided to validate the proposed joint designs. The results show the outage capacity-based design severely underestimates the required wireless resources.

This paper studies the robust beamforming design for simultaneous wireless information and power transfer (SWIPT)-enabled networks, where the rate-splitting (RS) scheme and the power-splitting (PS) energy harvesting (EH) receiver are adopted for secure information transfer and EH, respectively. In order to explore the worst-case energy efficiency (EE) performance limit of the system, an EE maximization problem is formulated with the elliptically bounded channel state information error model under the constraints of the quality of service (QoS) requirements of information decoding users, the EH requirements of EH users and the power budget at the transmitter. To tackle the formulated non-convex problem, a sequential minimal optimization-based algorithm is first proposed to construct a mapping table and the optimal PS ratios of the PS EH receiver are found by searching the table. Then, a dual-layer iterative algorithm is designed to obtain the maximal EE based on the Dinkelbach’s method in the inner loop and the successive convex approximation method in the outer loop. To accelerate the convergence of the outer loop, an efficient initialization algorithm is also designed. Simulation results show that the RS scheme contributes to the EE enhancement, and the PS EH receiver enlarges the rate-energy region restricted by the non-linear EH circuit. Moreover, traditional sum-rate maximization design and power minimization design may induce a notable worst-case EE performance loss at the high-power region and the low-QoS requirement region, respectively.

Massive internet of things (mIoT) could play an important role in the future smart high-speed railway (HSR), where grant-free multiple access technologies are required. Recently, tandem spreading multiple access (TSMA) has been raised for mIoT without mobility which achieves high connectivity and reliability. Meanwhile, orthogonal time frequency space (OTFS) modulation shows its potential to combat high mobility in point-to-point communication systems. To this end, in this article, we jointly design OTFS and TSMA, and propose OTFS-TSMA for HSR mIoT. The principle of OTFS-TSMA transceiver is described, where OTFS and TSMA are improved respectively. Especially, two-dimension cyclic shift of DD domain elements in OTFS is transformed into cyclic shift of Doppler elements, segments, symbols and chips by the proposed novel resource allocation and interleaving schemes. Data recovery approaches of the four categories of cyclic shift are given, thus massive user interference is mitigated. Simulation results illustrate that both high user connectivity and transmission reliability in HSR massive IoT can be achieved by OTFS-TSMA.

In this paper, we investigate a cell-free massive MIMO system with both access points (APs) and user equipments (UEs) equipped with multiple antennas over jointly-correlated Rayleigh fading channels. We study four uplink implementations, from fully centralized processing to fully distributed processing, and derive their achievable spectral efficiency (SE) expressions with minimum mean-squared error successive interference cancellation (MMSE-SIC) detectors and arbitrary combining schemes. Furthermore, the global and local MMSE combining schemes are derived based on full and local channel state information (CSI) obtained under pilot contamination, which can maximize the achievable SE for the fully centralized and distributed implementation, respectively. We study a two-layer decoding implementation with an arbitrary combining scheme in the first layer and optimal large-scale fading decoding (LSFD) in the second layer. Besides, we compute novel closed-form SE expressions for the two-layer decoding implementation with maximum ratio (MR) combining. In the numerical results, we compare the SE performance for different implementation levels, combining schemes, and channel models. It is important to note that increasing the number of antennas per UE may degrade the SE performance.

This paper investigates a joint beamforming design in a multiuser multiple-input single-output (MISO) communication network aided with an intelligent reflecting surface (IRS) panel. The symbol-level precoding (SLP) is adopted to enhance the system performance by exploiting the multiuser interference (MUI) with consideration of bounded channel uncertainty. The joint beamforming design is formulated into a nonconvex worst-case robust programming to minimize the transmit power subject to single-to-noise ratio (SNR) requirements. To address the challenges due to the constant modulus and the coupling of the beamformers, we first study the single-user case. Specifically, we propose and compare two algorithms based on the semidefinite relaxation (SDR) and alternating optimization (AO) methods, respectively. It turns out that the AO-based algorithm has much lower computational complexity but with almost the same power to the SDR-based algorithm. Then, we apply the AO technique to the multiuser case and thereby develop an algorithm based on the proximal gradient descent (PGD) method. The algorithm can be generalized to the case of finite-resolution IRS and the scenario with direct links from the transmitter to the users. Numerical results show that the SLP can significantly improve the system performance. Meanwhile, 3-bit phase shifters can achieve near-optimal power performance.

In massive machine-type communications (mMTC), the conflict between millions of potential access devices and limited channel freedom leads to a sharp decrease in spectral efficiency. The sparse nature of mMTC provides a solution by using compressive sensing (CS) to perform multiuser detection (MUD) but suffers conflict between the high computation complexity and low latency requirements. In this paper, we propose a novel Dual-network for joint channel estimation and data recovery. The proposed Dual-Net utilizes the sparse consistency between the channel vector and data matrix of all users. Experimental results show that the proposed Dual-Net outperforms existing CS algorithms and general neural networks in computation complexity and accuracy, which means reduced access delay and more supported devices.

Different from the traditional terrestrial intelligent reflecting surface (IRS), aerial IRS (AIRS) can provide some unique advantages, such as flexible deployment and wider-view signal reflection. In this paper, a three-dimensional (3D) single cylinder simulation channel model is proposed for AIRS-aided multiple-input multiple-output (MIMO) communication systems, where the considered propagation scenario consists of a fixed base station (BS) and a mobile station (MS). Based on the model, the channel impulse response (CIR), spreading function, and channel capacity are derived. Then, some heuristic algorithms are proposed to obtain the phase shifts of the IRS elements. It is found that multipath fading and Doppler effects stemming from the movement of MS can be effectively mitigated via adjusting the tunable phase shifts of the IRS elements. Moreover, the channel capacity of the system could also be improved by the proposed schemes. These findings can be used to lay a foundation for developing intelligent and controllable propagation environments.

In this paper, a general framework for deep learning-based power control methods for max-min, max-product and max-sum-rate optimization in uplink cell-free massive multiple-input multiple-output (CF mMIMO) systems is proposed. Instead of using supervised learning, the proposed method relies on unsupervised learning, in which optimal power allocations are not required to be known, and thus has low training complexity. More specifically, a deep neural network (DNN) is trained to learn the map between fading coefficients and power coefficients within short time and with low computational complexity. It is interesting to note that the spectral efficiency of CF mMIMO systems with the proposed method outperforms previous optimization methods for max-min optimization and fits well for both max-sum-rate and max-product optimizations.

Caching popular contents at a large number of access points and edge-clouds is a promising solution to alleviate the increasing backhaul congestion in beyond fifth-generation (B5G) networks. By integrating with cell-free massive multiple-input multiple-output (CF mMIMO), wireless caching can harness their combined virtues, i.e., almost uniform service quality, strong macro-diversity, and reduction of the data traffic from the core network. In this paper, we consider an offline cache-aided scenario with two caching strategies to minimize the total energy consumption (TEC), which are evaluated from the cache hit probability (CHP). The TEC minimization is showed to be NP-complete and, hence, dealt with a proposed greedy algorithm. An adaptive power control policy is proposed to reduce the TEC. We compare CF mMIMO with small cells in terms of the successful content delivery probability (SCDP) and TEC, respectively. The numerical results show that CF mMIMO can offer a much more uniform service, significantly higher SCDP, and lower average TEC when compared to than SC.

This paper considers an intelligent reconfigurable surface (IRS) assisted high-speed train communication system to address blockage issues, thus enhancing the spectrum efficiency and reducing the outage probability. However, instantaneous channel state information (CSI) at the base station is hard to be acquired in high-speed railway communication scenarios. To deal with the channel uncertainty, the outage performance is investigated by exploiting the statistical CSI in the downlink multiple-input multiple-output system, and we derive a closed-form expression of the outage probability. Moreover, an outage probability minimization problem is formulated to facilitate the IRS phase shift design and transceiver beam-forming design at the BS and the mobile relay on the train, subject to the transmit power constraint. Simulation results validate the efficacy of the proposed algorithm in terms of outage probability and effective rate, which is in close agreement with the theoretical analysis. Besides, the impacts of critical system parameters including the transmit power and the signal-to-noise ratio (SNR) threshold on the system performance are presented.

Recently, unmanned aerial vehicle (UAV) communications have drawn significant research interests. Meanwhile, cell-free (CF) massive multiple-input multiple-output (MIMO) is proposed as a promising technology to achieve the ultimate performance limits. In this paper, we investigate the UAV communication with wireless power transfer (WPT) aided CF massive MIMO systems, where the harvested energy (HE) from the downlink WPT is used to support both uplink data and pilot transmission. Take hardware impairments of UAV into account, novel closed-form downlink HE and uplink spectral efficiency (SE) expressions are derived. UAV communications with small cell (SC) and cellular massive MIMO enabled WPT systems are also considered for comparison. Our results reveal that CF massive MIMO achieves two and four times higher 95%-likely uplink SE than the ones of SC and cellular massive MIMO, respectively. To this end, SE is a concave function of the time-splitting fraction, and the optimal time-splitting fraction for maximizing SE is determined by the altitude and hardware impairment factor of the UAV.

Ambient backscatter is a new green technology that can enable batteryless tags or sensors to communicate with each other. In this paper, we combine ambient backscatter technology into traditional point-to-point wireless communication systems, and investigate its capacity and energy efficiency. Specifically, we build up the mathematical model for the new system with a sensor aided by ambient backscatter, derive its channel capacity, define the energy efficiency gain, and compare the capacity and energy efficiency performance of both traditional systems and backscatter aided systems. Simulations are then provided to corroborate our proposed studies.

Recently, reconfigurable intelligent surface (RIS) has drawn much attention due to its capability of improving coverage and communication performance. In this paper, a geometric RIS-assisted multiple-input multiple-output channel model is proposed for fixed-to-mobile (F2M) communications based on a three-dimensional cylinder model. The receiver is regarded as in motion, and the time-varying angles and propagation distances are derived to describe non-stationary channels. Based on the proposed channel model, the effect of RIS on spacetime correlation function is investigated. The results can be used to evaluate and optimize the performance of RIS-assisted F2M communication systems.

Abstract The OFDM system with multiple transceiver antennas is attractive to increase the spectrum or energy efficiency. Depending on the channel state information (CSI), precoding techniques offer array and multiplexing gain in such systems. However, if terminal equipment is fast moving, the CSI feedback would be outdated when it is applied to data transmission. This paper proposes a novel channel prediction method applicable for high‐speed environment. Benefiting from the proposed structure, this method greatly improves the prediction accuracy, and meanwhile retains the low feedback overhead. Numerical results show that the performance of the proposed method outperforms conventional precoding methods in high‐speed scenarios.

As the paradigm of the next high-speed railway (HSR) evolution, the smart railway attracts increasing attention from various professionals. With the maturity of 5G technologies, numerous intelligent applications are expected to be enabled. Therein, the Internet of Things (IoT) for railway can be supported by the massive machine-type communications (mMTCs) system. Recently, a novel multiple access scheme named tandem spreading multiple access (TSMA) has been proposed for the grant-free random access procedure in mMTC. However, high mobility has not been considered in TSMA so that it cannot be employed in IoT for railway currently. In this article, multicarrier TSMA (MC-TSMA) is introduced and the high-speed adaptability is investigated. Particularly, the impact of Doppler shift and time-varying channel is analyzed. In the meantime, the corresponding improvement designs are proposed. The simulation results show that the proposed scheme can effectively mitigate the impact of high mobility on MC-TSMA.

As the high-speed railway (HSR) industry Internet-of-Things chain matures, HSR wireless communication technology has become an increasingly important research field. The efficient management of time-frequency resources for Internet-of-Things networking is the core issue of HSR wireless communication optimization. The lack of time-frequency resources in LTE-R is still severe. In this article, a new LTE-R time-frequency resource allocation optimization method based on the probabilistic graphical theory is proposed. Considering the regularity that high-speed trains always pass by the same geographical location in similar time periods, we can do some research on opportunistic spectrum accessibility in the existing LTE time-frequency resource algorithm. The probabilistic graphical theory is suitable for finding the appropriate communication access opportunity in an HSR environment. The simulation results show that our method can effectively improve the performance of various traditional LTE time-frequency resource allocation algorithms.

How to meet the demand for increasing number of users, higher data rates, and stringent quality-of-service (QoS) in the beyond fifth-generation (B5G) networks? Cell-free massive multiple-input multiple-output (MIMO) is considered as a promising solution, in which many wireless access points cooperate to jointly serve the users by exploiting coherent signal processing. However, there are still many unsolved practical issues in cell-free massive MIMO systems, whereof scalable massive access implementation is one of the most vital. In this paper, we propose a new framework for structured massive access in cell-free massive MIMO systems, which comprises one initial access algorithm, a partial large-scale fading decoding (P-LSFD) strategy, two pilot assignment schemes, and one fractional power control policy. New closed-form spectral efficiency (SE) expressions with maximum ratio (MR) combining are derived. The simulation results show that our proposed framework provides high SE when using local partial minimum mean-square error (LP-MMSE) and MR combining. Specifically, the proposed initial access algorithm and pilot assignment schemes outperform their corresponding benchmarks, P-LSFD achieves scalability with a negligible performance loss compared to the conventional optimal large-scale fading decoding (LSFD), and scalable fractional power control provides a controllable trade-off between user fairness and the average SE.

Sparse signal recovery problems from noisy linear measurements appear in many areas of wireless communications. In recent years, deep learning (DL) based approaches have attracted interests of researchers to solve the sparse linear inverse problem by unfolding iterative algorithms as neural networks. Typically, research concerning DL assume a fixed number of network layers. However, it ignores a key character in traditional iterative algorithms, where the number of iterations required for convergence changes with varying sparsity levels. By investigating on the projected gradient descent, we unveil the drawbacks of the existing DL methods with fixed depth. Then we propose an end-to-end trainable DL architecture, which involves an extra halting score at each layer. Therefore, the proposed method learns how many layers to execute to emit an output, and the network depth is dynamically adjusted for each task in the inference phase. We conduct experiments using both synthetic data and applications including random access in massive MTC and massive MIMO channel estimation, and the results demonstrate the improved efficiency for the proposed approach.

Unmanned aerial vehicle (UAV) communications are expected to play a major role in future space-air-ground integrated networks (SAGINs). In this paper, a geometric three-dimensional (3D) non-stationary channel model operating at millimeter-wave (mmWave) band is proposed for wideband UAV multiple-input multiple-output (MIMO) communications based on a multiple-layer cylinder reference model, where both stationary and moving clusters around transmitter (Tx) and receiver (Rx) are considered. Unlike the existing UAV-based GBSMs, the proposed model considers both local and far clusters in the propagation environments. On this basis, a continuous-time Markov model with two states is used to model the dynamic properties of clusters (i.e., clusters appear/disappear with time), and the closed-form expressions of the survival probabilities of clusters are derived. Furthermore, we derive and investigate some significant statistical properties, including space-time-frequency correlation function, quasi-stationary interval, and Doppler power spectrum. Numerical results show that the local mobile cluster (LMC) component leads to higher time correlation compared with the local stationary cluster (LSC) component. In addition, it is found that the LMC component leads to larger quasi-stationary interval compared with the LSC component. Finally, it is found that the motion of transceivers and clusters, and the changes of carrier frequency introduce significant fluctuations in Doppler power spectrum. These observations and conclusions can be considered as a guidance for mmWave UAV MIMO system design.

Cell-free (CF) massive multiple-input multiple-output (MIMO) is a promising solution to provide uniform good performance for unmanned aerial vehicle (UAV) communications. In this paper, we propose the UAV communication with wireless power transfer (WPT) aided CF massive MIMO systems, where the harvested energy (HE) from the downlink WPT is used to support both uplink data and pilot transmission. We derive novel closed-form downlink HE and uplink spectral efficiency (SE) expressions that take hardware impairments of UAV into account. UAV communications with current small cell (SC) and cellular massive MIMO enabled WPT systems are also considered for comparison. It is significant to show that CF massive MIMO achieves two and five times higher 95%-likely uplink SE than the ones of SC and cellular massive MIMO, respectively. Besides, the large-scale fading decoding receiver cooperation can reduce the interference of the terrestrial user. Moreover, the maximum SE can be achieved by changing the time-splitting fraction. We prove that the optimal time-splitting fraction for maximum SE is determined by the number of antennas, altitude and hardware quality factor of UAVs. Furthermore, we propose three UAV trajectory design schemes to improve the SE. It is interesting that the angle search scheme performs best than both AP search and line path schemes. Finally, simulation results are presented to validate the accuracy of our expressions.

Orthogonal time frequency space (OTFS) modulation has been widely considered for high-mobility scenarios. Satellite-to-ground communications have recently received much attention as a typical high-mobility scenario and face great challenges due to the high Doppler shift. To enable reliable communications and high spectral efficiency in satellite mobile communications, we evaluate OTFS modulation performance for geostationary Earth orbit and low Earth orbit satellite-to-ground channels at sub-6-GHz and millimeter-wave bands in both line-of-sight and non-line-of-sight cases. The minimum mean squared error with successive detection (MMSE-SD) is used to improve the bit error rate performance. The adaptability of OTFS and the signal detection technologies in satellite-to-ground channels are analyzed. Simulation results confirm the feasibility of applying OTFS modulation to satellite-to-ground communications with high mobility. Because full diversity in the delay-Doppler domain can be explored, different terminal movement velocities do not have a significant impact on the performance of OTFS modulation, and OTFS modulation can achieve better performance compared with classical orthogonal frequency division multiplexing in satellite-to-ground channels. It is found that MMSE-SD can improve the performance of OTFS modulation compared with an MMSE equalizer.

Reconfigurable Intelligent Surface (RIS) can create favorable multipath to establish strong links that are useful in millimeter wave (mmWave) communications. While previous works assumed Rayleigh or Rician fading, we use the fluctuating two-ray (FTR) distribution to model the small-scale fading in mmWave frequency. First, we obtain the statistical characterizations of the product of independent FTR random variables (RVs) and the sum of product of FTR RVs. For the RIS-aided and amplify-and-forward (AF) relay systems, we derive exact end-to-end signal-to-noise ratio (SNR) expressions. To maximize the end-to-end SNR, we propose a novel and simple way to obtain the optimal phase shifts at the RIS elements. The optimal power allocation scheme for the AF relay system is also proposed. Furthermore, we evaluate important performance metrics including the outage probability and the average bit-error probability. To validate the accuracy of our analytical results, Monte-Carlo simulations are subsequently conducted to provide interesting insights. It is found that the RIS-aided system can attain the same performance as the AF relay system with low transmit power. More interestingly, as the channel conditions improve, the RIS-aided system can outperform the AF relay system using a smaller number of reflecting elements.

Cell-free massive multiple-input multiple-output (MIMO) provides more uniform spectral efficiency (SE) for users (UEs) than cellular technology. The main challenge to achieve the benefits of cell-free massive MIMO is to realize signal processing in a scalable way. In this paper, we consider scalable full-pilot zero-forcing (FZF), partial FZF (PFZF), protective weak PFZF (PWPFZF), and local regularized ZF (LRZF) combining by exploiting channel statistics. We derive closed-form expressions of the uplink SE for FZF, PFZF, and PWPFZF combining with large-scale fading decoding over independent Rayleigh fading channels, taking channel estimation errors and pilot contamination into account. Moreover, we investigate the impact of the number of pilot sequences, antennas per AP, and APs on the performance. Numerical results show that LRZF provides the highest SE. However, PWPFZF is preferable when the number of pilot sequences is large and the number of antennas per AP is small. The reason is that PWPFZF has lower computational complexity and the SE expression can be computed in closed-form. Furthermore, we investigate the performance of PWPFZF combining with fractional power control and the numerical results show that it improves the performance of weak UEs and realizes uniformly good service for all UEs in a scalable fashion.

Reconfigurable intelligent surface (RIS)-aided wireless communications have drawn significant attention recently. We study the physical layer security of the downlink RIS-aided transmission framework for randomly located users in the presence of a multi-antenna eavesdropper. To show the advantages of RIS-aided networks, we consider two practical scenarios: Communication with and without RIS. In both cases, we apply the stochastic geometry theory to derive exact probability density function (PDF) and cumulative distribution function (CDF) of the received signal-to-interference-plus-noise ratio. Furthermore, the obtained PDF and CDF are exploited to evaluate important security performance of wireless communication including the secrecy outage probability, the probability of nonzero secrecy capacity, and the average secrecy rate. Monte-Carlo simulations are subsequently conducted to validate the accuracy of our analytical results. Compared with traditional MIMO systems, the RIS-aided system offers better performance in terms of physical layer security. In particular, the security performance is improved significantly by increasing the number of reflecting elements equipped in a RIS. However, adopting RIS equipped with a small number of reflecting elements cannot improve the system performance when the path loss of NLoS is small.

In the last decade, the number of vehicles worldwide has increased every year, and this growth is projected to continue unabated. Thus, the congestions, incidents, and environmental pollution which are caused by the increasing number of road vehicles and traffics have resulted in hundreds of millions of losses and become a major challenge to the sustainable development of recent human society. Both academia and industry have already reached a consensus that vehicular communication is a vital element to extend the sensing ability of vehicles for ensuring safety driving. Unfortunately, current vehicular communication cannot meet the security, reliability, and effectiveness and other needs of ITS. The industry needs a more intelligent vehicular communication to support secure and reliable transmission of data. Therefore, the research community has to focus more on enhanced and completely new communication techniques.

Scenario identification plays an important role in improving communication system performance. Considering that the scenarios of vehicle communications are dynamic due to movements of vehicles, and there are obvious differences in channel characteristics, vehicle speeds, traffic densities between various scenarios, the requirement for real-time scenario identification of vehicular communications is increasingly urgent. Vehicular communication systems can select appropriate channel models and transmission mode by correctly identifying the current scenarios to maintain an effective and reliable operating state. This paper presents a machine-learning-based scenario identification model for intelligent vehicular communications. Channel characteristics extracted from channel measurements in different scenarios form the datasets used to training, then a back-propagation neural network (BPNN) is trained, and a scenario identification model is obtained. Furthermore, the model configuration scheme is explored and presented which can make the proposed identification model achieves optimal performance. Subsequently, identification accuracy is verified by using validation data of the corresponding scenarios. The results show that the identification accuracies are all above 98 % in four typical scenarios of urban areas, highways, tunnels, and vehicle obstructions, which indicates that the model proposed in this paper shows good performance in scenario identification for intelligent vehicular communications.

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

Vehicle-to-vehicle (V2V) wireless communications have many envisioned applications for ensuring traffic safety and for addressing traffic congestion. However, developing suitable communication systems and standards for this purpose requires developers to have accurate models for the V2V propagation channel. Likewise, the dynamic evolution of multipath components (MPCs) in V2V channels has not been well modeled in existing models. In this paper, we propose a geometry-based stochastic channel model for a lightly built-up urban environment and then parametrize the model from measurements. The MPCs are extracted based on a high-resolution parameter estimation; they are tracked and clustered through a joint algorithm. The identified clusters are classified as line-of-sight, reflections from static scatterers, reflections from mobile scatterers, multiple-bounce reflections, and diffuse scattering. Specifically, the multiple-bounce reflections are modeled as twin clusters that follow the COST 273/COST2100 approach. The paper gives a full parameterization of the channel model and supplies a step-by-step implementation recipe. We verify the model by comparing two second-order statistics, i.e., the root-mean-square (RMS) delay spread and the angular spreads of arrival/departure derived from the channel model, to the results obtained directly from the measurements. Furthermore, we also identify several key factors that strongly impact the synthetic channel performance.

In this paper, we investigate the impact of channel aging on the performance of cell-free (CF) massive multiple-input multiple-output (MIMO) systems with both spatial correlation and pilot contamination. We derive novel closed-form uplink and downlink spectral efficiency (SE) expressions that take imperfect channel estimation into account. More specifically, we consider large-scale fading decoding and matched-filter receiver cooperation in the uplink. The uplink performance of a small-cell (SC) system is derived for comparison. The CF massive MIMO system achieves higher 95%-likely uplink SE than the SC system. In the downlink, the coherent transmission has four times higher 95%-likely per-user SE than the non-coherent transmission. Statistical channel cooperation power control (SCCPC) is used to mitigate the inter-user interference. SCCPC performs better than full power transmission, but the benefits are gradually weakened as the channel aging becomes stronger. Furthermore, strong spatial correlation reduces the SE but degrades the effect of channel aging. Increasing the number of antennas can improve the SE while decreasing the energy efficiency. Finally, we use the maximum normalized Doppler shift to design the SE-improved length of the resource block. Simulation results are presented to validate the accuracy of our expressions and prove that the CF massive MIMO system is more robust to channel aging than the SC system.

In this paper, the vehicle‐to‐vehicle (V2V) channel characteristics in peak hours at the 5.9 GHz band in two typical urban road scenarios, the urban straight road and the intersection, are investigated. The channel characteristics, such as path loss, root mean square (RMS) delay spread, and angular spread, are derived from the ray‐tracing (RT) simulations. Due to the low height of antennas at both the transmitter (Tx) and the receiver (Rx), the line of sight (LOS) between the Tx and the Rx will often be obstructed by other vehicles. Based on the RT simulation results, the shadowing loss is modelled by the multimodal Gaussian distribution, and path loss models in both LOS and non‐LOS (NLOS) conditions are obtained. And the RMS delay spread in two scenarios can be modelled by the Weibull distribution. In addition, the deployment of an antenna array is discussed based on the statistics distribution of the angular spread.

We implement a wireless indoor tracking system based on the variational Gaussian process state-space model (GPSSM) with smartphone-collected WiFi received signal strength and inertial measurement unit readings. We adapt the existing variational GPSSM framework to wireless tracking scenarios, and provide a practical learning procedure for the variational GPSSM. The proposed system explores both the expressive power of the non-parametric Gaussian process model and its natural mechanism for integrating the state-of-the-art tracking techniques designed upon state-space model. Experimental results obtained from a real office environment validate the outstanding performance of the variational GPSSM in comparison with the traditional parametric state-space model in terms of tracking accuracy.

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

The integration of unmanned aerial vehicles (UAVs) and millimeter wave (mmWave) technology can provide high data rate for the fifth generation (5G) and beyond wireless networks. In this paper, a non-stationary geometric mmWave multiple-input multiple-output (MIMO) channel model is proposed for air-to-air (A2A) communications based on a three-dimensional (3D) cylinder model. To describe the real A2A propagation environments, a two-state continuous-time Markov process is introduced to model the dynamic properties (appearance and disappearance) of scatterers. The movements of the transmitter and receiver result in time-varying angles and propagation distances that make the model more general. Based on the proposed model, the space-time-frequency correlation functions are derived using the survival probabilities of scatterers. The effects of some important model parameters on channel correlation and non-stationarity are investigated. The observations and conclusions can be used to evaluate and optimize the performance of mmWave UAV A2A communication systems.

Achieving high data transmission rate in the highspeed railway (HSR) communications has always been the significant yet challenging goal. Unfortunately, through an enormous number of measurements, current spectrum efficiency in HSR scenarios is still far from satisfactory to meet the data rate requirements for HSR passengers. To tackle this problem, we investigate the power allocation in the extremely time-varying millimeter-wave (mmWave) HSR systems with hybrid beamforming in the paper. With the purpose of maximizing the achievable sum rate, we first formulate a joint hybrid beamforming and power allocation problem in the mmWave HSR channel model. Then we obtain the solution of beamforming design. Finally, for the sake of achieving dynamic power control, a low-complexity iterative optimization approach is proposed. The numerical results indicate that the proposed iterative algorithm is capable of achieving a significantly higher spectral efficiency compared with the existing schemes.

Railway is evolving into the new era of smart railway. Unfortunately, the challenge of obtaining accurate instantaneous channel state information in high-speed railway (HSR) scenario makes it difficult to apply conventional power allocation schemes. In this paper, we propose an innovative experience-driven power allocation algorithm which is capable of learning power decisions from its own experience instead of the accurate mathematical model, just like one person learns one new skill, e.g. driving. To be specific, with the purpose of maximizing the achievable sum rate, we first formulate a joint hybrid beamforming and power allocation problem based on the millimeter-wave HSR channel model. Then, both at the transmitters (TXs) and receivers (RXs), we obtain the solution of beamforming design. Finally, experience-driven power allocation algorithm with multi-agent deep reinforcement learning is proposed. The numerical results indicate that the spectral efficiency of proposed algorithm significantly outperforms the existing state-of-the-art schemes.

In this paper, we investigate the impact of channel aging on the performance of cell-free (CF) massive multiple-input multiple-output (MIMO) systems with pilot contamination. To take into account the channel aging effect due to user mobility, we first compute a channel estimate. We use it to derive novel closed-form expressions for the uplink spectral efficiency (SE) of CF massive MIMO systems with large-scale fading decoding and matched-filter receiver cooperation. The performance of a small-cell system is derived for comparison. It is found that CF massive MIMO systems achieve higher 95%-likely uplink SE in both low-and high-mobility conditions, and CF massive MIMO is more robust to channel aging. Fractional power control (FPC) is considered to compensate to limit the inter-user interference. The results show that, compared with full power transmission, the benefits of FPC are gradually weakened as the channel aging grows stronger.

As the sophisticated applications with latency constrained are difficult to operate on mobile devices with lower computing capability, fog-computing based radio access networks (F-RANs) have become a research hotspot as a revolutionary network architecture. It can offload a proportion of user's input tasks and provide scalable computation services at the fog-computing access points (F-APs), which reduce the task processing time and extend the battery life of local devices. In addition, the incorporation of millimeter-wave (mm-wave) band further improves the network performance with the uploading rate greatly increased. In this paper, we consider the multi-user uplink scenario, and formulate the problem of minimizing the total energy consumption of all users within the required latency. On the one hand, the optimization of user association joint with sub-channel allocation is studied to determine the connection status between users and edge F-AP nodes. It is modeled as a two-sided matching game representing the resource competition among users. As a result, the sub-optimal solution is obtained at lower complexity. On the other hand, the optimization of computation offloading for all users is also performed. Local devices can adjust CPU computing speed using dynamic voltage scaling (DVS) technology, and the offloading ratio is solved by convex programming. Both yield closed-form solutions. To this end, performance evaluation under multiple system parameters demonstrates the lower energy consumption and higher effectiveness of our proposed scheme in comparison with the baseline schemes.

This paper considers non-orthogonal multiple access (NOMA) based cell-free massive multiple-input multiple-output (mMIMO) systems over spatially correlated Rician fading channels. Closed-form downlink achievable sum-rate expression is derived by taking into account spatial correlation among multi-antenna access points, inter-cluster interference, intra-cluster pilot contamination, and imperfect successive interference cancellation (SIC). In particular, we propose an intra-cluster power allocation design for improving the system performance. Furthermore, we investigate the downlink performance with both minimum mean-squared error (MMSE) and element-wise MMSE channel estimation. It is interesting to find out that the correlation magnitude has a negligible effect on the system sum-rate in spatially correlated Rician fading channels. The numerical results validate the correctness of the presented results and confirm the effectiveness of the proposed power allocation.

Cell-free massive multiple-input multiple-output (MIMO) is a promising technology for future wireless networks. One main challenge of realizing practical cell-free massive MIMO is the high power consumption and huge hardware cost for employing high-resolution analog-to-digital converters (ADCs). To tackle this issue, a promising solution is to use low-resolution ADCs. In this paper, we investigate the cell-free massive MIMO system with low-resolution ADCs over spatially correlated channels. We generalize three levels of receiver cooperation and derive a tight closed-form expression of the spectral efficiency (SE) for a centralized receiver cooperation with large-scale fading decoding as a function of the resolution of ADCs. We also investigate the impact of spatial correlation magnitude on the sum SE. Moreover, we proposed a low-complexity power control method for maximizing the sum SE. Numerical results show that the centralized receiver cooperation needs more quantization bits to achieve the ideal performance and the proposed power control is efficient for improving the system performance.

The B5G is expected to support multiple massive machine-type communication (mMTC) services. However, limited resources impede the access of a large number of machine-type devices, and existing access frameworks are not efficient for transmitting small data packets. In this paper, we propose a compressive sensing (CS) approach for contention based random access scheme to support massive connections (≥ 106devices) within a certain time-frequency resource. Different from the four-step contention based access scheme in LTE, we adopt a one-step access scheme to save the energy and spectrum resources. It breaks the bottleneck of existing CS multiuser detection methods which have poor scalability (e.g., assuming ≤ 104devices in relevant literatures) due to the high computational complexity of CS. The improved performance of the newly proposed method is observed in our experimental results.

Fast time-varying channel parameters and large penetration loss for signals passing through train carriages are two well-known challenges for wireless communications on high speed rails (HSRs). In this paper we introduce, for the first time, backscatter technology into wireless communication systems on HSRs which can address the two challenges and meanwhile have low complexity of signal processing and low cost of circuit implementation compared with traditional solutions such as relay or beamforming technology. Specifically, we propose a backscatter aided wireless transmission (BAWT) scheme and compare its performance with existing direct wireless transmission (DWT) scheme. We also propose the transceiver design for both BAWT and DWT, including joint carrier frequency offset (CFO) and channel estimator, and signal detector. We show that BAWT, instead of DWT, can obtain the channel statistical information in practical applications due to fixed train antennas and unchanged tracks, which can be utilized to facilitate channel estimation. Finally simulation results are provided to corroborate the proposed studies.

In this paper, we investigate a QoE-aware coordinated caching scheme to adaptively satisfy the diversified video streaming requirements in a high-speed railway network, which consists of multiple cells endowed with cache storage capacity. However, traditional QoS-based video streaming schemes may not accurately reflect the influence of video adaption on user satisfaction. What's more, dynamic user demands and frequent handover impose serious challenges in terms of unknown uncertainties and short dwelling time. To overcome these challenges, we first define a perceptual QoE considering both video quality and video stalling. Second, we formulate a long-term optimization problem to maximize the perceptual QoE, with the cache storage and system stability constraints. Then, we propose a distributed online algorithm based on the Lyapunov optimization theory, which does not require future system information and is efficient for implementation. Finally, theoretical analysis and simulation results are presented to validate the effectiveness of the proposed algorithm.

In the real propagation environments, multipath components (MPCs) in wireless channel usually exist as clusters. Cluster based structure of MPCs has been widely used in wireless channel modeling. In this paper, a novel MPC clustering algorithm is proposed based on spectral clustering. Considering that MPCs having strong power should usually be grouped into different clusters, the algorithm introduces a power-weighted processing to identify the similarity of each MPC. The process of weighting the power is conducted by generating similarity matrix using the full link method of Gaussian kernel function in the traditional spectral clustering algorithm. In order to achieve high clustering accuracy, the dimensionality reduction method of Laplacian Eigenmap is decomposed by using the normalized cut method, the obtained eigenvectors are re-clustered to cut the generated similarity matrix for MPC clustering. In the simulation results, it is found that this algorithm can well separate MPCs with high powers into different clusters and achieves better clustering performance compared with KPowerMeans, Kmeans, and the traditional spectral clustering algorithms.

In this paper, we study the channel selection problem in edge computing-empowered cognitive machine-to-machine (CM2M) communications, where a massive number of machine type devices (MTDs) offload their computational tasks to a nearby edge server by opportunistically using the spectra that are temporarily unoccupied by primary users (PUs). We formulate the channel selection problem as an adversarial multi-armed bandit (MAB) problem, and combine the exponential-weight algorithm for exploration and exploitation (EXP3) and Lyapunov optimization to develop a learning-based energy-efficient solution named SEB-EXP3. It can find the long-term optimal channel selection strategy with guaranteed performance based on local information, while simultaneously achieving service reliability awareness, energy awareness, and data backlog awareness. Four heuristic algorithms are compared with SEB-EXP3 to demonstrate its effectiveness and reliability under various simulation settings.

Unmanned aerial vehicle (UAV) communications are envisioned to support numerous applications in the fifth and sixth generations wireless networks. In this paper, a three-dimensional (3D) non-stationary semi-spherical geometrical model is proposed for narrowband multi-input multi-output (MIMO) UAV channels. The Gauss-Markov mobility model is used to characterize the UAV rotation for the first time, which results in time-varying elevation and orientation angles of the antenna array and further leads to channel non-stationarity. Based on the proposed model, the space-time correlation function is derived and investigated in terms of the UAV movements (including pitch, roll, and heave). These observations and conclusions can be used as a reference for the system design and performance analysis of UAV-MIMO communication systems.

Vehicle obstruction is a special scenario in vehicle-to-vehicle (V2V) communications. In this case, channel characteristics including path loss and spatial distributions are obviously different from other typical vehicular communication scenarios. However, the vehicle obstruction scenario is difficult to be identified by global navigation satellite systems (GNSS) or radars, so it is difficult for V2V communication systems to respond to sudden changes in channel characteristics due to vehicle obstructions. Therefore, by correctly identifying the vehicle obstruction scenarios, V2V communication systems can select appropriate propagation channel models to maintain an effective and reliable operating state. For this reason, this paper presents a machine-learning-based vehicle obstruction scenario identification approach for V2V communications. Channel characteristics extracted from measurements form the datasets used to training, then the back-propagation neural network (BPNN) is trained and a scenario identification model is obtained. Subsequently, identification accuracy is verified by using validation data. The results show that the identification accuracy for vehicle obstruction scenarios is more than 97%, which indicates that the approach proposed in this paper shows good performance in vehicle obstruction scenario identification in V2V communications.

The millimeter wave (mmWave) communication with large bandwidth is a key enabler for both the fifth-generation mobile communication system (5G) and smart rail mobility. Thus, in order to provide realistic channel fundamental, the wireless channel at 22.6 GHz is characterized for a typical high-speed railway (HSR) environment in this paper. After importing the three-dimensional environment model of a typical HSR scenario into a self-developed high-performance cloud-computing Ray-Tracing platform - CloudRT, extensive raytracing simulations are realized. Based on the results, the HSR channel characteristics are extracted and analyzed, considering the extra loss of various weather conditions. The results of this paper can help for the design and evaluation for the HSR communication systems enabling smart rail mobility.

Powered by the Internet of Things (IoT), the cellular, vehicular, and other emerging networks, such as space-air-ground integrated networks, are expected to comprehensively evolve into the new era of the Internet of Everything (IoE) in which the propagation links between the IoT devices or sensors become diversified. Thus, a general propagation channel model is required. The line-of-sight (LOS) probability plays an essential role in the channel modeling, especially for built-up environments where the blockages from buildings are unfavorable to the signal transmission. The 4-D LOS probability model with considering the 3-D environment and frequency is comprehensively investigated in this article. Using the geometry-based stochastic method, the LOS probability is derived for arbitrary sizes, heights, and orientations of buildings in finely defined urban scenarios. The major contribution is the universality, simplicity, and good extension of the proposed model with comprehensive considerations of the influence of frequency, the type of urban, and the height of transceiver. The simulation results show that the propagation with higher frequency has a higher LOS probability. Moreover, the size, height, and density of building in urban environments are closely related to the LOS probability. The good agreements of the comparisons with the ray-tracing (RT) model and standard models show the accuracy of our proposed model. These results will be useful in the modeling of various channels and the design of IoT wireless communications systems.

Fast time-varying channel parameters and large penetration losses for signals passing through train carriages are two well-known challenges for wireless communications on high speed rails (HSRs). In this paper, we introduce, for the first time, backscatter technology into HSR wireless communications, which can address these two challenges and yet have low complexity of signal processing and low cost of circuit implementation compared with traditional solutions such as relaying or beamforming. Specifically, we propose a backscatter aided wireless transmission (BAWT) scheme and demonstrate that it outperforms the existing direct wireless transmission (DWT) scheme. We derive the upper and lower bounds of channel capacity for BAWT and prove that it exceeds that of DWT on certain conditions. We also propose the transceiver design for both BAWT and DWT, including joint carrier frequency offset and channel estimator, and signal detector. We show that BAWT, rather than DWT, can obtain the channel statistical information in practical applications due to fixed train antennas and unchanged tracks, which can be utilized to facilitate channel estimation. Finally, simulation results are provided to corroborate the proposed solutions.

Railway communications has attracted significant attention from both academia and industries due to the booming development of railways, especially high-speed railways (HSRs). To be in line with the vision of future smart rail communications, the rail transport industry needs to develop innovative communication network architectures and key technologies that ensure high-quality transmissions for both passengers and railway operations and control systems. Under high mobility and with safety, eco-friendliness, comfort, transparency, predictability, and reliability. Fifth-generation (5G) technologies could be a promising solution to dealing with the design challenges on high reliability and high throughput for HSR communications. Based on our in-depth analysis of smart rail traffic services and communication scenarios, we propose a network slicing architecture for a 5G-based HSR system. With a ray tracing-based analysis of radio wave propagation characteristics and channel models for millimeter wave (mmWave) bands in railway scenarios, we draw important conclusions with regard to appropriate operating frequency bands for HSRs. mymargin Specifically, we have identified significant 5G-based key technologies for HSRs, such as spatial modulation, fast channel estimation, cell-free massive multiple-input-multiple-output (MIMO), mmWave, efficient beamforming, wireless backhaul, ultrareliable low latency communications, and enhanced handover strategies. Based on these technologies, we have developed a complete framework of 5G technologies for smart railways and pointed out exciting future research directions.

Exploiting the latent low-rankness of tensors is crucial in tensor denoising. Classically, many methods use the Tucker model to find the low-rank structure of a tensor. Recently, the tensor train (TT) model has drawn wide attention owing to its powerful representation ability, and well-balanced matricization scheme for a tensor, and it has been successfully applied to various problems in signal processing, and machine learning applications. In this letter, we propose a tensor denoising method using the TT singular value decomposition, and information criteria, where we leverage the minimum description length to automatically estimate the TT rank. Furthermore, we establish the relationship between Tucker decomposition, and TT decomposition. In specific, the low Tucker rank of a tensor is the sufficient but unnecessary condition to the low TT rank. It unveils in theory the potential advantages of the TT model in characterizing the latent low-rankness of tensor. Denoising experiments on both synthetic data, and real HSI dataset demonstrate its superiority against Tucker-based methods.

Artificial intelligence (AI) based downlink channel state information (CSI) prediction for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems has attracted growing attention recently. However, existing works focus on the downlink CSI prediction for the users under a given environment and is hard to adapt to users in new environment especially when labeled data is limited. To address this issue, we formulate the downlink channel prediction as a deep transfer learning (DTL) problem, and propose the direct-transfer algorithm based on the fully-connected neural network architecture, where the network is trained in the manner of classical deep learning and is then fine-tuned for new environments. To further improve the transfer efficiency, we propose the meta-learning algorithm that trains the network by alternating inner-task and across-task updates and then adapts to a new environment with a small number of labeled data. Simulation results show that the direct-transfer algorithm achieves better performance than the deep learning algorithm, which implies that the transfer learning benefits the downlink channel prediction in new environments. Moreover, the meta-learning algorithm significantly outperforms the direct-transfer algorithm, which validates its effectiveness and superiority.

In this paper, we present a comprehensive framework for the performance analysis of dual-hop relaying communications with variable gain amplify-and-forward (AF) relays and operating in the presence of both multipath fading and shadowing, modeled by the Fisher-Snedecor F-distribution. Novel closed-form expressions for the probability density function (PDF) and the cumulative distribution function (CDF) of the end-to-end signal-to-noise ratio (SNR) of the considered system subject to hardware impairments are first derived. Single-integral expressions for the numerical evaluation of the n-th moment of the end-to-end SNR, the outage probability (OP), the ergodic capacity under different adaptive transmission schemes, the effective capacity and the average bit error rate (ABER) of M-ary modulation schemes are further presented. The proposed analytical expressions are valid for most of the well-known fading distributions, provided that the moment generating function (MGF) of the inverse SNR of each hop is readily available. For the special case of ideal hardware, it is shown that the above performance metrics can be expressed in closed-form. It is worth pointing out that the proposed analysis is valid even when the destination node is equipped with multiple antennas and maximal ratio combining (MRC) is employed. The correctness of the proposed mathematical analysis is validated through extensive numerically evaluated results accompanied with Monte-Carlo simulations.

Due to the bursty nature of Internet traffic, network service providers (NSPs) are forced to expand their network capacity in order to meet the ever-increasing peak-time traffic demand, which is however costly and inefficient. How to shift the traffic demand from peak time to off-peak time is a challenging task for NSPs. In this paper, we study the implementation of time-dependent pricing (TDP) for bandwidth slicing in software-defined cellular networks under information asymmetry and price discrimination. Congestion prices indicating real-time congestion levels of different links are used as a signal to motivate delay-tolerant users to defer their traffic demands. We formulate the joint pricing and bandwidth demand optimization problem as a two-stage Stackelberg leader-follower game. Then, we investigate how to derive the optimal solutions under the scenarios of both complete and incomplete information. We also extend the results from the simplified case of a single congested link to the more complicated case of multiple congested links, where price discrimination is employed to dynamically adjust the price of each congested link in accordance with its real-time congestion level. Simulation results demonstrate that the proposed pricing scheme achieves superior performance in increasing the NSP's revenue and reducing the peak-to-average traffic ratio (PATR).

Discriminating between line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, orLOS identification, is important for a variety of purposes in wireless systems, including localization and channel modeling. LOS identification is especially challenging in vehicle-to-vehicle (V2V) networks since a variety of physical effects that occur at different spatial/temporal scales can affect the presence of LOS. This paper investigates machine learning techniques for LOS identification in V2V networks using an extensive set of measurement data and then develops robust and efficient identification solutions. Our approach exploits several static and time-varying features of the channel impulse response (CIR), which are shown to be effective. Specifically, we develop a fast identification solution that can be trained by using the power angular spectrum. Moreover, based on the measurement data, we also compare three different machine learning methods, i.e., support vector machine, random forest, and artificial neural network, in terms of their ability to train and generate the classifier. The results of our experiments conducted under various V2V environments, which were then validated using$K$-fold cross-validation, show that our techniques can distinguish the LOS/NLOS conditions with an error rate as low as 1%. In addition, we investigate the impact of different training and validating strategies on the identification accuracy.

A reliable vehicle-to-vehicle (V2V) channel model is necessary for intelligent transportation systems (ITSs) design. Due to the high mobility of vehicles and the low heights of antennas, the line-of-sight (LOS) propagation paths in V2V communications are more likely to be obstructed by large vehicles such as buses. Therefore, it is worthwhile to conduct in-depth investigations on obstructed line-of sight (OLOS) propagation channels caused by vehicle obstructions. In this paper, actual V2V channel measurements with large vehicle obstructions at 5.9 GHz band are conducted. Based on the measured data, it can be found that the obstructions of large vehicles not only cause additional attenuation, but also significantly affect the angular distribution of multipath components (MPCs). In addition, a cluster-based dynamic V2V channel model is proposed for OLOS scenarios. In the proposed model, the influences of vehicle obstructions on path loss, delay and angle dispersion are intuitively embodied as changes in the statistical distribution of MPCs clusters. Finally, the rationality and accuracy of the proposed model is validated by comparing the measured and simulated channels. The results in the paper are useful for enriching the understanding of V2V channels and provide supports for vehicular communication systems design and performance evaluation.

Intelligent Transportation System (ITS) is more and more crucial in the modern transportation field, such as the applications of autonomous vehicles, dynamic traffic light sequences, and automatic road enforcement. As the upcoming fifth-generation mobile network (5G) is entering the deployment phase, the idea of cellular vehicle-to-everything (C-V2X) is proposed. The same 5G networks, coming to mobile phones, will also allow vehicles to communicate wirelessly with each other. Hence, 3.5 GHz, as the main sub-6 GHz band licensed in 5G, is focused in our study. In this paper, a comprehensive study on the channel characteristics for vehicle-to-infrastructure (V2I) link at 3.5 GHz frequency band is conducted through channel measurements and ray-tracing (RT) simulations. Firstly, the channel parameters of the V2I link are characterized based on the measurements, including power delay profile (PDP), path loss, root-mean-square (RMS) delay spread, and coherence bandwidth. Then, the measurement-validated RT simulator is utilized to conduct the simulations in order to supplement other channel parameters, in terms of the Ricean K-factor, angular spreads, the cross-correlations of abovementioned parameters, and the autocorrelation of each parameter itself. This work is aimed at helping the researchers understand the channel characteristics of the V2I link at 3.5 GHz and support the link-level and system level design for future vehicular communications of 5G.

In this paper, we investigate the edge caching and content delivery problem for both high-speed train (HST) passengers and low-mobility cellular users. Under multi-dimensional resources constraints, we formulate an optimization problem to minimize the content retrieval delay of HST passengers and meanwhile guarantee the delay requirements of cellular users. As the formulated problem is a mixed-integer nonconvex optimization problem, which is intractable directly, we propose an efficient iterative algorithm that optimizes the three decision variables (i.e., content placement, subchannel allocation, and transmission power allocation) alternately. In specific, Lagrangian multiplier is introduced to convert the constrained optimization, which transforms the content caching problem into a Lagrangian relaxed knapsack problem. Afterwards, the subchannel assignment problem is solved by the Hungarian algorithm with polynomial time complexity, and the power allocation strategy is obtained by the bisection method. Extensive simulations are carried out and results demonstrate that our proposed caching strategy can reduce the content retrieval delay by up to 25% in comparison with the benchmark strategy.

As one of the three major scenarios of fifth-generation (5G) communication system, massive machine type communications (mMTC) raises more challenges for development of new radio access technology. Unlike the human-to-human communications, the mMTC typically exhibit features such as massive number of devices, small-sized packets and sporadic transmission, which requires novel solutions to satisfy these features. In this paper, we propose a novel grant-free compressive sensing based solution with system level design to jointly conduct the active devices detection, channel estimation and data recovery without knowing the number of active devices. Specifically, in proposed solution, we integrate the sparsity information of channel coefficients and data symbols and exploit the constellation information of modulation and the data diversity of different devices to enhance the performance of receiver. Simulation results demonstrate that the proposed solutions have improve the performance of active device detection, channel estimation, data recovery and the throughput of the system.

Multiuser multiple input multiple output (MU-MIMO) millimeter wave (mmWave) communication is considered as a key technology to provide multi-gigabit train-to-ground wireless connections in the high-speed railway (HSR) system. Considering the power consumption and hardware constraint, the hybrid beamforming (BF), which combines analog BF and digital BF, is widely adopted in the MU-MIMO mmWave systems. In this paper, we target on an efficient hybrid BF structure design in HSR scenario with taking the practical HSR mmWave channel model into consideration. Specifically, the hybrid BF design aims at maximizing the overall throughput and is formulated as an optimization problem which is proved to be nonconvex and NP-hard. Therefore, a suboptimal yet efficient two-stage solution is proposed, where a weighted minimum mean square error (WMMSE) based beamforming strategy is exploited to devise the hybrid beamformer at the base station (BS) at the first stage, and the orthogonal matching pursuit (OMP) approach is leveraged to decouple the digital BF and analog BF at BS at the second stage. Simulation results demonstrate that higher overall throughput can be achieved by the proposed hybrid BF scheme compared to other state-of-the-art benchmarks.

The surge of demand of the Internet of Things (IoTs) poses more requirements for cellular communication systems, such as higher levels of reliability, latency and supported number of devices. A common scenario of IoT is massive machinetype communications (mMTC). The characteristics of mMTC, i.e. the massive number of sensors, small packets and sporadic transmission, require novel methods to detect the active devices, estimate their channel state information and recovery their data with high accuracy and low latency. In this paper, we propose a new compressive sensing based method to jointly conduct active device detection, channel estimation and data recovery. As a high level description of the proposed method, we improve the performance of active device detection and channel estimation by using side information brought from the data recovery, and improve the performance of data recovery in successive interference cancellation by using the side information brought from the active device detection. Simulation results demonstrate that the proposed method has improved data recovery performance.

Vehicular fog computing (VFC) has emerged as a cost-efficient solution for task processing in vehicular networks. However, how to realize stable and reliable task offloading under information uncertainty remains a critical challenge. In this paper, we propose a matching-learning-based task offloading algorithm to address this challenge. First, a low-complexity and stable task offloading mechanism is proposed to minimize the total network delay based on the pricing-based matching. Second, we extend the work to the scenario of information uncertainty, and develop a matching-learning-based task offloading algorithm by combining matching theory and upper confidence bound (UCB) algorithm. Simulation results demonstrate that the proposed algorithm can achieve bounded deviation from the optimal performance without the global information.

Massive machine-type communication (MTC) with sporadically transmitted small packets and low data rate requires new designs on the PHY and MAC layer with light transmission overhead. Compressive sensing based multiuser detection (CS-MUD) is designed to detect active users through random access with low overhead by exploiting sparsity, i.e., the nature of sporadic transmissions in MTC. However, the high computational complexity of conventional sparse reconstruction algorithms prohibits the implementation of CS-MUD in real communication systems. To overcome this drawback, in this paper, we propose a fast Deep learning based approach for CS-MUD in massive MTC systems. In particular, a novel block restrictive activation nonlinear unit, is proposed to capture the block sparse structure in wide-band wireless communication systems (or multi-antenna systems). Our simulation results show that the proposed approach outperforms various existing algorithms for CS-MUD and allows for ten-fold decrease of the computing time.

High-speed railway (HSR) wireless communications are required to support high data rate with seamless connectivity. In this paper, we investigate the outage performance of a downlink single-cell non-orthogonal multiple access (NOMA) based wireless network in HSR scenarios, where it is challenging to derive the perfect channel state information (CSI) and the distribution of the users are quite different from the traditional cellular scenarios. More specifically, the performance of NOMA over composite large-scale and Rician fading channel is studied with two kinds of partial CSI, e.g., imperfect small-scale CSI and no small-scale CSI. We derive the exact closed-form expression of the outage probability based on partial CSI, respectively, by using the Gauss-Chebyshev quadrature method. Finally, simulation results evidence the validity of the derived results and show that the average outage probability of NOMA systems outperforms conventional orthogonal multiple access systems.

Unmanned aerial vehicles (UAVs) communications are considered as a promising technology in various areas. In this paper, a three-dimensional (3D) non- stationary geometry-based stochastic model (GBSM) is proposed for UAV air-to- air (A2A) communication environments. The proposed GBSM considers not only both the ground surface and roadside reflections, but also the arbitrary trajectories of both UAV terminals. Based on the proposed model, some important statistical properties such as time- variant time-frequency correlation function and the Doppler power spectrum are derived and analyzed. Finally, numerical results show that a variation of the velocity and moving direction of the UAV has major impacts on the statistical properties of the radio channels, which indicates its usefulness for the performance analysis of UAV communication systems under non- stationary conditions.

For radio propagation, the wireless channel generally changes more significantly in the non-line-of-sight (NLOS) scenario compared to the line-of-sight (LOS) scenario. For vehicle-to-vehicle (V2V) communications, the most typical NLOS scene is large vehicle obstructions. Therefore, in order to understand the radio propagation mechanism and channel characteristics in V2V communication, it is necessary to carry out indepth investigations on V2V channel under the condition of large vehicle obstructions. In this paper, actual V2V channel measurements with large vehicle obstructions in the urban environment at 5.9 GHz are carried out. Based on the measured data, extraction and analysis of channel parameters are performed. Specifically, this paper focuses on the directional analysis of V2V channel in the case of large vehicle obstructions. It can be find that the occlusion of large vehicles not only causes additional attenuation of signal energy, but also significantly affects the angular distribution of multipath components in three- dimensional space, and brings larger angular dispersion. These results would be useful for establishing a more accurate channel model and serveing the design of V2V communication system.

Powered by the Internet of Things, the vehicular ad-hoc networks are expected to evolve into the Internet of Intelligent Vehicles in which each vehicle can be much more efficient in various vehicular and transportation applications. In order to realize this vision, a seamless low-latency and ultrareliable vehicle-to-vehicle (V2V) communication network is required. Thus, it is of importance to characterize the V2V channels in various realistic environments, especially when the line-of-sight between transmitter (Tx) and receiver (Rx) is obstructed. In this paper, we characterize obstructed V2V channels in the 5-GHz band through measurement-calibrated ray-tracing (RT) simulations. To begin, the main objects in the real world are divided into two groups: 1) the small-scale structures (e.g., lampposts, traffic signs, etc.) and 2) the large-scale structures (such as buildings and ground). Then, we integrate the radar cross sections of the small-scale structures into our RT simulator through a framework based on high frequency prediction techniques. For the large-scale structures, we calibrate the electromagnetic and scattering parameters of the large-scale structures through V2V channel measurements. After such integration and calibration, extensive RT simulations for V2V channels with Tx and Rx located on vehicles traveling in the opposite or same direction are realized with various antenna deployments in urban and open space environments, with and without sloped terrain. Based on the RT results, we characterize the path loss, shadow fading, and delay spread of the channel for each case, and show agreement with measured results in the literature for all these channel characteristics.

Hyperparameter optimization still remains the core issue in Gaussian processes (GPs) for machine learning. The classical hyperparameter optimization scheme based on maximum likelihood estimation is impractical for big data processing, as its computational complexity is cubic in terms of the number of data points. With the rapid development of efficient parallel data processing on ever cheaper and more powerful hardware, distributed models and algorithms will become ubiquitous. In this letter, we propose an alternative distributed GP hyperparameter optimization scheme using the efficient proximal alternating direction method of multipliers, proposed by Honget al.in 2016, and we derive the closed-form solution for the local sub-problems. In contrast to the existing schemes of similar kind, our proposed one well balances the computational load on each local machine and the communication overhead required for global consensus of the local hyperparameter estimates. The proposed scheme can work in either a synchronous or an asynchronous manner, thus very flexible to be adopted in different computing facilities. Experimental results with both synthetic and real datasets validate the outstanding performance of the proposed scheme.

In recent years, unmanned aerial vehicle (UAV) communication technology has played an important role in both military and civilian applications. However, with the rapid development of military equipment, the execution efficiency of single UAVs is often limited, for which complex combat missions cannot be completed well. Therefore, UAV swarm has become an important research trend in the field of UAVs. In this paper, we consider the problem of channel estimation and self-positioning for the UAV swarm, where multiple small UAVs are displaced by arbitrarily unknown displacements due to the dynamic moving. To explore the physical characteristics of UAV swarm, the parameters of the channel are decomposed into the direction of arrival (DOA) information, the relative position information, and the channel gain information. Utilizing the rank reduction (RARE) estimator, DOAs of the different target users can be estimated efficiently, regardless of the position of the UAVs. After obtaining the DOA information, we estimate the channel gain information using small amount of training resources, which significantly reduces the training overhead and the feedback cost. Moreover, the unknown displacements among UAVs can be self-recovered from the mixed integer nonlinear programming (MINLP). To reduce the computational complexity, we develop both the sphere decoding (SD) and the least square (LS) based methods. The deterministic Cramér-Rao bound (CRB) of the self-positioning estimation is derived in closed-form. Finally, numerical examples are provided to corroborate the proposed studies.

High power consumption and expensive hardware are two bottlenecks for practical massive multiple-input multiple-output (mMIMO) systems. One promising solution is to employ low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). In this paper, we consider a general multipair mMIMO relaying system with a mixed-ADC/DAC architecture, in which some antennas are connected to low-resolution ADCs/DACs, while the rest of the antennas are connected to high-resolution ADCs/DACs. Leveraging on the additive quantization noise model, both exact and approximate closed-form expressions for the achievable rate are derived. It is shown that the achievable rate can approach the unquantized one by using only 2-3 bits of resolutions. Moreover, a power scaling law is presented to reveal that the transmit power can be scaled down inversely proportional to the number of antennas at the relay. We further propose an efficient power allocation scheme by solving a complementary geometric programming problem. In addition, a tradeoff between the achievable rate and power consumption for different numbers of low-resolution ADCs/DACs is investigated by deriving the energy efficiency. Our results reveal that the large antenna array can be exploited to enable the mixed-ADC/DAC architecture, which significantly reduces the power consumption and hardware cost for practical mMIMO systems.

The papers in this special section provides a forum to present recent advances on 5G mobile communications f(5G) tactile Internet. The Internet, which was created to provide resilient and interoperable communication across the globe, evolved to transport a vast amount of content with which to enrich our real-life experience. Pervasive ultra-broadband, programmable networks, and cost reduction of IT systems are paving the way to new services and commoditization of telecommunications infrastructure while lowering entry barriers for new players and giving rise to new value chains. Today, it provides a depth of information and social sophistication that rivals the real world. The Tactile Internet, the next evolutionary step, will enable remote, real-time physical interactionwith real and virtual objects, creating a two-way interactive experience in which boundaries between the real world and virtual world will blur.

Multi-link transmission is one of the promising communication techniques capable of improving system capacity and cell-edge user spectral efficiency in railway communication networks. The performance of multi-link transmission heavily depends on the propagation characteristics of radio channels, especially the correlation property between multiple radio channels. Thus, it is important to characterize the multi-link channel in railway communication networks. In this paper, extensive wideband measurements in a viaduct railway environment at 460 MHz with two base stations and one mobile station are performed. Large-scale parameters (LSPs), including large-scale fading, Ricean K-factor, delay spread, and angle spread, are extracted from the measurement data. Based on the measurements, auto-correlation and cross-correlation properties of each LSP are investigated. A Markov-based multi-link tapped-delayline model for railway communications is established, where the Markov chains are introduced to model the birth and death state of multipath components in multi-link scenarios. Using the relationship between the correlation coefficients of complex random variables (RVs), the amplitude and the phase of taps with different delays are modeled as correlated RVs. The proposed channel model is implemented and validated with measurements.

Characterization and modeling of massive multiple-input multiple-output (MIMO) channel has been one of the research hotspots in the field of wireless communications. One important feature of the massive MIMO channel is the spatial non-stationarity. To statistically model the spatial non-stationary massive MIMO channels, a cluster-based channel model is proposed in this paper. The model incorporates both inter- and intra-cluster properties and the cluster evolution over the large-scale array. A hybrid data processing scheme is applied to extract the multipath components (MPCs) and clustering the MPCs over a large-scale antenna array. The global angular spread, cluster angular spread, and cluster delay spread are modeled with log-normal distributions. Observed cluster length and MPC length within clusters, which are introduced to describe the cluster existence over the array and MPCs existence within the cluster, respectively, are statistically modeled with the exponential distributions. Moreover, both the cluster and MPC arrival intervals, which are used, respectively, to describe the cluster occurrence position on the array and MPC occurrence position within the cluster, can be statistically modeled with the uniform distributions. Finally, the model implementation is validated by comparing with the different channel performance metrics between measurements and simulations.

With the rapid development of wireless communications, the understanding of three-dimensional (3D) propagation channels becomes essential for design and testing of some new wireless technologies, e.g., massive multiple-input multiple-output (MIMO) and full dimensional beamforming. To not only fully exploit the 3D multiplexing but also circumvent the size limitation of base station (BS), antenna elements in massive MIMO are usually arranged both horizontally and vertically. Based on an elaborate channel measurement campaign conducted at 11 GHz in a lobby environment, a 3D extended cluster-based channel model is proposed in this paper for massive multiple-input single-output (MISO) multi-user communications. In the model, the channel characteristics in both azimuth and elevation dimensions, and the visibility regions which are parametrized by observed cluster lengths across the large-scale array in both horizontal and vertical directions, are taken into consideration. Moreover, the spherical wavefront phenomenon observed from the measurements is also incorporated in the model. Model parametrization, implementation, and validation are presented in detail. Validations show that the proposed model can accurately reflect the realistic channel, and the spatial non-stationarity and the spherical wavefront should be carefully considered in the channel models for large-scale array communications.

In this paper, we consider the problem of self- positioning for the unmanned aerial vehicle (UAV) swarm, where multiple small UAVs are arranged by unknown displacement due to the dynamic moving. These multiple small UAVs also formulate a virtual massive antenna array that can estimate the direction of arrivals (DOAs) of target users efficiently, regardless of the relative position of the UAVs. After obtaining the DOA information, the unknown displacements among UAVs can also be self-recovered, automagically realizing the important functionality of self-positioning for UAV swarm. The self-positioning problem falls into the category of the mixed integer nonlinear programming (MINLP). To reduce the computational complexity, we develop a novel self-positioning algorithm based on least square (LS) method. Moreover, the deterministic Cramer-Rao bound (CRB) of the self-positioning estimation is derived in closed- form. Finally, numerical examples are provided to corroborate the proposed studies.

As the advent of cognitive radios, blind encoder identification has attracted increasingly attentions since it plays an important role in blind signal processing. The existing works mainly focus on additive white Gaussian noise (AWGN) channel, while the blind identification in multipath scenarios has not been sufficiently investigated. In this paper, we consider the blind low-density parity-check (LDPC) codes identification in the presence of unknown multipath fading channel. Then, a likelihood-based classifier is proposed using the expectation maximization (EM) algorithm to obtain the maximum likelihood estimates of the unknown parameters, including multipath fading channel and modulated symbols. Then, we adopt an average log-likelihood ratio (LLR) estimator to classify the unknown encoder. Numerical results show that the proposed algorithm provides promising identification performance in multipath channels, especially in the low signal- to-noise ratio region.

This paper investigates the transceiver design in a wireless multiple-input multiple-output (MIMO) switching network in which multiple users exchange messages via a multi-antenna relay. Previous work assumed that perfect channel state information (CSI) is known at the relay, which is intractable in practice. Regarding different types of CSI imperfection of uplink and downlink transmission, a statistical and a norm- bounded uncertainty model are adopted to characterize the imperfect CSI of uplink and downlink respectively for the transceiver design. An optimization problem is formulated by minimizing the worst-case mean square error (MSE) with respect to channel uncertainty in the constraint of the maximum transmit power of the relay. Since the problem is non-convex and difficult to solve, we divide the original problem into two subproblems in which the channel uncertainty of uplink and downlink are treated individually. For the uplink subproblem, we propose an iterative approach to determine a closed- form solution of the robust transceiver. In addition, a Sylvester equation is formulated which closed-form solution is provided explicitly for the downlink channel uncertainty subproblem. An overall iterative algorithm is proposed by combining the two algorithms for the subproblems, which can solve the original problem efficiently in a low complexity. Simulation results show that the proposed iterative algorithm reduces the sum MSE significantly in the channel uncertain scenarios.

With the increasing popularity of unmanned aerial vehicles (UAVs), it is foreseen that they will play an important role in broadening the horizon of mobile crowd sensing (MCS). However, the on- board battery capacity of UAVs imposes a limitation on their endurance capability and performance. In this paper, we investigate the joint optimization of route planning and task assignment for UAV-aided MCS from an energy efficiency perspective. The formulated NP-hard problem is transformed into a two-sided two-stage matching problem, in which the route planning problem is solved in the first stage based on dynamic programming (DP), and the task assignment problem is addressed in the second stage by exploring the Gale-Shapley (GS) algorithm. Numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme.

With the advent of vehicle-to-vehicle (V2V) communication, the research on the propagation channel modeling between vehicles becomes a vital topic. In this paper, measurements of V2V radio channel are conducted in a suburban scenario. The measurements are performed at the center frequency of 5.9 GHz with a bandwidth of 50 MHz. A single omnidirectional antenna is placed at the transmitter (Tx) vehicle, and a 16 dual-polarized cylindrical antenna array is adopted at the receiver (Rx) vehicle. The multipath components (MPCs) are extracted based on the Space- Alternating Generalized Expectation-maximization (SAGE) algorithm, and we also apply an automatic clustering and tracking algorithm to cluster the MPCs and track the time-variant clusters for our measured V2V channel. Under such a scheme, a time- variant cluster-based channel modeling approach is proposed. The model parameters include a number of inter-cluster parameters and some intra-cluster parameters, which are provided with detailed analysis. The proposed model is useful for characterizing the time-variant V2V channel with high accuracy and low complexity.

Massive multiple-input multiple-output (MIMO) transmission is one of the key technologies utilized in the fifth generation (5G) wireless communication system. For the algorithm design and the performance evaluation of massive-MIMO, channel characterization based on real measurements is necessary. In this paper, we introduced a measurement campaign conducted in a Line-of-Sight (LoS) scenario of a suburban outdoor environment. The channel sounder is equipped with a 64-element transmitter array and a 64-element receiver array. Multiple aspects of massive-MIMO channel characteristics are considered, including the power delay profile, the K-factor and the delay-spread. We found that for the outdoor LoS scenario considered, the polarization plays an important role generating orthogonal channels observed through large-scale antenna arrays.

With the increasing quality of service (QoS) demands for indoor multimedia applications, millimeter wave (mmWave) communications are emerging as a promising candidate for the wireless personal area networks (WPANs). On the one hand, it has the advantage of providing several-Gbps transmission rate. However, due to the unique characteristics in 60-GHz frequency band, such as high propagation loss, beamforming is fully exploited for mmWave links to achieve directional transmission and reception. In this paper, we propose a novel QoS aware scheduling algorithm for concurrent transmission in mmWave WPANs based on coalition game. First, we formulate the problem of concurrent transmission scheduling into a non-convex integer programming problem. Then, we propose a coalition game based algorithm to maximize the number of flows satisfying the corresponding QoS requirements, while improving the network resource utilization effectively. Besides, our proposed algorithm converges to a Nash-stable equilibrium with greatly reduced complexity. Through extensive simulations under various system parameters, we demonstrate our scheme achieves better network performance in terms of the throughput and the number of flows scheduled successfully, compared with existed protocols.

Massive multiple-input multiple-output (MIMO) has been selected as one of the key technologies of the fifth generation mobile communication system (5G). It can provide high spectral and power efficiency, thus it is suitable to be deployed in different hotspot scenarios. In this paper, a channel measurement campaign using a wideband channel sounder and a 256-element virtual uniform rectangular array (URA) for massive MIMO communications is presented. The measurements were respectively conducted at 6 GHz and 11 GHz, and the subway station environment was considered. The typical channel parameters, root mean square (RMS) delay spread and coherence bandwidth, are analyzed based on the measurements. Moreover, the channel characteristics in angular domain are obtained by applying the space-alternating generalized expectation-maximization algorithm (SAGE). The SAGE estimates are validated by relating them to the physical environment. The overall elevation angle distribution of multipaths is found to be fitted well with Laplace distributions. The global angular spread, including the azimuth spread of departure (ASD) and elevation spread of departure (ESD), are both fitted well with Lognormal distributions. The results in this paper can be fed into the new channel simulator for massive MIMO, and are useful for the design and application of the practical massive MIMO system in the future.

Massive multiple-input multiple-output (MIMO) is one of the key technologies of the fifth generation mobile communication system (5G). For development of massive MIMO systems, the directional properties of their wireless channel are of great importance. In this paper, directional analysis is presented based on a channel measurement campaign for massive MIMO communications in a theater environment. The measurements were conducted at 11 GHz with a bandwidth of 200 MHz. A 256-element virtual uniform rectangular array (URA) was used during the measurements, and the channel characteristics in angular domain are determined by applying the space-alternating generalized expectation-maximization algorithm (SAGE). According to the SAGE estimates, channel nonstationarity over the large-scale array is discussed. The dominant scatterers are identified, by directly relating the SAGE estimates to the physical objects in the measurement environment. Moreover, direction spread over the large-scale array at the Tx side is estimated, and channel performance on beamforming is evaluated. Corresponding analysis is given in detail. These results are useful for the design and application of the practical massive MIMO system in the future.

In this paper, a wireless wideband downlink transmission through a multipath and highly time-varying channel is considered for high-speed railway. In this scenario, non-Gaussian noise is generally involved at the receiver side which may lead to communication outage. In previous studies, the optimal receiver algorithms we designed are only applicable for Gaussian noise and time-invariant channels. But for impulsive noise and time-varying multipath channels, new algorithms are needed to be proposed. In order to resolve the issue, a synthesis scheme is proposed in this paper. First, we adopt impulsive noise detection algorithm to discriminate impulsive noise from the received signal. Then the discriminated noise can be suppressed by the blanking algorithm. After that, the refined signal goes through a well-designed beamforming network, which transforms the received signal with varying frequency offsets into an angle domain. Thus, the frequency offset in each angle is near-constant which can be accurately estimated. The simulation results indicate that the proposed scheme significantly improves the reliable performance of the high-speed railway communication systems.

As a combination of spatial modulation (SM) system and massive multiple‐input multiple‐output (MIMO) system, massive SM aided MIMO (SM‐MIMO) system is recently proposed. In broadband scenarios, single‐carrier (SC) modulation is introduced to massive SM‐MIMO system, thus massive SC‐SM MIMO system is proposed for uplink multi‐user transmission over frequency‐selective fading channels. In this study, the uplink spectral efficiency (SE) of multi‐cell massive SC‐SM MIMO system is analysed, meanwhile the pilot contamination effect is taken into consideration. A tight SE lower bound of multi‐cell massive SC‐SM MIMO system is proposed with maximum ratio (MR) combining, which also takes into account the imperfect channel estimation, transmit antenna correlation and path loss. The tightness of the authors' proposed closed‐form SE lower bound is shown via simulation results. The optimal pilot reuse factor can be determined with different system configurations by simulations, and the pilot reuse factor that is larger than one is more suitable for less TAs and user equipments.

With the increasing popularity of unmanned aerial vehicles (UAVs), it is foreseen that they will play an important role in broadening the horizon of mobile crowd sensing (MCS). Specifically, UAV-aided MCS allows autonomous data collection anytime and anywhere due to the capability of fast deployment and controllable mobility. However, the on-board battery capacity of UAVs imposes a limitation on their endurance capability and performance. In this paper, we consider the fixed-wing UAV-aided MCS system and investigate the corresponding joint route planning and task assignment problem from an energy efficiency perspective. The formulated joint optimization problem is transformed into a two-sided two-stage matching problem, in which the route planning problem is solved in the first stage based on either dynamic programming or genetic algorithms, and the task assignment problem is addressed in the second stage by exploring the Gale-Shapley algorithm. We provide a comprehensive theoretical analysis, and elaborate the procedures of practical implementation. Numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme.

In industrial internet of things (IIoT), massive machine-type communications (mMTC) system is introduced to provide communication services for large-scale industrial devices. To achieve massive access with scarce radio resources in mMTC, multiple access protocol has to be reconsidered to cope with the resulting collision problem. Currently, various novel multiple access schemes have been proposed. Among them, coded tandem spreading multiple access (CTSMA) is an emerging physical (PHY) layer multiple access scheme to resolve the collision in mMTC. In this paper, a multislot design scheme of CTSMA is proposed to further promote the collision resolution capability. In this scheme, CTSMA is combined with the MAC layer scheme coded slotted ALOHA (CSA). The analysis shows that the multislot design can effectively take advantage of CTSMA and CSA to enhance the mMTC system performance for the short uplink contention period, which is suitable for the IIoT applications.

Following the tremendous success of 2G and 3G mobile networks and the fast growth of 4G, the next generation mobile networks (5G) was proposed aiming to provide infinite networking capability to mobile users. Differentiated from 4G, a benefit offered by 5G is much more than the increased maximum throughput. It aims to involve and benefit from many current technical advances including Industrial Internet of Things (IIoT). As the IIoT integrates many heterogeneous networks, such as Wireless Sensor Networks (WSNs), Wireless Local Area Networks (WLANs), Mobile Communication Networks (3G/4G/LTE/5G), Wireless Mesh Networks (WMNs) and wearable health care systems, it is critical to design self-organizing and smart protocols for heterogeneous ad hoc networks in various IoT applications, such as cyber-physical systems, cloud computing for heterogeneous ad hoc networks, large-scale sensor networks, data acquisition from distributed smart devices, green communication and applications, environmental monitoring and control, etc. Moreover, based on the survey conducted by the World Health Organization, the world will lack 12.9 million healthcare workers by 2035. Hence, it is important to develop wearable healthcare systems to perform self-health monitoring. In general, wearable healthcare systems demands low power consumption and high measurement accuracy. Smart technologies including green electronics, green radios, fuzzy neural approaches and intelligent signal processing techniques play important roles in the developments of the wearable healthcare systems. Therefore, this special issue provides a forum to discuss the recent advances on 5G and beyond mobile technologies and applications for IIoT.

More people prefer to using rail traffic for travel or for commuting due to its convenience and flexibility. As the record of the maximum speed of rail has been continuously broken and new applications are foreseen, the high-speed railway (HSR) communication system requires higher data rate with seamless connectivity, and therefore, the system design faces new challenges to support high mobility. Millimeter-wave (mmWave) technologies are considered as candidates to provide wideband communication. However, mmWave is rarely explored in HSR scenarios. In this paper, channel characteristics are studied in the 5G mmWave band for typical HSR scenarios, including urban, rural, and tunnel, with straight and curved route shapes. Based on the wideband measurements conducted in the tunnel scenario by using the “mobile hotspot network” system, a 3-D ray tracer (RT) is calibrated and validated to explore more channel characteristics in different HSR scenarios. Through extensive RT simulations with 500-MHz bandwidth centered at 25.25 GHz, the power contributions of the multipath components are studied, and the dominant reflection orders are determined for each scenario. Path loss is analyzed, and the breakpoint is observed. Other key parameters, such as Doppler shifts, coherence time, polarization ratios, and so on, are studied. Suggestions on symbol rate, sub-frame bandwidth, and polarization configuration are provided to guide the 5G mmWave communication system design in typical HSR scenarios.

Heterogeneous cellular networks (HCNs) with millimeter wave (mm-wave) communications included are emerging as a promising candidate for the fifth generation mobile network. With highly directional antenna arrays, mm-wave links are able to provide several Gbps transmission rate. However, mm-wave links are easily blocked without line of sight. On the other hand, device to device (D2D) communications have been proposed to support many content-based applications and need to share resources with users in HCNs to improve spectral reuse and enhance system capacity. Consequently, an efficient resource allocation scheme for D2D pairs among both mm-wave and the cellular carrier band is needed. In this paper, we first formulate the problem of the resource allocation among mm-wave and the cellular band for multiple D2D pairs from the view point of game theory. Then, with the characteristics of cellular and mm-wave communications considered, we propose a coalition formation game to maximize the system sum rate in statistical average sense. We also theoretically prove that our proposed game converges to a Nash-stable equilibrium and further reaches the near-optimal solution with fast convergence rate. Through extensive simulations under various system parameters, we demonstrate the superior performance of our scheme in terms of the system sum rate compared with several other practical schemes.

Non-stationary mobile-to-mobile (M2M) channel modeling has gained strong momentum as it is vital for developing M2M communications technology. Traditional geometry-based channel models (GSCMs) for M2M communications usually assume fixed velocity and moving direction, which differs from the realistic M2M scenarios and also makes it difficult to incorporate non-stationarity of channel into the regular-shaped GSCMs. In this paper, a mobility model-based method is proposed to incorporate non-stationarity into M2M channel modeling by introducing dynamic velocities and trajectories. A revised Gauss-Markov mobility model is first presented together with the cluster-based two-ring M2M reference model. The mobility model uses tuning parameters to adjust the degree of mobility randomness and covers different M2M mobility trajectories. Then, a closed-form time-variant time-frequency correlation function and the Doppler power spectrum are derived from the model. Based on the numerical analysis, it is found that for a regular-shaped GSCM with a fixed M2M scattering environment, the motion does not introduce non-stationarity, however, the dynamic motion (i.e., the changes of velocity and moving direction) leads to non-stationarity, which is reflected by the time-variant time correlation function and Doppler spectrum. Different propagation modes, cluster number, and intra-cluster nonisotropic scattering also have major impacts on channel non-stationarity. Moreover, the randomness of the mobility model is found to significantly increase the degree of channel non-stationarity. These conclusions are useful for M2M non-stationary channel simulation and communication system evaluation.

With the explosive growth of mobile traffic, diverse mobile applications with high throughput demands have gained considerable attention from both academia and industry. However, dynamics due to human mobility impose serious issues on high throughput communications. Although millimeter-wave (mm-wave), relays, and concurrent transmission have been applied for throughput improvement, how to achieve high throughput transmission in mobility-aware scenarios is still challenging. In this paper, we propose a throughput-efficient service scheduling (TESS) scheme, which exploits multi-hop relay and concurrent transmissions with the consideration of human mobility. In TESS, we first propose a mobility-aware transmission scheduling scheme in single mm-wave cell. The proposed scheduling scheme consists of a relay path planning algorithm and a global time scheduling algorithm. In the relay path planning algorithm, we establish multi-hop relay transmission paths from base station to service points. In the global time scheduling algorithm, we schedule concurrent transmission in relay paths. Furthermore, the proposed TESS scheme is extended for multi-cell scenarios. Through extensive performance evaluations under realistic human mobility trajectories, we demonstrate the superior performance of TESS in terms of system throughput compared with the state-of-the-art schemes.

With the thriving of internet of things (IoT) industry, the importance of massive machine type communication (mMTC) is increasingly significant. Due to massive connections in mMTC, grant-free random access is preferred for the sake of saving the control signaling overheads. Currently, various techniques has been proposed to address the challenges in the grant-free random access system. Among them, tandem spreading is a novel spreading mechanism to solve the collision problem. In this paper, a generalized version of tandem spreading is introduced as the coded tandem spreading multiple access (CTSMA). Compared to the previous work, CTSMA enables multiple redundancy segments to support more simultaneous transmissions. Moreover, a flexible tandem spreading codebook design is introduced. Simulation results show the advantage of CTSMA on other related schemes and indicate the factors to influence the CTSMA performance under different conditions.

Channel measurements of large-scale multiuser multiple-input multiple-output (MU-MIMO) radio propagation channels are presented. In the setup, three users with patch antennas communicate simultaneously with a base station (BS) equipped with a large antenna array in an indoor environment. Both a uniform linear array (ULA) and a uniform planar array (UPA) are used, and their relative ability to separate MU-MIMO signals is examined. At the mobile station (MS) side, the effect of inter-user spacing (i.e., the spacing between different users) is investigated. This evaluation is done by means of the correlation matrix distance metric (between each pair of users) and the singular value spread of the system. Our investigation shows that the users can be spatially separated in a large antenna array system in line-of-sight propagation conditions even when they are located close to each other. Furthermore, the users tend to be more separable when a ULA is adopted, compared to using a UPA. Finally, we also confirm that larger user separation distance results in increased channel orthogonality by measurements.

In this paper, we present a measurement campaign of indoor massive MIMO channels by using a wideband channel sounder and a 64-element virtual linear array. The measurements are conducted at 6 GHz, with a bandwidth of 200 MHz. Both the light-of-sight (LOS) and obstructed-line-of-sight (OLOS) scenarios are considered in measurements. The Frequency Domain Space-Alternating Generalized Expectation maximization algorithm (FD-SAGE) is used to determine the channel characteristics in angular domain. In order to validate the obtained FD-SAGE estimates, the power azimuth spectrum (PAS) has been calculated using the Bartlett Beamformer (BBF). The non-stationarity of radio channels, which is reflected by power of the estimated MPCs, azimuth of departure (AOD), and azimuth of arrival (AOA), is discussed. The direction spread is estimated, which reflects spatial dispersion of wireless channel. It is also found that the diffuse scattering components at 6 GHz in OLOS scenario is richer than that in LOS scenario, and the magnitude of the diffuse scattering components are comparable with the specular components. It is suggested that the non-stationarity of the diffuse scattering components should not be neglected in the indoor massive MIMO channel modeling at the frequency bands below 6 GHz.

In the real-world environments, multipath components (MPCs) of wireless channels are generally distributed as groups, i.e., clusters. Modeling the clustered MPCs is important and necessary for channel modeling and an automatic clustering algorithm is thus required. This paper proposes a novel Kernel-power-density (KPD) based algorithm for MPC clustering. It uses the Kernel density to incorporate the modeled behavior of MPCs and takes into account the power of the MPCs. The proposed algorithm only considers the K nearest MPCs in the density estimation to better identify the local density variations of MPCs. Simulations validate the KPD algorithm and almost no performance degradation is found even with a large number of clusters and large cluster angular spread. The KPD algorithm enables applications with no prior knowledge about the clusters such as number and initial locations. It can be used for the cluster based channel modeling for 4G#x002F;5G communications.

The millimeter wave (mmWave) communications and massive multiple-input multiple-output (MIMO) are both widely considered to be the candidate technologies for the fifth generation mobile communication system. It is thus a good idea to combine these two technologies to achieve a better performance for large capacity and high data-rate transmission. However, one of the fundamental challenges is the characterization of mmWave massive MIMO channel. Most of the previous investigations in mmWave channel only focus on single-input single-output links or MIMO links, whereas the research of massive MIMO channels mainly focus on a frequency band below 6 GHz. This paper investigates the channel behaviors of massive MIMO at a mmWave frequency band around 26 GHz. An indoor mmWave massive MIMO channel measurement campaign with 64 and 128 array elements is conducted, based on which, path loss, shadow fading, root-mean-square (RMS) delay spread, and coherence bandwidth are extracted. Then, by using our developed ray-tracing simulator calibrated by the measurement data, we make the extensive ray-tracing simulations with 1024 antenna elements in the same indoor scenario, and get insights into the variation tendency of mean delay and the RMS delay with different array elements. It is observed that the measurement and the ray-tracing-based simulation results have reached a good agreement.

We present a measurement campaign to characterize an indoor massive multiple-input multiple-output (MIMO) channel system, using a 64-element virtual linear array, a 64-element virtual planar array, and a 128-element virtual planar array. The array topologies are generated using a 3D mechanical turntable. The measurements are conducted at 2, 4, 6, 11, 15, and 22 GHz, with a large bandwidth of 200 MHz. Both line-of-sight (LOS) and non-LOS (NLOS) propagation scenarios are considered. The typical channel parameters are extracted, including path loss, shadow fading, power delay profile, and root mean square (RMS) delay spread. The frequency dependence of these channel parameters is analyzed. The correlation between shadow fading and RMS delay spread is discussed. In addition, the performance of the standard linear precoder—the matched filter, which can be used for intersymbol interference (ISI) mitigation by shortening the RMS delay spread, is investigated. Other performance measures, such as entropy capacity, Demmel condition number, and channel ellipticity, are analyzed. The measured channels, which are in a rich-scattering indoor environment, are found to achieve a performance close to that in independent and identically distributed Rayleigh channels even in an LOS scenario.

Massive machine-type communication (MMTC) is the key technology to meet the flourishing Internet of things industry. One prominent characteristic of MMTC is massive connections which brings a critical challenge of multiple access interference (MAI) on channel estimation, user identification, and data detection. Currently, various techniques have been proposed to mitigate the MAI, but those MAI alleviations are limited. Therefore, this paper proposes a novel technique called tandem spreading network-coded division multiple access (TSNDMA) to effectively cope with the MAI problem. In this technique, first a novel channel-reciprocity-based precompensation scheme is proposed for channel estimation. Then, a tandem spreading mechanism and a physical-layer network coding-based redundancy design are introduced for user identification and data detection with corresponding algorithms at the receiver. Simulation results show that TSNDMA can effectively mitigate the MAI to achieve a favorable system performance.

Cluster-based channel modeling has been an important trend in the development of channel model, as it maintains accuracy while reducing complexity. Whereas a large number of channel measurements have shown that multipath components (MPCs) are distributed as groups, i.e., clusters, existing clustering algorithms have various drawbacks with respect to complexity, threshold choices, and/or assumptions about prior knowledge. In this paper, a kernel-power-density (KPD)-based algorithm is proposed for MPC clustering. It uses the kernel density of MPCs to incorporate the modeled behavior of MPCs and takes into account the power of the MPCs. Furthermore, the KPD algorithm only considers the K nearest MPCs in the density estimation to better identify the local density variations of MPCs. A heuristic approach of cluster merging is used to improve the performance. Both simulation and channel measurements validate the KPD algorithm, and almost no performance degradation is found even with a large number of clusters and large cluster angular spread, which outperforming other algorithms. The KPD algorithm enables applications in multipleinput-multiple-output channels with no prior knowledge about the clusters, such as number and initial locations. It also has a fairly low computational complexity and can be used for clusterbased channel modeling.

In this contribution, an algorithm based on the space-alternating generalized expectation-maximization principle is proposed for estimating the locations of scatterers involved in the last-hops of propagation paths when a large-scale antenna array is used in a receiver for channel measurement. The underlying generic parametric model is constructed under the spherical wave-front assumption, which allows characterizing a path with a new parameter, i.e., the distance between the scatterer at the last-hop of the path and a specific receiving antenna, additional to the conventional parameters characterizing a specular path under the plane wave-front assumption. Cramér-Rao lower bounds of mean squared errors are derived for the parameter estimators in a single-path scenario, and their accuracy is evaluated through Monte Carlo simulations. The performance of the algorithm when being applied in reality is also evaluated through experiments conducted in an office with a carrier frequency of 9.5 GHz, a bandwidth of 500 MHz, and the receiver equipped with a 121-element virtual array. The proposed signal model and algorithm can be extended to the case of localizing the scatterers in the first- and last-hops of paths when large-scale antenna arrays are used in both the transmitter and the receiver.

Rail traffic is widely acknowledged as an efficient and green transportation pattern and its evolution attracts a lot of attention. However, the key point of the evolution is how to develop the railway services from traditional handling of the critical signaling applications only to high data rate applications, such as real-time videos for surveillance and entertainments. The promising method is trying to use millimeter wave which includes dozens of GHz bandwidths to bridge the high rate demand and frequency shortage. In this paper, the channel characteristics in an arched railway tunnel are investigated owing to their significance of designing reliable communication systems. Meantime, as millimeter wave suffers from higher propagation loss, directional antenna is widely accepted for designing the communication system. The specific changes that directional antenna brings to the radio channel are studied and compared to the performances of omnidirectional antenna. Note that the study is based on enhanced wide-band ray tracing tool where the electromagnetic and scattering parameters of the main materials of the tunnel are measured and fitted with predicting models.

High-speed train (HST) communications at millimeter-wave (mmWave) band have received a lot of attention due to their numerous high-data-rate applications enabling smart rail mobility. Accurate and effective channel models are always critical to the HST system design, assessment, and optimization. A distinctive feature of the mmWave HST channel is that it is rapidly time-varying. To depict this feature, a geometry-based multipath model is established for the dominant multipath behavior in delay and Doppler domains. Because of insufficient mmWave HST channel measurement with high mobility, the model is developed by a measurement-validated ray tracing (RT) simulator. Different from conventional models, the temporal evolution of dominant multipath behavior is characterized by its geometry factor that represents the geometrical relationship of the dominant multipath component (MPC) to HST environment. Actually, during each dominant multipath lifetime, its geometry factor is fixed. To statistically model the geometry factor and its lifetime, the dominant MPCs are extracted within each local wide-sense stationary (WSS) region and are tracked over different WSS regions to identify its “birth” and “death” regions. Then, complex attenuation of dominant MPC is jointly modeled by its delay and Doppler shift both which are derived from its geometry factor. Finally, the model implementation is verified by comparison between RT simulated and modeled delay and Doppler spreads.

In this paper, we consider the sequential detection problem in a new cognitive radio (CR) scenario when the primary user (PU) works with more than one transmit power levels. The targets of the secondary user (SU) is not only to detect the presence of PU but also to recognize PU's transmit power levels. We formulate a valid sequential detection approach via the modified Neyman Pearson (NP) criterion and then derive the closed-form decision region for each PU's transmit power level. Moreover, the average sample number (ASN), a key metric for any sequential detection method, is analytically derived in closed-form to facilitate the performance evaluation. Finally simulation result is presented to verify the correctness of the proposed studies.

In this paper, we explore the underlay cognitive relay networks in the presence of hardware impairment. To avoid interfering with the primary user's communications, the transmission powers of the secondary source and relays are adaptively adjusted by considering the following three practical effects: i) the maximum transmission power constraint at the secondary transmission nodes, ii) the interference power constraint at the primary receiver, and iii) imperfect hardware. By taking the correlations among the received signal-to-interference-plus-noise ratios into account, exact closed form expressions for the outage probabilities are respectively derived for the cases with and without a direct secondary link over Rayleigh fading channels. We further conduct an asymptotic outage probability to evaluate the impact of hardware impairment on diversity order, and show both cases with and without direct secondary link can achieve the full diversity order. Finally, simulation results are presented to verify the correctness of our analytical derivations.

Massive machine type communication (mMTC) will become the key factor of the future industrial automation area. Also, with the rapid development of Internet of Things (IoT) technologies, more and more researchers in industries and universities have engaged in exploring the problems in mMTC. Currently, one attractive problem is how to cope the requirement of massive connections. One powerful candidate technique to tackle this problem is the Compressive Sensing based Multi-User Detection (CS-MUD) which exploit the sparsity characteristic of the small packet transmission. In this paper, we will perform the performance evaluations with different cases. The simulation results inspire us that in order to fully utilize CS-MUD, the industrial applications should judiciously set the mMTC parameters according to the property of their own business demand.

Due to the extra loss resulting from tunnel curve, most of the theoretical models cannot be directly used for propagation inside curved tunnels. In this paper,extensive simulations are made for propagation at different frequencies in different types of curved tunnels, by using a ray-tracing simulator that is validated by numerous measurements. From the simulated received power, the extra losses of tunnel curve in various cases are extracted, analyzed, and stochastically modeled by normal distributions. Thus, with the parameters presented in this paper, the total propagation loss inside curved tunnels can be predicted by adding the stochastically generated extra loss to the propagation loss in the straight tunnel. This provides a fairly simple and effective way to extend the theoretical propagation models to fulfill network planning and system design for communication systems in real curved tunnels.

In this paper, we propose a novel channel impulse response (CIR) clustering algorithm using a sparsity-based method, which exploits the feature of CIR that power of multipath component (MPC) is exponentially decreasing with increasing delay. We first use a sparsity-based optimization to recover CIRs, which can be well solved by using reweighted ℓ1minimization. Then a heuristic approach is provided to identify clusters in the recovered CIRs, which leads to improved clustering accuracy in comparison to identifying clusters directly in the raw CIRs. The proposed algorithm incorporates the physical behaviors of MPCs into the clustering framework and enables applications with no prior knowledge of the clusters, such as number and initial locations of clusters. The results in this paper can be used to parameterize the CIR model of radio channels.

It has been widely recognized that relaying is an important method for increasing the reliability and spectral efficiency of communications systems, and it is thus helpful for improving the performance of vehicle-to-vehicle (V2V) communication systems. However, designing and evaluating V2V relay networks require understanding the effect of shadowing, as this critically impacts the performance of the relay system. Even though the theoretic performances of various relaying schemes have been well investigated, there is a lack of empirical test that incorporates realistic shadowing effects. In this paper, we analyze the performance of relaying transmission in V2V scenarios based on measurements in scenarios where shadowing occurs through large vehicles such as buses. We investigate several potential locations for the relay nodes, and the measurements are performed with two static transmitters (TX) and one dynamic receiver (RX). Outage probabilities of several relaying schemes such as multi-hop decode-and- forward, multi-hop amplify-and-forward, and diversity-amplify-and-forward are estimated and discussed based on the measured instantaneous end- to-end signal-to-noise ratio (SNR). It is found that: (i) shadowing effect caused by the bus between V2V line-of-sight (LOS) links increases the outage probability for the non-LOS (NLOS) direct transmission; (ii) using relay node on the bus roof can significantly improve transmission, however, a strong shadowing effect may reduces the acceptable communication distance of relaying scheme; and (iii) the diversity-amplify-and- forward relaying scheme generally has the best performance. Our results can be used to design a relay system for V2V communications.

Mobile Hotspot Network (MHN) is designed as a new communication system for high speed mobile group vehicles and aiming at providing multi-Giga bps data rate by using millimeter wave (mmWave). According to the MHN setup, the channel at 30 GHz band is simulated by a ray-tracing tool in a typical straight subway tunnel. In order to assess the system physical layer performance, system simulations based on entire channel characteristics provide an profound significance. the channel parameters such as path loss, power delay profile, decorrelation distance, Rician K-factor, Doppler characteristics, etc., are extracted and analyzed for verifying system feasibility and can serve as a guide for physical layer design.

Massive MIMO has been chosen as one of the candidate technologies of the fifth-generation mobile communication system (5G), and channel modeling of massive MIMO is of great importance. The most direct and effective approach to investigate the propagation characteristics of massive MIMO channels is channel measurements. However, there are only few measurements of massive MIMO channels, and there still lacks deep investigations of massive MIMO channel characteristics. In this paper, we present a measurement campaign of indoor massive MIMO channels, by using a linear large-scale array with 64 elements. The measurements are conducted at 2 GHz, 4 GHz, and 6 GHz, respectively, with a bandwidth of 200 MHz. Both LOS and NLOS propagation scenarios are considered in the measurements. The basic channel parameters are extracted, including path loss, delay spread, and coherence bandwidth. The non-stationarity of radio channels, which is reflected by the variations of delay spread and coherence bandwidth over different array locations, is discussed. The impact of carrier frequency on the above channel parameters is further discussed. The results would be useful for the design of massive MIMO system in the indoor environments.

In this paper, the impact of mutual coupling on LTE-R MIMO capacity for antenna array configurations in high speed railway scenario is investigated. In order to evaluate the impact of mutual coupling for different antenna array configurations, the extended 3D clustered channel model is presented primarily in this paper. The impact of mutual coupling can be added to channel models using coupling matrix which is modeled based on S-parameters obtained from CST Microwave Studio. We study the impact of coupling on LTE-R MIMO capacity from the perspective of antenna array configurations: antenna array deployment, interelement spacing and fixed total physical space. The simulations illustrate that the impact on the capacity caused by mutual coupling is significant and differs among various antenna array configurations, which yields meaningful insight into the LTE-R antenna array configurations in high speed railway scenario.

With the explosive development in Internet of Things (IoT) and Internet of Vehicles (IoV) technologies, massive connections with sporadic transmission will commonly exist in the future communication network. Upon that, compressive sensing based multi-user detection (CS-MUD) technique was proposed in previous works. However, CS-MUD has not been considered in high mobility scenario which will be widely applied in the future communication. With the existence of high mobility, the frequency synchronization is no longer valid because the Doppler shift appears and results in the carrier frequency offset (CFO). In this paper, CS-MUD will be analyzed and evaluated in high mobility scenarios. It can be shown that the user activity detection of CS-MUD will be influenced by the CFO from the Doppler shift. In addition, constant amplitude zero auto-correlation sequences (CAZAC) are applied as the spreading sequences in this paper. The correlation property of the CAZAC sequences can help the CS-MUD to mitigate the CFO sensitivity to make the system robust to the mobility.

The local average power estimation is needed by communications system for use in coverage assessment, power control, and handoff. Aiming at satisfying the broadband communications and higher safety requirements of next generation communications system, this paper proposes novel criteria of local mean power estimation, including the statistical averaging length, sample number, and sample interval. The multi-path fading is Nakagami-m distributed and the basic procedure is similar to Lee Criteria. The performance of the estimation algorithm is compared with the Lee method which is based on Rayleigh distribution. When it is LOS propagation, which covers the most cases in railway scenario, the measured length necessary to obtain the local average power is determined to be in the range of 10 to 25 wavelengths. The sufficient number of samples depends on one parameter of Nakagami-m distribution and varies from 1 to 14. It is based on the 95 percent and 99 percent confidence interval and less than 1 dB error in estimating. The sample interval increases greatly in comparison with Lee criteria, which can reduce the measurement overhead while remaining the high estimating accuracy.

The rapidly time-varying channel in high-speed railway poses tough design challenges, which necessitates the research of accurate channel models. Existing researches focus on the isolated high-speed railway scenarios, and mainly deal with path loss, shadowing fading, Ricean K-factor and delay spread. However, few studies have been done in Doppler domain. In this paper, a deterministic channel model that employs ray- tracing algorithm is presented. The proposed deterministic modeling approach is applied in composite high-speed railway scenario rather than isolated one. The scenario is flexibly reconstructed through SketchUp. The simulation results are validated by the data measured in the same scenario. The channel characteristics in Doppler domain and the effect of Doppler shift are statistically analyzed based on the deterministic channel model. The transition regions in the composite scenario are emphatically investigated, and the results are compared with those of prior studies.

In this study, a problem of efficient spectrum sensing and power allocation is studied. A network of cognitive radio adopting the technique of two‐way relay is considered. A relay node, sitting between two secondary users, carries out spectrum sensing and helps the two secondary users to realise bidirectional relay communications. For such a system, an optimisation problem which targets at maximising the average rate between the two secondary users while guaranteeing the detection probability above a predefined threshold, is formulated. Although the problem is shown to be non‐convex which means the global optimal solution is hard to be obtained, the authors solve the formulated problem global optimally by using a combination of bilevel optimisation and monotonic programming. Numerical results are provided to present the effectiveness of the authors’ proposed algorithm.

Unlike solid obstacles, the excess loss of semiclosed obstacles (SCOs) can be considerably overestimated by directly applying existing diffraction models, i.e., multiedge diffraction models. By regarding the propagation situation as a superposition of the cases of the “Open Field” and the “Closed Obstacle,” this paper presents a simple way to model the excess loss of SCOs that widely exists in intelligent transportation systems. By estimating two weight coefficients according to the specific situation, this model structure can be applied to different SCOs. To illustrate our modeling concepts, two typical cut-and-cover tunnels in high-speed railway are studied in detail. Combining this case with our previous implementations for train stations and crossing bridges, a complete set of coefficients for the excess loss of the main SCOs in railway settings is presented. This case study shows that the proposed approach provides an effective and fairly simple way to include various SCOs in the network planning, simulation, and design of communication systems. As our approach has determined the coefficients empirically, the proposed model structure can provide the foundation for future work that aims to streamline the excess loss prediction via estimation of coefficients either analytically or via a reduced set of measurements.

A new geometry-based stochastic scattering model (GBSSM) for wideband multiple-input-multiple-output vehicle-to-vehicle (V2V) channels is proposed in this paper. The proposed GBSSM with cross-polarized antennas combines three-dimensional two cylinders to model the stationary scatterers and two-dimensional multirings to imitate the moving scatterers. The channel realization by using the channel matrix in this paper is much more straightforward and concise to study the channel characteristics compared with the too complicated analytical solutions available so far. Because V2V propagation channels are nonstationary, the time-varying channel properties and parameters are further investigated based on the proposed GBSSM and realized channels, which can be used in the link and system-level simulations in V2V radio systems.

In this paper, we propose a cognitive transmission scheme for Amplify-and-Forward (AF) two-way relay networks (TWRNs) and investigate its joint sensing and transmission performance. Specifically, we derive the overall false alarm probability, the overall detection probability, the outage probability of the cognitive TWRN over Rayleigh fading channels. Furthermore, based on these probabilities, the spectrum hole utilization efficiency of the cognitive TWRN is defined and evaluated. It is shown that smaller individual or overall false alarm probability can result in less outage probability and thus larger spectrum hole utilization efficiency for cognitive TWRN, and however produce more interference to the primary users. Interestingly, it is found that given data rate, more transmission power for the cognitive TWRN does not necessarily obtain higher spectrum hole utilization efficiency. Moreover, our results show that a maximum spectrum hole utilization efficiency can be achieved through an optimal allocation of the time slots between the spectrum sensing and data transmission phases. Finally, simulation results are provided to corroborate our proposed studies. Copyright © 2016 John Wiley & Sons, Ltd.

To statistically model time-variant vehicle-to-vehicle (V2V) channels, the dynamic multi-path components (MPCs) are characterized based on suburban measurements conducted at 5.3 GHz. The correlation matrix distance (CMD) is used to determine the size of local wide-sense stationary (WSS) region. Within each WSS time window, MPCs are extracted using wideband spatial spectrum of Bartlett beamformer. A MPC distance (MCD)-based tracking algorithm is used to identify the "birth" and "death" of MPCs over different WSS regions, and the lifetime of MPC is modeled with a truncated Gaussian distribution. Distributions of number of MPCs and their positions are statistically modeled. The MPC characterization considers both angular and delay domain properties as well as the dynamic evolution of MPCs over different WSS regions. The results shows insight into the dynamic behaviors of MPCs in V2V environments, and is useful for the scatterer modeling in the geometry-based stochastic channel modeling.

Most of the existing channel models are based on the assumption of wide sense stationary uncorrelated scattering (WSSUS) properties, which are not suitable for time-varying vehicle-to-vehicle (V2V) channels. A method for generating correlated taps in stochastic V2V channel model is proposed in this paper, which represents the non-WSSUS properties adequately. With the method for generating correlated taps, the proposed tapped delay line channel model is correlated both in the amplitude part and the phase part. In the model, the amplitude statistics follows the Weibull distribution, and the phase statistics follows the linear function of uniform distribution, which are more accurate to represent the fading properties of V2V channel. Furthermore, simulation results show that the proposed model can generate correlated V2V channel with arbitrary amplitude and phase correlation coefficients accurately.

Massive multiple input and multiple output (MIMO) systems can increase the spectrum and energy efficiency of existing cells, and because of this, massive MIMO has been considered as a potential technique for next generation wireless communication networks. Since a thorough knowledge of the propagation channel is a prerequisite of reliable communication systems, massive MIMO channels are of great current interest. In this paper, based on realistic measurements in a stadium scenario in two frequency bands, the stationarity of three basic channel parameters is investigated by using the reverse arrangements test. The results show that channel behaviors in our higher frequency band are stationary over the linear antenna array, whereas this appears untrue at the low frequency band. This non-stationarity phenomenon in the line of sight propagation environment is mainly caused by the stronger reflection and smaller path loss at the low frequency band, which allows more and stronger multipath components, and this leads to substantial channel changes over the large size antenna array.

Channel measurements and modelling have been long considered as the foundation for effective and efficient wireless communication system designs. Recently, there has been an explosive growth of research work dedicated to the so‐called device‐to‐device (D2D) communications. In the mean time, however, measurements and modelling of D2D channels seem to somewhat fall behind. To promote research on these aspects, in this study, the authors provide a critical overview of the current state of research on D2D channels, and comprehensively discuss future trends and research directions.

The recent development of high-speed trains (HSTs), as a high-mobility intelligent transportation system, and the growing demands of broad-band services for HST users, introduce new challenges to wireless communication systems for HSTs. The deployment of mobile relay stations on top of the train carriages is one of the promising solutions for HST wireless systems. For a proper design and evaluation of HST wireless communication systems, we need accurate channel models that can mimic the underlying channel characteristics for different HST scenarios. In this paper, a novel nonstationary geometry-based stochastic model (GBSM) is proposed for wideband multiple-input multiple-output HST channels in rural macrocell scenarios. The corresponding simulation model is then developed with angle parameters calculated by the modified method of equal areas. Both channel models can also be used to model nonstationary vehicle-to-infrastructure channels in vehicular communication networks. The system functions and statistical properties of the proposed channel models are investigated based on a theoretical framework that describes nonstationary channels. Numerical and simulation results demonstrate that the proposed channel models have the capability to characterize the nonstationarity of HST channels. The statistical properties of the simulation model, verified by the simulation results, can match those of the proposed theoretical GBSM. An excellent agreement is achieved between the stationary intervals of the proposed simulation model and those of relevant measurement data, demonstrating the utility of the proposed channel models.

Wave propagation characteristics in curved tunnels are of importance for designing reliable communications in subway systems. This paper presents the extensive propagation measurements conducted in two typical types of subway tunnels—traditional arched “Type I” tunnel and modern arched “Type II” tunnel—with 300- and 500-m radii of curvature with different configurations—horizontal and vertical polarizations at 920, 2400, and 5705 MHz, respectively. Based on the measurements, statistical metrics of propagation loss and shadow fading (path-loss exponent, shadow fading distribution, autocorrelation, and crosscorrelation) in all the measurement cases are extracted. Then, the large-scale fading characteristics in the curved subway tunnels are compared with the cases of road and railway tunnels, the other main rail traffic scenarios, and some “typical” scenarios to give a comprehensive insight into the propagation in various scenarios where the intelligent transportation systems are deployed. Moreover, for each of the large-scale fading parameters, extensive analysis and discussions are made to reflect the physical laws behind the observations. The quantitative results and findings are useful to realize intelligent transportation systems in the subway system.

The high-speed railway (HSR) propagation channel has a significant impact on the design and performance analysis of wireless railway control systems. This paper derives a stochastic model for the HSR wireless channel at 930 MHz. The model is based on a large number of measurements in 100 cells using a practically deployed and operative communication system. We use the Akaike information criterion to select the distribution of the parameter distributions, including the variations from cell to cell. The model incorporates the impact of directional base station (BS) antennas, includes several previously investigated HSR deployment scenarios as special cases, and is parameterized for practical HSR cell sizes, which can be several kilometers. The proposed model provides a consistent prediction of the propagation in HSR environments and allows a straightforward and time-saving implementation for simulation.

High-speed railways (HSRs) have been widely introduced to meet the increasing demand for passenger rail travel. While it provides more and more conveniences to people, the huge cost of the HSR has laid big burden on the government finance. Reducing the cost of HSR has been necessary and urgent. Optimizing arrangement of base stations (BS) by improving prediction of the communication link is one of the most effective methods, which could reduce the number of BSs to a reasonable number. However, it requires a carefully developed propagation model, which has been largely neglected before in the research on the HSR. In this paper, we propose a standardized path loss/shadow fading model for HSR channels based on an extensive measurement campaign in 4594 HSR cells. The measurements are conducted using a practically deployed and operative GSM-Railway (GSM-R) system to reflect the real conditions of the HSR channels. The proposed model is validated by the measurements conducted in a different operative HSR line. Finally, a heuristic method to design the BS separation distance is proposed, and it is found that using an improved propagation model can theoretically save around 2/5 cost of the BSs.

This paper proposes a novel non-stationary wideband multi-confocal ellipse two dimensional (2-D) channel model for massive multiple-input multiple-output (MIMO) communication systems. Spherical wavefront is assumed in the proposed channel model, instead of the plane wavefront assumption used in conventional MIMO channel models. In addition, the birth-death process is incorporated into the proposed model to capture the dynamic properties of clusters on both the array and time axes. Statistical properties of the channel model such as the space-time-frequency correlation function and power imbalance on the antenna array are studied. The impact of the spherical wavefront assumption on the statistical properties of the channel model is investigated. Furthermore, numerical analysis shows that the proposed channel model is able to capture specific characteristics of massive MIMO channel as observed in measurements.

In a relay-assisted cellular network, the transmission mode either direct transmission or relaying and the transmit power of the source and relay nodes affect not only transmission rates of individual links but also the rates of other links sharing the same channel. In this paper, we propose a cross-layer design that jointly considers the transmission mode/relay node selection MRS with power allocation PA to optimize the system rate. We first formulate an optimization problem for a cellular system, where the same frequency channel can be reused in different cells. A low complexity heuristic MRS scheme is proposed on the basis of the link and interference conditions of the source and potential relay nodes. Given the transmission mode and relay node if the relaying mode is chosen of each link, the transmit power of the source and relay nodes can be solved by geometric programming. This method for MRS and PA can achieve a close-to-optimum performance, but implementing the PA requires heavy signalling exchanged among cells. To reduce the signalling overheads, we finally proposed a heuristic and distributed method for MRS and PA inspired by some asymptotic analysis. Numerical results are conducted to demonstrate the rate performance of the proposed methods.Copyright ©2013 John Wiley & Sons, Ltd.

Vehicle-to-Vehicle (V2V) communication is an en-abler for improved traffic safety and congestion control. As for any wireless system the ultimate performance limit is determined by the propagation channel. A particular point of interest is the shadowing effect of large vehicles such as trucks and buses, as this might affect the communication range significantly. In this paper we present measurement results and model the propagation channel in which a bus acts as a shadowing object between two passenger cars. The measurement setup is based on a WARP FPGA software radio as transmitter, and a Tektronix RSA5106A real-time complex spectrum analyzer as receiver. We analyze the influence of the bus location and car separation distance on the large-scale path loss, shadowing, and small-scale fading. The main effect of the bus is that it is acting as an obstruction creating an additional 15–20 dB attenuation. A Nakagami distribution is found to describe the statistics of the small-scale fading, by using Akaike's Information Criterion and the Kolmogorov-Smirnov test. The distance-dependency of the path loss is analyzed, and a stochastic model is developed to reflect the impact.