The forthcoming 6G wireless systems must simultaneously achieve extreme capacity, ultra-low latency, and quantum-grade security. While classical physical-layer security techniques provide probabilistic protection, free-space quantum key distribution (QKD) suffers degradation under mobile and turbulent conditions. This paper introduces a Quantum-Enhanced Reconfigurable Intelligent Surface (QE-RIS) framework that synergistically integrates quantum photonics with classical electromagnetic control via hierarchical AI orchestration. Our design embeds quantum-capable devices into RIS unit cells operating at 12.5GHz (RF) and 850nm (optical) wavelengths and employs a dual-loop control mechanism, a fast loop at 5 ms and a mid loop at 50 ms, to dynamically select classical, quantum, or hybrid modes based on real-time signal-to-noise ratio (SNR) and quantum bit error rate (QBER). Monte Carlo simulations with 20 independent channel realisations demonstrate SNR gains up to 29.3 dB for RIS withN= 512 elements and 4-bit phase quantisation, QBER reductions ranging from 20% to 35% compared to free-space QKD, control latencies below 10 ms on edge-class hardware, and secure key rates exceeding 1 Mbit s−1under moderate turbulence. In this paper, we detail implementation assumptions, control information elements (IEs), and key performance indicators (KPIs) aligned with emerging standardisation efforts. The proposed framework offers a rigorous and reproducible foundation for advancing quantum-enhanced wireless communications.

In this paper, we propose a framework for energy-efficient (EE) design in reconfigurable intelligent surface (RIS)-assisted multi-satellite Internet of Things (IoT) networks, taking into account the imperfect channel state information (CSI). In this framework, the multi-satellite network is used to enhance communication capabilities, while the RIS is deployed to further improve EE performance. Our objective is to maximize the EE of the proposed network by jointly optimizing the active beamforming and scheduling of the satellites and the phase shifts of RIS under the transmit power constraint for each satellite, the elevation angle constraints, and the phase shift constraints of the RIS. To handle this non-convex and NP-hard optimization problem, we propose two efficient algorithms, i.e., the Dinkelbach-BigM-Successive-Penalty (DBSP) algorithm and the Lagrangian Dual Majorization (LDM) algorithm. The DBSP algorithm is based on the alternating optimization approach, which can effectively solve the formulated non-convex optimization problem with multiple dual and complex optimization variables. Specifically, we first employ the Dinkelbach method, successive convex approximation, big-M formulation, and semidefinite relaxation method to optimize the active beamforming and the scheduling of the satellites. In addition, the penalty convex-concave procedure approach is utilized to design the phase shifts of RIS. To reduce the complexity and improve computational efficiency, we propose the LDM algorithm and derive an analytical solution for active beamforming and phase shifts by exploiting the Lagrangian dual transform, quadratic transform, and majorization-minimization algorithms. Numerical simulations are conducted to demonstrate the efficiency and convergence behavior of the proposed algorithms. Moreover, it is also demonstrated that the proposed algorithms are superior to other benchmarks, corroborating the benefits of deploying an RIS in the multi-satellite network.

Implementing federated learning (FL) over wireless networks faces critical research challenges, such as high communication costs, inevitable communication latency, and significant energy consumption for model transmission and training, primarily caused by device heterogeneity and unpredictable dynamic channel conditions. This paper proposes Graph-based Resource Optimization with Compression for FL (GROC-FL), a unified framework that jointly coordinates wireless resource allocation and collaborative model compression. By leveraging Graph Neural Networks (GNNs) to model wireless topology and Deep Reinforcement Learning (DRL) to optimize communication and computation resources together with a globally consistent sparse update mechanism, GROC-FL minimizes the overall energy consumption of clients over wireless networks. In order to address the intrinsic topology dependence of wireless FL, we develop a graph-augmented DRL agent based on a graph convolutional network (GCN) that captures resource competition and network topology. We further develop a collaborative model compression module, termed Federated Parameter Negotiation (FPN), which enables clients to negotiate a global sparse mask and further reduce energy consumption during FL training. Experimental results demonstrate that GROC-FL outperforms the baselines in energy consumption, training performance, and client fairness.

In the Internet of Vehicles (IoV), vehicles have the capability to offload their computational tasks to the Mobile Edge Computing (MEC) servers in order to reduce service delay. However, the majority of existent task offloading and computational resource allocation schemes are static and lack consideration of the heterogeneous nature of tasks. Furthermore, in scenarios involving both collaborative and competitive resource utilization, there remains considerable room for performance enhancement for delay-sensitive tasks. To address these challenges, this paper proposes a novel Global Heterogeneous Multi-Agent Reinforcement Learning (GHMARL) that is an enhancement to the general MARL. In GHMARL, each vehicle and MEC server is represented by an agent, and intelligent collaboration and dynamic resource allocation are employed to balance resource usage and delay performance. In particular, GHMARL introduces a global Critic network and a local Critic network, working in a collaborative manner. The former is responsible for guiding the overall system performance to ensure the service delay performance, and the latter is responsible for MEC servers’ performance to ensure the resource usage efficiency. Simulation results demonstrate that, in comparison with alternative schemes, GHMARL significantly enhances the overall system performance, particularly with regard to resource usage efficiency. Furthermore, GHMARL offers distinct advantages in balancing task delay and resource consumption under various system dynamics, making it a robust solution to address issues of resource wastage and delay violations for IoV systems.

Integrated sensing and communication (ISAC) technology endows users with environmental awareness capabilities, which will play a crucial role in future mobile edge computing (MEC) systems. In this paper, we consider the design of sensingassisted beam alignment and investigate resource management for a task-oriented mmWave integrated sensing, communication, and computing (ISCC) system, which can be formulated as a preference-agnostic multi-objective optimization problem. To solve this problem, we first introduce a multi-objective Markov decision process (MOMDP) to reformulate the original problem and innovatively propose a preference-agnostic multi-objective soft actor-critic (PA-MOSAC) algorithm. To demonstrate the effectiveness of our proposed system architecture and resource management algorithm, we also introduce a traditional mmWave MEC (T-MEC) system based on the same set of system parameters as a benchmark. The proximal policy optimization (PPO) algorithm, known for its robustness, is used to address resource management in the T-MEC system. Through a comprehensive comparative analysis of the two systems and algorithms, we discover that our proposed sensing-assisted beam alignment can reduce task execution delay by 25% with only 1% increase in energy consumption. We also verify the convergence of our proposed PA-MOSAC algorithm and demonstrate its superior performance over the benchmark scheme.

This paper investigates the uplink massive grant-free random access in low-earth-orbit (LEO) satellite-based Internet-of-Things systems, where accurate joint activity detection and channel estimation (JADCE) is essential for reliable data recovery. However, the resource constraint and high-dynamic characteristic of LEO scenarios pose significant challenges to traditional JADCE schemes in terms of both estimation accuracy and computational complexity. To overcome these limitations, we propose a novel deep-unfolding JADCE framework based on the synergistic multi-stage optimization iterative shrinkage thresholding algorithm network, referred to as SMO-ISTA-Net, to facilitate efficient massive device access. Specifically, we first develop a synergistic attention module, where an inertia-guided optimization strategy is introduced into the gradient descent process to improve convergence stability and adaptability to fast-varying satellite channels. In order to mitigate the temporal feature inconsistency caused by asynchronous access and multi-path propagation, we further design a lightweight cross-attention mechanism that enables efficient channel feature fusion and facilitates multi-stage information interaction. Moreover, we propose a memory-enhanced proximal-mapping module that incorporates a high-throughput short-term memory mechanism into the unfolded structure, so as to significantly reduce information loss and maximize memory retention of the network. Extensive simulations under diverse LEO scenarios demonstrated that our scheme can achieve superior convergence speed, estimation accuracy, and preamble efficiency, while maintaining low computational complexity and short runtime, compared to the state-of-the-art model-driven JADCE schemes.

In this paper, we propose a hybrid simultaneous wireless information and power transfer (SWIPT)–enabled multiple-input multiple-output (MIMO) architecture, where the base station (BS) uses a conventional radio-frequency (RF) transmitter for downlink transmission and a Rydberg atomic quantum receiver (RAQR) for receiving uplink signals from Internet of Things (IoT) devices. To fully exploit this integration, we jointly design the transmission scheme and the power-splitting strategy to maximize the weighted sum rate, which leads to a non-convex problem. To address this challenge, we first derive closed-form lower bounds on the uplink achievable rates for maximum ratio combining (MRC) and zero-forcing (ZF), as well as on the downlink rate and harvested energy for maximum ratio transmission (MRT) and ZF precoding. Building upon these bounds, we propose an iterative algorithm relying on the best monomial approximation and geometric programming (GP) to solve the non-convex problem. Finally, simulations validate the tightness of our derived lower bounds and demonstrate the superiority of the proposed algorithm over benchmark schemes. Importantly, by integrating RAQR with SWIPT-enabled MIMO, the BS can reliably detect weak uplink signals from IoT devices powered only by harvested energy, enabling battery-free IoT networks.

This paper investigates the finite-blocklength performance of reconfigurable holographic surfaces (RHS) for ultra-reliable low-latency communication (URLLC). A physics-consistent RHS model is established, and the information-theoretic dispersion is derived in closed form using the Mellin transform method, we derive a closed-form probability density function of the RHS-induced channel gain. Then, we apply second-order Taylor expansion and saddle point approximation to obtain analytical expressions for the mutual information and unconditional information variance, which explicitly capture the amplitude-induced anisotropic variance. Finally, leveraging the Berry–Esseen theorem, the closed-form achievability and converse bounds are established which quantify the impact of RHS design parameters, blocklengthn, and average error probability ϵ on the rate. Analytical and simulation results demonstrate that RHS reduces the variance of the effective channel power by 35–50% in required blocklength compared with reconfigurable intelligent surfaces (RIS) under identical resource budgets. The findings identify RHS as a physically scalable and mathematically tractable architecture for short-packet 6G communication.

This paper presents a fundamental performance analysis of joint location and velocity estimation in a cell-free (CF) MIMO integrated sensing and communication (ISAC) system. Unlike prior studies that primarily rely on continuous-time signal models, we consider a more practical and challenging scenario in the discrete-time digital domain. Specifically, we first formulate a logarithmic likelihood function (LLF) and corresponding maximum likelihood estimation (MLE) for both single- and multiple-target sensing. Building upon the proposed LLF framework, closed-form Cramer-Rao lower bounds (CRLBs) for joint location and velocity estimation are derived under deterministic, unknown, and spatially varying radar cross-section (RCS) models. These CRLBs can serve as a fundamental performance metric to guide CF MIMO-ISAC system design. To enhance tractability, we also develop a class of simplified closed-form CRLBs, referred to as approximate CRLBs, along with a rigorous analysis of the conditions under which they remain accurate. Furthermore, we investigate how the sampling rate, squared effective bandwidth, and time width influence CRLB performance. For multi-target scenarios, the concepts of safety distance and safety velocity are introduced to characterize the conditions under which the CRLBs converge to their single-target counterparts. Extensive simulations using orthogonal frequency division multiplexing (OFDM) and orthogonal chirp division multiplexing (OCDM) validate the theoretical findings and provide practical insights for CF MIMO-ISAC system design

In this paper, an affine frequency division multiplexing aided grant-free random access (AFDM-GF-RA) system is designed for low Earth orbit (LEO) satellite Internet of Things (SIoT) networks, where users employ the GF-RA to access the satellite concurrently by configuring the AFDM modulation. In order to evaluate the system’s performance, average bit error probability (ABEP) bounds are first derived by Cholesky decomposition, which are verified by simulation results. Furthermore, a block sparse prior expectation propagation detection (BSPEP) is developed for joint active users detection (AUD) and data detection (DD). User activity probability is considered as a prior for joint detection. Concurrently, the accuracy of AUD is further improved by combining the block sparsity characteristics of the user transmission signals. Simulation results show that the proposed detector outperforms the classical oracle least squares (OLS) benchmark and approaches the theoretical ABEP bound at high signal-to-noise ratio (SNR).

This paper, for the first time, presents a closed-form error performance analysis of uplink power-domain non-orthogonal multiple access (PD-NOMA) with dynamic successive interference cancellation (SIC) decoding, where the decoding order is adapted to the instantaneous channel conditions. We first develop an analytical framework that characterizes how dynamic ordering affects error probabilities in uplink PD-NOMA systems. For a two-user system over independent and non-identically distributed Rayleigh fading channels, we derive closed-form probability density functions (PDFs) of ordered channel gains and the corresponding unconditional pairwise error probabilities (PEPs). To address the mathematical complexity of characterizing ordered channel distributions, we employ a Gaussian fitting to approximate truncated distributions while maintaining analytical tractability. Finally, we extend the bit error rate analysis for various $M$-quadrature amplitude modulation schemes (QAM) in both homogeneous and heterogeneous scenarios. Numerical results validate the theoretical analysis and demonstrate that dynamic SIC eliminates the error floor issue observed in fixed-order SIC, achieving significantly improved performance in high signal-to-noise ratio regions. Our findings also highlight that larger power differences are essential for higher-order modulations, offering concrete guidance for practical uplink PD-NOMA deployment.

This paper proposes an integrated anti-jamming covert communication and sensing system assisted by reconfigurable intelligent surfaces (RIS). By jointly optimizing beamforming vectors and RIS phase shifts, the system maximizes the sum transmission rate while enhancing communication security, sensing accuracy, and anti-jamming capability. We present two comprehensive optimization schemes: a perfect scheme under ideal channel conditions and a robust scheme for practical scenarios. The perfect scheme jointly optimizes beamforming and phase shifts when perfect channel state information (CSI) is available, establishing a performance upper bound. The robust scheme addresses practical transmission challenges by transforming stochastic uncertainties from imperfect CSI and phase shift errors into deterministic constraints through statistical expectation analysis and worst-case formulations, ensuring reliable system performance under realistic conditions. Both schemes effectively solve the resulting non-convex problems through innovative mathematical reformulations using fractional programming, quadratic transformation techniques, and the alternating direction method of multipliers. Comprehensive simulation results demonstrate significant advantages of our proposed framework in communication reliability, sensing accuracy, and resilience against the jammer compared to conventional approaches.

As a critical component of sixth-generation (6G) wireless networks, ultra-reliable and low-latency communication (URLLC) is expected to support real-time and reliable information exchange in low-altitude environments. However, achieving URLLC often incurs significant resource overhead, including increased bandwidth consumption, higher transmit power, and denser access point (AP) deployment, which pose significant challenges to both spectral efficiency (SE) and energy efficiency (EE). Besides, existing iterative optimization algorithms are computationally intensive and struggle to meet the latency requirements of URLLC. To address these challenges, we propose a hybrid aerial-terrestrial cell-free massive MIMO (CF-mMIMO) network to support diverse services, along with a channel prediction network and a deep mixture of experts (MoE) network for uplink optimization. First, we design a channel prediction network (CP-Net) to mitigate channel aging caused by high-mobility user equipment (UE). CP-Net employs three Transformer-based sub-networks for aged channel state information (CSI) prediction, while a channel quality-aware loss function is introduced to improve the prediction accuracy of weak links. Based on the predicted CSI, we develop a deep MoE network (MoE-Net) for power allocation comprising three expert models targeting different objectives. Then, we introduce a weighted gating network (WT-Net) to learn an efficient adaptive combination of expert outputs. The proposed framework better captures heterogeneous UE requirements and improves communication performance under URLLC constraints. Numerical results demonstrate the effectiveness of the proposed method.

Affine frequency division multiplexing (AFDM) is a promising chirp-assisted multicarrier waveform for future high-mobility communications. This paper is devoted to enhanced receiver design for multiple-input–multiple-output AFDM (MIMO-AFDM) systems. Firstly, we introduce a unified variational inference (VI) approach to approximate the target posterior distribution, under which the belief propagation (BP) and expectation propagation (EP)-based algorithms are derived. As both VI-based detection and low-density parity-check (LDPC) decoding can be expressed by bipartite graphs in MIMO-AFDM systems, we construct a joint sparse graph (JSG) by merging the graphs of these two for low-complexity receiver design. Then, based on this graph model, we present the detailed message propagation of the proposed JSG. Additionally, we propose an enhanced JSG (E-JSG) receiver based on the linear constellation encoding model. The proposed E-JSG eliminates the need for interleavers, de-interleavers, and log-likelihood ratio transformations, thus leading to concurrent detection and decoding over the integrated sparse graph. To further reduce detection complexity, we introduce a sparse channel method by approaximating multiple graph edges with insignificant channel coefficients into a single edge on the VI graph. Simulation results show the superiority of the proposed receivers in terms of computational complexity, detection and decoding latency, and error rate performance compared to the conventional ones.

In this paper, we investigate a cell-free massive multiple-input and multiple-output (MIMO)-enabled integration communication, computation, and sensing (ICCS) system, aiming to minimize the maximum overall latency to guarantee the stringent sensing requirements. We consider a two-tier offloading framework, where each multi-antenna terminal can optionally offload its local tasks to either multiple mobile-edge servers for distributed computation or the cloud server for centralized computation. The above offloading problem is formulated as a mixed-integer programming and non-convex problem, which can be decomposed into three sub-problems, namely, distributed offloading decision, beamforming design, and execution scheduling mechanism. First, the continuous relaxation and penalty-based techniques are applied to tackle the distributed offloading strategy. Then, the weighted minimum mean square error (WMMSE) and successive convex approximation (SCA)-based lower bound are utilized to design the integrated communication and sensing (ISAC) beamforming. Finally, the other resources can be judiciously scheduled to minimize the maximum latency. A rigorous convergence analysis and numerical results substantiate the effectiveness of our method. Furthermore, simulation results demonstrate the benefits of multi-point cooperation in cell-free massive MIMO-enabled ICCS and reveal the trade-off between the number of involved APs and the resulting latency, highlighting the inherent interplay among communication, sensing, and computation.

Affine frequency division multiplexing (AFDM) has garnered significant attention due to its superior performance in high-mobility scenarios, as well as multiple waveform parameters that provide greater degrees of freedom for system design. This paper proposes a novel secure affine frequency division multiplexing (SE-AFDM) system, which enhances physical-layer security by dynamically varying an AFDM pre-chirp parameter across subcarriers and over AFDM symbols. In the SE-AFDM system, the pre-chirp parameter is dynamically generated from a codebook controlled by a long-period pseudo-noise (LPPN) sequence. Instead of applying spreading in the data domain, our parameter-domain spreading approach provides additional security while maintaining reliability and high spectral efficiency. We also propose a synchronization framework to solve the problem of reliably and rapidly synchronizing the dynamic parameter in fast time-varying channels. The theoretical derivations prove that unsynchronized eavesdroppers cannot eliminate the nonlinear impact of the time-varying parameter and further provide useful guidance for codebook design. Simulation results demonstrate the security advantages of the proposed SE-AFDM system in high-mobility scenarios, and the hardware prototype validates the effectiveness of the proposed SE-AFDM system and the proposed synchronization framework.

Physical layer security (PLS) has emerged as a promising technology to protect critical and sensitive information against unauthorized devices. To address the key challenge of acquiring channel state information (CSI) of passive eavesdroppers in PLS implementation, we propose a novel sensing-assisted PLS scheme with the aid of reflecting reconfigurable intelligent surface (STAR-RIS). It employs a self-refine sensing scheme utilizing the artificial noise (AN) signals to iteratively estimate the eavesdroppers’ positions for CSI calculation. We aim to maximize the secrecy capacity based on the sensing-estimated CSI while tracking the eavesdroppers in full-duplex (FD) mode with integrated sensing and communication (ISAC) signals comprising artificial noise (AN). This is achieved by jointly designing the beamforming vector of information signals, the beamforming vector of AN signals, and the coefficients of the STAR-RIS. To optimize these coupled variables, we introduce an alternating optimization (AO) scheme to solve the problem recursively. In particular, we tackle the non-convexity of the beamforming optimizations for information and AN signals with the successive convex approximation (SCA) scheme and adopt a semi-definite relaxation (SDR) scheme to design the reflection and refraction coefficients of the STAR-RIS. The numerical results validate that the proposed scheme ensures secure communications against multiple eavesdroppers without any prior eavesdropper channel information. In addition, the proposed scheme can significantly improve SC performance by up to 66. 7% compared to the benchmarks without the sensing-assisted function.

In this paper, we propose a novel active reconfigurable intelligent surface (RIS)-assisted amplitude-domain reflection modulation (ADRM) transmission scheme, termed as ARIS-ADRM. This innovative approach leverages the additional degree of freedom (DoF) provided by the amplitude domain of the active RIS to perform index modulation (IM), thereby enhancing spectral efficiency (SE) without increasing the costs associated with additional radio frequency (RF) chains. Specifically, the ARIS-ADRM scheme transmits information bits through both the modulation symbol and the index of active RIS amplitude allocation patterns (AAPs). To evaluate the performance of the proposed ARIS-ADRM scheme, we provide an achievable rate analysis and derive a closed-form expression for the upper bound on the average bit error probability (ABEP). Furthermore, we formulate an optimization problem to construct the AAP codebook, aiming to minimize the ABEP. Simulation results demonstrate that the proposed scheme significantly improves error performance under the same SE conditions compared to its benchmarks. This improvement is due to its ability to flexibly adapt the transmission rate by fully exploiting the amplitude domain DoF provided by the active RIS.

Researchers from both academia and industry have started exploring the potential of sixth generation cellular networks, envisioning novel concepts and futuristic capabilities. An empirical analysis of real-world fifth generation (5G) deployments serves a compass to direct the next stage of evolution and provides insights on the additional improvements required for the future services and applications. However, acquiring real-world measurement data at a city or county scale poses substantial challenges in terms of time and cost. To address this issue, this paper presents a practical and cost-effective data collection testbed and a methodology that harnesses the existing services provided by municipal council authorities, including curbside waste collections to generate large-scale realtime network coverage maps. Rich datasets of measurement data collected from multiple fourth generation (4G) and 5G cells over seven months in Nottingham, United Kingdom (UK) for all four major UK network operators, namely EE, Vodafone, O2, and Three Mobile, are provided. These large datasets can be utilized for analyzing network deployment options, coverage, and future service provisioning as well as designing and training artificial intelligence and machine learning algorithms to further optimize the mobile networks. In addition, the paper reviews the latest empirical 5G network analysis tools and techniques, which were not seen in previous generations.

As satellite communications play an increasingly important role in future wireless networks, the issue of limited link budget in satellite systems has attracted significant attention in current research. Although semantic communications emerge as a promising solution to address these constraints, it introduces the challenge of increased computational resource consumption in wireless communications. To address these challenges, we propose a multi-layer hybrid bit and generative semantic communication framework which can adapt to the dynamic satellite communication networks. Furthermore, to balance the semantic communication efficiency and performance in satellite-to-ground transmissions, we introduce a novel semantic communication efficiency metric (SEM) that evaluates the trade-offs among latency, computational consumption, and semantic reconstruction quality in the proposed framework. Moreover, we utilize a novel deep reinforcement learning (DRL) algorithm group relative policy optimization (GRPO) to optimize the resource allocation in the proposed network. Simulation results demonstrate the flexibility of our proposed transmission framework and the effectiveness of the proposed metric SEM, illustrate the relationships among various semantic communication metrics.

Massive machine-type communication in satellite networks presents significant challenges for random access systems, particularly due to the limited number of terminals that can successfully access the network concurrently. To address this issue, this paper proposes a novel binary circular polarization modulation based non-orthogonal multiple access slotted ALOHA (BCPM-NOMA-SA) scheme that fully exploits the polarization characteristics of satellite transmission. The proposed approach first achieves orthogonal signal separation in the polarization domain through BCPM. Subsequently, an equivalent noise model is established by analyzing the Cramér-Rao lower bound (CRLB) of channel estimation errors. Finally, the upper bound of system throughput is derived, providing a theoretical foundation for the synergistic gains of polarization and power domain multiplexing. Simulation results demonstrate that the proposed scheme achieves 20-35% throughput improvement compared to conventional NOMA-SA and BCPM-SA schemes, showing significant performance advantages.

Integrating orthogonal time frequency space (OTFS) modulation into low-Earth-orbit (LEO) satellite communication systems can significantly enhance the robustness against severe Doppler effects caused by fast time-varying channels. However, since the high-dynamic characteristic of terrestrial-to-satellite links considerably constrains the OTFS frame duration, the inevitable fractional Doppler shifts will result in the emergence of inter-Doppler interference (IDI), thereby degrading channel estimation performance. To tackle this challenge, we develop an efficient correlation information-aided channel estimation scheme based on Zadoff-Chu (ZC) sequences in the presence of fractional Doppler shifts. The proposed scheme enables a fast coarse estimation of channel parameters by leveraging the magnitude distribution regularity of the periodical correlation results of ZC pilot under IDI, and remodels channel estimation as a non-convex constrained least squares optimization problem to obtain the fine channel parameters for further resistance of the inter-path interference. Complexity analysis and simulation results validate that our scheme can attain a high estimation accuracy while achieving substantial reductions in both the peak-to-average power ratio and computational complexity, in comparison to the state-of-the-art ones.

To ensure fair bandwidth allocation for diverse application flows from data centers, effective bandwidth management in switches is critical. Modern switches often adopt shared memory architectures to enhance efficiency. Fair queuing mechanisms can achieve fair bandwidth allocation in switches. However, the state-of-the-art fair queuing mechanisms in shared memory switches suffer from excessive packet drops, leading to suboptimal network utilization. In this paper, we propose Dynamic Fair Queuing (DFQ), a novel mechanism that leverages a limited number of priority queues to achieve both high network utilization and fair bandwidth allocation. DFQ is based on two key novel ideas. First, DFQ presents dynamic admission thresholds to manage packet enqueuing by monitoring the accumulated arrived packet bits and the remaining buffer of the queues in real time. Second, DFQ employs queue splitting and merging to maximize the utilization of the shared memory pool while guaranteeing fairness. Simulation results demonstrate that DFQ significantly improves throughput, fairness, and network utilization, while reducing flow completion time by up to 44.1%.

Radio Frequency Fingerprinting (RFF) exploits the unique characteristics of device hardware and has become a key technology in IoT device authentication and network security. Identification of unknown devices is a key challenge for radio frequency fingerprinting (RFF) in open-set scenarios, the similarity of device hardware characteristics further exacerbates the difficulty of the task. This paper proposes an open-set RFF recognition framework called SV-NPR (Siamese VGG16 with Negative Prototype Rejection). The framework combines the advantages of the VGG16 network in local feature extraction with the contrastive learning mechanism of the siamese network, and can efficiently capture the distribution of local detail features in RF signals. In addition, the introduction of a dynamic rejection mechanism based on negative prototypes improves the robustness and generalization ability of the model for unknown categories. Experimental results show that SV-NPR significantly outperforms the state-of-the-art on the Oracle dataset and exhibits leading recognition capabilities in open-set scenarios.

The parameter size of modern large language models (LLMs) can be scaled up to the trillion-level via the sparsely-activated Mixture-of-Experts (MoE) technique to avoid excessive increase of the computational costs. To further improve training efficiency, pipelining computation and communication has become a promising solution for distributed MoE training. However, existing work primarily focuses on scheduling tasks within the MoE layer, such as expert computing and all-to-all (A2A) communication, while neglecting other key operations including multi-head attention (MHA) computing, gating, and all-reduce communication. In this paper, we propose FlowMoE, a scalable framework for scheduling multi-type task pipelines. First, FlowMoE constructs a unified pipeline to consistently scheduling MHA computing, gating, expert computing, and A2A communication. Second, FlowMoE introduces a tensor chunk-based priority scheduling mechanism to overlap the all-reduce communication with all computing tasks. We implement FlowMoE as an adaptive and generic framework atop PyTorch. Extensive experiments with 675 typical MoE layers and four real-world MoE models across two GPU clusters demonstrate that our proposed FlowMoE framework outperforms state-of-the-art MoE training frameworks, reducing training time by14%-57%, energy consumption by 10%-39%, and memory usage by 7%-32%. FlowMoE’s code is anonymously available at https://anonymous.4open.science/r/FlowMoE.

This paper proposes interference suppression strategies for Low Earth Orbit (LEO) multi-satellite communication systems. A two-dimensional satellite-cell matching algorithm based on a utility function is introduced to mitigate partial interference. Additionally, a beam hopping (BH) pattern optimization algorithm is developed using satellite priority to minimize inter-satellite interference and enable collaborative BH transmission. To further suppress interference and address the impact of imperfect channel state information (CSI), a robust precoding algorithm leveraging multi-satellite cooperation is presented. Numerical results validate the effectiveness of the proposed algorithms in interference suppression, demonstrating their resilience in channel estimation errors.

In this paper, a low-complexity beam hopping pattern design with frequency reuse (FR) is proposed to efficiently adapt to the non-uniform traffic demands characteristics of the terrestrial cells. Concretely, an FR-based beam hopping (BH) downlink transmission model is conceived for multi-beam satellite systems. Next, a heuristic BH pattern optimization algorithm with FR and load balancing (BH-FR-LB) is proposed to improve the communication capacity, which is divided into two sub-problems with low complexity. Specifically, the cells are allocated into different frequency sub-bands to optimize the traffic load. The selection of the BH pattern is updated based on the remaining traffic demands of the terrestrial cells at each time slot, thereby achieving a trade-off between spectrum efficiency and inter-beam interference mitigation. Simulation results show that the proposed scheme obtains higher traffic satisfaction rate, while only requiring fewer time slots to achieve the data transmission under low traffic demands.

Tracking micro-unmanned aerial vehicles (micro-UAVs) in low signal-to-noise ratio (SNR) environments poses significant challenges due to their weak radar cross-section (RCS) and the inherent limitations of traditional Detect-Before-Track (DBT) radar algorithms. This paper proposes a novel Track-Before-Detect (TBD) approach based on a Markov Chain Monte Carlo-Enhanced Particle Filter (MCMC-EPF), leveraging 4D Frequency Modulated Continuous Wave (FMCW) MIMO radar. By directly processing unthresholded multi-frame 4D-FFT radar data, the method achieves joint detection and tracking, effectively preserving weak target information that is typically lost in DBT methods. Experimental results on real radar data demonstrate that the proposed algorithm achieves robust and accurate UAV tracking, maintaining a root-mean-square error (RMSE) within 1 meter and an average relative tracking error below 2.5% under low SNR conditions. These results highlight the method’s potential for reliable UAV surveillance in challenging operational environments.

In some application scenarios, strong spatio-temporal correlations exist between remote sensing data streams. For instance, during earthquake relief efforts, users in nearby locations may simultaneously request remote sensing data from an area of interest within the same time period. This leads to large data volumes transmitted in a limited spatio-temporal range, causing network traffic imbalance and reduced transmission efficiency. To address this issue, this paper proposes the Aggregated Transmission Strategy for Remote Sensing Data based on Spatio-Temporal Association (AFRST). According to the spatio-temporal attributes of remote sensing data and the location of users, AFRST utilizes the mapping relationship between naming and demand in Named Data Networking(NDN) and generates a demand association set when the demand between users in the same area reaches the association threshold, and the data in the overlapping area in the set is transmitted only once. We also uniformly assign transmission paths to the association set to improve the data transmission efficiency. Furthermore, AFRST takes into account the network state and user demand, constructing a Transmission-Load Balancing Control Model (TLBCM) based on the network utility maximization framework. This model maximizes the data transmission rate and balances the network load under constraints such as link capacity and other factors in each time slot, optimizing network service performance. The performance of AFRST is evaluated using ndnSIM, and the experimental results demonstrate the effectiveness of AFRST in terms of transmission latency, throughput, and number of completions. Compared to DCT and DPCCP, the average completion time per demand is increased by 29.9% and 18.1%, the overall transmission rate is increased by 43.5% and 22.8%, the overall average increase in the number of completions is about 42.4% and 22.9%, and the throughput is about 16.1% and 15.7% higher.

Edge computing improves the Internet of Vehicles (IoV) by offloading heavy computations from in-vehicle devices to high-capacity edge servers, typically roadside units (RSUs), to ensure rapid response times for intensive and latency-sensitive tasks. However, maintaining Quality of Service (QoS) remains challenging in dense urban settings and remote areas with limited infrastructure. To address this, we propose an software-defined networking (SDN)-driven model for uncrewed aerial vehicle (UAV)-assisted vehicular edge computing (VEC), integrating RSUs and UAVs to provide computing services and gather global network data via an SDN controller. UAVs serve as adaptable platforms for mobile-edge computing (MEC), filling gaps left by traditional MEC frameworks in areas with high vehicle density or sparse network resources. An optimal offloading mechanism, designed to minimize the Age of Information (AoI) while balancing energy consumption and rental costs, is implemented through a soft actor-critic (SAC)-based algorithm that jointly optimizes UAV trajectory, user association, and offloading decisions. Experimental results demonstrate the model’s superior performance, achieving up to 87.2% energy savings in energy-limited settings and a 50% reduction in time-sensitive scenarios, consistently outperforming traditional strategies across various task sizes.

With line-of-sight mode deployment and fast response, unmanned aerial vehicle (UAV), equipped with the cutting-edge integrated sensing and communication (ISAC) technique, is poised to deliver high-quality communication and sensing services in maritime emergency scenarios. In practice, however, the real-time transmission of ISAC signals at the UAV side cannot be realized unless the reliable wireless fronthaul link between the terrestrial base station and UAV are available. This paper proposes a multicarrier-division duplex based joint fronthaul-access scheme, where mutually orthogonal subcarrier sets are leveraged to simultaneously support four types of fronthaul/access transmissions. In order to maximize the end-to-end communication rate while maintaining an adequate sensing quality-of-service (QoS) in such a complex scheme, the UAV trajectory, subcarrier assignment and power allocation are jointly optimized. The overall optimization process is designed in two stages. As the emergency area is usually far away from the coast, the optimal initial operating position for the UAV is first found. Once the UAV passes the initial operating position, the UAV’s trajectory and resource allocation are optimized during the mission period to maximize the end-to-end communication rate under the constraint of minimum sensing QoS. Simulation results demonstrate the effectiveness of the proposed scheme in dealing with the joint fronthaul-access optimization problem in maritime ISAC networks, offering the advantages over benchmark schemes.

The Internet of Things (IoT) introduces diverse requirements and ubiquitous connections, necessitating efficient and affordable energy consumption as the ecosystem continues to grow. To address this challenge, we investigate a pure nonorthogonal multiple access (pure-NOMA) beamforming scheme to enhance system capacity by accommodating more IoT devices within the same spectrum. An energy efficiency (EE) maximization problem is formulated, jointly optimizing the beamforming matrix, power allocation, and device clustering. Due to the dynamic nature of the transmission channel and the coupling nonconvex mixed integer nonlinear programming (MINLP) problem, it is challenging to solve this problem by conventional mathematical methods. Additionally, the high dimensionality and coupling nonconvex MINLP problem pose significant challenges for traditional reinforcement learning (RL) methods. To overcome these issues, we propose a curiosity-driven approach that leverages intrinsic information from the base station (BS) to achieve energy efficient resource allocation. Simulation results demonstrate that pure-NOMA offers up to a 25% improvement in EE compared to hybrid-NOMA, while the curiosity-driven learning method outperforms baseline techniques, including deep RL (DRL), zero-forcing, and random methods, achieving a 14.78% reward gain over the DRL approach. The effectiveness of the proposed method is validated across various beam settings, device counts, quality-of-service requirements, and time consumption metrics, all while maintaining comparable computational complexity.

In this paper, we introduce a novel framework consisting of hybrid bit-level and generative semantic communications for efficient downlink image transmission within space-air-ground integrated networks (SAGINs). The proposed model comprises multiple low Earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and ground users. Considering the limitations in signal coverage and receiver antennas that make the direct communication between satellites and ground users unfeasible in many scenarios, thus UAVs serve as relays and forward images from satellites to the ground users. Our hybrid communication framework effectively combines bit-level transmission with several semantic-level image generation modes, optimizing bandwidth usage to meet stringent satellite link budget constraints and ensure communication reliability and low latency under low signal-to-noise ratio (SNR) conditions. To reduce the transmission delay while ensuring reconstruction quality for the ground user, we propose a novel metric to measure delay and reconstruction quality in the proposed system, and employ a deep reinforcement learning (DRL)-based strategy to optimize resource allocation in the proposed network. Simulation results demonstrate the superiority of the proposed framework in terms of communication resource conservation, reduced latency, and maintaining high image quality, significantly outperforming traditional solutions. Therefore, the proposed framework can ensure the real-time image transmission requirements in SAGINs, even under dynamic network conditions and user demand.

Sparse code multiple access (SCMA) is a promising non-orthogonal multiple access scheme for enabling massive connectivity in next generation wireless networks. However, current SCMA codebooks are designed with the same size, leading to inflexibility of user grouping and supporting diverse data rates. To address this issue, we propose a variable modulation SCMA (VM-SCMA) that allows users to employ codebooks with different modulation orders. To guide the VM-SCMA design, a VM matrix (VMM) that assigns modulation orders based on the SCMA factor graph is first introduced. We formulate the VM-SCMA design using the proposed average inverse product distance and the asymptotic upper bound of sum-rate, and jointly optimize the VMM, VM codebooks, power and codebook allocations. The proposed VM-SCMA not only enables diverse date rates but also supports different modulation order combinations for each rate. Leveraging these distinct advantages, we further propose an adaptive VM-SCMA (AVM-SCMA) scheme which adaptively selects the rate and the corresponding VM codebooks to adapt to the users’ channel conditions by maximizing the proposed effective throughput. Simulation results show that the overall designs are able to simultaneously achieve a high-level system flexibility, enhanced error rate results, and significantly improved throughput performance, when compared to conventional SCMA schemes.

In this paper, we analyse the reliability performance for the orthogonal time frequency space (OTFS) based low earth orbit (LEO)-satellite-air-terrestrial (LSAT) communication system. To facilitate the downlink transmission from the LEO satellite to the terrestrial node, a group of randomly distributed mobile unmanned aerial vehicles (UAVs) are employed to serve as the relays with decode-and-forward (DF) scheme. With the aid of stochastic geometry approach, the distribution of mobile UAVs is modeled by Poisson point processes (PPP) process with two motion modes: user dependent model (UDM) and user independent model (UIM). We derive the approximate closed-form expressions for the outage probabilities of the LSAT system under two UAV motion modes. Finally, the simulation results demonstrate that the reliability of the LSAT system can be significantly enhanced by using OTFS scheme, and by properly adjusting the UAV deployment parameters, corroborating the theoretical derivation.

Millimeter wave (mmW) is a promising technology for the next generation of mobile communications. However, the transmission efficiency and communication reliability of heterogeneous mobility user networks are limited by the frequent mmW beam alignment and the severe Doppler shift in the time-varying channel, respectively. To address this challenge, a joint resource allocation in frequency domain, time domain and power domain is investigated for the mmW communication network based on non-orthogonal multiple access (NOMA) and orthogonal time-frequency space (OTFS) techniques. The average spectral efficiency of high-mobility user equipment (H-UE) is maximized by the joint optimization of UE scheduling, beamwidth and transmit power. In order to solve the non-convex mixed integer problem of rate maximization, we propose the multi-dimensional resource allocation scheme based on alternate optimization method (AO-MRA), which decouples the initial intractable problem into two solvable sub-problems. In particular, based on the majorization-minimization approach, the scheduling algorithm is proposed to find the best UE scheduling for the NOMA groupings of heterogeneous mobility UEs, and the joint beamwidth and transmit power (JBP) algorithm is further designed for the optimal transmission time and power of base station in the mmW communication network. The proposed AO-MRA scheme can obtain effective suboptimal solutions of the initial problem. Simulation results demonstrate that the AO-MRA scheme is superior to other benchmark schemes in maximizing transmission efficiency. Moreover, the AO-MRA scheme is more suitable for low-power and high-bandwidth situations, and has superior spectral efficiency in terms of delay and Doppler high-resolution.

Time-frequency localization (TFL) based intelligent wideband spectrum sensing is capable of achieving precise dynamic spectrum management. Recent studies demonstrate that object detectors can achieve excellent TFL performance in simple electromagnetic scenarios when trained with massive and labeled datasets. However, in real-world concurrent cognitive scenarios that allow users to reuse the same frequency band under a certain interference constraint, the phenomenon of signal overlapping in the time-frequency domain will seriously degrade the performance of object detector. To the best of our knowledge, no comprehensive analysis has been conducted to assess the impact of overlapping in TFL. To fill this research gap, we analyze the impact of overlapping and identify three challenges: variety of overlapping, hard to label, and feature destruction. To enhance the robustness of the detector, we first adopt a self-supervised learning (SSL) framework based on a masked autoencoder. This framework aims to pre-train a backbone with excellent feature extraction ability using unlabeled dataset to overcome variety of overlapping and labeling difficulties. Subsequently, we develop a transformer based robust TFL (TRTFL) detector. This detector is designed to leverage both time-frequency correlation and fine-grained features, effectively addressing issues related to feature destruction. Finally, simulation results demonstrate the superiority of the proposed method and the effectiveness of SSL framework and TRTFL. Compared to existing detectors, the TRTFL achieves superior feature extraction, yielding a mean average precision (mAP) of 90.70% in overlapping signal scenarios. Moreover, the TRTFL with SSL can achieve an mAP of up to 95.08% outperforming the state-of-the-art.

In this paper, we propose a novel composition aided generalized quadrature spatial modulation (C-GQSM) scheme to improve the spectral efficiency (SE) of the GQSM systems by exploiting the power domain degree of freedom. The C-GQSM scheme constitutes a hybridization of GQSM and composition modulation (CM) principles, allowing the information bits to encompass not only the antenna activation patterns (AAPs) and amplitude/phase modulated (APM) constellation symbols, but also the energy allocation patterns (EAPs). In addition, we present two low-complexity detection techniques for the proposed C-GQSM system. The first one is based on the ordered successive interference cancellation (OSIC) technique, while the other based on the weighted coordinate descent (WCD) algorithm. Moreover, the upper bound of the average bit error probability (ABEP) of the proposed C-GQSM scheme is derived under both uncorrelated and correlated channel conditions. Simulation results show that the proposed C-GQSM outperforms both the conventional CM and GQSM systems in terms of SE without sacrificing the bit error rate (BER) performance.

In this paper, we propose a fluid antenna (FA) enabled joint transmit and receive index modulation (FA-JTR-IM) transmission mechanism for reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) communication systems. By integrating the methodologies of FA and IM, the proposed scheme achieves enhanced spectral efficiency (SE) while requiring only a single radio frequency (RF) chain at both the transmitter and receiver. The proposed scheme offers a low hardware cost and power consumption transmission mechanism for the RIS-aided mmWave communication systems. Specifically, the encoding of information bits encompasses not only the modulated symbol but also the indices of transmit FA positions and receive antennas. To achieve a reliability-complexity trade-off, two types of detectors are introduced for the proposed FA-JTR-IM scheme, including the optimal maximum likelihood (ML) detector and two-step sequential (TSS) detector. Based on the ML detector, we derive the expression for the conditional pair-wise error probability of the proposed FA-JTR-IM scheme. Additionally, we provide the closed-form expressions for the unconditional PEP under the finite-path and infinite-path channel conditions, respectively. Simulation results demonstrate the superiority of the proposed FA-JTR-IM scheme in terms of error performance over its conventional benchmark schemes under the same SE condition.

Traditional grid-based compressed sensing algorithms usually suffer from the base mismatch effect in channel estimation problems. To address this, we propose a novel gridless uplink/downlink (UL/DL) channel estimation strategy for millimeter wave (mmWave) massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. By exploiting inherent sparsity in the angle-delay domain of the mmWave channel, we first formulate the UL channel estimation problem as a joint sparse signal recovery problem. Then, we introduce the reweighted atomic norm for enhancing angular resolution of the mmWave channel on continuous Fourier dictionaries; we suggest a novel reweighted atomic norm minimization (NRAM) algorithm to solve the channel estimation problem by leveraging the Hankel-Toeplitz block model with multiple measurement vectors (MMVs), and the original NRAM problem is approximated by the solution of a semi-definite programming (SDP) problem with structured sparsity, which is efficiently solved by a low-complexity alternating direction multiplier method (ADMM). Subsequently, in the frequency division duplex (FDD) system, we design a simplified DL channel estimation scheme by leveraging the angle-delay reciprocity of UL and DL channels. This scheme reconstructs the DL channel matrix using the angle and path delay estimated from the UL channel, along with the channel gain obtained through least squares (LS). Finally, simulation results validate that our proposed approach achieves superior channel estimation accuracy and reduces pilot overhead compared to conventional UL/DL channel estimation techniques.

Sparse code multiple access (SCMA) and multiple input multiple output (MIMO) are considered as two efficient techniques to provide both massive connectivity and high spectrum efficiency for future machine-type wireless networks. This paper proposes a single sparse graph (SSG) enhanced expectation propagation algorithm (EPA) receiver, referred to as SSG-EPA, for uplink MIMO-SCMA systems. Firstly, we reformulate the sparse codebook mapping process using a linear encoding model, which transforms the variable nodes (VNs) of SCMA from symbol-level to bit-level VNs. Such transformation facilitates the integration of the VNs of SCMA and low-density parity-check (LDPC), thereby emerging the SCMA and LDPC graphs into a SSG. Subsequently, to further reduce the detection complexity, the message propagation between SCMA VNs and function nodes (FNs) are designed based on EPA principles. Different from the existing iterative detection and decoding (IDD) structure, the proposed EPA-SSG allows a simultaneously detection and decoding at each iteration, and eliminates the use of interleavers, de-interleavers, symbol-to-bit, and bit-to-symbol LLR transformations. Simulation results show that the proposed SSG-EPA achieves better error rate performance compared to the state-of-the-art schemes.

With the anticipation of sixth-generation (6G) networks escalating, the integrated sensing and communication (ISAC) and millimeter-wave (mmWave) unmanned aerial vehicle (UAV) communications emerge as the key focus areas. This paper presents an innovative approach to ISAC waveform design tailored for mmWave UAV communications. The orthogonal chirp division multiplexing (OCDM), characterized by a brunch of orthogonal chirp signals, is firstly introduced in UAV scenarios to offer dual sensing and communication functionalities.We propose an holistic waveform design which incorporates OCDM with the state-of-the-art mmWave frequency-modulated continuous wave (FMCW) radar. Specifically, one subcarrier in OCDM is chosen as the dedicated sensing signal to facilitate the FMCW processing at the UAV receiver, while the rest of OCDM subcarriers can be used to enhance communication data rate. Such OCDM-FMCW scheme significantly reduces the required hardware complexity, particularly in analog-to-digital converter, which provides an energy-efficient ISAC solution for the resource-constraint UAVs. Simulation results demonstrate the effectiveness and superiority of the proposed scheme. It can surpass traditional methods like OFDM and OTFS, by trading off the sensing performance, communication performance, and hardware complexity.

This paper investigates the amalgamation of affine frequency division multiplexing (AFDM) with sparse code multiple access (SCMA), termed as AFDM-SCMA, to facilitate massive connectivity in high-mobility scenarios. We start by introducing the basic principles of SCMA and AFDM systems and then present the proposed AFDM-SCMA system with multiple input and multiple output (MIMO) for both downlink and uplink channels. A two stage detector is proposed for the multi-user detection of the downlink channels. Additionally, to reduce the detection complexity and exploit the channel sparsity, we propose an expectation propagation algorithm (EPA)-aided low complexity receiver for uplink channels. Through numerical simulations, we validate the enhanced performance of the proposed AFDM-SCMA systems compared to conventional orthogonal frequency division multiplexing-empowered SCMA (OFDM-SCMA) systems in terms of error rate performance.

This article studies an intelligent reflecting surface (IRS)-empowered wireless-powered communication network (WPCN) in Internet of Things (IoT) networks. In particular, a power station (PS) with multiple antennas uses energy beamforming to enable wireless charging to multiple IoT devices, in the downlink wireless energy transfer (WET) phase; then, during the uplink wireless information transfer (WIT) phase, these IoT devices utilize the harvested energy to concurrently transmit their individual information signal to a multiantenna access point (AP), which equips with multiuser decomposition (MUD) techniques to reconstruct the IoT devices’ signal. An IRS is deployed to improve the energy collection and information transmission capabilities in the WET and WIT phases, respectively. To examine the performance of the system under study, we maximize the sum throughput with the aim of jointly designing the optimal solutions for the active PS energy beamforming, AP receive beamforming, passive IRS beamforming, and time scheduling. Due to the multiple coupled variables, the resulting formulation is nonconvex, and a two-level scheme to solve the problem is proposed. At the outer level, a 1-D search method is applied to find the optimal time scheduling, while at the inner level, an iterative block coordinate descent (BCD) algorithm is proposed to design the optimal receive beamforming, energy beamforming, and IRS phase shifts. In particular, the receive beamforming part is designed by considering the equivalence between sum rate maximization and sum mean square error (MSE) minimization, thereby deriving a closed-form solution. Furthermore, we alternately optimize the energy beamforming and IRS phase shifts using Lagrange dual transformation (LDT), quadratic transformation (QT), and alternating direction method of multipliers (ADMMs) methods. Finally, numerical results are presented to showcase the performance of the proposed solution and highlight its advantages compared to some typical benchmark schemes.

Wireless powered mobile edge computing (WPMEC) has been recognized as a promising solution to enhance the computational capability and sustainable energy supply for lowpower wireless devices (WDs). However, when the communication links between the hybrid access point (HAP) and WDs are hostile, the energy transfer efficiency and task offloading rate are compromised. To tackle this problem, we propose to employ multiple intelligent reflecting surfaces (IRSs) to WP-MEC networks. Based on the practical IRS phase shift model, we formulate a total computation rate maximization problem by jointly optimizing downlink/uplink IRSs passive beamforming, downlink energy beamforming, and uplink multiuser detection (MUD) vector at HAPs, task offloading power and local computing frequency of WDs, and the time slot allocation. Specifically, we first derive the optimal time allocation for downlink wireless energy transmission (WET) to IRSs and the corresponding energy beamforming. Next, with fixed time allocation for the downlink WET to WDs, the original optimization problem can be divided into two independent subproblems. For the WD charging subproblem, the optimal IRSs passive beamforming is derived by utilizing the successive convex approximation (SCA) method and the penaltybased optimization technique, and for the offloading computing subproblem, we propose a joint optimization framework based on the fractional programming (FP) method. Finally, simulation results validate that our proposed optimization method based on the practical phase shift model can achieve a higher total computation rate compared to the baseline schemes.

In order to overcome the vicious competition between different high-volume services, we study the wireless resource sharing problem in the transmission process of the MEC-caching-coexist (MCCe) system with the capability of mmWave communications. The multiobjective Markov decision process (MOMDP) is introduced to model the task scheduling and resource allocation problem for the mmWave links, which aims to minimize the transmission delay and energy consumption simultaneously. Note that, for practical consideration, the exact channel information of all links are not known. We propose a novel multiobjective deep reinforcement learning with discrete-continuous hybrid action space (MODRL/HA) algorithm. In particular, the envelope updated design (EUD) is designed to realize the multiobjective optimization from the perspective of the Bellman operator. On the other hand, the parameterized network design (PND) is developed to deal with the hybrid action space of discrete task scheduling and continuous beamwidth and power variables. Our simulations show that, the MODRL/HA algorithm can improve 22% performance in terms of the tradeoff between delay and energy consumption compared with the benchmark schemes, which are original deep deterministic policy gradient (DDPG) and multiobjective DDPG (MODDPG) algorithms.

In this paper, we focus on buffer-aided wireless powered Internet of Things (IoTs) comprising of one wireless access point (AP) and multiple devices, where the AP provides energy to all devices via downlink radio frequency (RF) energy beams. All devices utilize the harvested energy to transmit their data to the AP in a time-division multiple access (TDMA) manner. Every device is assumed to be provisioned with energy storage and data buffer to store the collected energy from the AP and its data, respectively. The problem of minimizing the long-term average age of information (AoI) of the system is formulated in this paper. By solving the problem under the Lyapunov optimization framework, the AoI-aware adaptive transmission scheme is obtained, in which downlink RF energy beamforming, downlink energy transfer and uplink access, as well as transmit power and transmission rate by every device, will be jointly adjusted in order to minimize average weightede AoI according to the underlying channel state information (CSI), the buffer state information (BSI), the energy-consumption status information (ESI) of all terminals, as well as the AoI status information (ASI). Our analysis unveils that, the status update rate at devices has a significant impact on the achievable AoI performance, and the minimum average weighted AoI can only be realized at a reasonable status update rate, which is neither too high nor too low. Moreover, flexible AoI-aware scheme can be realized by adjusting either the AoI priority level or the AoI weighting coefficient.

This article proposes an intelligent reflecting surface (IRS) assisted integrated sensing and communication (ISAC) system operating in the millimeter-wave band. Specifically, the ISAC system consists of a radar subsystem and a communication subsystem to detect multiple targets and communicate with the users simultaneously. The IRS is used to configure the radio propagation environment by changing the phase of the radio signal to enhance the communication transmission rate. In the proposed scheme, we first derive a closed-form solution for the radar signal covariance matrix to generate a radar beampattern in the angle of interest. Then, we jointly optimize the beamforming vector of the communication subsystem and the IRS phase shifts to enhance the communication transmission rate. To decouple the multiple variables to be optimized, the alternating optimization and quadratic transformation methods are applied to determine the communication beamforming vector and the IRS phase shifts. Specifically, we utilize the majorization minimization and the complex circle manifold methods to compute the IRS phase shifts. Simulation results verify the effectiveness of the proposed algorithm and demonstrate that an IRS can improve the performance of ISAC systems.

The space-air-ground integrated network (SAGIN) has become a crucial research direction in future wireless communications due to its ubiquitous coverage, rapid and flexible deployment, and multi-layer cooperation capabilities. However, integrating hierarchical federated learning (HFL) with edge computing and SAGINs remains a complex open issue to be resolved. This paper proposes a novel framework for applying HFL in SAGINs, utilizing aerial platforms and low Earth orbit (LEO) satellites as edge servers and cloud servers, respectively, to provide multi-layer aggregation capabilities for HFL. The proposed system also considers the presence of inter-satellite links (ISLs), enabling satellites to exchange federated learning models with each other. Furthermore, we consider multiple different computational tasks that need to be completed within a limited satellite service time. To maximize the convergence performance of all tasks while ensuring fairness, we propose the use of the distributional soft-actor-critic (DSAC) algorithm to optimize resource allocation in the SAGIN and aggregation weights in HFL. Moreover, we address the efficiency issue of hybrid action spaces in deep reinforcement learning (DRL) through a decoupling and recoupling approach, and design a new dynamic adjusting reward function to ensure fairness among multiple tasks in federated learning. Simulation results demonstrate the superiority of our proposed algorithm, consistently outperforming baseline approaches and offering a promising solution for addressing highly complex optimization problems in SAGINs.

In recent years, the amalgamation of satellite communications and aerial platforms into space-air-ground integrated network (SAGINs) has emerged as an indispensable area of research for future communications due to the global coverage capacity of low Earth orbit (LEO) satellites and the flexible Deployment of aerial platforms. This paper presents a deep reinforcement learning (DRL)-based approach for the joint optimization of offloading and resource allocation in hybrid cloud and multi-access edge computing (MEC) scenarios within SAGINs. The proposed system considers the presence of multiple satellites, clouds and unmanned aerial vehicles (UAVs). The multiple tasks from ground users are modeled as directed acyclic graphs (DAGs). With the goal of reducing energy consumption and latency in MEC, we propose a novel multi-agent algorithm based on DRL that optimizes both the offloading strategy and the allocation of resources in the MEC infrastructure within SAGIN. A hybrid action algorithm is utilized to address the challenge of hybrid continuous and discrete action space in the proposed problems, and a decision-assisted DRL method is adopted to reduce the impact of unavailable actions in the training process of DRL. Through extensive simulations, the results demonstrate the efficacy of the proposed learning-based scheme, the proposed approach consistently outperforms benchmark schemes, highlighting its superior performance and potential for practical applications.

To liberate indoor industrial cell-free massive multiple-input multiple-output (CF-mMIMO) networks from wired fronthaul, this paper proposes a multicarrier-division duplex (MDD)-enabled two-tier terahertz (THz) fronthaul scheme. In our scheme, two layers of fronthaul links rely on the mutually orthogonal subcarrier sets in the same THz band, while access links are implemented over sub-6G band. However, the proposed scheme leads to a complicated mixed-integer nonconvex optimization problem incorporating access point (AP) clustering, device selection, the assignment of subcarrier sets and the resource allocation at both the central processing unit (CPU) and APs. Hence, in order to address the formulated problem, we first resort to the low-complexity but efficient heuristic methods thereby relaxing the involved binary variables. Then, the overall end-to-end optimization is implemented by iteratively optimizing the assignment of subcarrier sets and the number of AP clusters. Furthermore, an advanced MDD frame structure consisting of three parallel data streams is tailored for the proposed scheme. Simulation results demonstrate the effectiveness of the proposed dynamic AP clustering approach in dealing with the networks of varying sizes. Moreover, benefiting from the well-designed frame structure, MDD is capable of outperforming TDD in the two-tier fronthaul networks. Additionally, the effect of the THz bandwidth on system performance is analyzed, and it is shown that empowered by sufficient bandwidth, our proposed two-tier fully-wireless fronthaul scheme can achieve a comparable performance to the fiber-optic based systems. Finally, the superiority of the proposed MDD-enabled fronthaul scheme is verified in a practical scenario with realistic ray-tracing simulations.

This paper proposes a grant-free massive access scheme based on the millimeter wave (mmWave) extra-large-scale multiple-input multiple-output (XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency, high data rate, and high localization accuracy in the upcoming sixth-generation (6G) networks. The XL-MIMO consists of multiple antenna subarrays that are widely spaced over the service area to ensure line-of-sight (LoS) transmissions. First, we establish the XL-MIMO-based massive access model considering the near-field spatial non-stationary (SNS) property. Then, by exploiting the block sparsity of subarrays and the SNS property, we propose a structured block orthogonal matching pursuit algorithm for efficient active user detection (AUD) and channel estimation (CE). Furthermore, different sensing matrices are applied in different pilot subcarriers for exploiting the diversity gains. Additionally, a multi-subarray collaborative localization algorithm is designed for localization. In particular, the angle of arrival (AoA) and time difference of arrival (TDoA) of the LoS links between active users and related subarrays are extracted from the estimated XL-MIMO channels, and then the coordinates of active users are acquired by jointly utilizing the AoAs and TDoAs. Simulation results show that the proposed algorithms outperform existing algorithms in terms of AUD and CE performance and can achieve centimeter-level localization accuracy.

Physical layer security (PLS) technologies are expected to play an important role in the next-generation wireless networks, by providing secure communication to protect critical and sensitive information from illegitimate devices. In this paper, we propose a novel secure communication scheme where the legitimate receiver use full-duplex (FD) technology to transmit jamming signals with the assistance of simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) which can operate under the energy splitting (ES) model and the mode switching (MS) model, to interfere with the undesired reception by the eavesdropper. We aim to maximize the secrecy capacity by jointly optimizing the FD beamforming vectors, amplitudes and phase shift coefficients for the ES-RIS, and mode selection and phase shift coefficients for the MS-RIS. With above optimization, the proposed scheme can concentrate the jamming signals on the eavesdropper while simultaneously eliminating the self-interference (SI) in the desired receiver. To tackle the coupling effect of multiple variables, we propose an alternating optimization algorithm to solve the problem iteratively. Furthermore, we handle the non-convexity of the problem by the the successive convex approximation (SCA) scheme for the beamforming optimizations, amplitudes and phase shifts optimizations for the ES-RIS, as well as the phase shifts optimizations for the MS-RIS. In addition, we adopt a semi-definite relaxation (SDR) and Gaussian randomization process to overcome the difficulty introduced by the binary nature of mode optimization of the MS-RIS. Simulation results validate the performance of our proposed schemes as well as the efficacy of adapting both two types of STAR-RISs in enhancing secure communications when compared to the traditional self-interference cancellation technology.

The communication bottleneck severely constrains the scalability of distributed deep learning, and efficient communication scheduling accelerates distributed DNN training by overlapping computation and communication tasks. However, existing approaches based on tensor partitioning are not efficient and suffer from two challenges: 1) the fixed number of tensor blocks transferred in parallel can not necessarily minimize the communication overheads; 2) although the scheduling order that preferentially transmits tensor blocks close to the input layer can start forward propagation in the next iteration earlier, the shortest per-iteration time is not obtained. In this paper, we propose an efficient communication framework called US-Byte. It can schedule unequal-sized tensor blocks in a near-optimal order to minimize the training time. We build the mathematical model of US-Byte by two phases: 1) the overlap of gradient communication and backward propagation, and 2) the overlap of gradient communication and forward propagation. We theoretically derive the optimal solution for the second phase and efficiently solve the first phase with a low-complexity algorithm. We implement the US-Byte architecture on PyTorch framework. Extensive experiments on two different 8-node GPU clusters demonstrate that US-Byte can achieve up to 1.26x and 1.56x speedup compared to ByteScheduler and WFBP, respectively. We further exploit simulations of 128 GPUs to verify the potential scaling performance of US-Byte. Simulation results show that US-Byte can achieve up to 1.69x speedup compared to the state-of-the-art communication framework.

The full-duplex (FD) communication can achieve higher spectrum efficiency than conventional half-duplex (HD) communication; however, self-interference (SI) is the key hurdle. This paper is the first work to propose the intelligent omni surface (IOS)-assisted FD multi-input single-output (MISO) FD communication systems to mitigate SI, which solves the frequency-selectivity issue. In particular, two types of IOS are proposed, energy splitting (ES)-IOS and mode switching (MS)-IOS. We aim to maximize data rate and minimize SI power by optimizing the beamforming vectors, amplitudes and phase shifts for the ES-IOS and the mode selection and phase shifts for the MS-IOS. However, the formulated problems are non-convex and challenging to tackle directly. Thus, we design alternative optimization algorithms to solve the problems iteratively. Specifically, the quadratic constraint quadratic programming (QCQP) is employed for the beamforming optimizations, amplitudes and phase shifts optimizations for the ES-IOS and phase shifts optimizations for the MS-IOS. Nevertheless, the binary variables of the MS-IOS render the mode selection optimization intractable, and then we resort to semidefinite relaxation (SDR) and Gaussian randomization procedures to solve it. Simulation results validate the proposed algorithms’ efficacy and show the effectiveness of both the IOSs in mitigating SI compared to the case without an IOS.

This work presents a novel K-Repetition based Hybrid Automatic Repeat reQuest (HARQ) scheme for uplink sparse code multiple access (SCMA) systems. Our core idea is to apply network coding (NC) principle to re-encode different packets (after channel coding and interleaving) or their fragments, where K-Repetition is an emerging HARQ technique (recommended in 3GPP Release 15) for enhanced reception in future massive machine-type communications. Such a proposed scheme is referred to as the NC aided K-repetition SCMA (NCK-SCMA) in this paper. We aim to understand the optimal NCK-SCMA design criteria for maximizing the channel diversity as well as the efficient receiver processing for superior error rate performances. It is found that NC can enable a larger diversity order for NCK-SCMA with fewer resources (i.e., higher spectrum efficiency). Toward this objective, some novel design criteria are developed for the efficient configuration of NCK-SCMA. Moreover, we propose an iterative network decoding and SCMA detection (INDSD) algorithm for robust and low-complexity recovery of the transmit data from a low-density parity-check (LDPC) coded uplink NCK-SCMA system. Simulation results demonstrate that the proposed NCK-SCMA lead to higher throughput and improved reliability over the conventional K-SCMA.

In order to maximize the spectral efficiency (SE) in multicarrier-division duplex (MDD) enabled cell-free massive MIMO (CF-mMIMO), a heterogeneous graph neural network (HGNN), referred to as CF-HGNN, is specifically introduced to optimize the power allocation (PA). To efficiently manage the interference invoked, a meta-path based mechanism is applied in CF-HGNN to enable individual access point (AP) and mobile station (MS) nodes to aggregate information from the interfering and communication paths with different priorities during message passing. Moreover, the proposed CF-HGNN employs the adaptive node embedding layer and adaptive output layer to make it scalable to the various numbers of APs, MSs and subcarriers. For comparison, a quadratic transform and successive convex approximation (QT-SCA) algorithm is proposed to solve the PA problem in classic way. Numerical results show that CF-HGNN is capable of achieving 99% of the SE achievable by QT-SCA but using only 10−4 times of its operation time, and it can outperform the conventional learning-based and greedy unfair methods in terms of SE performance. Furthermore, CF-HGNN exhibits good scalability to the CF networks with various numbers of nodes and subcarriers, and also to the large-scale CF networks when assisted by user-centric clustering.

Sparse code multiple access (SCMA) is a promising technique for the enabling of massive connectivity in future machine-type communication networks, but it suffers from a limited diversity order which is a bottleneck for significant improvement of error performance. This paper aims for enhancing the signal space diversity of sparse code multiple access (SCMA) by introducing quadrature component delay to the transmitted codeword of a downlink SCMA system in Rayleigh fading channels. Such a system is called SSD-SCMA throughout this work. By looking into the average mutual information (AMI) and the pairwise error probability (PEP) of the proposed SSD-SCMA, we develop novel codebooks by maximizing the derived AMI lower bound and a modified minimum product distance (MMPD), respectively. The intrinsic asymptotic relationship between the AMI lower bound and proposed MMPD based codebook designs is revealed. Numerical results show significant error performance improvement in the both uncoded and coded SSD-SCMA systems.

This paper studies the affine frequency division multiplexing (AFDM)-empowered sparse code multiple access (SCMA) system, referred to as AFDM-SCMA, for supporting massive connectivity in high-mobility environments. First, by placing the sparse codewords on the AFDM chirp subcarriers, the input-output (I/O) relation of AFDM-SCMA systems is presented. Next, we delve into the generalized receiver design, chirp rate selection, and error rate performance of the proposed AFDM-SCMA. The proposed AFDM-SCMA is shown to provide a general framework and subsume the existing OFDM-SCMA as a special case. Third, for efficient transceiver design, we further propose a class of sparse codebooks for simplifying the I/O relation, referred to as I/O relation-inspired codebook design in this paper. Building upon these codebooks, we propose a novel iterative detection and decoding scheme with linear minimum mean square error (LMMSE) estimator for both downlink and uplink channels based on orthogonal approximate message passing principles. Our numerical results demonstrate the superiority of the proposed AFDM-SCMA systems over OFDM-SCMA systems in terms of the error rate performance. We show that the proposed receiver can significantly enhance the error rate performance while reducing the detection complexity.

This paper investigates the maximal achievable rate for a given maximal error probability, and blocklength for the reconfigurable intelligent surface (RIS) assisted multiple-input and multiple-output (MIMO) system. The result consists of a finite blocklength and finite alphabet constraints channel coding achievability and converse bounds based on the Berry-Esseen theorem, the Mellin transform and the closed-form expression of the mutual information and the unconditional variance. The numerical evaluation shows a fast speed of convergence to the maximal achievable rate as the blocklength increases and also proves that the channel variance is a sound measurement of the backoff from the maximal achievable rate due to finite blocklength.

Compressed sensing (CS)-based techniques have been widely applied in the grant-free non-orthogonal multiple access (NOMA) to a single-antenna base station (BS). In this paper, we consider the multi-antenna reception at the BS for uplink grant-free access for the massive machine type communication (mMTC) with limited channel resources. To enhance the overloading performance of the BS, we develop a general framework for the synergistic amalgamation of the spatial division multiple access (SDMA) technique with the CS-based grant-free NOMA. We derive a closed-form statistical beamforming and a dynamic beamforming scheme for the inter-cluster interference suppression when applying SDMA. Based on this, we further develop a joint adaptive beamforming and subspace pursuit (J-ABF-SP) algorithm for the multiuser detection and data recovery, with a novel sparsity level decision method without the accurate knowledge of the noise level. To further improve the data recovery performance, we propose an interference cancellation-based J-ABF-SP scheme (J-ABF-SP-IC) by using the initial signal estimates generated from the J-ABF-SP algorithm. Illustrative simulations verify the superior user detection and signal recovery performance of our proposed algorithms in comparison with existing CS-based grant-free NOMA techniques.

Most of the existing research on degrees-of-freedom (DoF) with imperfect channel state information at the transmitter (CSIT) assume the messages are private, which may not reflect reality as the two receivers can request the same content. To overcome this limitation, we therefore consider the hybrid unicast and multicast messages. In particular, we characterize the optimal DoF region for the two-user multiple-input multiple-output (MIMO) broadcast channel (BC) with imperfect CSIT and hybrid messages. For the converse, we establish a three-step procedure to exploit the utmost possible relaxation. For the achievability, since the DoF region is with specific three-dimensional structure regarding antenna configurations and CSIT qualities, we verify the existence or non-existence of corner point candidates via the feature of antenna configurations and CSIT qualities categorization, and provide a hybrid message-aware rate-splitting scheme. Besides, we show that to achieve the strictly positive corner points, it is unnecessary to split the unicast messages into private and common parts. This implies adding a multicast message may mitigate the rate-splitting complexity.

In this paper, we propose a transmission mechanism for fluid antennas (FAs) enabled multiple-input multiple-output (MIMO) communication systems based on index modulation (IM), named FA-IM, which incorporates the principle of IM into FAs-assisted MIMO system to improve the spectral efficiency (SE) without increasing the hardware complexity. In FA-IM, the information bits are mapped not only to the modulation symbols, but also the index of FA position patterns. Additionally, the FA position pattern codebook is carefully designed to further enhance the system performance by maximizing the effective channel gains. Then, a low-complexity detector, referred to efficient sparse Bayesian detector, is proposed by exploiting the inherent sparsity of the transmitted FA-IM signal vectors. Finally, a closed-form expression for the upper bound on the average bit error probability (ABEP) is derived under the finite-path and infinite-path channel condition. Simulation results show that the proposed scheme is capable of improving the SE performance compared to the existing FAs-assisted MIMO and the fixed position antennas (FPAs)-assisted MIMO systems while obviating any additional hardware costs. It has also been shown that the proposed scheme outperforms the conventional FA-assisted MIMO scheme in terms of error performance under the same transmission rate.

This paper investigates a deep learning-based algorithm to optimize the unmanned aerial vehicle (UAV) trajectory and reconfigurable intelligent surface (RIS) reflection coefficients in UAV-RIS-aided cell-free (CF) hybrid non-orthogonal multiple-access (NOMA)/orthogonal multiple-access (OMA) networks. The practical RIS reflection model and user grouping optimization are considered in the proposed network. A double cascade correlation network (DCCN) is proposed to optimize the RIS reflection coefficients, and based on the results from DCCN, an inverse-variance deep reinforcement learning (IV-DRL) algorithm is introduced to address the UAV trajectory optimization problem. Simulation results show that the proposed algorithms significantly improve the performance in UAV-RIS-assisted CF networks.

In this paper, we investigate a cellular-connected unmanned aerial vehicle (UAV) network, where multiple UAVs receive messages from base stations (BSs) in the down-link, and in the meantime, BSs serve their paired ground user equipments (UEs). To effectively manage inter-cell interferences (ICIs) among UEs due to intense reuse of time-frequency resource block (RB) resource, a first p-tier based RB coordination criterion is adopted. Then, to enhance wireless transmission quality for UAVs while protecting terrestrial UEs from being interfered by ground-to-air (G2A) transmissions, a radio resource management (RRM) problem of joint dynamic RB coordination and time-varying beamforming design is formulated to minimize UAV's ergodic outage duration (EOD). To cope with conventional optimization techniques' inefficiency in solving the formulated RRM problem, a deep reinforcement learning (DRL)-aided solution is proposed, where deep double duelling Q network (D3QN) and twin delayed deep deterministic policy gradient (TD3) are invoked to deal with RB coordination in the discrete action domain and beamforming design in the continuous action regime, respectively. Numerical results illustrate the effectiveness of the proposed hybrid D3QNTD3 algorithm, compared to representative baselines.

In MIMO multi-user networks, inter-user interference (IUI) significantly affects the system performance. To handle this problem, this paper proposes the reconfigurable intelligent surface assisted cooperative interference alignment scheme (RIS-CIA). The core idea of this work is that the base station and full-duplex users jointly design space-time precoding matrices, which can reduce the dimension of the interference space on the user side. Besides, the additional interference caused by the information exchange process is split into sub-blocks by space-time precoding, then eliminated by interference nulling assisting by the passive RIS. The simulation results show that the RIS-CIA scheme with few numbers of elements obtains higher DoF than that of benchmark schemes with a huge number of elements.

This paper investigates a learning-based approach autonomously and jointly optimizing the trajectory of unmanned aerial vehicle (UAV), phase shifts of reconfigurable intelligent surfaces (RIS), and aggregation weights for federated learning (FL) in wireless communications, forming an autonomous RIS-assisted UAV-enabled network. The proposed network considers practical RIS reflection models and FL transmission errors in wireless communications. To optimize the RIS phase shifts, a double cascade correlation network (DCCN) is introduced. Additionally, the deep deterministic policy gradient (DDPG) algorithm is employed to address the optimization problem of UAV trajectory and FL aggregation weights based on the results obtained from DCCN. Simulation results demonstrate the substantial improvement in FL performance within the autonomous RIS-assisted UAV-enabled network setting achieved by the proposed algorithms compared to the benchmarks.

In the upcoming B5G/6G era, devices will generate a amount of heterogeneous data at the network edge. As a paradigm for implementing distributed and privacy-preserving machine learning (ML), Federated Learning (FL) has drawn great attention to secure data sharing in edge networks. However, FL takes too much time and communication resources to train and transmit model parameters, which is unaffordable for edge devices with limited capabilities. To achieve a trade-off between resource and efficiency, it is crucial to select appropriate training nodes. While existing works about node selection focus on the resources allocation and pay less attention to the node mobility and seamless service. In this paper, we considering mobility, computation capability, and transmission power of training nodes to minimize the FL system cost. We propose an algorithm and mechanism respectively for different scenarios of node speed. An algorithm based on Deep Reinforcement Learning (DRL) matches with stationary and low-speed training nodes. A heuristic mechanism is used for nodes with high mobility. Simulation results show that the proposed schemes select appropriate training nodes effectively, and reduce the system cost by up to 20%.

High mobility scenarios may be typical for different applications such as low earth orbit (LEO) satellite and vehicle-to-everything (V2X) communications. A standardized approach to dealing with high mobility scenarios is using flexible sub-frame structures including a higher pilot density in the time domain, which leads to reduced spectrum efficiency. We propose a supplementary algorithm to improve multiple antenna receiver performance in high mobility scenarios for the given sub-frame structure compared to the conventional 3GPP pilot and data based interference rejection receivers. The main feature of high mobility (non-stationary) scenarios is that different symbols in the desired signal sub-frame may be received under different propagation and/or interference conditions. Recently, we have addressed a non-stationary interference rejection scenario in slowly varying propagation environment with asynchronous (intermittent) interference by means of developing an interference rejection combining algorithm, where the pilot based estimate of the interference plus noise covariance matrix is regularized by the data based estimate of the covariance matrix. In this paper, we: 1) extend the data regularized solution to the general high mobility scenarios, and 2) demonstrate its efficiency compared to the conventional pilot and data based receivers for different sub-frame formats in the uplink transmissions in the LEO satellite scenario with high residual Doppler frequency with and without hardware impairments.

The Dijkstra algorithm has been widely used in the routing protocol design. However, the shortest path tree calculation method based on the Dijkstra algorithm fails to take advantage of the characteristics of the graph structure in topologies of satellite networks and incurs a heavy computational burden. In this paper, we propose a low-complexity and efficient algorithm for satellite routing according to the special topology and link latency distribution of LEO satellite constellations. The simulation results show that the proposed scheme achieves the same optimal performance as the Dijkstra algorithm but with much-reduced complexity. In particular, under the network scale of 60×30, the average calculation time of our algorithm is less than $\frac{1}{{10}}$ of that of the Dijkstra algorithm.

In this paper, a novel index and composition modulation (ICM) transmission scheme, termed as grouped generalized composition and spatial modulation (G-GCSM), is proposed for massive multiple-input multiple-output (MIMO) systems. Specifically, it amalgamates the concepts of composition modulation (CM), generalized spatial modulation (GSM) and spatial multiplexing to attain high spectral efficiency (SE) and low implementation complexity. In the G-GCSM scheme, transmit antennas are divided into several groups and the GCSM transmission structure is employed independently in each group, facilitating the bit-to-index mapping issue in massive MIMO scenarios. Additionally, at the receiver side, an improved expectation propagation (EP) detector is designed for the proposed G-GCSM scheme, which exploits the inner sparsity of the transmitted vector in G-GCSM. Simulation results demonstrate the superiority of the proposed scheme over the existing GSM schemes in terms of bit error rate (BER) performance under the same SE conditions. Moreover, the proposed improved EP detector is able to provide a significant performance gain over the conventional minimum-mean-squared error (MMSE) detector in both determined and under-determined massive MIMO systems.

In this paper, a novel intelligent transmission surface (ITS) assisted terahertz (THz) wideband massive multiple-input multiple-output (MIMO) non-terrestrial communication architecture is conceived, which is capable of reducing the hardware cost and power consumption remarkably compared to traditional architectures. To further address the beam squint impact in the THz wideband system, an angle-based hybrid beamformer is designed for the proposed architecture, which can effectively suppress the beam squint and maintain high spectral efficiency (SE) performance. Numerical results demonstrate that the proposed scheme is capable of approaching the optimal full-digital architecture in terms of the SE performance, and the proposed method can achieve significant energy efficiency performance gains over other existing architectures.

Non-orthogonal multiple access (NOMA) is a promising candidate radio access technology for future wireless communication systems, which can achieve improved connectivity and spectral efficiency. Without sacrificing error rate performance, link adaptation combining with adaptive modulation and coding (AMC) and hybrid automatic repeat request (HARQ) can provide better spectral efficiency and reliable data transmission by allowing both power and rate to adapt to channel fading and enabling re-transmissions. However, current AMC or HARQ schemes may not be preferable for NOMA systems due to the imperfect channel estimation and error propagation during successive interference cancellation (SIC). To address this problem, a reinforcement learning based link adaptation scheme for downlink NOMA systems is introduced in this paper. Specifically, we first analyze the throughput and spectrum efficiency of NOMA system with AMC combined with HARQ. Then, taking into account the imperfections of channel estimation and error propagation in SIC, we propose SINR and SNR based corrections to correct the modulation and coding scheme selection. Finally, reinforcement learning (RL) is developed to optimize the SNR and SINR correction process. Comparing with a conventional fixed look-up table based scheme, the proposed solutions achieve superior performance in terms of spectral efficiency and packet error performance.

This article studies secure communication in an Internet of Things (IoT) network, where the confidential signal is sent by an active refracting reconfigurable intelligent surface (RIS)-based transmitter, and a passive reflective RIS is utilized to improve the secrecy performance of users in the presence of multiple eavesdroppers. Specifically, we aim to maximize the weighted sum secrecy rate by jointly designing the power allocation, transmit beamforming (BF) of the refracting RIS, and the phase shifts of the reflective RIS. To solve the nonconvex optimization problem, we propose a linearization method to approximate the objective function into a linear form. Then, an alternating optimization (AO) scheme is proposed to jointly optimize the power allocation factors, BF vector, and phase shifts, where the first one is found using the Lagrange dual method, while the latter two are obtained by utilizing the penalty dual decomposition method. Moreover, considering the demands of green and secure communications, by applying Dinkelbach’s method, we extend our proposed scheme to solving a secrecy energy maximization problem. Finally, simulation results demonstrate the effectiveness of the proposed design.

Upon exploiting massive spectrum resources, millimeter-wave (mmW) communication can significantly improve the transmission rate of mobile-edge computing (MEC) offloading, whereas the directional mmW links are constrained by shrunk beam coverage and demand extra phase for beam alignment. To enhance the accessing efficiency, we develop the nonorthogonal multiple access (NOMA) scheme-based mmW MEC mechanism, namely, NOMA-mmW MEC, therefore motivating to minimize the average delay of the MEC offloading, by jointly optimizing the beamwidth, user equipment (UE) scheduling, and transmit power. To tackle the mixed-integer nonlinear programming (MINLP) problem of delay minimization, we develop the alternative optimization (AO) approach-based RA scheme, namely, AO-RA, to obtain the close-optimum solutions. In the AO-RA scheme, we propose the matrix control many-to-one with externality (MC-M2OE) algorithm, to find the best UE scheduling for the NOMA groupings of different types of UEs. Upon the above, we further design the joint beamwidth and transmit power (JBTP) algorithm, which determines the optimal beamwidth and transmit power for the MEC offloading transmissions. Our simulation results show the effectiveness of the proposed AO-RA scheme in minimizing the offloading delay, where our MC-M2OE and JBTP algorithms can significantly outperform the existing approaches. From the simulation results, we may conclude that it needs to carefully address the tradeoff between beam alignment overhead and transmission gain while properly balancing the loading among different NOMA groups, for the practical consideration of NOMA-mmW MEC technology.

This article analyzes the maximal achievable rate for a given blocklength and maximal error probability over a multiple-antenna ambient backscatter channel. The result consists of a finite blocklength channel coding achievability bound and a converse bound for the legacy system with finite alphabet constraints and multiple-input-multiple-output based on the Neyman–Pearson test, the Berry–Esseen theorem, and the Mellin transform. Then, we derive the closed-form expression of the mutual information and the information variance to reduce the complexity of the computation. By applying the low-complexity maximum-likelihood detection, the relation between the maximal error probability of the RF source signal and the average error probability of the tag symbol with respect to the blocklength is proposed. Finally, numerical evaluation of these bounds shows fast convergence to the maximal achievable rate as the blocklength increases and also proves that the information variance is an accurate measure of the backoff from the maximal achievable rate due to finite blocklength.

6G edge networks strive to offer ubiquitous intelligent services, requiring a greater emphasis on network stability and reliability. However, current networks present a low automation degree of the operation, administration and maintenance process. Consequently, active service migration away from abnormal network nodes and links, as well as automatic and transparent service recovery from sudden anomalies, become challenging tasks. These conditions underscore the urgency for an innovative service self-healing mechanism for 6G edge networks. Digital twin (DT) technology uses modeling to represent physical entities, thereby facilitating lifecycle management. However, the application of DT technology in networks is still a burgeoning field of study. In this paper, we explore the DT-driven service self-healing mechanism in 6G edge networks. Initially, we design a DT-based architecture for service self-healing. Subsequently, we construct a performance prediction mechanism leveraging graph neural networks (GNNs) to devise an efficient prediction model, which aims to accurately infer network performance and promptly detect abnormal network conditions. To maintain fine-grained service stability amidst potential network anomalies, we propose a DT-driven service redeployment mechanism enhanced by GNNs. Comprehensive experimental results reveal that our proposed mechanism can accurately predict flow-level delays and identify abnormal links and nodes. Furthermore, the DT-driven service redeployment mechanism effectively reduces service delay and enhances network load balance.

In this paper, we investigate an intelligent reflecting surface (IRS)-assisted wireless powered Internet of Things (WP-IoT) network that operates in multiple resource blocks (RBs). Particularly, the IRS helps in both downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT), in a way that it improves energy reflection in WET from a power station (PS) to various IoT devices and boosts information delivery in WIT from the IoT devices to an access point (AP). Those IoT devices are capable of utilizing the collected energy, and adopting the time-division multiple access (TDMA) or non-orthogonal multiple access (NOMA) scheme in the uplink WIT. Aiming to maximize the average throughput as the overall performance indicator of the considered network, we jointly optimize the transmit power allocation of the PS, the time scheduling, and the IRS phase shifts. These coupled variables lead to the non-convexity of this optimization problem, which cannot be solved directly. To address this problem, we first design the optimal PS’s transmit power allocation for each RB. For the TDMA-based scheme, we design the closed-form IRS beam pattern of the uplink WIT. Then, the closed-form downlink and uplink time allocations are derived by the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions. In addition, the quadratic transformation (QT)-based Alternating Direction Method of Multipliers (ADMM) approach is proposed to iteratively derive the sub-optimal IRS beam pattern of the downlink WET in an alternated fashion. For the NOMA-based scheme, we propose to apply an alternating optimization (AO) algorithm to iteratively optimize the IRS phase shifts, where the uplink IRS beam pattern is iteratively designed by the Riemannian Manifold Optimization (RMO) approach, and the QT-based ADMM method is adopted to alternately derive the sub-optimal downlink IRS phase shifts. Finally, numerical results demonstrate the improved performance of the proposed solution approaches compared to the benchmark schemes, also highlight advantages of the application of IRS in multiple RB scenarios.

This paper exploits an intelligent reflecting surface (IRS) assisted wireless powered mobile edge computing and caching (WP-MECC) network. In particular, an IRS is utilized to reflect energy signals from a power station (PS) to various IoT devices for energy harvesting during uplink wireless energy transfer (WET). These devices collect energy to support their own partially local computing for computational tasks and their offloading capabilities to an access point (AP), with the help of IRS via time or frequency division multiple access (TDMA or FDMA). The AP is equipped with a local cache connected with a MEC server via a backhaul link, which prefetches the data to facilitate edge computing capabilities. The maximization of a utility function is formulated to evaluate the overall network performance, which is defined as the difference between the sum of computational bits (offloading bits and local computing bits) and total backhaul cost. Due to multiple coupled variables, we first design the optimal caching strategy. Then, an auxiliary vector is introduced to coordinate the energy consumption of local computing and offloading, where its optimal solution can be achieved by an exhaustive search. Moreover, we utilize the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions to derive the optimal time scheduling for the TDMA scheme or the optimal bandwidth allocation for the FDMA counterpart in closed form. The IRS phase shifts are iteratively designed by employing the quadratic transformation (QT) and the Riemannian Manifold Optimization (RMO). Finally, simulation results are demonstrated to validate the network utility performance and confirm the advantage of the employment of IRS, the optimal IRS phase shift design and caching strategy, in comparison to the benchmark schemes.

In this contribution, the concepts of polarization modulation (PM) and spatial modulation (SM) are integrated, to reap their respective advantages toward single-radio frequency (RF) multiple-input multiple-output (MIMO) transmissions. In the so-called polarization and spatial modulation (PSM) system, the information is conveyed by activated antenna indices as well as polarization modulated symbols, while at the receiver, a low-complexity near-optimal detection algorithm based on compressive sensing (CS) is proposed. Furthermore, a closed-form union bound of the average bit-error rate (BER) over fading channels is quantified by theoretical derivation, which is then extended to the case of spatial correlation (SC) as well as channel estimation error (CSE) toward practical use. Finally, our simulation results demonstrate the superiority of the developed PSM system over conventional PM in terms of BER performance over various fading channels.

Intelligent reflecting surface (IRS) is a promising technique to extend the network coverage and improve spectral efficiency. This paper investigates an IRS-assisted terahertz (THz) multiple-input multiple-output (MIMO)-nonorthogonal multiple access (NOMA) system based on hybrid precoding with the presence of eavesdropper. Two types of sparse RF chain antenna structures are adopted, i.e., sub-connected structure and fully connected structure. First, cluster heads are selected for each beam, and analog precoding based on discrete phase is designed. Then, users are clustered based on channel correlation, and NOMA technology is employed to serve the users. In addition, a low-complexity forced-zero method is utilized to design digital precoding in order to eliminate inter-cluster interference. On this basis, we propose a secure transmission scheme to maximize the sum secrecy rate by jointly optimizing the power allocation and phase shifts of IRS subject to the total transmit power budget, minimal achievable rate requirement of each user, and IRS reflection coefficients. Due to multiple coupled variables, the formulated problem leads to a non-convex issue. We apply the Taylor series expansion and semidefinite programming to convert the original non-convex problem into a convex one. Then, an alternating optimization algorithm is developed to obtain a feasible solution of the original problem. Simulation results verify the convergence of the proposed algorithm, and deploying IRS can bring significant beamforming gains to suppress the eavesdropping.

This paper proposes a multi-cluster wireless powered Internet of Things (WP-IoT) network assisted by multiple intelligent reflecting surfaces (multi-IRS). In this network, a power station (PS) first broadcasts wireless energy to the distributed IoT devices grouped into multiple clusters. The IoT devices then use the harvested energy to convey their information to an access point (AP), based on a hybrid time- and frequency-division multiple access (TDMA-FDMA) protocol. Furthermore, multiple IRSs are deployed to perform anomalous reflection for energy and information transfer, to improve energy harvesting and data transmission capabilities. Under the constraints of the unit-modulus phase shifts, the transmission time shared among clusters and the bandwidth shared by the devices in each cluster, the considered system is optimized by maximizing its sum throughput. The optimization problem is non-convex and with complicatedly coupled variables. To solve this problem, we propose to first apply the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions to derive closed-form solutions for transmission scheduling and bandwidth allocation, then the quadratic transformation (QT) and the alternating optimization (AO) algorithm are introduced to solve the downlink and uplink IRS phase shifts, whilst the Majorization-Minimization (MM) and Riemannian Manifold Optimization (RMO) methods are applied to iteratively derive their closed-form solutions. Additionally, we provide a benchmark scheme to facilitate the system design, where each IRS can control its “on/off” state to aid the downlink and uplink transmissions in the condition of at most one activated IRS during one certain time duration. Finally, simulation results are presented to verify the optimality of our proposed scheme and highlight the beneficial role of the IRS.

This paper conceives a novel sparse code multiple access (SCMA) codebook design which is motivated by the strong need for providing ultra-low decoding complexity and good error performance in downlink Internet-of-things (IoT) networks, in which a massive number of low-end and low-cost IoT communication devices are served. By focusing on the typical Rician fading channels, we analyze the pair-wise error probability of superimposed SCMA codewords and then deduce the design metrics for multi-dimensional constellation construction and sparse codebook optimization. For significant reduction of the decoding complexity, we advocate the key idea of projecting the multi-dimensional constellation elements to a few overlapped complex numbers in each dimension, called low projection (LP). An emerging modulation scheme, called golden angle modulation (GAM), is considered for multi-stage LP optimization, where the resultant multi-dimensional constellation is called LP-GAM. Our analysis and simulation results show the superiority of the proposed LP codebooks (LPCBs) including one-shot decoding convergence and excellent error rate performance. In particular, the proposed LPCBs lead to decoding complexity reduction by at least 97% compared to that of the conventional codebooks, whilst owning large minimum Euclidean distance. Some examples of the proposed LPCBs are available athttps://github.com/ethanlq/SCMA-codebook.

In this paper, physical layer security (PLS) in a non-orthogonal multiple access (NOMA)-based mobile edge computing (MEC) system is investigated, where hybrid successive interference cancellation (SIC) decoding is considered. Specifically, users intend to complete confidential tasks with the help of the MEC server, while an eavesdropper attempts to intercept the offloaded tasks. By jointly designing computational resource allocation, task assignment, and power allocation, a latency minimization problem is formulated. Based on the interactions between local computing time and MEC processing time, the closed-from solutions of computational resource allocation and task assignment are derived. After that, a strategy selection mechanism is established to select offloading strategies based on the corresponding conditions. Moreover, according to the analysis of hybrid SIC decoding, the conditions of different decoding orders in secure NOMA networks are derived. Furthermore, a reinforcement learning based algorithm is proposed to solve the power allocation problems for NOMA and OMA offloading strategies. This work is extended to a multi-user scenario, in which a matching-based algorithm is proposed to solve the formulated sub-channel assignment problem. Simulation results indicate that: i) the proposed solution can significantly reduce the latency and provide dynamic strategy selection for various scenarios; ii) the NOMA offloading strategy with hybrid SIC decoding can outperform other strategies in the considered system.

With exploiting massive spectrum resources, millimeter wave (mmWave) communications significantly improve the offloading capability for future mobile edge computing (MEC) techniques, which however is constrained by blockage problem in dynamic environments. In this paper, we study the resource allocation problem for the conceived mmWave MEC system with dynamic offloading process, in which the UEs are characterized by being mobile and having the imperfect knowledge of the offloading tasks coming. By introducing the multi-objective Markov decision process (MOMDP), the resource allocation problem is modeled by simultaneously minimizing the delay and energy consumption, where jointly considering the multi-beam assignment (mBA) and beamwidth and power optimization (BPO). To tackle this problem, we innovatively propose a matching-aided-learning (MaL) resource allocation scheme, with the aid of a learnable weight based attention mechanism (LW-AM) for adapting the dynamic offloading process. In particular, our MaL scheme includes many-to-one matching (M2O-M) based mBA algorithm and deep deterministic policy gradient (DDPG) based BPO algorithm, which are executed iteratively and converge with relatively low number of iterations. The simulation results show the practical value of the proposed MaL, which can approach the performance of benchmark scheme with perfect knowledge of offloading tasks.

In this paper, we propose a bufferless data center network architecture (CycleDCN). CycleDCN is implemented as three-stage Clos network with arrayed wavelength grating routers (AWGRs) and tunable wavelength converters (TWCs) as the core device. Multi-wavelength routing and multi-stage structure make the routing problem of CycleDCN more complex. Based on the unique topology of CycleDCN, we propose two methods for route scheduling and wavelength assignment. One is based on integer linear programming, and the other is the heuristic algorithm based on the wavelength contention probability of each flow, which is called low contention probability first (LCF). With a centralized controller and either of the above methods, the proposed DCN architecture can achieve high scalability, high throughput, and low latency without using any optical buffer. The simulation results show that our proposed architecture can achieve about 83% throughput and less than 200ns latency at full load while accommodating thousands of ToR connections.

This paper proposes an intelligent reflecting surface (IRS) assisted integrated sensing and communication (ISAC) system operating at the millimeter-wave (mmWave) band. Specifically, the ISAC system combines communication and radar operations and performs on the same hardware platform, detecting and communicating simultaneously with multiple targets and users. The IRS dynamically controls the amplitude or phase of the radio signal via the reflecting elements to reconfigure the radio propagation environment and enhance the transmission rate of the ISAC system in the mmWave band. By jointly designing the radar signal covariance (RSC) matrix, the beamforming vector of the communication system, and the IRS phase shift, the ISAC system transmission rate can be improved while matching the desired waveform for radar. The problem is non-convex due to multivariate coupling, and thus we decompose it into two separate subproblems. First, a closed-form solution of the RSC matrix is derived from the radar desired waveform. Next, the quadratic transformation (QT) technique is applied to the subproblem, and then alternating optimization (AO) is applied to determine the communication beamforming vector and the IRS phase shift. Also, we derive a closed-form solution for the formulated problem, effectively decreasing computational complexity. Finally, the simulations verify the effectiveness of the algorithm and demonstrate that the IRS can improve the performance of the ISAC system.

This paper considers a two-user downlink multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) system using minimum mean-squared error (MMSE) detection under imperfect channel estimation. By taking account of both errors in channel estimation and MMSE detection, we derive approximated users' capacities and design a closed-form power allocation scheme to maximize the minimum (max-min) of them. The design problem is equivalent to max-min optimization of users' signal-to-interference-plus-noise ratios (SINRs), and the solution can be obtained by SINR balancing. The proposed power allocation scheme involves solving two quadratic equations, and is easy to implement in practical applications. As compared with an existing robust MIMO-NOMA power allocation method based on generalized singular value decomposition and SINR balancing, the proposed one offers slightly worse bit-error-rate performance with much lower complexity.

This paper proposes a secure transmission in reconfigurable intelligent surfaces (RIS) aided non-terrestrial cooperative networks (NTCN), where the practical phase-dependent model is considered in which the RIS reflection amplitudes change with the corresponding discrete phase shifts. Moreover, we employ a full-duplex transmission scheme at the relay nodes to reduce the long-range signal loss and improve the security between the satellite and the relay node. To solve the complex nonconvex optimization problem of the joint RIS reflection coefficient and relay selection optimization, we propose the deep cascade correlation learning (DCCL) algorithm to enhance optimization efficiency. Simulation results show that the proposed DCCL-based method significantly improves the secrecy capacity compared to the random relay selection and RIS coefficient methods.

A cell-free massive multiple-input multiple-output (MIMO) uplink is investigated in this paper. We address a power allocation design problem that considers two conflicting metrics, namely the sum rate and fairness. Different weights are allocated to the sum rate and fairness of the system, based on the requirements of the mobile operator. The knowledge of the channel statistics is exploited to optimize power allocation. We propose to employ large scale-fading (LSF) coefficients as the input of a twin delayed deep deterministic policy gradient (TD3). This enables us to solve the non-convex sum rate fairness trade-off optimization problem efficiently. Then, we exploit a use-and-then-forget (UatF) technique, which provides a closed-form expression for the achievable rate. The sum rate fairness trade-off optimization problem is subsequently solved through a sequential convex approximation (SCA) technique. Numerical results demonstrate that the proposed algorithms outperform conventional power control algorithms in terms of both the sum rate and minimum user rate. Furthermore, the TD3-based approach can increase the median of sum rate by 16%-46% and the median of minimum user rate by 11%-60% compared to the proposed SCA-based technique. Finally, we investigate the complexity and convergence of the proposed scheme.

This paper investigates the impact of unreliable backhaul and channel estimation error on the performance of small-cell networks over independent and identically distributed Nakagami-m fading channels. To overcome the impact of these practical constraints, we propose an optimal selection scheme where the best small cell with respect to the maximal secrecy capacity is selected. The secrecy outage probability for the considered scheme is derived and compared with Monte-Carlo simulations. To gain additional insights on the impact of unreli-able backhaul and imperfect channel estimation, the asymptotic behaviour of secrecy outage probability is also obtained.

This article investigates a wireless-powered intelligent radio environment, where a fractional nonlinear energy harvesting (NLEH) is proposed to enable an intelligent reflecting surface (IRS)-assisted wireless-powered Internet of Things (WP IoT) network. The IRS engages in downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT). We aim to improve the overall performance of the considered network, and the approach is to maximize its sum throughput subject to constraints of two different types of IRS beam patterns and time durations. To solve the formulated problem, we first consider the Lagrange dual method and Karush–Kuhn–Tucker (KKT) conditions to optimally design the time durations in closed form. Then, a quadratic transformation (QT) is proposed to iteratively transform the fractional NLEH model into the subtractive form, where the IRS phase shifts are optimally derived by the complex circle manifold (CCM) method in each iteration. Finally, numerical results are demonstrated to promote the proposed scheme in comparison to the benchmark schemes, where the benefits are induced by the IRS compared with the benchmark schemes.

In this article, we propose an intelligent reflecting surface (IRS)-enabled wireless-powered caching system. In the proposed IRS model, a power station (PS) provides wireless energy to multiple Internet of Things (IoT) devices, delivering their information to an access point (AP) by utilizing the harvested power. The AP, equipped with a local cache, stores the IoT data to avoid waking up the IoT devices frequently. Meanwhile, we deploy the IRS involving in the wireless energy and information transfer process for performance enhancements. In this practical system, the PS and AP could belong to different service providers. Also, the AP requires to incentivize the PS to offer a provisional energy service. We model the interaction between the PS and AP as a Stackelberg game that jointly optimizes the transmit power of the PS, the energy price, the phase shifts of the wireless energy transfer (WET) and wireless information transfer (WIT) phases, as well as wireless caching strategies of the AP. In this way, we first derive the optimal solutions of the phase shifts and the transmit power of the PS in a closed form. We propose an alternating optimization (AO) algorithm to optimize the wireless caching strategies and the energy price iteratively. Finally, we present various numerical evaluations to validate the beneficial role of the IRS and the wireless caching strategies and the performance of the proposed scheme compared with the existing benchmark schemes.

This article proposes a novel machine-learning-based routing optimization for the multiple reconfigurable intelligent surfaces (M-RIS)-assisted multihop cooperative networks, in which a practical phase model for reconfigurable intelligent surface (RIS) with the amplitude variation based on the corresponding discrete phase shift is considered. We aim to maximize the end-to-end data rate in the proposed network by jointly optimizing the data transmission path, the passive beamforming design of RIS, and transmit power allocation. To tackle this complicated nonconvex problem, we divide it into two subtasks: 1) the passive beamforming design of the RIS and 2) joint routing and power allocation optimization. First, for the passive beamforming design of RIS, we develop a distributed learning algorithm that employs a cascade forward backpropagation network in each relay node to solve the RIS coefficients optimization problem by directly using the optimization target to train the cascade networks. This solution can avoid the curse of dimensionality of traditional reinforcement learning algorithms in the RIS optimization problem. Then, based on the result of RIS optimization, we introduce the proximal policy optimization (PPO) algorithm with the clipping method to find solutions for joint optimization of routing and power allocation via achieving the long-term benefit in the Markov decision process (MDP). Simulation results show that the proposed learning-based scheme can learn from the environment to improve its policy stability and efficiency in the iterative training process for optimizing routing and RIS and significantly outperform the benchmark schemes.

This article investigates the covert performance of an unmanned aerial vehicle (UAV) jammer-assisted cognitive radio (CR) network. In particular, the covert transmission of secondary users can be effectively protected by UAV jamming against the eavesdropping. For practical consideration, the UAV is assumed to only know certain partial channel distribution information (CDI), whereas not to know the detection threshold of an eavesdropper. For this sake, we propose a model-driven generative adversarial network (MD-GAN)-assisted optimization framework, consisting of a generator and a discriminator, where the unknown channel information and the detection threshold are learned weights. Then, a GAN-based joint trajectory and power optimization (GAN-JTP) algorithm is developed to train the MD-GAN optimization framework for covert communication, which results in the joint solution of the UAV’s trajectory and transmits power to maximize the covert rate and the probability of detection errors. Our simulation results show that the proposed GAN-JTP with a rapid convergence speed can attain near-optimal solutions of the UAV’s trajectory and transmit power for the covert communication.

Toward 6G networks, such as virtual reality (VR) applications, Industry 4.0, and automated driving, demand mobile-edge computing (MEC) techniques to offload computing tasks to nearby servers, which, however, causes fierce competition with traditional communication services. On the other hand, by introducing millimeter wave (mmWave) communication, it can significantly improve the offloading capability of MEC, enabling low latency and high throughput. For this sake, this article investigates the resource management for the offload transmission of the mmWave MEC system, when considering the data transmission demands from both communication-oriented users (CM-UEs) and computing-oriented users (CP-UEs). In particular, the joint consideration of user pairing, beamwidth allocation, and power allocation is formulated as a multiobjective problem (MOP), which includes minimizing the offloading delay of CP-UEs and maximizing the transmission rate of CM-UEs. By using the$\epsilon $-constraint approach, the MOP is converted into a single-objective optimization problem (SOP) without losing Pareto optimality, and then the three-stage iterative resource allocation algorithm is proposed. Our simulation results show that the gap between Pareto front generated by the three-stage iterative resource allocation algorithm and the real Pareto front is less than 0.16%. Furthermore, the proposed algorithm with much lower complexity can achieve the performance similar to the benchmark scheme of NSGA-II, while significantly outperforms the other traditional schemes.

Considering a reconfigurable intelligent surface (RIS) aided wireless powered Internet of Things (WP IoT) network. To address the energy-limitation issue, IoT devices in such a network can be wirelessly powered by a power station (PS) first and then connect with an access point (AP) using their own harvested energy. The RIS helps enhance energy and information receptions in the downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT), respectively. This work unveils the impact of phase shift error (PSE) and transceiver hardware impairment (THI) on the considered network. Our investigation starts with a scenario where only the impact of the PSE on system under study is considered, then moves toward a scenario with the compound effect of both PSE and THI. A maximization problem of the system sum throughput is formulated to evaluate the overall performance for these two scenarios, subject to the constraints of the adjustable RIS phase shifts, the statistical PSE and the transmission time scheduling. To handle the non-convexity of the formulated problem due to those coupled variables, we first adopt the Lagrange dual method and Karush-Kuhn-Tucker (KKT) conditions to derive the optimal time scheduling in closed-form. Next, we recast the stochastic PSE into the deterministic counterpart for its tractability. Then, we adopt a successive convex approximation (SCA) to iteratively derive the optimal WIT’s phase shifts, and element-wise block coordinate decent (EBCD) and complex circle manifold (CCM) methods to iteratively derive the optimal WET’s phase shifts. Finally, we complete our solution approach for the scenario with both PSE and THI. Simulation results highlight the performance of the proposed scheme and the benefits induced by the RIS in comparison to benchmark schemes.

The paper proposed a novel intelligent transmission surface (ITS) aided transmitter in an intelligent reflection surface (IRS) assisted multiuser multiple-input multiple-output (MIMO) network. The ITS deployed in the transmitter architecture can reduce the power consumption in signal beamforming at the base station (BS), and the IRS can help the information transfer from the ITS-aided transmitter to the users. We first maximize the weighted sum rate (WSR) of the users by jointly designing the beamforming vector at the BS and the phase shifts of ITS and IRS. To solve this non-convex optimization problem, we propose an effective algorithm in which the Lagrangian dual transform, the alternative optimization (AO) algorithm and the quadratic transform (QT) method are adopted to simplify the objective function. Then, the bisection search and the alternating direction method of multipliers (ADMM) algorithm are considered to design the optimal beamforming vector and phase shifts of ITS and IRS, respectively. Furthermore, the paper explores the energy efficiency (EE) maximization problem to emphasize the value of the ITS-assisted transmitter in terms of power savings. Finally, we compare the simulation results to various state-of-the-art techniques to see how much better the proposed algorithm is in terms of WSR and EE.

Vehicular networks, an enabling technology for Intelligent Transportation System (ITS), smart cities, and autonomous driving, can deliver numerous on-board data services, e.g., road-safety, easy navigation, traffic efficiency, comfort driving, infotainment, etc. Providing satisfactory Quality of Service (QoS) in vehicular networks, however, is a challenging task due to a number of limiting factors such as erroneous and congested wireless channels (due to high mobility or uncoordinated channel-access), increasingly fragmented and congested spectrum, hardware imperfections, and anticipated growth of vehicular communication devices. Therefore, it will be critical to allocate and utilize the available wireless network resources in an ultra-efficient manner. In this paper, we present a comprehensive survey on resource allocation schemes for the two dominant vehicular network technologies, e.g. Dedicated Short Range Communications (DSRC) and cellular based vehicular networks. We discuss the challenges and opportunities for resource allocations in modern vehicular networks and outline a number of promising future research directions.

In recent years, malware detection has become an active research topic in the area of Internet of Things (IoT) security. The principle is to exploit knowledge from large quantities of continuously generated malware. Existing algorithms practise available malware features for IoT devices and lack real-time prediction behaviours. More research is thus required on malware detection to cope with real-time misclassification of the input IoT data. Motivated by this, in this article, we propose an adversarial self-supervised architecture for detecting malware in IoT networks, SETTI, considering samples of IoT network traffic that may not be labeled. In the SETTI architecture, we design three self-supervised attack techniques, namely, Self-MDS , GSelf-MDS, and ASelf-MDS . The Self-MDS method considers the IoT input data and the adversarial sample generation in real-time. The GSelf-MDS builds a generative adversarial network model to generate adversarial samples in the self-supervised structure. Finally, ASelf-MDS utilises three well-known perturbation sample techniques to develop adversarial malware and inject it over the self-supervised architecture. Also, we apply a defence method to mitigate these attacks, namely, adversarial self-supervised training, to protect the malware detection architecture against injecting the malicious samples. To validate the attack and defence algorithms, we conduct experiments on two recent IoT datasets: IoT23 and NBIoT. Comparison of the results shows that in the IoT23 dataset, the Self-MDS method has the most damaging consequences from the attacker’s point of view by reducing the accuracy rate from 98% to 74%. In the NBIoT dataset, the ASelf-MDS method is the most devastating algorithm that can plunge the accuracy rate from 98% to 77%.

Sparse code multiple-access (SCMA) is an emerging technique to support massive connectivity in 5G networks and beyond. In SCMA transmissions, some resource-blocks may undergo certain contamination due to deep fading and/or jamming attacks, thus leading to severe performance degradation over such contaminated ones. Besides, the current SCMA infrastructure is normally deployed in single-cell. To deploy the SCMA into multicell networks under contaminated/jamming channels, we propose a novel frequency-hopping based SCMA (FH-SCMA) for quasi-synchronous multi-cell networks, in which the entire subcarrier-channels of every codeword keep hopping over the multiple resource-blocks according certain hopping pattern. We propose and design a pseudo-randomly orthogonal hopping pattern to adapt to the specific requirements of quasi-synchronous FH-SCMA multi-cell networks. Our analysis and simulation results indicate that the proposed FH-SCMA leads to both improved user capacity and error-rate performance, whilst remaining resilient to the inter-cell interference.

Terahertz (THz) communication has been regarded as one promising technology to enhance the transmission capacity of future Internet-of-Things (IoT) users due to its ultrawide bandwidth. Nonetheless, one major obstacle that prevents the actual deployment of THz lies in its inherent huge attenuation. Intelligent reflecting surface (IRS) and multiple-input-multiple-output (MIMO) represent two effective solutions for compensating the large path loss in THz systems. In this article, we consider an IRS-aided multiuser THz MIMO system with orthogonal frequency-division multiple (OFDM) access, where the sparse radio frequency chain antenna structure is adopted for reducing the power consumption. The objective is to maximize the weighted sum rate via jointly optimizing the hybrid analog/digital beamforming at the base station (BS) and reflection matrix at the IRS. Since the analog beamforming and reflection matrix need to cater all users and subcarriers, it is difficult to directly solve the formulated problem, and thus, an alternatively iterative optimization algorithm is proposed. Specifically, the analog beamforming is designed by solving a MIMO capacity maximization problem, while the digital beamforming and reflection matrix optimization are both tackled using semidefinite relaxation (SDR) technique. Considering that obtaining perfect channel state information (CSI) is a challenging task in IRS-based systems, we further explore the case with the imperfect CSI for the channels from the IRS to users. Under this setup, we propose a robust beamforming and reflection matrix design scheme for the originally formulated nonconvex optimization problem. Finally, simulation results are presented to demonstrate the effectiveness of the proposed algorithms.

Satellite communication system is expected to play a vital role for realizing various remote Internet-of-Things (IoT) applications in sixth-generation vision. Due to unique characteristics of satellite environment, one of the main challenges in this system is to accommodate massive random access (RA) requests of IoT devices while minimizing their energy consumptions. In this article, we focus on the reliable design and detection of RA preamble to effectively enhance the access efficiency in high-dynamic low-earth-orbit (LEO) scenarios. To avoid additional signaling overhead and detection process, a long preamble sequence is constructed by concatenating the conjugated and circularly shifted replicas of a single root Zadoff-Chu (ZC) sequence in RA procedure. Moreover, we propose a novel impulse-like timing metric based on length-alterable differential cross-correlation (LDCC), that is immune to carrier frequency offset (CFO) and capable of mitigating the impact of noise on timing estimation. Statistical analysis of the proposed metric reveals that increasing correlation length can obviously promote the output signal-to-noise power ratio, and the first-path detection threshold is independent of noise statistics. Simulation results in different LEO scenarios validate the robustness of the proposed method to severe channel distortion, and show that our method can achieve significant performance enhancement in terms of timing estimation accuracy, success probability of first access, and mean normalized access energy, compared with the existing RA methods.

In this paper, a novel terahertz (THz) spectroscopy technique and a new graphene-based sensor is proposed. The proposed sensor consists of a graphene-based metasurface (MS) that operates in reflection mode over a broad range of frequency band (0.2 - 6 THz) and can detect relative permittivity of up to 4 with a resolution of 0.1 and a thickness ranging from 5 μm to 600 μm with a resolution of 0.5 μm. To the best of author's knowledge, such a THz sensor with such capabilities has not been reported yet. Additionally, an equivalent circuit of the novel unit cell is derived and compared with two conventional grooved structures to showcase the superiority of the proposed unit cell. The proposed spectroscopy technique utilizes some unique spectral features of a broadband reflection wave including Accumulated Spectral power (ASP) and Averaged Group Delay (AGD), which are independent to resonance frequencies and can operate over a broad range of spectrum. ASP and AGD can be combined to analyse the magnitude and phase of the reflection diagram as a coherent technique for sensing purposes. This enables the capability to distinguish between different analytes with high precision which, to the best of author's knowledge, has been accomplished for the first time.

This paper investigates the performance of limited-fronthaul cell-free massive multiple-input multiple-output (MIMO) taking account the fronthaul quantization and imperfect channel acquisition. Three cases are studied, which we refer to as Estimate & Quantize, Quantize & Estimate, and Decentralized, according to where channel estimation is performed and exploited. Maximum-ratio combining (MRC), zero-forcing (ZF), and minimum mean-square error (MMSE) receivers are considered. The Max algorithm and the Bussgang decomposition are exploited to model optimum uniform quantization. Exploiting the optimal step size of the quantizer, analytical expressions for spectral and energy efficiencies are presented. Finally, an access point (AP) assignment algorithm is proposed to improve the performance of the decentralized scheme. Numerical results investigate the performance gap between limited fronthaul and perfect fronthaul cases, and demonstrate that exploiting relatively few quantization bits, the performance of limited-fronthaul cell-free massive MIMO closely approaches the perfect-fronthaul performance.

This paper proposes a joint iterative optimization based hybrid beamforming technique for massive MU-MIMO systems. The proposed technique jointly and iteratively optimizes the transmitter precoders and combiners, aiming to approach the global optimum solution for the system sum-rate maximization problem. The proposed technique develops an adaptive algorithm exploiting the stochastic gradients (SG) of the local beamformers and provides low-complexity closed-form solutions. Furthermore, an efficient adaptive scheme is developed based on the proposed adaptive algorithm and the closed-form solutions. The proposed algorithm requires the signal-to-interference-plus-noise ratio (SINR) feedback from each user and a limited size transition vector to be exchanged between the transmitter and receivers at each step to update beamformers locally. Analytic result shows that the proposed adaptive algorithm achieves low-complexity when the array size is large and is able to converge within a small number of iterations. Simulation result shows that the proposed technique is able to achieve superior performance comparing to the existing state-of-art techniques. In addition, the knowledge of instantaneous channel state information (CSI) is not required as the channels are also adaptively estimated with each coherence time which is a practical assumption since the CSI is usually unavailable or have time-varying nature in real-time applications.

This paper investigates the impact of intelligent reflecting surface (IRS) enabled wireless secure transmission. Specifically, an IRS is deployed to assist multiple-input multiple-output (MIMO) secure system to enhance the secrecy performance, and artificial noise (AN) is employed to introduce interference to degrade the reception of the eavesdropper. To improve the secrecy performance, we aim to maximize the achievable secrecy rate, subject to the transmit power constraint, by jointly designing the precoding of the secure transmission, the AN jamming, and the reflecting phase shift of the IRS. We first propose an alternative optimization algorithm (i.e., block coordinate descent (BCD) algorithm) to tackle the non-convexity of the formulated problem. This is made by deriving the transmit precoding and AN matrices via the Lagrange dual method and the phase shifts by the Majorization-Minimization (MM) algorithm. Our analysis reveals that the proposed BCD algorithm converges in a monotonically non-decreasing manner which leads to guaranteed optimal solution. Finally, we provide numerical results to validate the secrecy performance enhancement of the proposed scheme in comparison to the benchmark schemes.

The uplink of a reconfigurable intelligent surfaces (RIS)-aided cell-free massive multiple-input multiple-output (MIMO) system is analyzed, where the channel state information (CSI) is estimated using uplink pilots. First, we derive analytical expressions for the achievable rate of the system with zero forcing (ZF) receiver, taking into account the effects of pilot contamination, channel estimation error and the distributed RISs. The max-min rate optimization problem is considered with per-user power constraints. To solve this non-convex problem, we propose to decouple the original optimization problem into two sub-problems, namely, phase shift design problem and power allocation problem. The power allocation problem is solved using a standard geometric programming (GP) whereas a semidefinite programming (SDP) is utilized to design the phase shifts. Moreover, the Taylor series approximation is used to convert the nonconvex constraints into a convex form. An iterative algorithm is proposed whereby at each iteration, one of the sub-problems is solved while the other design variable is fixed. The max-min user rate of the RIS-aided cell-free massive MIMO system is compared to that of conventional cell-free massive MIMO. Numerical results indicate the superiority of the proposed algorithm compared with a conventional cell-free massive MIMO system. Finally, the convergence of the proposed algorithm is investigated.

We consider a cell-free massive multiple-input multiple-output (MIMO) system where the channel estimates and the received signals are quantized at the access points (APs) and forwarded to a central processing unit (CPU). Zero-forcing technique is used at the CPU to detect the signals transmitted from all users. To solve the non-convex sum rate maximization problem, a heuristic sub-optimal scheme is proposed to convert the problem into a geometric programme (GP). Exploiting a deep convolutional neural network (DCNN) allows us to determine both a mapping from the large-scale fading (LSF) coefficients and the optimal power by solving the optimization problem using the quantized channel. Depending on how the optimization problem is solved, different power control schemes are investigated; i) small-scale fading (SSF)-based power control; ii) LSF use-and-then-forget (UatF)-based power control; and iii) LSF deep learning (DL)-based power control. The SSF-based power control scheme needs to be solved for each coherence interval of the SSF, which is practically impossible in real time systems. Numerical results reveal that the proposed LSF-DL-based scheme significantly increases the performance compared to the practical and well-known LSF-UatF-based power control.

This paper analyses the New Radio (NR) air interface waveforms and numerologies in the context of current activities and studies of 3GPP related to the feasibility and standardisation of necessary adaptations for the 5G NR to support integrated-satellite-terrestrial networks with low earth orbit (LEO) satellites. Frequency-localized orthogonal frequency division multiplexing (OFDM)-based candidate waveforms are recommended by 3GPP as the waveforms for the NR in order to preserve the advantages of OFDM as well as maintain backward compatibility. 5G New Radio enables diverse service support, efficient synchronization and channel adaptability using a multinumerology concept, which defines a family of parameters of the parent waveform, that are related to each other by scaling. The major design challenges in the LEO satellite scenario are power limited link budget and high Doppler effects which can be addressed by choosing waveforms with small peak to average power ratio (PAPR) and sub-carrier bandwidth adaptation respectively. Hence, the selection of the right waveform and numerology is of prime relevance for the proper adaptation of 5G NR for LEO satellite terrestrial integration. The performance evaluation of the new air interface waveforms, with different numerologies, are carried out under the effect of carrier frequency offset (CFO), multipath effects, non-linearity, phase noise and additive white Gaussian noise (AWGN).

Spatial modulation (SM) is a new multiple-input multiple-output (MIMO) paradigm in which only one transmit antenna is activated over every symbol duration. So far, efficient SM training sequences (different from the existing design for conventional MIMO systems) remain largely open. Motivated by this research problem, we introduce a novel class of sequence pairs, called "cross Z-complementary pairs (CZCPs)", each displaying zero-correlation zone (ZCZ) properties for both their aperiodic autocorrelation sums and cross-correlation sums. A CZCP may be transmitted in two non-orthogonal SM channels and hence proper design should be conducted to minimize the cross-interference of the two constituent sequences. We construct perfect CZCPs based on selected Golay complementary pairs. We show that the training sequences derived from our proposed CZCPs lead to optimal channel estimation performance over frequency-selective SM channels.

A novel reconfigurable dielectric resonator antenna (DRA) employed a T-Shaped microstrip-fed structure in order to excite the dielectric resonator is presented. By carefully adjusting the location of the inverted U-shaped slot, switches, and length of arms, the proposed antenna can support WLAN wireless system. In addition, the presented DRA can be proper for cognitive radio because of availability switching between wideband and narrowband operation. The proposed reconfigurable DRA consisting of a Roger substrate with relative permittivity 3 and a size of 20 mm × 30 mm × 0.75 mm and a dielectric resonator (DR) with a thickness of 9 mm and the overall size of 18 mm × 18 mm. Moreover, the antenna has been fabricated and tested which test results have enjoyed a good agreement with the simulated results. As well as this, the measured and simulated results show the reconfigurability that the proposed DRA provides a dual-mode operation and also three different resonance frequencies as a result of switching the place of arms.

A machine learning (ML) technique has been used to synthesis a linear millimetre wave (mmWave) phased array antenna by considering the phase-only synthesis approach. For the first time, gradient boosting tree (GBT) is applied to estimate the phase values of a 16-element array antenna to generate different far-field radiation patterns. GBT predicts phases while the amplitude values have been equally set to generate different beam patterns for various 5G mmWave transmission scenarios such as multicast, unicast, broadcast and unmanned aerial vehicle (UAV) applications.

This work provides the first link level performance evaluation of the Rate-Splitting (RS) based precoding scheme in a downlink multi-user multiple input single output (MU-MISO) system. Contrary to the existing works on the RS precoding that mostly focused on the sum rate or minimum rate maximization, this work bridges the optimization results with the bit error rate (BER) performance, initiating the RS software implementation. We demonstrate that, in an overloaded scenario, the conventional precoding schemes suffer from the BER error floor that corresponds to their rate saturation, which can be overcome by the RS-based strategy that adding the message decodability of certain users with the interference-limited message rate.

Covert communication provides high-level security for protecting users' privacy information. In this paper, we analyze the joint impact of an external jammer and channel uncertainty on covert communication in multi-user cognitive radio networks. Meanwhile, to fairly schedule the covert communication over multi-user cognitive radio networks, we propose a fairness secondary user (SU) scheduling scheme, which enables each SU to have the same probability for sending information covertly with the aid of an external jammer. Then, the closed-form expression for the covert rate of the scheduled SU can be obtained. Our results show that the minimal detection error probability and covert rate of the scheduled SU can be significantly improved by exploiting the channel uncertainty and random variation of interference power. Moreover, the impact of interference power on the probability of detection error and the covert rate is noticeable when channel uncertainty is large.

Decentralized joint transmit power and beamforming selection for multiple antenna wireless ad hoc networks operating in a multi-user interference environment is considered. An important feature of the considered environment is that altering the transmit beamforming pattern at some node generally creates more significant changes to interference scenarios for neighboring nodes than variation of the transmit power. Based on this premise, a good neighbor algorithm is formulated in the way that at the sensing node, a new beamformer is selected only if it needs less than the given portion of the transmit power required for the current beamformer. Otherwise, it keeps the current beamformer and achieves the performance target only by means of power adaptation. Equilibrium performance and convergence behavior of the proposed algorithm compared to the best response and regret matching solutions is demonstrated by means of semi-analytic Markov chain performance analysis for small scale and simulations for large scale networks.

The fifth-generation (5G) new radio (NR) cellular system promises a significant increase in capacity with reduced latency. However, the 5G NR system will be deployed along with legacy cellular systems such as the long-term evolution (LTE). Scarcity of spectrum resources in low frequency bands motivates adjacent-/co-carrier deployments. This approach comes with a wide range of practical benefits and it improves spectrum utilization by re-using the LTE bands. However, such deployments restrict the 5G NR flexibility in terms of frame allocations to avoid the most critical mutual adjacent-channel interference. This in turns prevents achieving the promised 5G NR latency figures. In this we paper, we tackle this issue by proposing to use the mini-slot uplink feature of 5G NR to perform uplink acknowledgment and feedback to reduce the frame latency with selective blind retransmission to overcome the effect of interference. Extensive system-level simulations under realistic scenarios show that the proposed solution can reduce the peak frame latency for feedback and acknowledgment up to 33% and for retransmission by up to 25% at a marginal cost of an up to 3% reduction in throughput.

This article exploits a generic downlink symbiotic radio (SR) system, where a base station (BS) establishes a direct (primary) link with a receiver having an integrated backscatter device (BD). In order to accurately measure the backscatter link, the backscattered signal packets are designed to have finite block length. As such, the backscatter link in this SR system employs the finite blocklength channel codes. According to different types of the backscatter symbol period and transmission rate, we investigate the noncooperative and cooperative SR systems, and derive their average achievable rate of the direct and backscatter links, respectively. We formulate two optimization problems, i.e., transmit power minimization and energy-efficiency maximization. Due to the nonconvex property of these formulated optimization problems, the semidefinite programming (SDP) relaxation and the successive convex approximation (SCA) are considered to design the transmit beamforming vector. Moreover, a low-complexity transmit beamforming structure is constructed to reduce the computational complexity of the SDP relaxed solution. Finally, the simulation results are demonstrated to validate the proposed schemes.

In this article, we consider an edge cache-assisted millimeter-wave cloud radio access network (C-RAN). Each remote radio head (RRH) in the C-RAN has a local cache, which can prefetch and store the files requested by the actuators. Multiple RRHs form a cluster to cooperatively serve the actuators, which acquire their required files either from the local caches or from the central processor via multicast fronthaul links. For such a scenario, we formulate a beamforming design problem to minimize the secure transmission delay under transmit power constraint of each RRH. Due to the difficulty of directly solving the formulated problem, we divide it into two independent ones: 1) minimizing the fronthaul transmission delay by jointly optimizing the transmit and receive beamforming and 2) minimizing the maximum access transmission delay by jointly designing cooperative beamforming among RRHs. An alternatively iterative algorithm is proposed to solve the first optimization problem. For the latter, we first design the analog beamforming based on the channel state information of the actuators. Then, with the aid of successive convex approximation and $S$ -procedure techniques, a semidefinite program (SDP) is formulated, and an iterative algorithm is proposed through SDP relaxation. Finally, the simulation results are provided to verify the performance of the proposed schemes.

A cell-free massive multiple-input multiple-output (MIMO) uplink is considered, where quantize-and-forward (QF) refers to the case where both the channel estimates and the received signals are quantized at the access points (APs) and forwarded to a central processing unit (CPU) whereas in combine-quantize-and-forward (CQF), the APs send the quantized version of the combined signal to the CPU. To solve the non-convex sum rate maximization problem, a heuristic sub-optimal scheme is exploited to convert the power allocation problem into a standard geometric programme (GP). We exploit the knowledge of the channel statistics to design the power elements. Employing large-scale-fading (LSF) with a deep convolutional neural network (DCNN) enables us to determine a mapping from the LSF coefficients and the optimal power through solving the sum rate maximization problem using the quantized channel. Four possible power control schemes are studied, which we refer to as i) small-scale fading (SSF)-based QF; ii) LSF-based CQF; iii) LSF use-and-then-forget (UatF)-based QF; and iv) LSF deep learning (DL)-based QF, according to where channel estimation is performed and exploited and how the optimization problem is solved. Numerical results show that for the same fronthaul rate, the throughput significantly increases thanks to the mapping obtained using DCNN.

To fully reap the benefits of massive multiple-input multiple-output hybrid analog and digital precoding in frequency division duplexing single-cell systems, a two-stage precoder is developed utilizing the signal-to-leakage-plus-noise ratio metric. The main idea of this technique is to jointly design the analog precoder based only on the long-term channel statistics information at the transmitter, i.e., the channel mean and reconstructed reduced rank covariance statistics, while the digital precoder is designed based on the instantaneous channel state information of the reduced dimensionality effective reconstructed channel. Consequently, we can significantly reduce the downlink training and uplink feedback overhead analogously to the rank of the resultant effective channel. The two extremes of full channel state information at the transmitter (CSIT) and statistical CSIT are also investigated. The performance gap between the full and statistical CSIT corroborates the importance of the proposed two-stage CSIT approach. These precoders are then extended to multi-cell systems. It is shown that the digital baseband precoder design problem reduces to the generalized Rayleigh quotient problem, while the analog precoder design problem reduces to the quotient trace problem, also known as the ratio trace problem. These dimensionality reduction problems are solved via the generalized eigenvalue decomposition method. Finally, in the presence of multiuser diversity where only a subset of the users are scheduled, to considerably alleviate the channel estimation and feedback overhead burden, a low-complexity one-stage and two-stage CSIT joint user scheduler and precoder algorithms are developed.

The downlink (DL) of a non-orthogonal-multiple-access (NOMA)-based cell-free massive multiple-input multiple-output (MIMO) system is analyzed, where the channel state information (CSI) is estimated using pilots. It is assumed that the users are grouped into multiple clusters. The same pilot sequences are assigned to the users within the same clusters whereas the pilots allocated to all clusters are mutually orthogonal. First, a user's bandwidth efficiency (BE) is derived based on his/her channel statistics under the assumption of employing successive interference cancellation (SIC) at the users' end with no DL training. Next, the classic max-min optimization framework is invoked for maximizing the minimum BE of a user under per-access point (AP) power constraints. The max-min user BE of NOMA-based cell-free massive MIMO is compared to that of its orthogonal multiple-access (OMA) counter part, where all users employ orthogonal pilots. Finally, our numerical results are presented and an operating mode switching scheme is proposed based on the average per-user BE of the system, where the mode set is given by Mode = { OMA, NOMA }. Our numerical results confirm that the switching point between the NOMA and OMA modes depends both on the length of the channel's coherence time and on the total number of users.

In this paper, we investigate the downlink secure beamforming (BF) design problem of cloud radio access networks (C-RANs) relying on multicast fronthaul, where millimeter-wave and microwave carriers are used for the access links and fronthaul links, respectively. The base stations (BSs) jointly serve users through cooperating hybrid analog/digital BF. We first develop an analog BF for cooperating BSs. On this basis, we formulate a secrecy rate maximization (SRM) problem subject both to a realistic limited fronthaul capacity and to the total BS transmit power constraint. Due to the intractability of the non-convex problem formulated, advanced convex approximated techniques, constrained concave convex procedures and semi-definite programming (SDP) relaxation are applied to transform it into a convex one. Subsequently, an iterative algorithm of jointly optimizing multicast BF, cooperative digital BF and the artificial noise (AN) covariance is proposed. Next, we construct the solution of the original problem by exploiting both the primal and the dual optimal solution of the SDP-relaxed problem. Furthermore, a per-BS transmit power constraint is considered, necessitating the reformulation of the SRM problem, which can be solved by an efficient iterative algorithm. We then eliminate the idealized simplifying assumption of having perfect channel state information (CSI) for the eavesdropper links and invoke realistic imperfect CSI. Furthermore, a worst-case SRM problem is investigated. Finally, by combining the so-called S-Procedure and convex approximated techniques, we design an efficient iterative algorithm to solve it. Simulation results are presented to evaluate the secrecy rate and demonstrate the effectiveness of the proposed algorithms.

In this treatise, we introduce a novel polarization modulation (PM) scheme, where we capitalize on the reconfigurable polarization antenna design for exploring the polarization domain degrees of freedom, thus boosting the system throughput. More specifically, we invoke the inherent properties of a dual polarized (DP) antenna for transmitting additional information carried by the axial ratio (AR) and tilt angle of elliptic polarization, in addition to the information streams transmitted over its vertical (V) and horizontal (H) components. Furthermore, we propose a special algorithm for generating an improved PM constellation tailored especially for wireless PM modulation. We also provide an analytical framework to compute the average bit error rate (ABER) of the PM system. Furthermore, we characterize both the discrete-input continuous-output memoryless channel (DCMC) capacity and the continuous-input continuous-output memoryless channel (CCMC) capacity as well as the upper and lower bounds of the CCMC capacity. The results show the superiority of our proposed PM system over conventional modulation schemes in terms of both higher throughput and lower BER. In particular, our simulation results indicate that the gain achieved by the proposed Q-dimensional PM scheme spans between 10dB and 20dB compared to the conventional modulation. It is also demonstrated that the PM system attains between 54% and 87.5% improvements in terms of ergodic capacity. Furthermore, we show that this technique can be applied to MIMO systems in a synergistic manner in order to achieve the target data rate target for 5G wireless systems with much less system resources (in terms of bandwidth and the number of antennas) compared to existing MIMO techniques.

In this paper, we propose a generalized space-time block coded spatial modulation (GSTBC-SM) scheme for open-loop massive multiple-input and multiple-output (MIMO) downlink communication systems. Specifically, we firstly partition the information bits into multiple groups with each group modulated by the spatial modulation (SM), where the SM symbols are invoked for orthogonal STBC (OSTBC) and quasi-orthogonal STBC (Q-OSTBC) structures. Then, message passing (MP) and block minimum mean square equalization (B-MMSE) detectors are designed for our GSTBC-SM systems, to achieve near-optimal performance with significantly reduced complexity in massive MIMO configurations. Finally, we derive the theoretical average bit error probability (ABEP) of the proposed scheme. The main contribution is that the propose scheme achieves high transmission rate and diversity gain even with small number of radio frequency (RF) chains at the transmitter. Simulation results verify the theoretical derivations and show that the proposed GSTBC-SM scheme provides near 20 dB gain over the conventional GSTBC scheme under massive MIMO configurations.

A simplified rectangular differential spatial modulation (S-RDSM) scheme is conceived for massive multiple-input multiple-output (MIMO) systems dispensing with the channel state information (CSI). In the proposed S-RDSM scheme, the information bits are first mapped to a conventional SM symbol and then rectangular differential encoding is invoked between a pair of SM symbols. Then a non-coherent detector relying on a forgetting factor is developed, which requires no CSI at the receiver. Explicitly, a low-complexity hard limited maximum likelihood (HL-ML) detector is conceived for our generalized S-RDSM scheme, which is characterized by our theoretical analysis. Furthermore, we derive the optimal forgetting factor in closed form, which is capable of significantly reducing the complexity of the associated optimization. Finally, the upper bounds of the average bit error probability (ABEP) are derived using the moment generating function (MGF), and are validated by our simulation results. Both the theoretical and simulation results have shown that the proposed S-RDSM system outperforms the existing non-coherent schemes, despite operating at 10% of the benchmarker's complexity, whilst approaching the performance of its coherent SM counterpart at a comparable complexity.

This paper proposes a novel spatial-modulated multicarrier sparse code-division multiple access (SM/MC-SCDMA) system for achieving massive connectivity in device-centric wireless communications. In our SM/MC-SCDMA system, the advantages of both MC signalling and SM are amalgamated to conceive a low-complexity transceiver. Sparse frequency-domain spreading is utilized to mitigate the peak-to-average power ratio (PAPR) of MC signalling, as well as to facilitate low-complexity detection using the message passing algorithm. We then analyze the single-user bit error rate performance of SM/MC-SCDMA systems communicating over frequency-selective fading channels. Furthermore, the performance of SM/MC-SCDMA systems is evaluated based on both Monte-Carlo simulations and analytical results. We demonstrate that our low-complexity SM/MC-SCDMA transceivers are capable of achieving near-maximum likelihood (ML) performance even when the normalized user-load is as high as two, hence constituting a variable solution to support massive connectivity in device-centric wireless systems.

Mixed-numerology transmission is proposed to support a variety of communication scenarios with diverse requirements. However, as the orthogonal frequency division multiplexing (OFDM) remains as the basic waveform, the peak-to average power ratio (PAPR) problem is still cumbersome. In this paper, based on the iterative clipping and filtering (ICF) and optimization methods, we investigate the PAPR reduction in the mixed-numerology systems. We first illustrate that the direct extension of classical ICF brings about the accumulation of inter-numerology interference (INI) due to the repeated execution. By exploiting the clipping noise rather than the clipped signal, the noise-shaped ICF (NS-ICF) method is then proposed without increasing the INI. Next, we address the in-band distortion minimization problem subject to the PAPR constraint. By reformulation, the resulting model is separable in both the objective function and the constraints, and well suited for the alternating direction method of multipliers (ADMM) approach. The ADMM-based algorithms are then developed to split the original problem into several subproblems which can be easily solved with closed-form solutions. Furthermore, the applications of the proposed PAPR reduction methods combined with filtering and windowing techniques are also shown to be effective.

The flexibility in supporting heterogeneous services with vastly different technical requirements is one of the distinguishing characteristics of the fifth generation (5G) communication systems and beyond. One viable solution is to divide the system bandwidth into several bandwidth parts (BWPs), each having a distinct numerology optimized for a particular service. However, multiplexing of mixed numerologies over a unified physical infrastructure comes at the cost of induced interference. In this paper, we develop an analytical system model for inter-numerology interference (InterNI) analysis in orthogonal frequency-division multiplexing (OFDM) systems with and without filter processing in the presence of mixed numerologies. With the analytical model, the level of InterNI is quantified by the developed analytical metric, which is expressed as a function of several system parameters. This leads to an analysis and evaluation of these parameters for meeting a given distortion target. Moreover, a case study on power allocation utilizing the derived analysis is presented, where an optimization problem of maximizing the sum rate is formulated, and a solution is also provided. It is also demonstrated that a filtered-OFDM system better accommodates the coexistence of mixed numerologies. The proposed model provides an accurate analytical guidance for the multi-service design in 5G and beyond systems.

In this paper, we investigate the throughput performance of single-packet and multi-packet hybrid-automatic repeat request (HARQ) with blanking for downlink non-orthogonal multiple access (NOMA) systems. While conventional single-packet HARQ achieves high throughput at the expense of high latency, multi-packet HARQ, where several data packets are sent in the same channel block, can achieve high throughput with low latency. Previous works have shown that multi-packet HARQ outperforms single-packet HARQ in orthogonal multiple access (OMA) systems, especially in the moderate to high signal-to-noise ratio regime. This work amalgamates multi-packet HARQ with NOMA to achieve higher throughput than the conventional single-packet HARQ and OMA, which has been adopted in the legacy mobile networks. We conduct theoretical analysis for the throughput per user and also investigate the optimization of the power and rate allocations of the packets, in order to maximize the weighted-sum throughput. It is demonstrated that the gain of multi-packet HARQ over the single-packet HARQ in NOMA systems is reduced compared to that obtained in OMA systems due to inter-user interference. It is also shown that NOMA-HARQ cannot achieve any throughput gain with respect to OMA-HARQ when the error propagation rate of the NOMA detector is above a certain threshold.

Generalized index modulation (GIM) which implicitly conveys information by the activated indices is a promising technique for next-generation wireless networks. Due to the prohibitive challenge of bit-to-index combination (IC) mapping optimization, conventional GIM system obtains the bit-to-IC mapping table randomly, which may suffer from some performance loss. To circumvent this issue, we propose a low-complexity graph theory assisted bit-to-IC gray coding for GIM systems by minimizing the average hamming distance (HD) between any two ICs having one different value. Specifically, we decompose and transform the optimization problem into two subproblems using the graph theory, i.e., 1) Select an IC set whose corresponding graph has the minimum degree; 2) Design a bit-to-IC mapping principle to minimize the weight of the selected graph. Low-complexity algorithms are developed to solve the subproblems with a significant reduced complexity. Both simulation and theoretical results are shown that the GIM systems with our proposed mapping table are capable of providing significant performance gains over the conventional counterparts without the need for any additional feedback-link and without extra computational complexity. It is also shown that the proposed bit-to-IC mapping table is straightforward for any GIM systems over generalized fading channels.

The concept of massive spatial modulation (SM) assisted vertical bell labs space-time (V-BLAST) (SM-VBLAST) system [1] is proposed, where SM symbols (instead of conventional constellation symbols) are mapped onto the VBLAST structure. We show that the proposed SM-VBLAST is a promising massive multiple input multiple output (MIMO) candidate owing to its high throughput and low number of radio frequency (RF) chains used at the transmitter. For the generalized massive SM-VBLAST systems, we first derive both the upper bounds of the average bit error probability (ABEP) and the lower bounds of the ergodic capacity. Then, we develop an efficient error correction mechanism (ECM) assisted compressive sensing (CS) detector whose performance tends to achieve that of the maximum likelihood (ML) detector. Our simulations indicate that the proposed ECM-CS detector is suitable both for massive SM-MIMO based point-to-point and for uplink communications at the cost of a slightly higher complexity than that of the compressive sampling matching pursuit (CoSaMP) based detector in the high SNR region.

A clear understanding of mixed-numerology signals multiplexing and isolation in the physical layer is of importance to enable spectrum efficient radio access network (RAN) slicing, where the available access resource is divided into slices to cater to services/users with optimal individual design. In this paper, a RAN slicing framework is proposed and systematically analyzed from the physical layer perspective. According to the baseband and radio frequency (RF) configurations imparities among slices, we categorize four scenarios and elaborate on the numerology relationships of slices configurations. By considering the most generic scenario, system models are established for both uplink and downlink transmissions. Besides, a low out of band emission (OoBE) waveform is implemented in the system for the sake of signal isolation and inter-service/slice-band-interference (ISBI) mitigation. We propose two theorems as the basis of algorithms design in the established system, which generalize the original circular convolution property of discrete Fourier transform (DFT). Moreover, ISBI cancellation algorithms are proposed based on a collaboration detection scheme, where joint slices signal models are implemented. The framework proposed in the paper establishes a foundation to underpin extremely diverse use cases in 5G that implement on a common infrastructure.

In this paper, we investigate spectrum sharing ultra- reliable and low- latency communications (URRLC) in an un- manned aerial vehicle (UAV)-aided cognitive radio (CR) internet of thing (IoT) network. Particularly, the secondary IoT devices opportunistically accesses the radio resource provided by a primary network and directly transmits short packets to the mobile UAV. A novel performance metric is proposed with finite block-length codes is adopted in the secondary UAV-aided IoT network. We aim to maximize the minimum average finite block-length rate for the secondary UAV-aided IoT network, subject to a probabilistic interference power constraint to the primary network based on imperfect channel state information (CSI). This formulated problem is non-convex due to the binary time scheduling, the power allocation, and the UAV altitude. In order to circumvent this issue, we develop an alternating method to solve this problem. Specifically, we first exploit the time scheduling optimization of the IoT devices for given power allocation and UAV altitude. Next, the monotonicity of the average finite block-length rate is analyzed to gain more insights for given time scheduling and UAV altitude. By capitalizing on this property, an optimal power control policy is proposed, followed by closed-form expressions and approximations for the optimal average power and the achievable average rate in the finite block- length regime. The optimal altitude of the UAV can be obtained by one-dimensional line search. Numerical results validate the effectiveness and accuracy of the derived theoretical results.

In this paper, non-orthogonal-multiple-access (NOMA)-based cell-free massive multiple-input multiple-output (MIMO) is investigated, where the users are grouped into multiple clusters. Exploiting conjugate beamforming, the bandwidth efficiency (BE) of the system is derived while the assumption that the users performing realistic successive interference cancellation (SIC) based on only the knowledge of channel statistics. The max-min fairness problem of maximizing the lowest user BE is investigated and an iterative bisection method is developed to determine the optimal solution to the max-min BE problem. Numerical results are presented for validating the proposed design's performance, and a mode switching scheme is conceived for selecting a specific Mode = {OMA, NOMA} that maximizes the system's BE.

We investigate the energy efficiency performance of cell-free Massive multiple-input multiple-output (MIMO), where the access points (APs) are connected to a central processing unit (CPU) via limited-capacity links. Thanks to the distributed maximum ratio combining (MRC) weighting at the APs, we propose that only the quantized version of the weighted signals are sent back to the CPU. Considering the effects of channel estimation errors and using the Bussgang theorem to model the quantization errors, an energy efficiency maximization problem is formulated with per-user power and backhaul capacity constraints as well as with throughput requirement constraints. To handle this non-convex optimization problem, we decompose the original problem into two sub-problems and exploit a successive convex approximation (SCA) to solve original energy efficiency maximization problem. Numerical results confirm the superiority of the proposed optimization scheme.

Low latency and energy efficiency are two important performance requirements in various fifth-generation (5G) wireless networks. In order to jointly design the two performance requirements, in this paper a new performance metric called effective energy efficiency (EEE) is defined as the ratio of the effective capacity (EC) to the total power consumption in a cellular network with underlaid device to device (D2D) communications. We aim to maximize the EEE of the D2D network subject to the D2D device power constraints and the minimum rate constraint of the cellular network. Due to the non-convexity of the problem, we propose a two-stage difference-of-two-concave (DC) function approach to solve this problem. Towards that end, we first introduce an auxiliary variable to transfer the fractional objective function into a subtractive form. We then propose a successive convex approximation (SCA) algorithm to iteratively solve the resulting non-convex problem. The convergence and the global optimality of the proposed SCA algorithm are both analyzed. The numerical results are presented to demonstrate the effectiveness of the proposed algorithm.

In this paper, we investigate the hybrid precoding design for joint multicast-unicast millimeter wave (mmWave) system, where the simultaneous wireless information and power transform is considered at receivers. The subarray-based sparse radio frequency chain structure is considered at base station (BS). Then, we formulate a joint hybrid analog/digital precoding and power splitting ratio optimization problem to maximize the energy efficiency of the system, while the maximum transmit power at BS and minimum harvested energy at receivers are considered. Due to the difficulty in solving the formulated problem, we first design the codebook-based analog precoding approach and then, we only need to jointly optimize the digital precoding and power splitting ratio. Next, we equivalently transform the fractional objective function of the optimization problem into a subtractive form one and propose a two-loop iterative algorithm to solve it. For the outer loop, the classic Bi-section iterative algorithm is applied. For the inner loop, we transform the formulated problem into a convex one by successive convex approximation techniques, which is solved by a proposed iterative algorithm. Finally, simulation results are provided to show the performance of the proposed algorithm.

Millimeter wave (mmWave) communication is a promising technology in future wireless networks because of its wide bandwidths that can achieve high data rates. However, high beam directionality at the transceiver is needed due to the large path loss at mmWave. Therefore, in this paper, we investigate the beam alignment and power allocation problem in a nonorthogonal multiple access (NOMA) mmWave system. Different from the traditional beam alignment problem, we consider the NOMA scheme during the beam alignment phase when two users are at the same or close angle direction from the base station. Next, we formulate an optimization problem of joint beamwidth selection and power allocation to maximize the sum rate, where the quality of service (QoS) of the users and total power constraints are imposed. Since it is difficult to directly solve the formulated problem, we start by fixing the beamwidth. Next, we transform the power allocation optimization problem into a convex one, and a closed-form solution is derived. In addition, a one-dimensional search algorithm is used to find the optimal beamwidth. Finally, simulation results are conducted to compare the performance of the proposed NOMA-based beam alignment and power allocation scheme with that of the conventional OMA scheme.

An index modulation (IM) assisted Discrete Cosine Transform based Orthogonal Frequency Division Multiplexing (DCT-OFDM) with Enhanced Transmitter Design (termed as EDCT-OFDM-IM) is proposed. It amalgamates the concept of Discrete Cosine Transform assisted Orthogonal Frequency Division Multiplexing (DCT-OFDM) and Index Modulation (IM) to exploit the design freedom provided by the double number of available subcarrier under the same bandwidth. In the proposed EDCT-OFDM-IM scheme, the maximum likelihood (ML) detector used for symbol bits and index bits recovering is derived and the sophisticated designing guidelines for EDCT-OFDM-IM are provided. Based on the derived pairwise error event probability, a theoretical upper bound on the average bit-error probability (ABEP) of EDCT-OFDM-IM is provided over multipath fading channels. Furthermore, the maximum peak-to-average power ratio (PAPR) of our proposed EDCT-OFDM-IM scheme is derived and compared to than the general Discrete Fourier Transform (DFT) based OFDM-IM counterpart.

Physical layer security (PLS) technologies have attracted much attention in recent years for their potential to provide information-theoretically secure communications. Artificial Noise (AN)-aided transmission is considered as one of the most practicable PLS technologies, as it can realize secure transmission independent of the eavesdropper's channel status. In this paper, we reveal that AN transmission has the dependency of eavesdropper's channel condition by introducing our proposed attack method based on a supervised-learning algorithm which utilizes the modulation scheme, available from known packet preamble and/or header information, as supervisory signals of training data. Numerical simulation results with the comparison to conventional clustering methods show that our proposed method improves the success probability of attack from 4.8% to at most 95.8% for the QPSK modulation. It implies that the transmission to the receiver in the cell-edge with low order modulation will be cracked if the eavesdropper's channel is good enough by employing more antennas than the transmitter. This work brings new insights into the effectiveness of AN schemes and provides useful guidance for the design of robust PLS techniques for practical wireless systems.

In this paper, an ultra-wideband, Dielectric Resonator Antenna (DRA) has been proposed. The proposed antenna is based on isosceles triangular DRA (TDRA), which is fed from the base side using a 50Ω probe. For bandwidth enhancement and radiation characteristics improvement, a partially cylindrical-shape hole is etched from its base side which approached probe feed to the center of TDRA. The dielectric resonator (DR) is located over an extended conducting ground plane. This technique has significantly enhanced antennas bandwidth from 48.8% to 80% (5.29-12.35 GHz), while the biggest problem was radiation characteristics. The basis antenna possesses negative gain in a wide range of bandwidth from 7.5 GHz to 10.5 GHz down to -13.8 dBi. Using this technique improve antenna gain over 1.6 dBi for whole bandwidth, while peak gain is 7.2 dBi.

In this paper, a high flat gain waveguide-fed aperture antenna has been proposed. For this purpose, two layers of FR4 dielectric as superstrates have been located in front of the aperture to enhance the bandwidth and the gain of the antenna. Moreover, a conductive shield, which is connected to the edges of the ground plane and surrounding aperture and superstrates, applied to the proposed structure to improve its radiation characteristics. The proposed antenna has been simulated with HFSS and optimized with parametric study and the following results have been obtained. The maximum gain of 13.0 dBi and 0.5-dBi gain bandwidth of 25.9 % (8.96-11.63 GHz) has been achieved. The 3-dBi gain bandwidth of the proposed antenna is 40.7% (8.07-12.20 GHz), which has a suitable reflection coefficient (≤ -10dBi) in whole bandwidth. This antenna comprises a compact size of (1.5λ×1.5λ), easy structure and low-cost fabrication.

In this paper, Generalized Space-Time Block Coded Spatial Modulation (GSTBC-SM) is proposed for Multiple-Input and Multiple-Output (MIMO) system, which can be extended into an arbitrary even number of Transmit Antennas (TAs). The proposed GSTBC-SM scheme employs the hybrid concepts of Generalized Space-Time Block Coding (GSTBC) and Spatial Modulation (SM) to further exploit the diversity benefits of GSTBC using sparse Radio Frequency (RF) chains. To be more specific, the information bits are divided into Nugroups and each group is modulated by SM scheme. Finally, the Nusymbols are invoked for GSTBC structure. In order to demonstrated the advantages of our proposed GSTBC-SM schemes, the theoretical Average Bit Error Probability (ABEP) of our proposed GSTBC-SM is derived. Both our analytical and simulation results demonstrated that the proposed GSTBC-SM scheme is capable of providing considerable performance gains over the corresponding GSTBC schemes at the same transmit rate associated with the same number of RF chains.

Orthogonal Frequency Division Multiplexing (OFDM) with Index Modulation (OFDM-IM), which conveyed information bits via the activated indices and constellation symbols is a promising technique in the next wireless communications. In the OFDM-IM scheme, only part of subcarriers are activated to transmit information, the inactive subcarriers transmit zero symbols, so that the conventional differential coding is not suitable for the adjacent subcarriers. In order to address this issue, in this paper, a novel Rectangular Differential OFDM-IM (RD-OFDM-IM) scheme is proposed to exploit the benefits of OFDM-IM dispensing with Channel State Information (CSI). In the proposed RD-OFDM-IM scheme, N subcarriers are partitioned into G subblocks and index modulation is employed in each subblock first. Then rectangular differential coding is invoked during two adjacent subblocks, so that non-coherent detection can be employed for the proposed RD-OFDM-IM scheme. Simulation results are shown that the proposed RD-OFDM-IM scheme is capable of providing considerable performance gain over conventional Differential OFDM (D-OFDM) scheme with lower Peak Average Power Ratio (PAPR).

Cell-free massive multiple-input-multiple-output (MIMO) is considered, where distributed access points (APs) multiply the received signal by the conjugate of the estimated channel, and send back a quantized version of this weighted signal to a central processing unit (CPU). For the first time, we present a performance comparison between the case of perfect fronthaul links, the case when the quantized version of the estimated channel and the quantized signal are available at the CPU, and the case when only the quantized weighted signal is available at the CPU. The Bussgang decomposition is used to model the effect of quantization. The max-min problem is studied, where the minimum rate is maximized with the power and fronthaul capacity constraints. To deal with the non-convex problem, the original problem is decomposed into two sub-problems (referred to as receiver filter design and power allocation). Geometric programming (GP) is exploited to solve the power allocation problem whereas a generalized eigenvalue problem is solved to design the receiver filter. An iterative scheme is developed and the optimality of the proposed algorithm is proved through uplink-downlink duality. A user assignment algorithm is proposed which significantly improves the performance. The numerical results demonstrate the superiority of the proposed schemes.

By blending the concepts of non-orthogonal multiple access (NOMA) and orthogonal frequency division multiplexing, in this paper, a novel hybrid scheme is conceived for supporting diverse services in future wireless systems. Motivating to maximize energy efficiency (EE), the joint resource management of user clustering (UC) and power allocation is investigated for the downlink hybrid NOMA systems. Under two different power consumption cases, the optimal resource allocation (Opt-RA) algorithm is developed with the help of converting the original mixed integer non-linear programming (MINLP) problem to the tractable decoupled problems. For practical implementation, the heuristic resource allocation (Heur-RA) algorithm is also proposed, and it includes a low-complexity UC algorithm based on the candidate search-and-allocation approach. Our simulation results show that, both the Opt-RA and Heur-RA algorithms achieve significantly higher EE performance than other existing algorithms. Further, the results also prove that, the hybrid NOMA conceived is able to exploit the advantages of NOMA scheme, and is superior to conventional orthogonal multiple access (OMA) in terms of EE, as well as achieving higher flexibility for system configuration than NOMA.

A novel multiple-input and multiple-output (MIMO) transmission scheme termed as generalized quadrature spatial modulation (G-QSM) is proposed. It amalgamates the concept of quadrature spatial modulation (QSM) and spatial multiplexing for the sake of achieving a high throughput, despite relying on a low number of radio frequency (RF) chains. In the proposed G-QSM scheme, the conventional constellation points of the spatial multiplexing structure are replaced by the QSM symbols, hence the information bits are conveyed both by the antenna indices as well as by the classic amplitude/phase modulated (APM) constellation points. The upper bounds of the average bit error probability (ABEP) of the proposed G-QSM system in high throughput massive MIMO configurations are derived. Furthermore, an efficient multipath orthogonal matching pursuit (EM-OMP)-based compressive sensing (CS) detector is developed for our proposed G-QSM system. Both our analytical and simulation results demonstrated that the proposed scheme is capable of providing considerable performance gains over the existing schemes in massive MIMO configurations.

The development of modellings and analytical tools to structurise and study the allocation of resources through noble user competitions become essential, especially considering the increased degree of heterogeneity in application and service demands that will be cornerstone in future communication systems. Stochastic asymmetric Blotto games appear promising to modelling such problems, and devising their Nash equilibrium (NE) strategies by anticipating the potential outcomes of user competitions. In this regard, this paper approaches the generic energy efficiency problem with a new stochastic asymmetric Blotto game paradigm to enable the derivation of joint optimal bandwidth and transmit power allocations by setting multiple users to compete in multiple auction-like contests for their individual resource demands. The proposed modelling innovates by abstracting the notion of fairness from centrally-imposed to distributed-competitive, where each user's pay-off probability is expressed as quantitative bidding metric, so as, all users' actions can be interdependent, i.e., each user attains its utility given the allocations of other users, which eliminates the chance of low-valued carriers not being claimed by any user, and, in principle, enables the full utilisation of wireless resources. We also contribute by resolving the allocation problem with low complexity using new mathematical techniques based on Charnes-Cooper transformation, which eliminate the additional coefficients and multipliers that typically appear during optimisation analysis, and derive the joint optimal strategy as a set of linear single-variable functions for each user. We prove that our strategy converges towards a unique, monotonous and scalable NE, and examine its optimality, positivity and feasibility properties in detail. Simulation comparisons with relevant studies confirm the superiority of our approach in terms of higher energy efficiency performance, fairness index and quality-of-service provision.

This paper investigates a secure wireless-powered integrated service system with full-duplex self-energy recycling. Specifically, an energy-constrained information transmitter (IT), powered by a power station (PS) in a wireless fashion, broadcasts two types of services to all users: a multicast service intended for all users and a confidential unicast service subscribed to by only one user while protecting it from any other unsubscribed users and an eavesdropper. Our goal is to jointly design the optimal input covariance matrices for the energy beamforming, the multicast service, the confidential unicast service, and the artificial noises from the PS and the IT, such that the secrecy-multicast rate region (SMRR) is maximized subject to the transmit power constraints. Due to the non-convexity of the SMRR maximization (SMRRM) problem, we employ a semidefinite programming-based two-level approach to solve this problem and find all of its Pareto optimal points. In addition, we extend the SMRRM problem to the imperfect channel-state information case, where a worst-case SMRRM formulation is investigated. Moreover, we exploit the optimized transmission strategies for the confidential service and energy transfer by analyzing their own rank-one profile. Finally, numerical results are provided to validate our proposed schemes.

This paper investigates a two-tier heterogeneous networks (HetNets), where millimeter wave (mmWave) frequency is employed at the macro base station (MBS), and the small cell BSs (SBSs) consider orthogonal frequency division multiple access (OFDMA). Subarray structure based hybrid analog/digital precoding scheme is studied to reduce the hardware cost and energy consumption. Our goal is to maximize the energy efficiency (EE) of the HetNets with limited wireless backhaul capacity and all users' quality of service (QoS) constraints. Due to nonconvexity of the mixed integer nonlinear fraction programming (MINLFP), the formulated problem cannot be solved directly. In order to circumvent this issue, we propose a two-loop iterative resource allocation algorithm. Specifically, we reformulate the outer-loop problem into a difference of convex programming (DCP) by employing integer relaxation and Dinkelback method. In addition, the first-order approximation is adopted to linearize this inner-loop DCP problem into a convex optimization framework. Lagrange dual method is adapted to achieve the optimal power allocation. Furthermore, the convergence of the proposed iterative algorithm is analyzed. Numerical results are presented to demonstrate our proposed algorithms.

In time-division-duplexing (TDD) massive multiple-input multiple-output (MIMO) systems, channel reciprocity is exploited to overcome the overwhelming pilot training and the feedback overhead. However, in practical scenarios, the imperfections in channel reciprocity, mainly caused by radio-frequency mismatches among the antennas at the base station side, can significantly degrade the system performance and might become a performance limiting factor. In order to compensate for these imperfections, we present and investigate two new calibration schemes for TDD-based massive multi-user MIMO systems, namely, relative calibration and inverse calibration. In particular, the design of the proposed inverse calibration takes into account a compound effect of channel reciprocity error and channel estimation error. We further derive closed-form expressions for the ergodic sum rate, assuming maximum ratio transmissions with the compound effect of both errors. We demonstrate that the inverse calibration scheme outperforms the traditional relative calibration scheme. The proposed analytical results are also verified by simulated illustrations.

This paper introduces a millimeter-wave multiple-input-multiple-output (MIMO) antenna for autonomous (self-driving) cars. The antenna is a modified four-port balanced antipodal Vivaldi which produces four directional beams and provides pattern diversity to cover 90 deg angle of view. By using four antennas of this kind on four corners of the car's bumper, it is possible to have a full 360 deg view around the car. The designed antenna is simulated by two commercially full-wave packages and the results indicate that the proposed method can successfully bring the required 90 deg angle of view.

This study examines the effect of different pressures on the radiation characteristics of the loop-shaped plasma antenna filled by two gases; Argon and Nitrogen. Proposed loop plasma antennas operating at LTE and Wi-Fi frequency bands have been designed and its performance studied at three different pressures of 2.28, 5 and 10 Torr. The radiation characteristics of the both loop-shaped plasma antennas have been investigated and presented for three different pressures. To analyze the performance of the proposed antenna, full-wave simulation were run using the finite integral method software, CST Microwave Studio.

This paper investigates a wireless powered sensor network, where multiple sensor nodes are deployed to monitor a certain external environment. A multiantenna power station (PS) provides the power to these sensor nodes during wireless energy transfer phase, and consequently the sensor nodes employ the harvested energy to transmit their own monitoring information to a fusion center during wireless information transfer (WIT) phase. The goal is to maximize the system sum throughput of the sensor network, where two different scenarios are considered, i.e., PS and the sensor nodes belong to the same or different service operator(s). For the first scenario, we propose a global optimal solution to jointly design the energy beamforming and time allocation. We further develop a closed-form solution for the proposed sum throughput maximization. For the second scenario in which the PS and the sensor nodes belong to different service operators, energy incentives are required for the PS to assist the sensor network. Specifically, the sensor network needs to pay in order to purchase the energy services released from the PS to support WIT. In this case, this paper exploits this hierarchical energy interaction, which is known as energy trading. We propose a quadratic energy trading-based Stackelberg game, linear energy trading-based Stackelberg game, and social welfare scheme, in which we derive the Stackelberg equilibrium for the formulated games, and the optimal solution for the social welfare scheme. Finally, numerical results are provided to validate the performance of our proposed schemes.

In this paper, a comprehensive design and analysis of multiple-input multiple-output (MIMO) full-duplex (FD) relaying systems in a multi-cell environment is investigated, where a multi-antenna amplify-and-forward FD relay station serves multiple half-duplex (HD) multi-antenna users. The pivotal obstacles of loopback self-interference (LI) and multiple co-channel interferers (CCI) at the relay and destination when employing FD relaying in cellular networks are addressed. In contrast to the HD relaying mode, the CCI in the FD relaying mode is predicted to double since the uplink and downlink communications are simultaneously scheduled via the same channel. In this paper, the optimal layout of transmit (receive) precoding (decoding) weight vectors which maximizes the overall signal-to-interference-plus-noise ratio is constructed by a suitable optimization problem, and then a closed-form sub-optimal formula based on null space projection is presented. The proposed hop-by-hop rank-1 zero-forcing (ZF) beamforming vectors are based on added ZF constraints used to suppress the LI and CCI channels at the relay and destination, i.e., the source and relay perform transmit ZF beamforming, while the relay and destination employ receive ZF combining. To this end, unified accurate expressions for the outage probability and ergodic capacity are derived in closed form. In addition, simpler tight lower bound formulas for the outage probability and ergodic capacity are presented. Moreover, the asymptotic approximations for outage probability are considered to gain insights into system behavior in terms of the diversity order and array gain. Numerical and simulation results show the accuracy of the presented exact analytical expressions and the tightness of the lower bound expressions. The case of hop-by-hop maximum-ratio transmission/maximal-ratio combining beamforming is included for comparison purposes. Furthermore, our results show that while multi-antenna terminals improve the system performance, the detrimental effect of CCI on FD relaying is clearly seen. Therefore, our findings unveil that MIMO FD relaying could significantly improve the system performance compared to its conventional MIMO HD relaying counterpart.

Filtered orthogonal frequency division multiplexing (F-OFDM) system is a promising waveform for 5G and beyond to enable the multi-service system and spectrum efficient network slicing. However, the performance for F-OFDM systems has not been systematically analyzed in the literature. In this paper, we first establish a mathematical model for an F-OFDM system and derive the conditions to achieve the interference-free one-tap channel equalization. In the practical cases (e.g., insufficient guard interval, asynchronous transmission, and so on), the analytical expressions for inter-symbol interference, inter-carrier interference, and adjacent-carrier interference are derived, where the last term is considered as one of the key factors for asynchronous transmissions. Based on the framework, an optimal power compensation matrix is derived to make all of the subcarriers having the same ergodic performance. Another key contribution of this paper is that we propose a multi-rate F-OFDM system to enable low-complexity low-cost communication scenarios, such as narrow-band Internet of Things, at the cost of generating inter-subband interference (ISubBI). Low computational complexity algorithms are proposed to cancel the ISubBI. The result shows that the derived analytical expressions match the simulation results, and the proposed ISubBI cancelation algorithms can significantly save the original F-OFDM complexity (up to 100 times) without significant performance loss.

This paper proposes a low-complexity hybrid beamforming design for multi-antenna communication systems. The hybrid beamformer is comprised of a baseband digital beamformer and a constant modulus analog beamformer in the radio frequency (RF) part of the system. As in singular-value-decomposition (SVD)-based beamforming, hybrid beamforming design aims to generate parallel data streams in multi-antenna systems, however, due to the constant modulus constraint of the analog beamformer, the problem cannot be solved similarly. To address this problem, mathematical expressions of the parallel data streams are derived in this paper and desired and interfering signals are specified per stream. The analog beamformers are designed by maximizing the power of desired signal while minimizing the sum-power of interfering signals. Finally, digital beamformers are derived by defining the equivalent channel observed by the transmitter/receiver. Regardless of the number of the antennas or type of channel, the proposed approach can be applied to a wide range of MIMO systems with hybrid structure wherein the number of the antennas is more than the number of the RF chains. In particular, the proposed algorithm is verified for sparse channels that emulate mm-wave transmission as well as rich scattering environments. In order to validate the optimality, the results are compared with those of the state-of-the-art and it is demonstrated that the performance of the proposed method outperforms state-of-the-art techniques, regardless of type of the channel and/or system configuration.

The discrete cosine transform (DCT) based multicarrier modulation (MCM) system is regarded as one of the promising transmission techniques for future wireless communications. By employing cosine basis as orthogonal functions for multiplexing each real-valued symbol with symbol period of T, it is able to maintain the subcarrier orthogonality while reducing frequency spacing to 1/(2T) Hz, which is only half of that compared to discrete Fourier transform (DFT) based multicarrier systems. In this paper, following one of the effective transmission models by which zeros are inserted as guard sequence and the DCT operation at the receiver is replaced by DFT of double length, we reformulate and evaluate three classic detection methods by appropriately processing the post-DFT signals both for single antenna and multiple-input multiple-output (MIMO) DCT-MCM systems. In all cases, we show that with our reformulated detection approaches, DCT-MCM schemes can outperform, in terms of error-rate, conventional OFDM-based systems.

This paper presents a complex-valued discrete multicarrier modulation (MCM) system based on the real-valued discrete Hartley transform (DHT) and its inverse (IDHT). Unlike the conventional discrete Fourier transform (DFT), the DHT cannot diagonalize multipath fading channels due to its inherent properties, and this results in mutual interference between subcarriers of the same mirror-symmetrical pair. We explore this interference pattern in order to seek an optimal solution to utilize channel diversity for enhancing the bit error rate (BER) performance of the system. It is shown that the optimal channel diversity gain can be achieved via pairwise maximum likelihood (ML) detection, taking into account not only the subcarrier's own channel quality but also the channel state information of its mirror-symmetrical peer. Performance analysis indicates that DHT-based MCM can mitigate fast fading effects by averaging channel power gains of each mirror-symmetrical pair of subcarriers. Simulation results show that the proposed scheme has a substantial improvement in BER over the conventional DFT-based MCM system.

We propose channel estimation algorithms and pilot signal optimization for the universal filtered multi-carrier (UFMC) system based on the comb-type pilot pattern. By considering the least square linear interpolation (LSLI), discrete Fourier transform (DFT), minimum mean square error (MMSE) and relaxed MMSE (RMMSE) channel estimators, we formulate the pilot signals optimization problem by minimizing the estimation MSE subject to the power constraint on pilot tones. The closed-form optimal solutions and minimum MSE are derived for LSLI, DFT, MMSE and RMMSE estimators.

Network slicing has been identified as one of the most important features for 5G and beyond to enable operators to utilize networks on an as-a-service basis and meet the wide range of use cases. In physical layer, the frequency and time resources are split into slices to cater for the services with individual optimal designs, resulting in services/slices having different baseband numerologies (e.g., subcarrier spacing) and / or radio frequency (RF) front-end configurations. In such a system, the multi-service signal multiplexing and isolation among the service/slices are critical for the Physical-Layer Network Slicing (PNS) since orthogonality is destroyed and significant inter-service/ slice-band-interference (ISBI) may be generated. In this paper, we first categorize four PNS cases according to the baseband and RF configurations among the slices. The system model is established by considering a low out of band emission (OoBE) waveform operating in the service/slice frequency band to mitigate the ISBI. The desired signal and interference for the two slices are derived. Consequently, one-tap channel equalization algorithms are proposed based on the derived model. The developed system models establish a framework for further interference analysis, ISBI cancelation algorithms, system design and parameter selection (e.g., guard band), to enable spectrum efficient network slicing.

Sparse code multiple access (SCMA) is a novel non‐orthogonal multiple access scheme for 5G systems, in which the logarithm domain message passing algorithm (Log‐MPA) is applied at the receiver to achieve near‐optimum performance. However, the computational complexity of Log‐MPA detector is still a big challenge for practical implementation, especially for energy‐sensitive user equipments in the downlink scenario. A region‐restricted detector with an improved Log‐MPA (RRL detector) is proposed for downlink SCMA systems, in which the complexity is reduced from two perspectives. To avoid unnecessary calculations when searching the superposition constellation exhaustively, the proposed RRL detector updates the function nodes only within a restricted search region. While constellation points outside the search region are neglected, the performance is well maintained which is verified by simulations. Besides, the original Log‐MPA heavily relies on exponential operations, resulting in high computational complexity. To solve this problem, an improved Log‐MPA is also put forward in this study to make a better compromise between complexity and performance. Simulation results show that the complexity of the RRL detector is reduced considerably while the bit error rate performance degrades unnoticeably.

Orthogonal frequency division multiplexing with index modulation (OFDM‐IM) has attracted considerable interest recently. The technique uses the subcarrier indices as a source of information. In filter bank multicarrier (FBMC) system, double‐dispersive channels lead to inter‐carrier interference and/or inter‐symbol interference, which are caused by the neighbouring symbols in the frequency and/or time domain. When the authors introduce index modulation to the FBMC system, the interference power will be smaller comparing with that of the conventional FBMC system as some subcarriers carry nothing but zeros. In this study, the advantages of FBMC with index modulation (FBMC‐IM) are investigated by comparing the signal to interference ratio with that of the conventional FBMC system. However, the bit error rate (BER) performance is affected since there exists interference in the FBMC‐IM system. To improve the BER performance, the authors propose an optimal combination‐selection algorithm and an optimal combination‐mapping rule. By abandoning some combinations whose error probability are larger and by mapping the remaining combinations into specified bits, a better BER performance can be achieved compared with that without optimisation. The theoretical analysis and simulation results clearly show the FBMC‐IM system has a good BER performance under double‐dispersive channels.

The authors propose a novel turbo detection scheme based on the factor graph (FG) serial‐schedule belief propagation equalisation algorithm with low complexity for single‐carrier faster‐than‐Nyquist (SC‐FTN) and multi‐carrier FTN (MC‐FTN) signalling. In this work, the additive white Gaussian noise channel and multi‐path fading channels are both considered. The iterative FG‐based equalisation algorithm can deal with severe intersymbol interference and intercarrier interference introduced by the generation of SC and MC‐FTN signals, as well as the effect of multi‐path fading. With the application of Gaussian approximation, the complexity of the proposed equalisation algorithm is significantly reduced. In the turbo detection, low‐density parity check code is employed. The simulation results demonstrate that the FG‐based turbo detection method can achieve satisfactory performance with low complexity.

Simultaneous localization and tracking (SLAT) in wireless sensor networks (WSNs) involves tracking the mobile target while calibrating the nearby sensor node locations. In practice, localization error propagation (EP) phenomenon will arise, due to the existence of the latest tracking error, target mobility, measurement error, and reference node location errors. In this case, the SLAT performance limits are crucial for the SLAT algorithm design and WSN deployment, and the study of localization EP principle is desirable. In this paper, we focus on the EP issues for the received signal strength-based SLAT scheme, where the measurement accuracy is assumed to be spatial-temporal-domain doubly random due to the target mobility, environment dynamics, and different surroundings at different reference nodes. First, the Cramer-Rao lower bound (CRLB) is derived to unveil both the target tracking EP and the node location calibration EP. In both cases, the EP principles turn out to be in a consistent form of the Ohm's Law in circuit theory. Second, the asymptotic CRLB analysis is then presented to reveal that both EP principles scale with the inverse of sensor node density. Meanwhile, it is shown that, the tracking and calibration accuracy only depends on the expectation of the measurement precision. Third, the convergence conditions, the convergence properties, and the balance state of the target tracking EP and the location calibration EP are examined to shed light on the EP characteristics of the SLAT scheme Finally, numerical simulations are presented to corroborate the EP analysis.

Cooperative network localization plays an important role in wireless sensor network (WSN), wherein neighboring sensor nodes will help each other to calibrate their locations. However, due to the dynamic wireless propagation environment and different surroundings, the measurement accuracy at different network nodes is different and varies overtime. In this paper, the uncertainties in both measurement accuracy and reference node locations are considered to account for the impact of different surrounding environments and the initial node location errors on the cooperative network localization. A mean-field variational inference-based positioning (VIP) algorithm is proposed for cooperative network localization. The mechanism of the proposed VIP algorithm, the convergence properties, implementation complexity, and the parallel implementation structure are presented to show that the VIP algorithm provides an effective mechanism to incorporate and share the localization information among all network nodes for an improved localization performance. Finally, a concise Cramer-Rao lower bound (CRLB) is derived to reveal the principle of localization error propagation. It is disclosed that the localization error propagation principle is similar to the Ohm's Law in circuit theory, which provides a new insight into the impact of the measurement accuracy, the reference node location errors and the number of reference nodes on the cooperative network localization performance.

Flexibly supporting multiple services, each with different communication requirements and frame structure, has been identified as one of the most significant and promising characteristics of next generation and beyond wireless communication systems. However, integrating multiple frame structures with different subcarrier spacing in one radio carrier may result in significant inter-service-band-interference (ISBI). In this paper, a framework for multi-service (MS) systems is established based on a subband filtered multi-carrier system. The subband filtering implementations and both asynchronous and generalized synchronous (GS) MS subband filtered multi-carrier (SFMC) systems have been proposed. Based on the GS-MS-SFMC system, the system model with ISBI is derived and a number of properties on ISBI are given. In addition, low-complexity ISBI cancelation algorithms are proposed by precoding the information symbols at the transmitter. For asynchronous MS-SFMC system in the presence of transceiver imperfections, including carrier frequency offset, timing offset, and phase noise, a complete analytical system model is established in terms of desired signal, inter-symbol-interference, inter-carrier-interference, ISBI, and noise. Thereafter, new channel equalization algorithms are proposed by considering the errors and imperfections. Numerical analysis shows that the analytical results match the simulation results, and the proposed ISBI cancelation and equalization algorithms can significantly improve the system performance in comparison with the existing algorithms.

A well known problem of regularization (diagonal loading) of the interference rejection combining (IRC) and IRC / maximum ratio combining (MRC) switching is addressed. Different empirical loading factor selection rules adjusted to specific scenarios have been introduced in the literature. It is expected that future network will be characterized by variety of scenarios, transmission modes, and receiver configurations. Empirical IRC regularization may not be suitable for such networks. In this study we consider the Expected Likelihood (EL) criterion for estimation/selection of the interference plus noise covariance matrix and demonstrate that it gives the diagonal loading selection rules that are effective in wide range of scenarios and receiver configurations.

Decentralized dynamic spectrum allocation (DSA) that exploit adaptive antenna array interference mitigation (IM) diversity at the receiver, is studied for interference-limited environments with high level of frequency reuse. The system consists of base stations (BSs) that can optimize uplink frequency allocation to their user equipments (UEs) to minimize impact of interference on the useful signal, assuming no control over band allocation of other BSs sharing the same bands. To this end, “good neighbor” (GN) rules allow effective trade off between the equilibrium and transient decentralized DSA behavior if the performance targets are adequate to the interference scenario. In this paper, we extend the GN rules by including a spectrum occupation control that allows adaptive selection of the performance targets corresponding to the potentially “interference free” DSA; define the semi-analytic absorbing Markov chain model for the GN DSA with occupation control and study the convergence properties including effects of possible breaks of the GN rules; and for higher-dimension networks, develop the simplified search GN algorithms with occupation and power control (PC) and demonstrate their efficiency by means of simulations in the scenario with unlimited requested network occupation.

To flexibly support diverse communication requirements (e.g., throughput, latency, massive connection, etc.) for the next generation wireless communications, one viable solution is to divide the system bandwidth into several service subbands, each for a different type of service. In such a multi-service (MS) system, each service has its optimal frame structure while the services are isolated by subband filtering. In this paper, a framework for multi-service (MS) system is established based on subband filtered multi-carrier (SFMC) modulation. We consider both single-rate (SR) and multi-rate (MR) signal processing as two different MS-SFMC implementations, each having different performance and computational complexity. By comparison, the SR system outperforms the MR system in terms of performance while the MR system has a significantly reduced computational complexity than the SR system. Numerical results show the effectiveness of our analysis and the proposed systems. These proposed SR and MR MS-SFMC systems provide guidelines for next generation wireless system frame structure optimization and algorithm design.

In this paper, we first consider a downlink multiple- input multiple-output non-orthogonal multiple access (MIMO-NOMA) system with a base station and four users, where the users are equally divided into two clusters and inter-cluster interference is induced in transmission. Then we propose a joint clustering and precoding algorithm for the system such that not only the inter-cluster interference can be effectively eliminated but also the sum capacity can be enhanced. Specifically, a set of precoder pairs (one pair having two precoders for the two clusters) are constructed based on the eigenspaces of channel matrices, and one of them is selected to maximize the sum capacity. The proposed joint algorithm involves a high-complexity issue due to the need of exhaustive search for the best precoder pair. To reduce the search complexity, we explore the channel gain of each user and construct a reduced-size precoding set for selection. Combined with orthogonal frequency division multiplexing, the proposed four-user scheme is further extended to the general multiuser case, where every four users share a subcarrier for MIMO-NOMA transmission. Simulation results show that the proposed MIMO-NOMA scheme can provide more system capacity than a MIMO orthogonal multiple access approach.

Recently proposed universal filtered multicarrier (UFMC) system is not an orthogonal system in multipath channel environments and might cause significant performance loss. In this paper, the authors propose a cyclic prefix (CP) based UFMC system and first analyze the conditions for interference-free one-tap equalization in the absence of transceiver imperfections. Then the corresponding signal model and output signal-to-noise ratio expression are derived. In the presence of carrier frequency offset, timing offset, and insufficient CP length, the authors establish an analytical system model as a summation of desired signal, intersymbol interference, intercarrier interference, and noise. New channel equalization algorithms are proposed based on the derived analytical signal model. Numerical results show that the derived model matches the simulation results precisely, and the proposed equalization algorithms improve the UFMC system performance in terms of bit error rate.

A specific characteristic for the isolator circuits used in small form-factor in-band full-duplex transceivers, used to attenuate the leakage of the transmit signal to the receive path (self-interference), is nonuniform performance over the wide frequency band as these circuits must tune at the operating frequency. Moreover active cancellation circuits are required in addition to the isolation circuit to attenuate the self-interference further. In this work an RF self-interference cancellation is proposed to attenuate further the remaining self-interference, on the top of achieved isolation, uniformly over the wide band. This approach divides the wideband signal into a number of narrower subbands and then self-interference cancellation is performed at each subband separately. This is an alternate approach to the previously proposed delay and attenuation network active RF self-interference cancellation. Simulation results indicate significant improvements in uniformly self-interference cancellation and system performance which is achieved in the expense of moderate extra loss.

An attractive hybrid method of mitigating the effects of the residual self-interference imposed by the signal propagation and analog/digital circuit non-idealities for Full-Duplex (FD) point-to-point multiple-input multiple-output (MIMO) systems is proposed. Furthermore, the effect of channel estimation errors on system performance is considered. The simulation results demonstrate that our proposed cancellation technique for FD systems achieve a significant gain over traditional Half-Duplex (HD) systems. For example, the sum rate of our proposed scheme of FD MIMO system is approximately 1.8 times higher than that of the HD transmission.

Filter-bank multicarrier (FBMC) with offset quadrature amplitude modulation (OQAM) is becoming popular in wireless communications due to its advantages over orthogonal frequency division multiplexing (OFDM) systems. However, in presence of intrinsic interference, channel estimation in FBMC is not as straightforward as OFDM systems especially in multiple antenna scenarios. In this paper we propose a channel estimation method which employs intrinsic interference pre-cancellation at the transmitter side. The results show that this method needs less pilot overhead compared to the popular intrinsic approximation methods (IAM) with better BER and MSE performance.

Full-duplex transceivers enable transmission and reception at the same time on the same frequency, and have the potential to double the wireless system spectral efficiency. Recent studies have shown the feasibility of full-duplex transceivers. In this paper, we address the radio resource allocation problem for full-duplex system. Due to the self-interference and inter-user interference, the problem is coupled between uplink and downlink channels, and can be formulated as joint uplink and downlink sum-rate maximization. As the problem is non-convex, an iterative algorithm is proposed based on game theory by modelling the problem as a noncooperative game between the uplink and downlink channels. The algorithm iteratively carries out optimal uplink and downlink resource allocation until a Nash equilibrium is achieved. Simulation results show that the algorithm achieves fast convergence, and can significantly improve the full-duplex performance comparing to the equal resource allocation approach. Furthermore, the full-duplex system with the proposed algorithm can achieve considerable gains in spectral efficiency, that reach up to 40%, comparing to half-duplex system.

In this paper, the capacity of OFDM/OQAM with isotropic orthogonal transfer algorithm (IOTA) pulse shaping is evaluated through information theoretic analysis. In the conventional OFDM systems the insertion of a cyclic prefix (CP) decreases the system's spectral efficiency. As an alternative to OFDM, filter bank based multicarrier systems adopt proper pulse shaping with good time and frequency localisation properties to avoid interference and maintain orthogonality in real field among sub-carriers without the use of CP. We evaluate the spectral efficiency of OFDM/OQAM systems with IOTA pulse shaping in comparison with conventional OFDM/QAM systems, and our analytical model is further extended in order to gain insights into the effect of utilizing the intrinsic interference on the performance of our system. Furthermore, the spectral efficiency of OFDM/OQAM systems is analyzed when the effect of inter-symbol and inter-carrier interference is considered.

This paper investigates the secrecy performance of full-duplex relay (FDR) networks. The resulting analysis shows that FDR networks have better secrecy performance than half duplex relay networks, if the self-interference can be well suppressed. We also propose a full duplex jamming relay network, in which the relay node transmits jamming signals while receiving the data from the source. While the full duplex jamming scheme has the same data rate as the half duplex scheme, the secrecy performance can be significantly improved, making it an attractive scheme when the network secrecy is a primary concern. A mathematic model is developed to analyze secrecy outage probabilities for the half duplex, the full duplex and full duplex jamming schemes, and the simulation results are also presented to verify the analysis.

In this paper, we investigate the secrecy performance for a multiple-input multiple-output (MIMO) wiretap channel in the presence of a multiantenna eavesdropper. In particular, the legitimate transmitter uses transmit antenna selection (TAS) to transmit on a single antenna with the largest signal-to-noise ratio (SNR) while both the legitimate receiver and the eavesdropper adopt maximal ratio combining (MRC) for reception. We derive exact closed-form expressions for the probabilities of achieving positive secrecy rate and secrecy outage in the case of imperfect feedback due to feedback delay and/or feedback error. Furthermore, we derive the asymptotic secrecy outage probability at high SNR, which accurately reveals the secrecy diversity loss due to imperfect feedback. Simulation results are provided to verify our analytical results and illustrate the impact of imperfect feedback on the secrecy performance of such a wiretap system.

Low density signature orthogonal frequency division multiplexing (LDS-OFDM) and low density parity-check (LDPC) codes are multiple access and forward error correction (FEC) techniques, respectively. Both of them can be expressed by a bipartite graph. In this paper, we construct a joint sparse graph combining the single graphs of LDS-OFDM and LDPC codes, namely joint sparse graph for OFDM (JSG-OFDM). Based on the graph model, a low complexity approach for joint multiuser detection and FEC decoding (JMUDD) is presented. The iterative structure of JSG-OFDM receiver is illustrated and its extrinsic information transfer (EXIT) chart is researched. Furthermore, design guidelines for the joint sparse graph are derived through the EXIT chart analysis. By offline optimization of the joint sparse graph, numerical results show that the JSG-OFDM brings about 1.5-1.8 dB performance improvement at bit error rate (BER) of 10-5over similar well-known systems such as group-orthogonal multi-carrier code division multiple access (GO-MC-CDMA), LDS-OFDM, and turbo structured LDS-OFDM.

In this paper, we first analyse bit error rate (BER) bounds of the distributed network coding (DNC) scheme based on the Luby-transform (LT) codes, which is a class of fountain codes, for wireless sensor networks (WSNs). Then we investigate the effect from two parameters of the degree distributions, i.e., the degree value and the proportion of odd degree, to the performance of the LT-based DNC scheme. Based on the analysis and investigation results, a degree distribution design criteria is proposed for the DNC scheme based on fountain codes over Rayleigh fading channels. We compare the performance of the DNC scheme based on fountain codes using degree distributions designed in this paper with other schemes given in the literature. The comparison results show that the degree distributions designed by using the proposed criteria have better performance.

Low density signature for OFDM (LDS-OFDM) is able to achieve satisfactory performance in overloaded conditions, but the existing LDS-OFDM has the drawback of slow convergence rate for multiuser detection (MUD) and high receiver complexity. To tackle these problems, we propose a serial schedule for the iterative MUD. By doing so, the convergence rate of MUD is accelerated and the detection iterations can be decreased. Furthermore, in order to exploit the similar sparse structure of LDS-OFDM and LDPC code, we utilize LDPC codes for LDS-OFDM system. Simulations show that compared with existing LDS-OFDM, the LDPC code improves the system performance.

In the literature, the Gaussian input is assumed in power optimization algorithms. However, this assumption is unrealistic, whereas practical systems use Finite Symbol Alphabet (FSA) input, (e.g., M-QAM). In this paper, we consider the optimal power for joint interweave and underlay CR systems given FSA inputs. We formulated our problem as convex optimization and solved it through general convex optimization tools. We observed that the total SU transmit power is always less than the power budget and remains in interference limited region only over the considered distance range. Therefore, we re-derive optimal power with interference constraint only in order to reduce the complexity of the algorithm by solving it analytically. Numerical results reveal that, for the considered distance range, the transmit power saving and the rate gain with the proposed algorithm is in the range 16–92% and 7–34%, respectively, depending on the modulation scheme (i.e., BPSK, QPSK and 16-QAM) used.

In Cognitive Radio (CR) systems, the data rate of the Secondary User (SU) can be maximized by optimizing the transmit power, given a threshold for the interference caused to the Primary User (PU). In conventional power optimization algorithms, the Gaussian input distribution is assumed, which is unrealistic, whereas the Finite Symbol Alphabet (FSA) input distribution, (i.e., M-QAM) is more applicable to practical systems. In this paper, we consider the power optimization problem in multiple input multiple output orthogonal frequency division multiplexing based CR systems given FSA inputs, and derive an optimal power allocation scheme by capitalizing on the relationship between mutual information and minimum mean square error. The proposed scheme is shown to save transmit power compared to its conventional counterpart. Furthermore, our proposed scheme achieves higher data rate compared to the Gaussian optimized power due to fewer number of subcarriers being nulled. The proposed optimal power algorithm is evaluated and compared with the conventional power allocation algorithms using Monte Carlo simulations. Numerical results reveal that, for distances between the SU transmitter and the PU receiver ranging between 50m to 85m, the transmit power saving with the proposed algorithm is in the range 13–90%, whereas the rate gain is in the range 5–31% depending on the modulation scheme (i.e., BPSK, QPSK and 16-QAM) used.

In the literature, optimal power allocation assuming Gaussian input has been evaluated in OFDM based Cognitive Radio (CR) systems to maximize the capacity of the secondary user while keeping the interference introduced to the primary user band within tolerable range. However, the Gaussian input assumption is not practical and Finite Symbol Alphabet (FSA) input distributions, i.e., M-QAM are used in practical systems. In this paper, we consider the power optimization problem under the condition of FSA inputs as used in practical systems, and derive an optimal power allocation strategy by capitalizing on the relationship between mutual information and minimum mean square error. The proposed scheme is shown to save transmit power in a CR system compared to its conventional counterpart, that assumes Gaussian input. In addition to extra allocated power, i.e., power wastage, the conventional power allocation scheme also causes nulling of more subcarriers, leading to reduced transmission rate, compared to the proposed scheme. The proposed optimal power algorithm is evaluated and compared with the conventional algorithm assuming Gaussian input through simulations. Numerical results reveal that for interference threshold values ranging between 1mW to 3mW, the transmit power saving with the proposed algorithm is in the range between 55-75%, 42-62% and 12-28%, whereas the rate gain is in the range between 16.8-12.4%, 13-11.8% and 3-5.8% for BPSK, QPSK and 16-QAM inputs, respectively.

The Long Term Evolution (LTE) may provide ubiquitous mobile broadband services with all IP architecture, however, the quality of service (QoS) of LTE systems is seriously affected by the network congestions, packet losses, jitters, latencies and other QoS issues in all IP networks. Thus it is valuable to investigate and design efficient resource scheduling algorithms to improve the performance of data services and enduser experiences. In this paper we propose an improved radio resource scheduling algorithm over the existing semi-continuous scheduling algorithm for the voice over Internet Protocol (VoIP) data packets. Through mapping the TCP (transmission control protocol) ACK (acknowledgement) packets into a higher priority logical channel, the probability of both the discarded ACK packets and congestions in the wireless channels are reduced. As the result, the scheme may avoid frequently opening the TCP congestion control mechanism. The simulation results have shown the advantages of our proposed algorithm, such as the RTT (Round-Trip Time) packet delay reduction, improved throughput, acceptable stability, desirable performance, and son on.

The optimized weight spectrum sequence (OWSS) is utilized as a design criterion in this paper to determine the periodic puncturing pattern and the non-periodic puncturing pattern of partially systematic RCPT (PS-RCPT) codes for LTE systems. It is shown that the OWSS-criterion based PS-RCPT codes outperform the pseudo-random puncturing (PRP) based PS-RCPT codes. Meanwhile, it is unveiled that, the puncturing ratio of information bits should be carefully determined in PS-RCPT codes generation to achieve reasonable tradeoff between the waterfall region and the error floor region performance.

In this paper, we introduce the concept of average per-user rate to the multiuser Multiple-Input, Multiple-Output (MIMO) system with the frequency domain packet scheduler (FDPS) at base stations, which provides an estimate of the rate that the system could provide for each admitted user. The proposed admission control is designed by comparing the user's quality of service (QoS) requirements with the transmission rate that the system can offer. The analytical model is based on the generalized 3GPP LTE downlink transmission for which two Spatial Division Multiplexing (SDM) multiuser MIMO schemes are investigated, namely, Single User (SU) and Multi-user (MU) MIMO schemes. The main contribution of this paper is the derivation of the achievable rate for each user in the SDM MIMO systems based on a mathematical model of the Signal to Interference plus Noise Ratio (SINR) distribution with the frequency domain packet scheduler. The achievable rate provides insights into the system's performance from a different perspective.

Orthogonal frequency division multiplexing (OFDM) technique has gained increasing popularity in both wired and wireless communication systems. However, in the conventional OFDM systems the insertion of a cyclic prefix (CP) and the transmission of periodic training sequences for purpose of channel estimation decrease the system's spectral efficiency. As an alternative to OFDM, isotropic orthogonal transform algorithm (IOTA)-based multicarrier system adopts a proper pulse shaping with good time and frequency localisation properties to avoid interference and maintain orthogonality in real field among sub-carriers without the use of CP. In this study, the authors propose linearly precoded IOTA-based multicarrier systems to achieve blind channel estimation by utilising the structure of auto-correlation and cross-correlation matrices introduced by precoding. The results show that the proposed IOTA-based multicarrier systems achieve better power and spectral efficiency compared with the conventional OFDM systems.

This work addresses joint transceiver optimization for multiple-input, multiple-output (MIMO) systems. In practical systems the complete knowledge of channel state information (CSI) is hardly available at transmitter. To tackle this problem, we resort to the codebook approach to precoding design, where the receiver selects a precoding matrix from a finite set of pre-defined precoding matrices based on the instantaneous channel condition and delivers the index of the chosen precoding matrix to the transmitter via a bandwidth-constraint feedback channel. We show that, when the symbol constellation is improper, the joint codebook based precoding and equalization can be designed accordingly to achieve improved performance compared to the conventional system.

The authors propose some robust adaptive multiuser detection schemes for direct-sequence code-division multiple-access multipath frequency-selective fading channels. Multiple access interference (MAI) and intersymbol interference (ISI) are presented in an identical format using expanded signal subspace, which facilitates multiuser detection in a symbol-by-symbol fashion. This study contributes to the theoretical aspect of adaptive multiuser detection by proving that the optimum linear multiuser detectors that achieve maximum signal-to-interference-plus-noise ratio (SINR) must exist in the signal subspace, and the theoretic SINR upper bound is also derived. Another contribution of this study is to propose the design of multiuser detectors in an expanded signal subspace, and introduce subspace estimation and Kalman filtering algorithms for their adaptive implementation. To robustify the adaptive detectors against subspace estimation and channel estimation errors, a modified projection approximation subspace tracking (PAST) algorithm is proposed for subspace tracking. It is demonstrated by simulations that these adaptive detectors effectively suppress both MAI and ISI and converge to the optimum SINR. They are robust against subspace estimation errors and channel estimation errors compared to the conventional Wiener minimum mean square error (MMSE) detector.

Different detection schemes for multiple-input, multiple-output (MIMO) systems are investigated. By enhancing the interference and noise estimation, we propose a novel MIMO receiver strategy, which is shown to achieve superior performance with moderate increase in computational complexity compared to conventional MIMO detection schemes.

A novel iterative detection scheme for MIMO-OFDM systems is proposed in this work. We show that the existing detection schemes are sub-optimum and the iterative process can be optimized by utilizing the non-circular property of the residual interference after interference cancellation. Results show that the proposed iterative scheme outperforms the conventional iterative soft interference cancellation (ISIC) and V-BLAST schemes by about 1.7 and 4.0 dB, respectively, in a 4 × 4 antennas system over exponentially distributed eleven path channels.

This paper presents a method to significantly reduce the preprocessing complexity of the sphere decoder (SD) in frequency-selective channels. The method consists of calculating an approximate QR decomposition (AQRD) of the channel matrix, making use of its special Toeptliz and block-Topelitz structure in single and multiple-antenna frequency-selective channels, respectively. The AQRD obtains the QR decomposition of a small submatrix of the channel matrix and extends that result to the rest of the matrix, resulting in a considerable complexity reduction compared to the original full QR decomposition (FQRD). Simulation results show that, despite the lower complexity of the AQRD, it causes only a small bit error rate (BER) performance degradation in the SD.

This paper presents a novel spatial frequency domain packet scheduling and frequency domain equalization (FDE) algorithm for uplink Single Carrier (SC) Frequency Division Multiple Access (FDMA) multiuser MIMO systems. Our analysis model is confined to 3GPP uplink SC-FDMA transmission with Multi-user (MU) Spatial Division Multiplexing (SDM). The results show that the proposed MU-MIMO scheduler in conjunction with the new FDE singificantly increases the maximum achievable rate and improves the bit error rate (BER) performance for the system under consideration.

The average channel capacity for 3GPP LTE downlink multiuser Multiple Input Multiple Output (MIMO) systems is analyzed in this paper. A packet scheduler is used to exploit the available multiuser diversity in all the three physical domains (i.e., space, time and frequency). A mathematical model is established to derive the channel capacity of multiuser MIMO systems with the frequency domain packet scheduler (FDPS). This work provides a theoretical reference for the future version of the LTE standard and a useful source of information for the practical implementation of the LTE systems.

Robust adaptive multiuser detection schemes are developed for direct-sequence code-division multiple-access (DS-CDMA) multipath frequency-selective fading channels. Multiple access interference (MAI) and intersymbol interference (ISI) are presented in identical format in the expanded signal subspace, which provides convenience for symbol-by-symbol multiuser detection. The proposed multiuse detectors are designed in the expanded signal subspace, and subspace estimation and Kalman filtering algorithms are developed for their adaptive implementation. It is demonstrated by simulation that these adaptive detectors are robust against subspace estimation error and can effectively suppress both MAI and ISI and converge to the optimum SINR.

A convolutionally coded M-ary orthogonal direct sequence code division multiple access (DS-CDMA) system in time-varying frequency-selective Rayleigh fading channels is considered in this work. We propose several novel soft demodulation algorithms based on interference cancellation and suppression techniques that can be coupled with soft decoding to improve the system performance in an iterative manner. The performance of the proposed demodulation algorithms is evaluated numerically and proved to achieve substantial bit error rate (BER) performance gain compared with the conventional detection schemes.

This letter derives mathematical expressions for the received signal-to-interference-plus-noise ratio (SINR) of uplink Single Carrier (SC) Frequency Division Multiple Access (FDMA) multiuser MIMO systems. An improved frequency domain receiver algorithm is derived for the studied systems, and is shown to be significantly superior to the conventional linear MMSE based receiver in terms of SINR and bit error rate (BER) performance.

The LTE downlink multiuser Multiple Input Multiple Output (MIMO) systems are analyzed in this paper. Two Spatial Division Multiplexing (SDM) multiuser MIMO schemes are investigated: Single User (SU) and Multi-user (MU) MIMO schemes. The main contribution of this paper is the establishment of a mathematical model for the Signal to Interference plus Noise Ratio (SINR) distribution for multiuser SDM MIMO systems with frequency domain packet scheduler.

Recently, full rate and full diversity two-group (2Gp) and four-group (4Gp) decodable space-time block codes (STBC) derived from quasi-orthogonal STBC (QSTBC) and designed under diversity product maximization criterion have been proposed. In this paper, we derive an upper bound of diversity product for those STBCs and discover that the diversity product of the current 2Gp-QSTBC and 4Gp-QSTBC has the potential to approach the upper bound for 8 transmit antennas. To this end, we propose an improved design of 2Gp and 4Gp STBC with increased diversity product for 8 transmit antennas by allowing sufficient number of dimensions for constellation rotation. The diversity product of the proposed two-group decodable STBC achieves the derived upper bound.

In this paper, we propose a novel iterative receiver strategy for uncoded multiple-input, multiple-output (MIMO) systems employing improper signal constellations. The proposed scheme is shown to achieve superior performance and faster convergence without the loss of spectrum efficiency compared to the conventional iterative receivers. The superiority of this novel approach over conventional solutions is verified by both simulation and analytical results.

This paper considers multiuser MIMO CDMA systems with high rate space-time linear dispersion codes (LDC) and orthogonal space-time block codes (O-STBC) in time-varying Rayleigh fading MIMO channels. We propose a multi-function process integrating multi-user detection, space-time decoding and symbol demodulation, which can be coupled with soft channel decoding to improve the system performance in an iterative fashion. We show that the space-time coded CDMA systems approach the single-user bound with only two iterations, and full diversity LDCs enable the systems to utilize the time diversity inherent in fast fading channels. The space-time coded CDMA systems are also compared to the MIMO CDMA system based on spatial multiplexing, some recommendations are made on how to design a practical MIMO CDMA system based on the comparative studies.

The performance of a downlink synchronous MCCDMA system with joint frequency-time domain spreading (MC-2D-CDMA) is investigated in this paper. We propose a two dimensional adaptive minimum mean square error (MMSE) receiver, which works in decision-directed mode after an initial training period. A subcarrier phase tracker, which comprises a bank of phase locked-loops (PLLs), is employed in the receiver to track the fading phase variability. Furthermore, a simplified phase tracker structure is proposed to reduce the system complexity. The performance of the data detector and the behavior of the phase tracker are analyzed theorectically in this paper and are shown to match the simulation results. Both analysis and simulation indicate that the proposed system outperforms the conventional MC-DS-CDMA systems by exploiting frequency diversity and facilitating subcarrier synchronization.

In this paper, we apply the Jacobi iterative algorithm to combat intersymbol interference caused by frequency selective channels. An analytical bound of the proposed equalizer is analyzed in order to gain an insight into its asymptotic performance. Due to the error propagation problem, the potential of this algorithm is not reached in an uncoded system. However, its extension to a coded system with the application of the turbo processing principle results in a new turbo equalization algorithm which demonstrates comparable performance with reduced complexity compared to some existing filter based turbo equalization schemes.

Channel estimation for multiple-input, multiple-output (MIMO) systems is studied in this paper. In particular, we present a simplified MIMO channel estimator based on orthogonal design. The performance of the proposed scheme is theoretically analyzed and compared to that of the optimum maximum likelihood estimator. The effect of non-orthogonality of the training sequences is investigated. Some modifications of the proposed estimator with sample stacking and averaging are introduced to further improve the estimation performance. This simplified scheme is evaluated in the context of the WiMAX MIMO systems in terms of mean square error for the channel estimation and bit error rate for the space-time turbo equalization. Both analytical and simulation results indicate that despite of its low computational complexity, this simplified estimator leads to minimum variance unbiased estimation and achieves identical performance to that of the maximum likelihood estimator.

A novel hybrid multiuser detection scheme that jointly uses linear and nonlinear interference suppression techniques is developed for high-speed direct-sequence code-division multiple-access communications in multipath frequency-selective fading channels. The detector detects signals in a symbol-by-symbol style. Conventional decorrelating detectors suffer from the noise enhancement problem, which becomes more serious for dispersive multipath channels. The proposed detector uses interference cancellation technology to reduce the rank of the expanded signal subspace and hence it preserves the advantages of the expanded decorrelating detector in terms of complete multiple access interference and intersymbol interference suppression and meanwhile avoids its disadvantage in terms of noise enhancement. Computer simulation shows clear superiority of the new detector to other existing methods.

Recently, a single-symbol decodable transmit strategy based on preprocessing at the transmitter has been introduced to decouple the quasi-orthogonal space-time block codes (QOSTBC) with reduced complexity at the receiver . Unfortunately, it does not achieve full diversity, thus suffering from significant performance loss. To tackle this problem, we propose a full diversity scheme with four transmit antennas in this letter. The proposed code is based on a class of restricted full-rank single-symbol decodable design (RFSDD) and has many similar characteristics as the coordinate interleaved orthogonal designs (CIODs), but with a lower peak-to-average ratio (PAR).

In this letter, the performance bound of the IEEE 802.16d channel is examined analytically in order to gain an insight into its theoretical potential. Different design strategies, such as orthogonal frequency division multiplexing (OFDM) and single-carrier frequency-domain equalization (SC-FDE), time-domain decision feedback equalization (DFE), and sphere decoder (SD) techniques are discussed and compared to the theoretical bound.

Broadband fixed wireless access (BFWA) is an ideal solution for providing high data rate communications where traditional landlines are either unavailable or too costly to be installed. In this paper we consider a number of alternative techniques to achieve high data rate and high quality of services requirements in these systems, including orthogonal frequency division multiplexing (OFDM), turbo equalization as well as multiple-input multiple-output (MIMO) techniques. In particular, the frequency domain OFDM scheme and time domain turbo equalization will be studied and compared in a MIMO BFWA context, in an attempt to provide some guidelines on how to design high data rate BFWA applications.

A low complexity transmit diversity scheme is derived in this paper in order to overcome the prohibitive complexity imposed by the maximum likelihood detection for the systems with space-time block code (STBC) over frequency selective channels. By taking advantage of multipath propagation and exploiting temporal diversity gain, the proposed turbo equalization algorithm significantly improves the system performance compared to the original Alamouti algorithm as well as the conventional minimum mean square error (MMSE) detection scheme.

A novel equalization algorithm utilizing improper nature of the intersymbol interference (ISI) is introduced in this paper. We show that full exploitation of the available information on the second-order statistics of the observed signal entails widely linear processing and that previously known linear minimum mean square error (MMSE) equalizers represent sub-optimum solutions. The proposed scheme is generally applicable for both real and complex signal constellations. The results show that accounting for the improper nature of the ISI leads to significant performance gain compared to conventional equalization schemes.

The systems under investigation are broadband fixed wireless access (BFWA) systems operating in multipath fading channels. Conventional detection methods, for example, coherent detection for single-input single-output systems and the Alamouti algorithm for multiple-input multiple-output systems are examined theoretically and shown to yield unsatisfactory performance. The theoretical analyses are validated by Monte-Carlo simulations and are demonstrated to be accurate. The asymptotic performance of the space–time block coded (STBC) system with the–Alamouti transmission scheme is also evaluated. However, the results indicate that the performance lower bound cannot be obtained in an uncoded system due to the error propagation problem, which can be tackled by concatenating the STBC system with an outer channel code and applying the turbo processing principle. Theoretical performance analysis provides an insight into the physical limitations imposed by BFWA channels and suggest solutions to improve the capacity and performance of future BFWA systems.

In this correspondence, we present a new method for the iterative equalization and decoding of multilevel trellis coded modulation (TCM) signals over frequency selective channels. Results show that the proposed algorithm achieves better performance compared to the previous work on the MMSE filter- based turbo equalization for a non-binary coded modulation scheme. The performance gain is accomplished by utilizing the combined modulation and coding nature of TCM and passing the refined signal obtained from different paths to the TCM decoder as the channel value in addition to the a priori probabilities.

The asymptotic performance of the space-time block coded systems using two transmit antennas over a broadband wireless fixed access (FWA) frequency selective channel is studied in this paper. The conducted theoretical analysis gives us an insight into the physical limitations imposed by the FWA channels and suggests solutions to improve the capacity and performance of future FWA systems.

The system under study is a coded asynchronous DS-CDMA system with orthogonal modulation in time-varying Rayleigh fading multipath channels. Information bits are convolutionally encoded, block interleaved, and mapped to M-ary orthogonal Walsh codes, where the last step is essentially a process of block coding. This paper aims at tackling the problem of joint iterative decoding of this serially concatenated inner block code and outer convolutional code and estimating frequency-selective fading channels in multiuser environments. The (logarithm) maximum a posteriori probability, (Log)-MAP criterion is used to derive the iterative decoding schemes. In our system, the soft output from inner block decoder is used as a priori information for the outer decoder. The soft output from outer convolutional decoder is used for two purposes. First, it may be fed back to the inner decoder as extrinsic information for the systematic bits of the Walsh codeword. Secondly, it is utilized for channel estimation and multiuser detection (MUD). We also show that the inner decoding can be accomplished without extrinsic information, and in some cases, e.g., when the system is heavily loaded, yields better performance than the decoding with unprocessed extrinsic information. This implies the need for correcting the extrinsic information obtained from outer decoder. Different schemes are examined and compared numerically, and it is shown that iterative decoding with properly corrected extrinsic information or with non-extrinsic/extrinsic adaptation enables the system to operate reliably in the presence of severe multiuser interference, especially when the inner decoding is assisted by decision directed channel estimation and interference cancellation techniques.

In this paper, we presents a novel method of turbo equalization and decoding multi-level trellis coded modulation (TCM) signals over frequency selective channels. Results show that the proposed algorithm achieves better performance with reduced complexity compared to previous work on the MMSE filter-based turbo equalization for non-binary coded modulation scheme. The performance gain is accomplished by passing the refined signal from different paths to the TCM decoder as channel value in addition to the a prior information. While the computational complexity is reduced by avoiding matrix inversion for each symbol estimate.

In this paper, we provide a theoretical evaluation for the multistage parallel interference cancellation (PIC) scheme in a DS-CDMA system with orthogonal modulation and long scrambling codes. The studied system operates on the reverse link in a time-varying multipath Rayleigh fading channel. Unequal powers are assumed among different paths, which is usually the case in practical situations. The proposed analysis gives insight into the performance and capacity one can expect from the PIC based receivers under different situations.

We present the channel estimation algorithms for the asynchronous direct-sequence code-division multiple access (DS-CDMA) systems employing the orthogonal signalling formats and long scrambling codes. The performance of a communication system depends largely on its ability to retrieve an accurate measurement of the underlying channel. We investigated channel estimation algorithms under different conditions. The estimated channel information is used to enable the coherent data detection to combat the detrimental effect of multipath propagation of the transmitted signal as well as multiple access interference (MAI). Different channel estimation schemes are evaluated and compared in terms of mean square error (MSE) of the channel estimate and the bit error rate (BER) performance. Based on our analysis and numerical results, some recommendations are made on how to choose appropriate channel estimators in practical systems.

In this paper, we present different linear and nonlinear iterative data detection schemes for the asynchronous direct-sequence code-division multiple access (DS-CDMA) systems employing orthogonal signalling formats and long scrambling codes. Compared to the conventional receiver and other noncoherent multiuser detectors, coherent multiuser detection schemes achieve much better performance provided that the channels are accurately estimated. To this end, we proposed several channel estimation algorithms to estimate multipath Rayleigh fading channels. Different data detection and channel estimation schemes are compared in terms of BER performance. Based on the numerical results, some recommendations are made on how to choose multiuser detectors and channel estimation algorithms in practical CDMA systems.

In this paper, we developed several algorithms to combat the impact of synchronization errors on demodulating M-ary orthogonal signaling formats in asynchronous DS-CDMA systems. The system under study resembles the uplink of an IS-95 system. The channel is assumed to be a time-varying flat Rayleigh-fading channel. Investigation shows that synchronization errors severely deteriorate the performance of multi-user detectors. We proposed an adaptive algorithm to estimate the errors in synchronization. Based on this information, remedial actions are taken to alleviate the performance degradation caused by sampling the received signals at the incorrect timing. Simulation results show considerable capacity gains when the proposed algorithms are performed to erroneously sampled signals.