Road transport accounts for approximately 75% of emissions within the transportation sector, highlighting the need not only for cleaner vehicles but also for intelligent, connected infrastructure. Cyber-physical infrastructure (CPI) enables emerging technologies such as intelligent transport systems (ITS) and digital twins (DT), providing a foundation for enhanced planning, decision-making, and real-time optimisation. The effectiveness of DT-enabled ITS depends on reliable, low-latency communication networks like 5G and beyond, which face challenges such as beam blockage due to urban mobility and obstructions. To address this challenge, we propose a configurable simulation framework that models realistic urban scenarios, including connected autonomous vehicles (CAVs) dynamics, traffic congestion, and roadside units (RSUs) deployment strategies. Through three case studies, we examine the influence of traffic density, RSU height, and RSU count on beam blockage events and received signal strength (RSS). Our findings highlight key trade-offs: while taller RSUs reduce beam blockages, they incur greater propagation losses; likewise, denser RSU deployments improve connectivity up to a point, beyond which additional units result in marginal improvements. These insights provide practical guidance for designing resilient, low-latency communication infrastructures and highlight the need for intelligent, adaptive solutions to proactively mitigate blockage events in real time for sustainable and time-sensitive ITS applications.

Modern smart factories accommodate numerous heterogeneous devices, making co-scheduling of Ultra-Reliable Low-Latency Communications (URLLC) and massive MachineType Communications (mMTC) services on shared physical resources are a critical challenge. This paper proposes a resource allocation mechanism for this convergence scenario, combining Signal-to-Noise Ratio (SNR) boosting strategy, flexible numerology configuration, and device priority control. By formulating a multi-objective optimization model that integrates delay constraints, channel conditions, and resource utilization, we design a low-complexity adaptive scheduling algorithm. The simulation results demonstrate that our proposed method improves URLLC schedulability by 19.8% while achieving an average latency of 0.843ms, substantially outperforming the baseline algorithms. This paper provides effective technical support for the establishment of industrial wireless communication systems capable of supporting coexisting Quality of Service (QoS) requirements.

Semantic communication (SemCom), a paradigm central to the next generation task-oriented vision, relies on a shared Knowledge Base (KB) between the transmitter and receiver. However, the prevalent assumption of a static KB is frequently invalidated in dynamic real-world environments, where KB "staleness" precipitates a collapse in semantic efficiency. This introduces a critical trade-off: either continue transmission with a suboptimal, stale KB, or allocate scarce wireless resources to a knowledge consensus protocol to update the KB, thereby incurring a significant opportunity cost. This paper addresses this dynamic resource allocation problem. We are the first to establish a system model that explicitly quantifies knowledge staleness K(t) as a state variable and models the update procedure as a resource-consuming consensus task. We formulate this trade-off as a complex, NP-hard 0-1 Mixed-Integer Non-Linear Program (MINLP), which uniquely incorporates the fixed activation costs associated with initiating the consensus protocol. To solve this intractable problem, we propose a low-complexity online control framework based on model predictive control (MPC), which embeds a novel heuristic algorithm termed iterative marginal cost allocation (IMCA). Simulation results demonstrate that the proposed MPC-IMCA framework significantly outperforms static Greedy and Periodic Update baselines in long-term cumulative semantic utility, exhibiting robust adaptability to varying environmental dynamics (δd) and update workloads (DKB).

Resource allocation in 6G digital twin-enabled Internet of Robotic Things (DT-IoRT) faces critical challenges from limited resources, dynamic demands, and heterogeneous service requirements, while existing solutions lack adaptability, incur high complexity, or fail to capture hierarchical pricing interactions. This paper proposes a learning-driven Stackelberg resource allocation framework for 6G DT-IoRT, where an Edge Base Station (EBS) dynamically optimizes bandwidth pricing using Proximal Policy Optimization (PPO), and digital twin agents optimize bandwidth demands through logarithmic utility maximization in response to announced prices. The pricing problem is formulated as a Markov Decision Process, enabling real-time adaptation without requiring knowledge of follower utility parameters. Simulation results show that the proposed approach improves follower utility by 12.4% and leader profit by 9.7% compared to the best static pricing baseline, while maintaining a Jain’s fairness index of approximately 0.80 across varying system scales, demonstrating its effectiveness and scalability for industrial IoRT scenarios.

Scheduling mission-critical tasks in sixth-generation (6G) vehicular networks over millimeter-wave (mmWave) links is challenging due to dynamic topology, fast-varying channels, and strict latency constraints. This paper addresses these challenges by proposing a lightweight cross-layer heuristic scheduling framework that integrates channel state information (CSI), task priority, and beamforming gain into a unified scoring function. The framework comprises two modules: (i) edge selection to balance load across nodes via link and queue metrics, and (ii) task scheduling to prioritize collision-warning tasks under favorable links. Unlike optimization-based methods, the proposed design enables real-time use with sub-millisecond execution and linear per-slot complexity. Overall, simulations show that 90% of tasks meet 5 ms deadlines, and the Packet Loss Rate (PLR) stays below 20%. The proposed framework reduces latency by 80% and 60%, and PLR by 27.5% and 57.5%, compared to the strategies based on Equal and CSI schemes, respectively. These results confirm the suitability of the framework for vehicular latency and 6G reliability requirements.

Reconfigurable Intelligent Surfaces (RIS) are increasingly viewed as a key technology for re-shaping wireless propagation in 6G networks. While existing work has examined RIS theory, algorithms, and hardware prototypes, far less attention has been given to the simulation tools that support reproducible, deployment-oriented evaluation. This paper presents a systematic literature review (SLR) of RIS simulators with emphasis on their readiness for real-world integration. Following PRISMA 2020 guidelines, we analyze over 310 publications and classify 112 relevant studies into three core dimensions: mobility models, channel estimation techniques, and RIS control strategies. The survey provides a unified taxonomy, evaluates leading simulators and testbeds, and maps their capabilities to six representative deployment scenarios defined by ETSI ISG RIS and ITU IMT-2030. Key findings highlight critical gaps, including the lack of real-time evaluation frameworks, weak support for high-mobility or non-terrestrial settings, and limited mechanisms for multi-RIS coordination. We also identify opportunities for AI-driven RIS control, standardized benchmarking, and simulation-to-hardware integration. By consolidating tools, scenarios, and open challenges, this review offers a practical reference for researchers and engineers aiming to bridge the gap between theoretical RIS models and field-ready

In the past decade, Digital Twins (DTs) have emerged as a key enabler of industry digitalisation. As digital representations of physical objects and processes, DTs integrate a range of technologies to support applications from process monitoring to policymaking. However, increased system integration expands their attack surface, heightening cybersecurity risks. Efforts to integrate DTs into larger ecosystems have further intensified these concerns. Cybersecurity is inherently a socio-technical challenge influenced by various organisational and governance considerations. However, cybersecurity research on DTs has remained predominantly technical. As such, the current understanding of how organisational cybersecurity challenges emerge in DT contexts is limited. This work addresses this gap through a critical analysis of literature, examining how cybersecurity is conceptualised in DT implementation and the extent to which organisational cybersecurity challenges are addressed. Our analysis demonstrates that cybersecurity is widely acknowledged as a challenge in DT implementation. Nevertheless, most research offers limited in-depth analysis of specific cybersecurity challenges in real-world contexts. When addressed, cybersecurity was primarily framed around confidentiality and privacy, while other elements including data integrity and system availability are overlooked. Where cybersecurity has been the primary focus, works have been overwhelmingly technical, overlooking organisational complexities that affect cybersecurity in practice. This highlights a clear gap in understanding of how organisational cybersecurity challenges emerge in DTs. We conclude by outlining an agenda for future research to support more effective and secure approaches to DT implementation.

Human Activity Recognition (HAR) using radar signals has gained significant attention due to its non-intrusive nature and robustness in various environments. However, the impact of radar signal preprocessing techniques on the performance of deep learning (DL) models remains an active area of research. This study investigates how different radar domain representations affect HAR accuracy by evaluating four preprocessing methods: Time-Range (TR) maps generated via Range-Fast Fourier Transform (FFT), Range-Doppler (RD) maps obtained through sequential FFTs, and Time-Doppler (TD) features extracted using Short Time Fourier Transform (STFT) and Smoothed Pseudo Wigner Ville Distribution (SPWVD). We employ a baseline Convolutional Neural Network (CNN) and state-of-the-art Transfer Learning (TL) models to assess whether advanced preprocessing or increased model complexity yields greater performance gains. The results reveal that high-resolution TD analysis using SPWVD does not significantly enhance classification performance and incurs substantial computational overhead, limiting its real-time applicability. Conversely, the TR representation offers computational efficiency but struggles to classify complex activities with the baseline CNN accurately. RD and STFT methods provide a favorable balance between classification accuracy and computational efficiency. Notably, transitioning from the baseline CNN to TL models leads to substantial improvements in recognition accuracy: up to 29.36% for TR, 21.42% for RD, 16.66% for STFT, and 11.11% for SPWVD representations. Overall, our findings demonstrate that TL models, when combined with computationally efficient radar preprocessing techniques like RD or STFT, significantly improve recognition accuracy and generalize well across datasets, as confirmed by evaluation on two publicly available radar-based HAR datasets. Among these, the RD representation combined with VGG-19 yielded the best trade-off between accuracy and latency, achieving a total processing time of 0.91 s per sample for a 10 s activity duration, making it highly suitable for latency-sensitive HAR applications.

Large Language Models (LLMs) have been increasingly applied to intelligent vehicular systems for tasks such as scene understanding, intent reasoning, and natural language interaction. However, their inference demands exceed onboard processing capabilities, making low-latency on-vehicle inference impractical. Although edge computing can partially offload computation, the prolonged nature of LLM inference often causes execution to exceed the residence time of vehicles within edge coverage areas, leading to frequent service interruption. To address these challenges, we propose a collaborative spatiotemporal resource orchestration architecture for LLM inference in vehicular-edge networks (CoInfer). CoInfer exploits the intrinsic decomposability of LLM inference by modeling each request as a Directed Acyclic Graph (DAG) of interdependent subtasks, which are then scheduled, migrated, and aggregated along the road network to preserve end-to-end inference continuity. To improve latency and resource efficiency, CoInfer integrates multi-agent reinforcement learning for coarse-grained task orchestration with a reactive scheduler for fine-grained resource adaptation, forming a closed-loop service optimization under dynamic resource conditions. The simulation results demonstrate that CoInfer achieves a task success ratio of up to 96.0% and reduces the end-to-end inference latency by 35.7% compared to representative baselines.

Semantic communication (SemCom) shifts the focus from bit-level accuracy to the preservation of meaning, enabling more efficient and robust transmission. To achieve a high utilization of the wireless channel in SemCom, in this paper, we propose a channel assignment approach for polar code-based SemCom that allocates polarized channels according to semantic importance. Specifically, by combining eye-tracking data with semantic segmentation, we define two metrics that capture the contribution and correlation of semantic entities within an image. Leveraging these semantic metrics and polarized channel reliabilities, we formulate a constrained 0-1 optimization problem for polarized channel assignment and develop a priority-based algorithm that dynamically prioritizes semantically important content. Simulation results demonstrate that our method significantly outperforms the traditional channel allocation policy, especially under harsh channel conditions, by preserving critical visual information while reducing overall transmission redundancy.

With the rapid increase of connected devices, securing IoT networks against sophisticated cyber threats has become a critical research priority. However, effective intrusion detection in IoT environments is hindered by several core challenges, including severe class imbalance in network traffic, limited availability of annotated data for supervised learning, and the sensitivity of deep learning models to hyperparameter configurations. To address these limitations, we propose a data-driven DL framework that combines data balancing, active learning, and hyperparameter optimization. We employ the proximity-weighted synthetic oversampling technique to mitigate class imbalance by generating weighted synthetic samples. To reduce labeling overhead, we propose an active learning-based, Entropy-based Convolutional Neural Network (EntroConvNet), an intrusion detector for selective annotation of the most uncertain samples. Additionally, a novel Random Search Optimized Convolutional Neural Network (RS-ConvNet) is proposed to maximize detection performance. Experimental results on the TON IoT dataset show that EntroConvNet outperforms the baseline models with improvements of 3.45% in accuracy, 3.57% in precision, 1.10% in recall, 2.30% in F1-score, 3.19% in Area Under the Receiver Operating Characteristics Curve (AUC-ROC), 6.67% in Cohen’s Kappa and Mathews Correlation Coefficient (MCC), and 16.67% reduction in log loss and Hamming loss. Furthermore, RS-ConvNet achieves superior gains of 4.60% in accuracy, 5.95% in precision, 1.10% in recall, 3.45% in F1-score, 2.13% in AUC-ROC, 8% in Cohen’s Kappa and MCC, and also reduces log loss and Hamming loss by 20% and 25%, respectively. These results validate the proposed framework’s ability to deliver accurate, and annotation-efficient intrusion detection systems in dynamic IoT network environments.

The coordinated control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) offers significant advantages in applications such as surveillance, navigation, and emergency response. Effective path planning is essential in such missions, especially in complex environments where UAVs must relay accurate environmental data to assist UGVs. However, in urban environments and disaster zones, wireless communication is often unstable due to severe interference and non-line-of-sight conditions, making it difficult to support timely and accurate path planning for UAV-UGV coordination. To this end, this paper proposes a semantic communication (SemCom) framework specifically designed to enhance the reliability for UAV/UGV cooperative path planning under unreliable wireless conditions. SemCom transmits only key information for path planning, reducing transmission volume without sacrificing accuracy. Based on this framework, a SemCom transceiver is designed to fulfill the requirements of UAV-UGV cooperative path planning. Simulation results show that, compared to conventional SemCom transceivers, the proposed transceiver significantly reduces data transmission volume while maintaining path planning accuracy, thereby enhancing system collaboration efficiency.

With the significant advances in generative AI (GAI) and the proliferation of mobile devices, providing high-quality AI-generated content (AIGC) services via wireless networks is becoming the future direction. However, the primary challenges of AIGC service delivery in wireless networks lie in unstable channels, limited bandwidth resources, and unevenly distributed computational resources. In this paper, we employ semantic communication (SemCom) in diffusion-based GAI models to propose a resource-aware workload-adjustable transceiver (ROUTE) for AIGC delivery in dynamic wireless networks. Specifically, to relieve the communication resource bottleneck, SemCom is utilized to prioritize semantic information of the generated content. Then, to improve computational resource utilization in both edge and local and reduce AIGC semantic distortion in transmission, modified diffusion-based models are applied to adjust the computing workload and semantic density in cooperative content generation. Simulations verify the superiority of our proposed ROUTE in terms of latency and content quality compared to conventional AIGC approaches.

The integration of semantic communication (SemCom) with non-orthogonal multiple access (NOMA) presents a promising approach to enhance spectrum efficiency and system capacity. SemCom focuses on accurate meaning delivery with less bits, while NOMA enables simultaneous access for multiple users on the same frequency, maximizing resource utilization. However, power allocation in NOMA-based SemCom systems is quite challenging as it should accommodate not only channel conditions and interference management but also the characteristics of semantic information and service requirements. In this paper, we investigate the power allocation strategy for NOMA-based SemCom systems, with the aim to maximize system throughput in semantic unit (STU). Successive interference cancellation requirements and resource budgets are taken into account as the constraints. To address this problem, we propose a modified water filling-based algorithm enhancing both STU and fairness. Simulation results demonstrate the superiority of our proposed algorithm in terms of STU performance and fairness compared to two existing baseline strategies.

Semantic communication (SemCom) has been recently deemed a promising technique to shape next-generation wireless networks with a focus on meaning delivery for significant spectrum savings and efficient information exchanges. It is foreseen that device-to-device (D2D) SemCom underlying cellular networks will be a very common and practical architecture, and in this paper, we jointly address the energy efficiency-driven power control and spectrum reuse problems for D2D SemCom networks. Concretely, we first construct a semantic triplet-based transmission model for both cellular and D2D SemCom users. Then, by taking into account each user's SemCom service preference, we leverage a novel metric of semantic value to determine the unique energy efficiency. Next, a corresponding energy efficiency maximization problem is formulated with variables of power and spectrum allocation subject to several SemCom-related and practical constraints. Afterward, we propose an optimal resource management scheme by employing a fractional-to-subtractive transformation approach and developing a threestage method with low computational complexity. Numerical results demonstrate the performance superiority of our proposed scheme in energy efficiency compared with two benchmarks.

Joint stiffness affects over 350 million people worldwide, creating demand for intelligent systems for detection and monitoring. Patients with joint stiffness need continuous rehab guided by experts, highlighting the need for tele-rehabilitation. This work presents a flexible, biodegradable, and economical strain sensor for joint stiffness monitoring, fabricated through a simple, cost-effective method. The sensor uses cotton fabric as a substrate and conductive paste from recycled dry-cell electronic waste for electrodes. A modified interdigitated capacitive (MIDC) structure is used, achieving high sensitivity with a GF value of 1006, response and recovery times of 0.38 sec. On-body testing on wrist, knee, and elbow joints yielded a minimum resolution of 5°. The proposed MIDC strain sensor is ideal for joint stiffness monitoring in tele-rehabilitation.

With the rapid advancement of autonomous driving technology, enhancing the intelligence of individual vehicles and Vehicle-to-Everything (V2X) networking is crucial for improving traffic efficiency and safety. Intelligent Transportation Systems (ITS) face challenges in evolving traditional traffic models, transitioning the focus from human reaction times to vehicle communication and system delays, as exemplified by scenarios like intersection startup delays. This paper presents a comprehensive validation of simultaneous control in Vehicle-to-Infrastructure (V2I) communication. It provides problem mathematical modeling, validation, and quantitative analysis specifically for connected autonomous vehicle lanes at 100% penetration. The real experiment showed that even with identical scenarios, hardware, and communication delays, startup times still varied due to complex internal and external factors affecting command execution. These factors cause cumulative errors over time, diminishing command execution precision despite advanced control algorithms and sensor calibrations. The investigation underscores the imperative for dynamic modifications and systematic recalibrations in control systems of autonomous vehicles, facilitated by fixed infrastructure, to sustain long-term accuracy and effectively navigate real-world complexities.

Recent advances in Byzantine Fault-Tolerant State Machine Replication have led to practical protocols for partially synchronous or asynchronous networks by combining classical methods with modern cryptographic tools like verifiable random functions. Despite improved performance in throughput and latency, these protocols remain limited by the FLP impossibility and the Dwork-Lynch-Stockmeyer bound, tolerating at most ⌊(n − 1) /3⌋ adversaries. In contrast, Proof-of-Work (PoW) achieves up to ⌊(n − 1) /2⌋ fault tolerance under asynchrony, albeit with high energy costs and probabilistic finality. Protocols like Avalanche similarly exceed the ⌊(n − 1) /3⌋ bound by relaxing deterministic guarantees. In this paper, we propose Halpha, a leaderless consensus protocol that tolerates nearly half Byzantine faults with overwhelming probability. Unlike PoW and Avalanche, which provide probabilistic finality post-termination, Halpha relaxes liveness during proposal. It leverages a probabilistic multi-valued validated Byzantine agreement (P-MVBA) to propose transaction sets, ensuring each referenced transaction is seen by at least one honest node. While P-MVBA may halt, it progresses under the intermittent existence of a bounded-delay interval, and ensures consistency with overwhelming probability without synchrony. Halpha then runs multiple asynchronous binary agreement instances, using verifiable random functions to support randomized liveness under Byzantine conditions and achieves finality when synchrony arrives. Experiments show Halpha achieves safety with high probability and low-latency deterministic finality in normal cases.

Ultra-reliable low-latency communication (URLLC) has become essential for mission-critical applications such as autonomous systems, remote surgery, and industrial automation. This growing demand has been a key driver behind the development and deployment of fifth-generation (5G) networks and is influencing the design of future 6G systems. However, dynamic traffic patterns and limited bandwidth, particularly during peak load conditions, lead to network congestion, packet loss, and reduced reliability, undermining URLLC performance. This paper proposes ALPHA-NET (Agentic Latency and Prioritization for High-Availability Networks), an Agentic AI framework that dynamically prioritizes bandwidth allocation to reduce latency and improve reliability in mission-critical 5G/6G networks. The proposed system outperforms traditional bandwidth management strategies by leveraging a decentralized multi-agent architecture and real-time monitoring execution. ALPHA-Net consists of three collaborative agents: a latency monitoring agent, a bandwidth slicing agent, and a traffic forecasting agent. These agents operate through an on-demand, distributed infrastructure to enable real-time decision-making with minimal overhead. Simulation results demonstrate a 62.8% reduction in packet loss and a 22% improvement in critical bandwidth allocation compared to baseline approaches. These findings highlight the potential of intelligent, adaptive coordination for resilient next-generation network management.

The Open Radio Access Network (O-RAN) architecture offers an innovative approach to wireless network design by enabling multi-vendor compatibility and dynamic resource allocation. However, extensive network configurations and high data traffic volumes present significant challenges to its sustainability in terms of energy consumption. Therefore, in this study, we develop a 5G O-RAN traffic steering and sleep mode control system based on Deep Q-Learning (DQN), targeting low-traffic scenarios where significant energy savings can be achieved through selective activation and deactivation of devices. The proposed scheme leverages a deep reinforcement learning algorithm to optimize the mappings from User Equipments (UEs) to Radio Units (RUs), from RUs to Distributed Units (DUs), and from DUs to Centralized Units (CUs), in order to maximize energy savings in O-RAN. Using UE Reference Signal Received Power (RSRP) and RU load levels as input states, the system generates energy-efficient mapping actions for dynamic traffic steering and sleep mode control. The simulation results demonstrate that the proposed mapping methods achieve 5∼12% energy savings compared to the benchmark scenario.

Light-emitting-diode based visible light positioning (VLP) system(s) can provide high-precision positioning accuracy, but require an unobstructed line-of-sight (LoS) environment. Visible light multipath signals are significantly affected by temporal and spatial dispersion, making them less effective for stringent positioning services. This work addresses the LoS limitation by optimizing multipath reflections and creating a sustainable alternative channel, even in a fully blocked LoS scenario. Accordingly, optical intelligent reflecting surfaces (IRS) are proposed to converge incident light toward a desired direction with an optimal energy threshold. Unlike recent multi-step IRS orientation and mismatched alignment techniques for position estimation, this paper aims to improve the positioning accuracy of a deterministic (fixed but unknown) receiver by modeling three types of reflections (i.e., fully diffuse, partially diffuse, and purely directive). In addition, a maximum likelihood estimation technique is proposed to leverage the simplicity and robustness of the received signal strength trilateration and direct positioning techniques within a synchronous indoor VLP system. Our simulation results demonstrate 88% improvement in positioning accuracy for partially diffused reflections over LoS-only case.

State-Of-the-art deep transfer learning methods depend on exhaustive, trial-and-error fine-tuning of pre-trained models—a process that is both computationally expensive and unreliable when data in target domain are scarce. To overcome these limitations, we propose a domain-informed fine-tuning strategy built upon a novel Innately-Intelligent Neural Network (IINN) architecture. Unlike how state-of-the-art deep learning models are heuristically constructed, IINN constructs each layer in a domain informed manner by directly mapping the mathematical operations of analytical equations (e.g., 3GPP propagation models) into it’s network architecture prior to any training. This "innate" design strategy inherently aligns each layer with specific physical parameters, making the model fully interpretable. As a result, we can pre-identify the exact layers associated with parameters that change between source and target domains and fine-tune only those—eliminating the need for iterative layer-by-layer retraining. This targeted fine tuning approach reduces computational overhead and data requirements. We validated IINN on radio-propagation modelling for cellular networks, achieving faster adaptation and higher accuracy than the conventional fine-tuning approach. Experimental evaluations demonstrate that our proposed domain-aware transfer learning framework achieves up to 16.4% improvement in sector-based performance and approximately 10.3% gain in adapting to varying base station heights, with overall average gains in the 10–15% range over state-of-the-art DNN transfer learning approaches. The proposed framework offers a promising direction for data-efficient learning in next-generation wireless systems.

Multi-modal image fusion, particularly between infrared (IR) and visible (VIS) images, integrates complementary information from diverse imaging sources to enhance perception in applications like autonomous driving and surveillance. While IR images capture thermal radiation and VIS images provide rich texture details, existing fusion methods face challenges in preserving infrared thermal signatures under high illumination, where overexposed VIS regions dominate fusion outputs. To address the above problems, we propose XOR-Fuse, a novel logical operation-driven fusion framework that explicitly captures complementary pixel-level discrepancies between IR and VIS modalities. The XOR-Fuse defines the pixel-wise analogy for the logical XOR operation, which rectifies the suppression of IR features caused by conventional maximum-intensity fusion rules under high illumination. To further reinforce IR feature preservation, we integrate multi-scale Gabor wavelet filtering and wavelet decomposition for illumination-invariant texture extraction and VGG-based semantic constraints, ensuring structural congruence between IR and VIS details. Experiments on MSRS, RoadScene, and TNO datasets demonstrate significant improvements over four baseline models (DeepFuse, SDNet, U2Fusion, and DATFuse). For instance, the enhanced DeepFuse achieves SD=48.27 and VIF=0.62 on MSRS, outperforming the original model (SD=33.79, VIF=0.42). Qualitative results under variable illumination confirm the recovery of suppressed IR details while retaining VIS textures.

With Internet of Vehicles and advanced onboard computing, connected autonomous vehicles (CAVs) can interact and process driving data in real time, enhancing safety and improving road efficiency. However, in mixed-autonomy traffic, the unpredictability of human-driven vehicles (HDVs) poses significant challenges for CAV control. Moreover, existing cooperative control methods often assume seamless real-time information sharing, leading to excessive communication demands that strain limited transmission resources. To address these issues, we propose a lightweight Digital Twin (DT) framework that models the traffic environment and surrounding vehicle behaviors to reduce uncertainty in CAV decision-making. The framework employs an attention-based multi-agent deep reinforcement learning method, enabling each CAV to dynamically adjust its communication frequency with neighboring vehicles according to the significance of their observations for its driving control decisions. Asynchronous updates in the DT space allow CAVs to expand their situational awareness, enabling lightweight coordination that mitigates HDV disturbances and fosters swarm intelligence. Simulations show our scheme improves traffic stability and achieves 30% faster high-speed flow than baselines, while maintaining strong performance under low bandwidth.

Decarbonizing the residential sector is challenging due to the complexities in balancing energy consumption optimisation, occupant comfort, and health. Digitalization has emerged as a critical enabler to address these challenges, which could offer data-driven insights to realize this balance and achieve scalable impact, which many existing studies lack. This paper introduces an integrated Cyber-Physical and AI-driven methodology to digitalize Scottish Social housing and tackle these challenges. Our approach employs a low-power Long Range Wide Area Network (LoRaWAN) based internet of Things (IoT) architecture, combining smart plugs, clamp sensors, and environmental sensors to monitor CO2, temperature, humidity, energy usage at the appliance level, and other critical parameters. Predictive analytics on air quality and energy patterns are also enabled, delivering actionable insights for retrofitting and behavioral optimization. A case study conducted in a Scottish house demonstrated the ability of the proposed system to identify energy inefficiencies. Results show that the system effectively identified energy waste: an average of approximately 30-60% of energy being consumed during unoccupied periods, across multiple appliances. Furthermore, the system achieved over 95 % accuracy in predicting CO2indoor ppm, enabling relevant proactive actions to future unhealthy air quality conditions. Furthermore, data analytics on thermal fluctuations enabled the identification of poorly insulated rooms, providing insights for actionable retrofitting strategies. The findings highlight the system's ability to reduce energy waste, lower operational costs, and optimize retrofit investments, offering a scalable pathway to achieve low-carbon living standards.

This study presents a machine learning (ML)-based localization method for improving location estimation accuracy in wireless networks, especially in challenging environments where traditional techniques often fall short. Conventional methods rely on a limited number of multipath components (MPCs), leading to inaccurate localization in complex environments. By leveraging a novel dataset generated from ray-tracing simulations in urban and campus environments, we propose a deep neural network (DNN)-based method that incorporates rich channel metrics such as angle of arrival (AoA), time of arrival (ToA), and received signal strength (RSS). The DNN is trained on diverse scenarios, including both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions, and outperforms traditional MPC-based methods, reducing localization error by up to 20%. Our approach challenges the conventional use of only 3 MPCs for localization and demonstrates that a larger number of MPCs enhances accuracy, particularly in urban and obstructed environments. This research provides important insights into the potential of ML-driven solutions for improving localization accuracy in next-generation wireless systems , such as 5G and beyond.

In the mobile network, digital twin (DT)-aided artificial intelligence (AI)-empowered link control is vital to enhance the performance of wireless communication. This paper proposes a deep reinforcement learning (DRL)-convex optimization enhanced time-frequency domain power allocation scheme to reduce the long-term average bit error rate (BER) in multi-user orthogonal frequency division multiplexing (OFDM) systems. To alleviate performance loss caused by trial-and-error during the training period of DRL algorithms, we design a novel practical DT-aided “prediction-then-decision” autonomous wireless link control framework considering the periodic interaction mechanism between the DT and its physical counterpart. A Transformer-based channel generator Mucomformer is implemented in the DT layer to generate large amounts of multi-user virtual channel state information (CSI) in future transmission frames. In addition, the DRL agent is trained over the DT channel in advance and executed in the real-world OFDM system to generate the optimal transmission strategy by considering the interaction mechanism between the DT and the physical counterpart. The simulation results demonstrate that the proposed Mucomformer has lower average prediction error of 2.51 dB compared to the Transformer baseline. The DRL and convex-based power allocation scheme further outperforms the classic strategy. Moreover, the practical DT-aided autonomous link control framework effectively mitigates the performance impairment, achieves an average BER performance gain 45.65% higher than that without DT and achieves faster convergence during the whole training period.

Compared to broader physical activities, detecting nuanced respiratory movements poses a significant challenge in indoor health monitoring systems. While respiratory activity can be conceptualized as periodic chest movements akin to mechanical vibrations, uncontrollable environmental factors often introduce noise into detected radar signals. The clutter in the field of view, especially metallic objects, such as hospital steel beds, degrades the performance of radar physiological monitoring: 1) amplifying noise of multipath effects and 2) misleading the informative localization module. In this article, we propose a preprocessing scheme of Search-Voxel Ellipse Normalization for respiratory detection system, including an ellipse normalization method combined with the fitting-cost voxel selection policy, to improve the respiration detection performance using MIMO frequency modulated continuous wave radar. This article provides an in-depth assessment of the designed system, including a metal-insulated room test, involving ten participants in different postures. The results show notable performance improvements of our proposed ENDTW-MVMD method, especially in lowering the mean absolute error from the best state-of-the-art 0.93–0.75 bpm and stabilization in voxel selection. The proposed approach is thoroughly evaluated against established methods across various dimensions, such as voxel selection, independent performance, frequency estimation, and ablation studies.

Intelligent transportation systems increasingly depend on wireless communication for broadcasting traffic messages and facilitating real-time vehicular communication. In this context, message authentication is crucial for establishing secure and reliable communication. However, security solutions must consider the dynamic nature of vehicular communication links, which fluctuate between line-of-sight (LoS) and non-line-of-sight (NLoS) due to obstructions. This paper proposes a lightweight cross-layer authentication scheme that employs public-key infrastructure (PKI)-based authentication for initial legitimacy detection/handshaking while using key-based physical-layer re-authentication for message verification. This approach reduces signature generation and signaling overheads associated with each transmission, thereby enhancing network scalability. However, the receiver operating characteristic (ROC;Pd: detection vs.PFA: false alarm probabilities) of the latter decreases with lower signal-to-noise ratio (SNR). To address this, we investigate the use of reconfigurable intelligent surfaces (RISs) to strengthen the SNR directed toward the designated vehicle in shadowed areas (i.e., NLoS scenarios), thereby improving the ROC. Theoretical analysis and practical implementation are conducted using a 1-bit RIS consisting of 64×64 reflective metasurfaces. Experimental results show a significant improvement inPd, increasing from 0.82 to 0.96 at SNR = −6 dB for an orthogonal frequency-division multiplexing (OFDM) system with 128 subcarriers. We also conducted informal and formal security analyses using Burrows-Abadi-Needham (BAN) logic to prove the scheme’s ability to resist passive and active attacks. Furthermore, the proposed scheme reduces computational and communication overheads by 43% and 13%, respectively, compared to traditional cryptographic methods, demonstrating its superiority for real-time, challenging communication scenarios.

This paper proposes a novel joint transceiver optimization framework for multi-carrier (MC) waveform design. Unlike conventional orthogonal frequency division multiplexing, which employs memoryless modulation and fixed inverse discrete Fourier transform-based waveform generation, our approach utilizes neural network (NN)-based modulation with memory and NN-driven waveform generation at the transmitter. On the receiver side, a large-kernel attention-based NN replaces the traditional demodulation process, effectively mitigating large-span inter-carrier interference. This architecture provides enhanced flexibility for MC waveform optimization, allowing better adaptation to spectral emission mask constraints and maximizing the utilization of allocated spectrum resources. Additionally, it achieves significant spectral efficiency gains across diverse channel conditions, including additive white Gaussian noise (AWGN) and linear time-varying (LTV) channels with delay and Doppler spread. Numerical evaluations demonstrate significant bit error rate performance improvements, with up to 10 dB signal-to-noise ratio gain in LTV channels and approximately 6 dB gain in AWGN channels, underscoring the superiority of the proposed framework over state-of-the-art schemes.

Several wireless networking problems are often posed as 0-1 mixed optimization problems, which involve binary variables (e.g., selection of access points, channels, and tasks) and continuous variables (e.g., allocation of bandwidth, power, and computing resources). Traditional optimization methods as well as reinforcement learning (RL) algorithms have been widely exploited to solve these problems under different network scenarios. However, solving such problems becomes more challenging when dealing with a large network scale, multi-dimensional radio resources, and diversified service requirements. To this end, in this paper, a unified framework that combines RL and optimization theory is proposed to solve 0-1 mixed optimization problems in wireless networks. First, RL is used to capture the process of solving binary variables as a sequential decision-making task. During the decision-making steps, the binary (0-1) variables are relaxed and, then, a relaxed problem is solved to obtain a relaxed solution, which serves as prior information to guide RL searching policy. Then, at the end of decision-making process, the search policy is updated via suboptimal objective value based on decisions made. The performance bound and convergence guarantees of the proposed framework are then proven theoretically. An extension of this approach is provided to solve problems with a non-convex objective function and/or non-convex constraints. Numerical results show that the proposed approach reduces the convergence time by about 30% over B&B in small-scale problems with slightly higher objective values. In large-scale scenarios, it can improve the normalized objective values by 20% over RL with a shorter convergence time.

Recently, semantic communication (SemCom) has shown great potential in significant resource savings and efficient information exchanges, thus naturally introducing a novel and practical cellular network paradigm where two modes of SemCom and conventional bit communication (BitCom) coexist. Nevertheless, the involved wireless resource management becomes rather complicated and challenging, given the unique background knowledge matching and time-consuming semantic coding requirements in SemCom. To this end, this paper jointly investigates user association (UA), mode selection (MS), and bandwidth allocation (BA) problems in a hybrid semantic/bit communication network (HSB-Net). Concretely, we first identify a unified performance metric of message throughput for both SemCom and BitCom links. Next, we specially develop a knowledge matching-aware two-stage tandem packet queuing model and theoretically derive the average packet loss ratio and queuing latency. Combined with practical constraints, we then formulate a joint optimization problem for UA, MS, and BA to maximize the overall message throughput of HSB-Net. Afterward, we propose an optimal resource management strategy by utilizing a Lagrange primal-dual transformation method and a preference list-based heuristic algorithm with polynomial-time complexity. Numerical results not only demonstrate the accuracy of our analytical queuing model, but also validate the performance superiority of our proposed strategy compared with different benchmarks.

Contactless vital signs detection has the potential to advance healthcare by offering precise and convenient patient monitoring. This groundbreaking approach not only streamlines the monitoring process, but also allows continuous, real-time assessment of vital signs, allowing early detection of anomalies and prompt intervention. This paper presents a novel framework for contactless vital sign s detection using continuous-wave (CW) radar and advanced signal processing techniques. We achieved unprecedented precision in capturing 1,261 samples for radar based heart sound waveforms compared to the ground truth ECG signal. Further, our heart sounds method yields highly accurate human heart pulse readings, surpassing previous benchmarks with a mean absolute percentage error (MAPE) of 0.0129 and mean absolute error (MAE) below one (0.8712). In addition, we derive d heart rates from the heart sound waveforms and compare them with conventional radar-derived heart rates and ground truth ECG signal. Through analysis, we identifi ed regions where conventional radar based methods exhibit limitations. Our approach demonstrates minimal errors and superior accuracy across all heart rate states, which can potentially set new standards for noninvasive vital sign monitoring.

As vehicle intelligence technology advances rapidly, the need for unrestricted, fully autonomous driving intensifies. Relying solely on single vehicle intelligence is no longer sufficient to meet the requirements high-level autonomous driving. Issues such as sensor range and occluded areas urgently require the assistance of roadside infrastructure. However, the lack of consensus or clear solutions and testing methods on how to unify heterogeneous sensors and information amplifies the urgency of our proposed solution. Integrated sensing and communication (ISAC) has already become a key candidate technology for 6G, and integrated sensing, communication, and computing (ISCC) has also been frequently mentioned recently. Therefore, this paper aims to coordinate the intelligence of vehicles and roads to establish and validate integrated intelligence, including integrated sensing, communication, computing and control (IS3C). Our research takes a safety-centric approach, using hazard degree as a unifying feature for heterogeneous devices and information. We integrate sensors, roadside units (RSUs), and mobile edge computers (MECs) into roadside infrastructure. By combining the sensor-set, local dynamic maps (LDMs), Vehicle-to-Everything (V2X), Infrastructure-to-Everything (I2X), edge computing, and centralized control, we have developed a practical IS3C structure for intelligent transportation systems (ITS). This structure has been modeled and deployed step-by-step on real robots, and its feasibility has been demonstrated through experiments, meeting the latency requirements for ITS. Additionally, our research leverages the advantages of IS3C, particularly “resource aggregation” by utilizing real-time environmental judgments and maximizing the potential of short-term historical information. We propose a new topic for future researchers studying ITS integrated control based on these integrated advantages.

With the significant advances in AI-generated content (AIGC) and the proliferation of mobile devices, providing high-quality AIGC services via wireless networks is becoming the future direction. However, the primary challenges of AIGC services provisioning in wireless networks lie in unstable channels, limited bandwidth resources, and unevenly distributed computational resources. To this end, this paper proposes a semantic communication (SemCom)-empowered AIGC (SemAIGC) generation and transmission framework, where only semantic information of the content rather than all the binary bits should be generated and transmitted by using SemCom. Specifically, SemAIGC integrates diffusion models within the semantic encoder and decoder to design a workload-adjustable transceiver thereby allowing adjustment of computational resource utilization in edge and local. In addition, aresource-aware workloadtrade-off (ROOT) scheme is devised to intelligently make workload adaptation decisions for the transceiver, thus efficiently generating, transmitting, and fine-tuning content as per dynamic wireless channel conditions and service requirements. Simulations verify the superiority of our proposed SemAIGC framework in terms of latency and content quality compared to conventional approaches.

Semantic communication (SemCom), a paradigm that emphasizes conveying the meaning of information, faces challenges in precise reasoning in semantic coding models. Knowledge graphs (KGs) offer a potential solution by providing structured triples (entities and relations), enabling inference via entity attributes and relational logic. Several key challenges exist in leveraging KGs within SemCom. The first challenge lies in developing methods to create semantic representations aligning and integrating source data and KG information. Second, reconstructing the original data using KGs becomes challenging particularly under poor communication conditions. Moreover, integrating KGs with source data inevitably increases the transmission overhead. In this paper, we propose a novel SemCom framework named KG-SemCom with sophisticated KG-based semantic encoding and decoding designs to solve these challenges. This framework aligns KG entities with message tokens, and then encodes messages into a semantic fusion of contextual and knowledge-based information. Furthermore, KG-SemCom can utilize the KG and contextual relationships to assist in predicting incomplete or distorted messages during the decoding process. Finally, simulation results demonstrate that KG-SemCom achieves higher accuracy and greater robustness compared to existing benchmarks without incorporating KGs, especially in challenging communication environments.

The advent of Federated Learning (FL) empowers IoT devices to collectively train a shared model without local data exposure. In order to address the issue of Non-IID that causes model performance degradation, the recently proposed federated codistillation framework has shown great potential. However, due to the system heterogeneity of devices, the federated codistillation framework still faces a synchronization barrier issue, resulting in a non-negligible waiting time with a fixed computation amount (epoch or batch size) assigned. In this paper, we propose Adaptive Computation Amount Allocation (ACAA) to accelerate federated codistillation. Specifically, we leverage a criterion, solution inexactness, to quantify the computation amount. We dynamically adjust the solution inexactness of devices based on their computing power and bandwidth to enable them nearly simultaneous completion of training, reducing synchronization waiting time without sacrificing the training performance. The minimum required computation amount is determined by the coefficient of the distillation term and the gradient dissimilarity bound of Non-IID. We theoretically analyze the convergence of ACAA. Extensive experiments show that, compared to benchmark algorithms, ACAA can accelerate training by up to 5×.

In a previous paper, we have shown that a recurrent neural network (RNN) can be used to detect cellular network radio signal degradations accurately. We unexpectedly found, though, that accuracy gains diminished as we added layers to the RNN. To investigate this, in this article, we build a parallel model to illuminate and understand the internal operation of neural networks (NNs), such as the RNN, which store their internal state to process sequential inputs. This model is widely applicable in that it can be used with any input domain where the inputs can be represented by a Gaussian mixture. By looking at RNN processing from a probability density function (pdf) perspective, we are able to show how each layer of the RNN transforms the input distributions to increase detection accuracy. At the same time we also discover a side effect acting to limit the improvement in accuracy. To demonstrate the fidelity of the model, we validate it against each stage of RNN processing and output predictions. As a result, we have been able to explain the reasons for RNN performance limits with useful insights for future designs for RNNs and similar types of NN.

Predicting signal blockages in millimetre-wave and terahertz networks is essential for enabling proactive handover (PHO) and ensuring seamless connectivity. Existing approaches utilising deep learning, multi-modal vision and wireless sensing data primarily depend on centralised model training. Although these techniques are effective, they come with high communication costs, inefficient bandwidth usage, and latency issues, which restrict their real-time applicability. This paper proposes a Semantic-Aware Federated Blockage Prediction (SFBP) framework, leveraging the lightweight computer vision technique MobileNetV3 for edge-based semantic extraction, lowering communication and computation costs. Furthermore, we introduce a Similarity-Driven Federated Averaging (SD-FedAVG) mechanism to enhance the robustness of the model aggregation process, effectively mitigating the impact of noisy updates and adversarial attacks. Our proposed SFBP framework achieves 97.1% blockage prediction accuracy, closely matching centralised learning methods, while reducing communication costs by 88.75% compared to centralised learning and by 57.87% compared to FL without semantic extraction. Moreover, on-device inference reduces the latency by 23% compared to centralised learning and 18% compared to FL without semantic extraction, improving real-time decision-making for PHO. Additionally, the SD-FedAVG mechanism improves prediction accuracy under noisy conditions, directly impacting the PHO by reducing the handover failure rate by 7%.

Vehicle-to-grid (V2G) energy trading based on distributed ledger technologies (DLT), such as blockchains, has attracted much attention due to its promising features, including ease of deployment, decentralization, transparency, and security. However, existing DLT-based models do not support microtransactions due to the low value of such transactions relative to the incentives offered to transaction verifiers. To address this issue, we propose an IOTA DLT-based efficient and secure energy trading model for V2G networks, where electric vehicles (EVs) and grids negotiate energy prices in an off-chain manner. The proposed model utilizes a privacy-preserving protocol to prevent real-time tracking of EV locations. We develop a Stackelberg game model to represent the interactions between the EVs and grids, from which we derive a pricing scheme and propose a deposit mechanism to prevent fake energy trading between the EVs and grids. Extensive simulations demonstrate that our proposed scheme outperforms existing V2G energy trading mechanisms regarding transaction efficiency, provides enhanced EV privacy, and improves resilience against fake energy trading. Offering robust computational performance and addressing computational complexity (time, space, and message), our model presents a comprehensive V2G energy trading solution, balancing efficiency, security, and privacy.

Abstract Unmanned aerial vehicles (UAVs), also known as drones, have gained significant attention in recent years due to a wide range of military as well as civilian applications. Due to high mobility, flying at different altitudes and in different environments, their design, structure, size, weight, connectivity, coverage, communication, and various other factors are of critical importance. UAV communication systems should be able to incorporate seamless connectivity, wide coverage, high-quality signal, and operation over a wide range of frequencies. Thus, antennas are critical to enhance received signal quality and to extend UAV coverage. Reconfigurable Intelligent Surfaces (RISs) and antennas have drawn significant attention recently due to their enormous potential in wireless communication systems and have been widely studied for UAVs too. This paper presents a comprehensive survey of reconfigurable intelligent surfaces and antennas and their deployment in UAVs. It also presents a critical analysis and evaluation of the research work conducted on reconfigurable antennas in UAVs. Challenges and future research directions have also been presented. Lastly, some significant UAV applications and prospects of UAV-based communication combined with modern technologies have been discussed.

This paper jointly investigates user association (UA), mode selection (MS), and bandwidth allocation (BA) problems in a novel and practical next-generation cellular network where two modes of semantic communication (SemCom) and conventional bit communication (BitCom) coexist, namely hybrid semantic/bit communication network (HSB-Net). Concretely, we first identify a unified performance metric of message throughput for both SemCom and BitCom links. Next, we comprehensively develop a knowledge matching-aware two-stage tandem packet queuing model and theoretically derive the average packet loss ratio and queuing latency. Combined with several practical constraints, we then formulate a joint optimization problem for UA, MS, and BA to maximize the overall message throughput of HSB-Net. Afterward, we propose an optimal resource management strategy by employing a Lagrange primal-dual method and devising a preference list-based heuristic algorithm. Finally, numerical results validate the performance superiority of our proposed strategy compared with different benchmarks.

Gesture-based teleoperation is a complex and essential task that enables remote object manipulation. Recent advancements in 3D human hand pose estimation, driven by affordable depth cameras, have proven its aptitude for this task. However, while previous vision-based approaches focus on mapping hand posture to end-effectors, they overlook the interaction between the robot and the object. This leaves a challenge in interpreting these hand joint estimates into practical robotic behaviour. In this paper, we propose a method that leverages the geometric information of the human hand to enable robots to perform human-like grasping and manipulation. Our approach incorporates a pointcloud-based hand joint regressor and the grasping direction analysis (GDA) to control the robot. The joint-wise regressor showed an improved mean joint error of 7.8mm on the MSRA dataset compared to the 8.5mm baseline. We demonstrate that the GDA-based teleoperation can successfully perform real-time robotic manipulator controlling and grasping for various tasks.

Teleoperation can be very difficult due to limited perception, high communication latency, and limited degrees of freedom (DoFs) at the operator side. Autonomous teleoperation is proposed to overcome this difficulty by predicting user intentions and performing some parts of the task autonomously to decrease the demand on the operator and increase the task completion rate. However, decision-making for mode-switching is generally assumed to be done by the operator, which brings an extra DoF to be controlled by the operator and introduces extra mental demand. On the other hand, the communication perspective is not investigated in the current literature, although communication imperfections and resource limitations are the main bottlenecks for teleoperation. In this study, we propose an intelligent mode-switching framework by jointly considering mode-switching and communication systems. User intention recognition is done at the operator side. Based on user intention recognition, a deep reinforcement learning (DRL) agent is trained and deployed at the operator side to seamlessly switch between autonomous and teleoperation modes. A real-world data set is collected from our teleoperation testbed to train both user intention recognition and DRL algorithms. Our results show that the proposed framework can achieve up to 50% communication load reduction with improved task completion probability.

Blockchain is an eminent technique to enhance the safety and robustness of the Internet of Things (IoT) network, due to its traits of decentralisation and transparency. Practical Byzantine Fault Tolerance (PBFT) blockchain consensus mechanism is well suited for wireless networks because of its low-computing requirement, low latency and high throughput. In this paper, we investigate the implementation of the base station (BS)-enabled wireless PBFT network, where the inter-node communications go through the BS in the normal case operation. The performance under such a scheme is analysed and evaluated through three metrics: consensus success probability, communication complexity, and average node transmit power. Results show that the proposed framework achieves higher scalability, lower communication complexity, and lower average node transmit power.

Emergency communication is critical but challenging after natural disasters when the ground infrastructure is devastated. Unmanned aerial vehicles (UAVs) have enormous potential for agile relief coordination in such scenarios. However, effectively leveraging UAV fleets poses additional challenges, in terms of security, privacy, and efficient collaboration across response agencies. This paper presents a robust blockchain-enabled framework to address these challenges by integrating a consortium blockchain model, smart contracts, and crypto-graphic techniques to securely coordinate UAV fleets for dis-aster response. Specifically, we make two key contributions: a consortium blockchain architecture for secure and private multi-agency coordination and an optimized consensus protocol balancing efficiency and fault tolerance using a delegated proof of stake practical Byzantine fault tolerance (DPoS-PBFT). Com-prehensive simulations show the framework's ability to enhance transparency, automation, scalability, and cyber-attack resilience for UAV coordination in post-disaster networks.

In the rapidly evolving landscape of wireless networks, accurate and resilient propagation models are essential to achieve optimal performance and reliability. This paper presents a novel domain-aware framework for interpretable and resilient propagation models. The proposed approach represents an innovative architecture framework that is not only interpretable but can also deal with training data size scarcity. Bridges domain knowledge with machine learning. The proposed approach leverages a combination of domain expertise, analytical modeling, and customized neural networks to construct interpretable models that excel in both identical distribution and non-identical distribution test-train dataset scenarios. Through a comprehensive analysis, we demonstrate the proposed approach's ability to adapt and refine models in response to real-world variations, ensuring consistent, high-quality performance. The proposed framework not only enhances our understanding of complex systems but also paves the way for the creation of digital twins for wireless networks. Furthermore, the root mean square error of the performance metric for the proposed approach is reported as 6.97 dB, further confirming its effectiveness in accurately predicting the results of wireless propagation.

Millimeter wave (mmWave) bands come with a deployment challenge of signal degradation when an obstacle blocks the line of sight (LOS) link. This paper proposes an intra-cell proactive handover (PHO) framework utilizing links from an intelligent reflective surface (IRS) to assist a 60GHz mmWave transmitter. Empowered by vision-aided wireless communication (VAWC) the PHO is aiming to replace a blocked LOS link with an IRS-assisted link. Evaluation of IRS-assisted link performance during the HO scenario is conducted to establish a solid understanding of deployment requirements and limitations. Estimations of the received signal strength indicator (RSSI) were performed to compare IRS-assisted links in the blocked area and LOS links in the absence of a blockage event. Results showed that for an IRS to provide a comparable signal level to the original LOS link, beam focusing must be the operating mode. IRS-assisted PHO scenarios were evaluated based on a range of IRS elements (64, 100 and 1000) to compare between the two links. Signal drop was between 30 to 15 dBm depending on the number of IRS elements and user location. The gap in signal level was further reduced to 10–5 dBm by increasing the number of antenna elements in the uniform linear array (ULA) sector transmitting to the IRS. Finally, the results showed that a HO to a 30GHZ IRS-assisted link with 64 ULA antenna elements and 1000 IRS elements will perform comparably to the LOS signal strength.

The millimetre-wave (mmWave) based frequency-modulated continuous wave (FMCW) radar is a pioneering technique for non-invasive vital sign monitoring, specifically designed for integration within 5G/Beyond 5G (B5G) network environments. In this study, we focus on evaluating the accuracy of the FMCW radar in measuring heart rate (HR) and respiratory rate (RR) at various heights relative to the subject's chest. To harness the low-latency benefits of advanced wireless technologies, the mmWave radar operates in tandem with the real-time data-capture adapter (DCA1000) evaluation module, facilitating the high-speed transmission of vital sign data. We employed two signal-processing methodologies, fast fourier transform (FFT) and peak count, to analyze and validate the radar's precision and consistency against reference sensors. Our results underscore the effectiveness of the peak count method, which demonstrates superior accuracy, with mean absolute error (MAE) values of 5.33 breaths/min for RR and 4.03 beats/min for HR, along with root mean square error (RMSE) values of 5.30 breaths/min for RR and 4.30 beats/min for HR, consistent across all tested heights. The proposed system significantly enhances the reliability and responsiveness of next-generation healthcare systems, offering a robust solution for patient monitoring in various interactive and intelligent real-time settings.

The research investigates the data rates of a wide-band multi-user system by optimizing the phases of the reconfigurable intelligent surfaces (RISs) elements and performing fair power allocation for subcarriers. A practical beamforming codebook is developed to select the RIS configuration that maximizes the signal-to-noise ratio (SNR). This guides the iterative power and semidefinite relaxation (SDR) algorithms for finding the optimal RIS configuration. The iterative power method is validated by comparing the achievable data rate and the computational complexity with the existing techniques in the literature. Simulation results revealed that the data rate performance of our proposed iterative power method is comparatively close to the well-known approaches such as the SDR and the strongest tap maximization (STM). Notably, our method exhibits superior performance to STM in non-line-of-sight (NLoS) channels, while also maintaining lower computational complexity compared to SDR.

The rise of connected vehicular networks (CVNs) holds promise for future intelligent transport systems, offering improvements in safety and road efficiency. CVNs face challenges due to data-driven perception and driving models, requiring extensive knowledge to navigate complex scenarios. In vehicular networks, federated learning (FL) is vital for privacy-preserving machine learning (ML). It allows collaborative training of a single ML model across edge devices while keeping data locally, preserving privacy. However, scalability remains a challenge, especially for large ML models, and can yield suboptimal results when local data distributions diverge. We present a robust and efficient Fed-aided multi-task temporal clustering (FeMTC) knowledge-sharing framework tailored to the demands of highly distributed vehicular networks. Our approach quantifies the temporal similarity between a pair of client vectors to group clients with higher similarity at the edge-base server and trains independently on single and multi-task cluster learning. Experiments show that FeMTC achieves faster convergence and up to 15% better performance than existing methods in some scenarios. It easily combines with other methods for improved performance and exhibits robust gains in various non-independent and identically distributed (non-IID) scenarios.

This paper introduces a compact dual-polarized antenna array designed for the 28/38 GHz frequency bands. The dimensions of the antenna array are 32×20 mm2, offering two broad impedance bandwidths: 2.7 GHz (26.6‒29.3 GHz) and 5.2 GHz (35‒40.2 GHz). To achieve polarization diversity, the antenna is excited by two orthogonal feed lines. Each element of the antenna array comprises a fractal radiating patch, enabling multiple resonances, and a rectangular slot in the radiating patch, enhancing the antenna's impedance bandwidth. The proposed antenna array exhibits unidirectional radiation patterns with gains of 13 dBi and 12.5 dBi at the lower and upper frequency bands, respectively. Furthermore, the isolation between the ports exceeds −10 dB for both operating bands. With its simple structure, compact size, and high gain, the proposed antenna is deemed suitable for integration into 5G communication systems.

The asymptomatic nature of cancer in its early stages has been a major problem in medical research. Terahertz (THz) radiation has great potential in cancer detection due to its outstanding properties of non-ionization, non-invasion, and accuracy. As an auspicious THz radiation source, the photoconductive antenna (PCA) is now becoming adopted in investigating and analyzing THz applications. In this paper, a fabricable novel structure of THz PCA working on THz band is proposed. The design of plasmonic contact fingers is applied to the antenna gap in order to improve the efficiency by enhancing the absorption of surface current, leading to a larger THz radiation. The proposed design is analysed and compared to the conventional design, indicating that the directivity and E-field magnitude of the proposed work are significantly improved in range of 0.3 to 0.6 THz, up to 7.33 dBi and 20.33 V/m. The E-field distribution inside the structure and at the electrodes-substrate interface is provided to show the superior optical absorption by the plasmonic contact fingers. The proposed PCA fulfils the requirements of cancer detection and other medical biosensing scenarios.

This paper presents the RF performance analysis of various designs of RF - MEMS shunt capacitive switches in coplanar configuration. The switches consist of slotted bridges of a variety of shapes such as circular, hexagonal, and rectangular. The design consisting of the rectangular slotted bridge exhibits the best performance with a maximum insertion loss of 0.08 dB, return loss of greater than 20 dB and isolation of more than 30 dB. The design has an operational frequency range from DC-40 GHz. The designs are modelled and simulated using High Frequency Structure Simulator (HFSS).

This paper presents a broadband circularly polar-ized (CP) antenna array for millimeter-wave (mmWave) applications. The overall size of the$1 \times 4$antenna array is$28\times 23\text{mm}^{2}$and covers the 28 GHz to 38 GHz bands. Each element of the antenna array consists of a wide square slot (WSS) with a T-shaped stub extended in the ground plane. The impedance matching and the CP bandwidth is improved by introducing asymmetric fractals in the ground plane. The antenna array elements are fed by a simple 4-way power divider network. The simulated and measured impedance bandwidth of the array is 49% (26.6 GHz to 42.9 GHz) and 49% (26.6 GHz to 42.4 GHz) respectively. The simulated and measured 3dB axial ratio (AR) bandwidth is 29.5% (34 GHz to 44 GHz) and 28.5% (35 GHz to 42GHz) respectively. The peak gain of 8.5-11.2 dBi is measured throughout the bandwidth. The proposed antenna array is suitable for commercial use in future 5G communication systems.

Due to the explosion of mobile users and the ever-increasing heterogeneity and scale of wireless networks, traditional communication protocols and optimizing methods can not satisfy future wireless network (FWN) requirements. As promising technologies, artificial intelligence (AI) and blockchain are deemed as the solution for the FWN. AI, famous for its big data processing ability, will enable the FWN to self-update itself to better adapt to the dynamic network condition. Blockchain, as a distributed ledger, can guarantee data integrity, security, and privacy. In this survey, we overview the concept of AI and blockchain and present their state-of-the-art applications in wireless networks. The potential of AI and blockchain is still huge and waiting to be fully explored in wireless networks. Therefore, we introduce how AI and blockchain can assist each other in FWNs. Furthermore, we explore the current constraints of applying both technologies in the FWNs. In the final part, we discuss the future direction of the deployment of AI and blockchain in FWNs.

Breathing pattern is a representation of human breathing in the rate, depth, and rhythm, which can reflect physical and mental health conditions. Capturing and identifying abnormal breathing patterns can help localize associated disorders and have important implications for the patient or the potential patient. In this paper, a breathing patterns recognition system is proposed to monitor and identify abnormal breathing patterns in a contactless, unobtrusive and comfortable way. The system utilize the designed prototype based on WiFi signal and deep learning architecture to achieve the reliable measurement and recognition of respiratory patterns. We first develop a series of data preprocessing method to capture accurately the time-domain breathing signal from received data. Then, we apply a combined convolutional–long short-term memory (CNN-LSTM) network model to classify six distinct respiratory patterns (Eupnea, Tachypnea, Bradypnea, Biots, Cheyne–Stokes, and Kussmaul). The experimental results demonstrate that the proposed system have the ability to effectively classify the afore-mentioned six breathing patterns, which combines a series of novel data processing methods with the obtained CNN-LSTM model. The accuracy, precision, recall and F1-scores obtained by the CNN-LSTM model on the collected test set were 97.8%, 97.9%, 97.8% and 97.8%, respectively. In addition, the proposed system achieved 96.7%, 97.5%, and 98.1% recognition accuracy in different indoor environments. Overall, the proposed contactless breathing patterns recognition system validates the feasibility of long-term continuous respiratory patterns recognition, and provides a potential solution for the auxiliary diagnosis of diseases.

With the fourth industrial revolution, the construction of the Internet of Things (IoT) has developed vigorously, and machine learning is also widely used in IoT management and data processing. Given the existence of massive distributed and private datasets generated by a large number of IoT devices, centralized machine learning is unsatisfactory. Therefore, federated learning (FL), as a distributed learning method, becomes a promising solution. In FL, clients can train models by transferring model parameters to the aggregation server while keeping private data locally. However, FL still relies on a central server, which has questionable reliability. The single point of failure and limited communication resources also hinder the application of FL in the IoT. In this paper, we propose a voting consensus based decentralized federated learning method (VCDFL) by incorporating the leader-candidate-follower hierarchical management method and the consensus based leader election mechanism to solve the single point of failure and exclude outlier models for accelerating convergence during aggregation. Then, we propose a joint decision method to exchange decision information rather than model transfer between clients to further protect privacy and reduce communication overhead while ensuring accuracy. Furthermore, we mathematically derive the probability of successfully electing a leader, the communication efficiency and the joint decision accuracy. We conduct our method in an image recognition scenario. The results show that our joint decision mechanism promotes the accuracy of both system and local decision-making. Meanwhile, the proposed scheme greatly reduces communication costs compared to benchmark learning methods.

Burdened by persistent traffic congestion, urban transportation is in a pressing need of more effective traffic guidance schemes. Existing traffic guidance approaches fall short in optimizing benefits, primarily due to their exclusive reliance on current road conditions for decision making and the prevalence of driving egoism within traditional patterns. Autonomous vehicles (AVs), liberated from human control and enhanced by the Internet of Vehicles and edge computing, provide new possibilities for traffic guidance. Nevertheless, it is tough to precisely determine the pertinent information to convey and establish an effective cooperative guidance mechanism in the face of the substantial number of AVs. This article proposes a social value orientation (SVO)-based cooperation mechanism for AVs, through which the driving routes are jointly determined by individual driving demands, local road network conditions, and global benefits. We design a digital twin-based Edge-to-Cloud traffic guidance architecture, leveraging real-time AV decisions and micro-driving characteristics for forthcoming road condition estimation. The hierarchical Edge-to-Cloud structure efficiently mitigates communication and computation overheads in traffic guidance by distributing tasks across different regions. Finally, an innovative method based on inverse reinforcement learning is proposed to address the challenge of adapting guidance policies in response to varying traffic densities and distributions. The simulation results show a 59.1% improvement in the travel achievement ratio under heavy road traffic load, with no significant change in the detour ratio. It indicates an enhanced system driving efficiency, while still safeguarding the individual benefits of AVs.

Life on Earth depends on healthy oceans, which supply a large percentage of the planet’s oxygen, food, and energy. However, the oceans are under threat from climate change, which is devastating the marine ecosystem and the economic and social systems that depend on it. The Internet of Underwater Things (IoUT), a global interconnection of underwater objects, enables round-the-clock monitoring of the oceans. It provides high-resolution data for training machine learning (ML) algorithms for rapidly evaluating potential climate change solutions and speeding up decision making. The sensors in conventional IoUT are battery powered, which limits their lifetime, and constitutes environmental hazards when they die. In this article, we propose a sustainable scheme to improve the throughput and enable wireless charging of underwater networks, enabling them to potentially operate indefinitely. The scheme is based on simultaneous wireless information and power transfer (SWIPT) from an autonomous underwater vehicle (AUV) used for data collection. We model the problem of jointly maximizing throughput and harvested power as a Markov decision process (MDP), and develop a model-free reinforcement learning (RL) solution. The model’s reward function incentivises the AUV to find optimal trajectories that maximize throughput and power transfer to the underwater nodes while minimising its own energy consumption. To the best of our knowledge, this is the first attempt at using RL for this application. The scheme is implemented in an open 3-D RL environment specifically developed in MATLAB for this study. The performance results show up 207% improvement in energy efficiency compared to those of a random trajectory scheme used as a baseline model.

As a novel distributed machine learning approach, federated learning (FL) is proposed to train a global model while preserving data privacy. However, some studies manifest that adversaries can still recover private information from the gradients. Differential privacy (DP) is a rigorous mathematical tool to protect records in a database against leakage. It has been widely applied in FL by perturbing the gradients. Nevertheless, while using DP in FL, the convergence performance of the global model is inevitably degraded. In this paper, we implement a DP-based FL scheme, which achieves local DP (LDP) by adding well-designed Gaussian noise on the gradients before clients upload them to the server. After that, we propose two strategies to improve the convergence performance of the DP-based FL. Both methods are realized by modifying the local objective function to limit the effect of LDP noise on convergence without degrading the privacy protection level. We then provide the detailed framework which adopts the LDP scheme and two strategies. The framework on different machine learning models is tested by simulation results, which show that our framework can improve the convergence performance up to 40% faster under different noise compared with other DP-based FL. Finally, we show the theoretical convergence guarantee of our proposed framework by first presenting the expected decrease in the global loss function for one round of training and then providing the upper convergence bound after multiple communication rounds.

In this paper, we establish a task-oriented cross-system design framework to minimize the required packet rate for timely and accurate modeling of a real-world robotic arm in the Metaverse, where sensing, communication, prediction, control, and rendering are considered. To optimize a scheduling policy and prediction horizons, we design a Constraint Proximal Policy Optimization (C-PPO) algorithm by integrating domain knowledge from relevant systems into the advanced reinforcement learning algorithm, Proximal Policy Optimization (PPO). Specifically, the Jacobian matrix for analyzing the motion of the robotic arm is included in the state of the C-PPO algorithm, and the Conditional Value-at-Risk (CVaR) of the state-value function characterizing the long-term modeling error is adopted in the constraint. Besides, the policy is represented by a two-branch neural network determining the scheduling policy and the prediction horizons, respectively. To evaluate our algorithm, we build a prototype including a real-world robotic arm and its digital model in the Metaverse. The experimental results indicate that domain knowledge helps to reduce the convergence time and the required packet rate by up to 50%, and the cross-system design framework outperforms a baseline framework in terms of the required packet rate and the tail distribution of the modeling error.

Cellular communication standards have been established to ensure connectivity across most urban environments, complemented by deployment hardware and facilities tailored for city life. At the same time, numerous initiatives seek to broaden connectivity to rural and developing areas. However, with nearly half the global population still offline, there is an urgent need to drive research toward enhancing connectivity in areas and conditions that deviate from the norm. This article delves into innovative communication solutions not only for hard-to-reach and extreme environments but also introduces “hard-to-serve” areas as a crucial, yet underexplored, category within the broader spectrum of connectivity challenges.We explore the latest advancements in communication systems designed for environments subject to extreme temperatures, harsh weather, excessive dust, or even disasters such as fires. Our exploration spans the entire communication stack, covering communications on isolated islands, sparsely populated regions, mountainous terrains, and even underwater and underground settings. We highlight system architectures, hardware, materials, algorithms, and other pivotal technologies that promise to connect these challenging areas. Through case studies, we explore the application of 5G for innovative research, long range (LoRa) for audio messages and emails, LoRa wireless connections, free-space optics, communications in underwater and underground scenarios, delay-tolerant networks, satellite links, and the strategic use of shared spectrum and TV white space (TVWS) to improve mobile connectivity in secluded and remote regions. These studies also touch on prevalent challenges such as power outages, regulatory gaps, technological availability, and human resource constraints, where we introduce the concept of peri-urban hard-to-serve areas where populations might struggle with affordability or lack the skills for traditional connectivity solutions. This article provides an exhaustive summary of our research, showcasing how 6G and future networks will play a crucial role in delivering connectivity to areas that are hard-to-reach, hard-to-serve, or subject to extreme conditions (ECs).

Reconfigurable intelligent surface (RIS) has been identified as a promising disruptive innovation to realize a faster, safer and more efficient communication system. In this paper, we study the broad beamwidth design of RIS. A problem is formulated to achieve broadbeam with maximum and equal power gain within a pre-defined angular region given constraints of the unit modulus weights of RIS. Since the formulated problem is non-convex, where the optimal solution cannot be analytically obtained, we propose the difference-of-convex-based semi-definite programming (DC-SDP) algorithm. In addition, as important guidance of signal coverage for arbitrary angular regions, we mathematically derive the relationship between the angular range of the spatial sector and the maximum average received power. The upper bounds of the average received power with different RIS configurations are also obtained, where uniform rectangular array (URA) and uniform linear array (ULA) are considered. Simulation results demonstrate the effectiveness of our derivations and verify that our proposed DC-SDP algorithm is applicable in practical applications and outperforms other baseline methods. Overall, this work can be viewed as a foundation for the practical implementation of RIS on coverage enhancement and can also be seen as an initial step towards achieving channel estimation.

This article introduces a new method to improve the dependability of millimeter-wave (mmWave) and terahertz (THz) network services in dynamic outdoor environments. In these settings, line-of-sight (LoS) connections are easily interrupted by moving obstacles like humans and vehicles. The proposed approach, coined as Radar-aided Dynamic blockage Recognition (RaDaR), leverages radar measurements and federated learning (FL) to train a dual-output neural network (NN) model capable of simultaneously predicting blockage status and time. This enables determining the optimal point for proactive handover (PHO) or beam switching, thereby reducing the latency introduced by 5G new radio procedures and ensuring high quality of experience (QoE). The framework employs radar sensors to monitor and track object movement, generating range-angle and range-velocity maps that are useful for scene analysis and predictions. Moreover, FL provides additional benefits such as privacy protection, scalability, and knowledge sharing. The framework is assessed using an extensive real-world dataset comprising mmWave channel information and radar data. The evaluation results show that RaDaR substantially enhances network reliability, achieving an average success rate of 94% for PHO compared to existing reactive HO procedures that lack proactive blockage prediction. Additionally, RaDaR maintains a superior QoE by ensuring sustained high throughput levels and minimising PHO latency.

Virtual reality (VR) technology has recently achieved notable success and been widely expected to interplay with more mobile multimedia services. To further enhance real-time immersive experience for VR applications, exploiting cooperative offloading among capable terminal devices should be emerged as an effective means. However, faced with diverse and surging mobile VR user requests, terminal-assisted offloading needs to support comprehensive cached content, ultra-low latency delivery, and continuous energy provisioning, to guarantee stringent quality of service requirements, which poses a critical challenge for resource-constrained terminals. Hence, this paper proposes a Cooperative Offloading framework for Terminal Clusters (named CO-TC), in which VR terminal clusters form several cooperation groups for sharing cached field of view (FoV) tiles and available computing resources to cooperatively perform FoV rendering and content delivery. To maximize energy efficiency in CO-TC, an optimization problem is formulated to jointly decide the task offloading and computing resource utilization. An intelligent offloading scheme is designed based on multi-agent reinforcement learning (MARL) specially using agent relation feature graph embeddings. Moreover, we theoretically prove the permutation invariance and convergence of the proposed algorithm and derive the optimal observation range of the agent to balance the performance gain and interaction overhead in the distributed MARL frame. Finally, simulation results show that the proposed offloading scheme outperforms other baselines in terms of VR service performance, including latency, energy consumption, and energy efficiency.

Video inpainting aims to fill in missing regions of a video after any undesired contents are removed from it. This technique can be applied to repair the broken video or edit the video content. In this paper, we propose a depth-guided deep video inpainting network (DGDVI) and demonstrate its effectiveness in processing challenging broken areas crossing multiple depth layers. To achieve our goal, we divide the inpainting into depth completion, content reconstruction, and content enhancement. Three corresponding modules are designed to implement a process-flow. Firstly, we develop a depth completion module based upon the spatio-temporal Transformer which is used to obtain the completed depth information for each video frame. Secondly, we design a content reconstruction module to generate initially inpainted video. With this module, the contents of the missing regions are composed via the depth-guided feature propagation. Thirdly, we construct a content enhancement module to enhance the temporal coherence and texture quality for the inpainted video. All of proposed modules are jointly optimized to guarantee the high inpainting efficiency. The experimental results demonstrate that our proposed method provides better inpainting results, both qualitatively and quantitatively, compared with the previous state-of-the-art.

Most existing studies consider the deep reinforcement learning (DRL) based Q-learning approach due to its ability to quickly converge to a near-optimal solution, resulting in effective allocation of resources and power. DRL-based Q-network discretizes the continuous power values which results in poor performance. It is challenging to allocate resources effectively in fast varying channel conditions in dynamic vehicular environments. In this work, we propose two approaches to overcome these challenges. First, we present a DRL-based energy-efficient resource allocation approach where we use a twin delayed deep deterministic policy gradient (TD3) scheme based on Thompson sampling to solve the power and resource allocation problem. Second, we present a dynamic meta-transfer learning framework to enhance the policy’s ability to adjust to new channel conditions. Simulation results shows that the proposed TD3 approach based on Thompson sampling enhances the system performance. Moreover, the proposed DRL-based dynamic meta-transfer learning framework takes 80% less samples to adapt to a new environment.

For multiple-input multiple-output (MIMO) frequency diverse array (FDA) communications, this paper proposes a frequency offsets index modulation (FOIM) scheme, which conveys extra information by selecting transmitting frequency offsets from a frequency offsets pool. To improve system spectral efficiency, we firstly design an orthogonal baseband waveform for FDA with the exact expression, then the corresponding receiver structure is proposed. Further, considering that the traditional maximum likelihood (ML) detection algorithm suffers from high complexity, an output combined maximum likelihood (OCML) approach is presented. Moreover, the closed-form expressions for the upper bound on bit error rates (BERs) of both ML and OCML methods are derived, as well as the counterpart of the system capacity. The simulation results show that the capacity of the proposed FOIM scheme outperforms the MIMO scheme, and meanwhile, our method achieves a higher communication rate when compared with the quadrature spatial modulation (QSM) scheme. Additionally, the proposed OCML algorithm can lead the BER performance of FOIM to be superior to that of the aforementioned approaches with significantly lower computational complexity.

Semantic communication (SemCom), as an emerging paradigm focusing on meaning delivery, has recently been considered a promising solution for the inevitable crisis of scarce communication resources. This trend stimulates us to explore the potential of applying SemCom to wireless vehicular networks, which normally consume a tremendous amount of resources to meet stringent reliability and latency requirements. Unfortunately, the unique background knowledge matching mechanism in SemCom makes it challenging to simultaneously realize efficient service provisioning for multiple users in vehicle-to-vehicle networks. To this end, this paper identifies and jointly addresses two fundamental problems of knowledge base construction (KBC) and vehicle service pairing (VSP) inherently existing in SemCom-enabled vehicular networks in alignment with the next-generation ultra-reliable and low-latency communication (xURLLC) requirements. Concretely, we first derive the knowledge matching based queuing latency specific for semantic data packets, and then formulate a latency-minimization problem subject to several KBC and VSP related reliability constraints. Afterward, a SemCom-empowered Service Supplying Solution (S4) is proposed along with the theoretical analysis of its optimality guarantee and computational complexity. Numerical results demonstrate the superiority of S4in terms of average queuing latency, semantic data packet throughput, user knowledge matching degree and knowledge preference satisfaction compared with two benchmarks.

In this paper, we focus on reconfigurable intelligent surface (RIS)-aided resource allocation under base stations (BSs)‘ non-cooperation scheme, where the RIS is solely controlled by one BS and should not affect the communication of the adjacent BS. The minimum quality-of-service (QoS) of users served by the RIS-aided BS, minimum effects on the channel quality of the adjacent BS, the sub-channel assignment rule, and the total transmit power constraint are taken into account. Based on these constraints, the sum-rate of users served by the RIS-aided BS is maximized by jointly optimizing the RIS passive beamforming, power allocation, and sub-channel assignment. To tackle the non-convex problem, an efficient algorithm exploiting the techniques of block coordinate descent (BCD) is developed. A two-sided matching (TSM) algorithm is firstly applied to solve the discrete sub-channel assignment optimization. Then the power allocation and RIS passive beamforming are optimized iteratively. To address the non-convexity in optimizing the power allocation and RIS beamforming, a successive convex upper bound approx-imation method and a multi-ratio fractional programming (FP) with Taylor series approximation-based successive cancellation algorithm (SCA) are used, respectively. The convergence of simulation results proves the validity of our proposed algorithm, and the effects of the numbers of RIS elements and the total transmit power are studied.

Due to the sharp growth of employing mobile devices and IoT (Internet of Things) sensors in daily life, tremendous generated or collected data become one of the most valuable assets for not only users but also numerous applications, which provide various services using user data. However, a large portion of such data is possessed by only a few giant companies in a centralized manner. This incurs the concerns of how user data are harnessed and used and who can use such data because of many cases of privacy violence and data leakage. Therefore, in this paper, we propose a decentralized data sharing marketplace using Ethereum to enable users to share their data in a privacy-preserving and self-governing manner. Users can only share parts of the data from their devices they want to share in the marketplace and gain rewards from the bidding of buyers anonymously. Furthermore, a federated learning use case is demonstrated as a privacy-enhanced application of the proposed marketplace to encourage users to share processed data to avoid raw data leakage.

The rise of intelligent reflecting surfaces (IRSs) is opening the door for unprecedented capabilities in visible light communication (VLC) systems. By controlling light propagation in indoor environments, it is possible to manipulate the channel conditions to achieve specific key performance indicators. In this paper, we investigate the role that IRSs can play in boosting the secrecy capacity of non-orthogonal multiple access (NOMA) VLC systems. More specifically, we propose an IRS-based physical layer security (PLS) mechanism that mitigates the information leakage risk inherent in NOMA. Our results demonstrate that the achieved secrecy capacity can be enhanced by up to 105% for a number of 80 IRS elements. To the best of our knowledge, this is the first paper that examines the PLS of NOMA-based IRS-assisted VLC systems.

To accommodate the growing number of heterogeneous radios with limited wireless resources, symbiotic communication (SC) inspired by biology has been recently proposed to establish a symbiotic radio (SR) ecosystem. In this SR ecosystem, through collaboratively optimizing service/resource exchange policies, radios can coevolve like organisms, thus enabling various radio resources (such as spectrum, energy, and computing power) to complement each other. However, one critical challenge is securing a trusted coevolution environment in an SR ecosystem since the SRs with different network operators should coevolve under unreliable wireless links with complex electromagnetic interference. Moreover, multidimensional resources participated and a wide array of service requirements pose additional challenges to service/resource exchange decision-making across massive SRs. In this paper, we propose a Blockchain-empowered Intelligent cOevolution scheme for SRs, named BIO-SR. Specifically, BIO-SR exploits the digital acyclic graph (DAG) blockchain consensus in securing a trusted environment of SRs and applies deep reinforcement learning (DRL) in service exchange decision-making. The simulation results show that the BIO-SR scheme outperforms conventional solutions in terms of transmission rate and latency under both non-attack and malicious attack scenarios.

An important issue for cellular network operators is how to maintain radio coverage so that any issues can be addressed before they impact user services. This is particularly important in dense small cell network deployment scenarios such as vehicular networks. Antenna electrical tilt is a key factor in this, as unintended deviations from the planned value can adversely affect coverage and service reliability. We propose a novel method to detect antenna tilt anomalies using existing data sources without the need for additional hardware to be deployed in the radio access network. Our approach goes beyond previous techniques by using federated unsupervised learning based on polar coordinates, together with a geometrical transformation to normalise data across multiple sites. By using this approach to combine scarce training data from multiple cells, we can achieve detection accuracy in excess of 95% in a way that minimises training data size as well as computing power and memory usage.

Semantic communication (SemCom) has recently been considered a promising solution for the inevitable crisis of scarce communication resources. This trend stimulates us to explore the potential of applying SemCom to vehicular networks, which normally consume a tremendous amount of resources to achieve stringent requirements on high reliability and low latency. Unfortunately, the unique background knowledge matching mechanism in SemCom makes it challenging to realize efficient vehicle-to-vehicle service provisioning for multiple users at the same time. To this end, this paper identifies and jointly addresses two fundamental problems of knowledge base construction (KBC) and vehicle service pairing (VSP) inherently existing in SemCom-enabled vehicular networks. Concretely, we first derive the knowledge matching based queuing latency specific for semantic data packets, and then formulate a latency-minimization problem subject to several KBC and VSP related reliability constraints. Afterward, a SemCom-empowered Service Supplying Solution (S4) is proposed along with the theoretical analysis of its optimality guarantee. Simulation results demonstrate the superiority of S4 in terms of average queuing latency, semantic data packet throughput, and user knowledge preference satisfaction compared with two different benchmarks.

Future robots will be very advanced with high flexibility and accurate control performance. They will have the ability to mimic human behaviours or even perform better, which raises the significant risk of robot attack. In this work, we study the robot mimic attack on the current keystroke-dynamic user authentication system. Specifically, we proposed a robot mimicry attack framework for keystroke-dynamics systems. We collected keyboard logging data and acoustical signal data from real users and extracted the timing pattern of keystrokes to understand victim's behaviour for robot imitation attacks. Furthermore, we develop a deep Q-Network (DQN) algorithm to control the velocity of robot which is one of the key challenges of forging the human typing timing features. We tested and evaluated our approach on the real-life robotic testbed. We presented our results considering user identification and user authentication performance. We achieved a 90.3% user identification accuracy with genuine keyboard logging data samples and 89.6% accuracy with robot-forged data samples. Furthermore, we achieved 11.1%, and 36.6% EER for user authentication performance with zero-effort attack, and robot mimicry attack, respectively.

Head movement holds significant importance in con-veying body language, expressing specific gestures, and reflecting emotional and character aspects. The detection of head movement in smart or assistive driving applications can play an important role in preventing major accidents and potentially saving lives. Additionally, it aids in identifying driver fatigue, a significant contributor to deadly road accidents worldwide. However, most existing head movement detection systems rely on cameras, which raise privacy concerns, face challenges with lighting conditions, and require complex training with long video sequences. This novel privacy-preserving system utilizes UWB-radar technology and leverages Deep Learning (DL) techniques to address the mentioned issues. The system focuses on classifying the five most common head gestures: Head 45L (HL45), Head 45R (HR45), Head 90L (HL90), Head 90R (HR90), and Head Down (HD). By processing the recorded data as spectrograms and leveraging the advanced DL model VGG16, the proposed system accurately detects these head gestures, achieving a maximum classification accuracy of 84.00% across all classes. This study presents a proof of concept for an effective and privacy-conscious approach to head position classification.

The rapid evolution of drone communication systems necessitates the development of novel approaches for optimal beam management in millimetre wave (mmWave) 6G networks. Beamforming is used to improve signal quality and enhance the signal-to-noise ratio (SNR); however, the existing beam management performs an exhaustive search over the pre-defined codebook, resulting in higher latency due to training overhead that makes it impractical for high-mobility applications. Therefore, this paper introduces an innovative technique for mmWave beam prediction, considering practical visual and communication scenarios. The approach proposed in this study utilizes computer vision (CV) and ensemble learning via stacking, combining multi-modal vision sensing and positional data to achieve accurate estimations of drone positions and orientations. The developed framework first fine-tunes "you look only once" version 5 (YOLO-v5), a CV model to obtain the bounding box (location) of the drone from RGB images. This filtered vision sensing information and position data are used to train two different sets of neural networks, and the output of each model is stacked to train a meta-learner, used for the prediction of K-beams from a pre-defined codebook. The proposed method outperforms with the top-1 accuracy of approximately 90% compared to 86% and 60% for vision and position models, respectively. Furthermore, top-3 and top-5 accuracies are approximately 100%, resulting in a significant receive signal strength.

The Open Radio Access Network (O-RAN) architecture has been identified as a promising technology for enhanced network deployment, innovation, improved competition, and reduction of capital and operating expenses (CAPEX/OPEX) of 5G and beyond networks because of its open interfaces, disaggregated network entities and functions, virtualization of network hardware and software, and intelligent control. However, the effect of this improved technology on the energy consumption of the RAN needs to be carefully investigated, so that the many advantages that can be obtained from the O-RAN are not overwhelmed by increased energy consumption. Hence, in this paper, we investigate the O-RAN from an Energy efficiency (EE) perspective by reviewing the state-of-the-art power consumption models, and EE techniques that have been proposed to minimize the energy consumption of O-RAN. In addition, the challenges associated with the optimization of the EE of O-RAN and opportunities for further research are highlighted.

6G is expected to revolutionize the Internet of things (IoT) applications toward a future of completely intelligent and autonomous systems. Conventional machine-learning approaches involve centralizing training data in a data center, where the algorithms can be used for data analysis and inference. To promote green computing in IoT applications, Machine-2-Machine (M2M) technologies are largely focused on lowering energy consumption and creating effective IT infrastructure. In this paper, we introduce an AI-enabled One-Shot Interference(O-SI) Knowledge-Driven unified model block sharing (K-Dumbs) framework in which actionable knowledge is aggregated from the training perception robots to facilitate others at the Edge in the vicinity. To demonstrate the practicality of the proposed concept, we explore a K-Dumb Fed-Average (FedAvg) algorithm to meet the massively distributed and unbalanced pattern and privacy requirement of the Internet of Robotic Things(IoRT). Simulation results show that, when compared to traditional Federated Learning (FL) systems, the proposed K-Dumb FedAvg architecture delivers higher information-sharing and learning quality. In addition, we validate our method using MNIST handwritten digits for training image processing with an accuracy that is close to the centralized solution for up to 80% reduction in the amount of exchange data with the O-SI method. Furthermore, the suggested solution reduces IoRT energy consumption by up to 10 times and protects privacy.

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are expected to be widely used in future wireless communication networks to improve spectrum and energy efficiency. In this paper, RIS-assisted UAV communication systems are studied and analysed by developing a comprehensive mathematical framework for examining their outage performance. In order to study the effect of the RIS on UAV communications, two system scenarios are considered: in the first scenario, the UAV acts as an aerial base station (BS) serving a ground user to offload the terrestrial network, and in the second system, the UAV acts as an aerial user served by a terrestrial BS. We present channel models considering the UAV’s unique characteristics, propose a closed-form approximation for the signal-to-noise-ratio (SNR) distribution, and derive an analytical expression for the relevant outage probability. Results show that RIS can significantly improve the performance of UAV communication systems by introducing energy-efficient and reliable links. This opens the door for UAV networks, which are highly scalable, adaptable, and robust to environmental changes. Furthermore, the results show that the UAV position and altitude optimisation significantly affects the outage performance.

Line of sight (LoS) links that use high frequencies are sensitive to blockages, making it challenging to scale future ultra-dense networks (UDN) that capitalise on millimetre wave (mmWave) and potentially terahertz (THz) networks. This paper embraces two novelties; Firstly, it combines machine learning (ML) and computer vision (CV) to enhance the reliability and latency of next-generation wireless networks through proactive identification of blockage scenarios and triggering proactive handover (PHO). Secondly, this study adopts federated learning (FL) to perform decentralised model training so that data privacy is protected, and channel resources are conserved. Our vision-aided PHO framework localises users using object detection and localisation (ODL) algorithm that feeds a multiple-output neural network (NN) model to predict possible blockages. This involves analysing images captured from the video cameras co-located with the base stations (BSs) in conjunction with wireless parameters to predict future blockages and subsequently trigger PHO. Simulation results show that our approach performs remarkably well in highly dynamic multi-user environments where vehicles move at different speeds, and achieves 93.6% successful PHO. Furthermore, the proposed framework outperforms the reactive-HO methods by a factor of 3.3 in terms of latency while maintaining a high quality of experience (QoE) for the users.

Joint communication and sensing technique has been adopted for smart home design and other applications recently. WiFi sensing, which utilizes mutually orthogonal channel response to monitor the changes in the medium, is regarded as one of key techniques in this field. Human activity recognition using wireless communication systems is a key function of future internet of things systems. The effective and inexpensive WiFi sensing system can help people with device-free controlling, and healthcare monitoring without concern of image information leakage that uses a camera system. In this article, we proposed a continuous angle of arrival and time of flight (AoA-ToF) maps based method that adopts multiple signals classification analysis on commercial and off-the-shelf WiFi devices to detect human activities. Our experimental results ensure the effectiveness of the proposed system for the human activity recognition (HAR) task with 8 activities among 5 users in three directions. The performance of our system achieves 85.6% accuracy on average. Meanwhile, we evaluate the performance of our system under different conditions, including direction and user identity. The results show the system’s robustness for human activity recognition under such conditions.

In wireless communications, various study findings have shown that a reconfigurable intelligent surface (RIS) may successfully alter wireless wave parameters like phase and amplitude without requiring sophisticated signal processing and decoding at the receiver. However, it is necessary to take into account designing the surface under a realistic frequency selective fading channel. Because of this, we chose a wideband OFDM multi-user communication system based on an actual RIS setup that considers mutual coupling (MC) and electromagnetic interference (EMI). We used Hadamard matrix in the pilot transmissions to estimate the uncontrollable and the controllable channels. The best pilot configuration was selected to initialize the gradient descent method in order to calculate the optimal reflection coefficient that maximize the data rate for each user in the presence of EMI and MC. Simulation results revealed that the data rate has been degraded when considering EMI and MC for around 30 Mbits/s for each user. This confirms that both EMI and MC must be given considerable attention in our research due to their inevitable effects on the system performance.

The spatial distribution of cellular networks has made them very promising to use for localization. By knowing the location of a user, cellular networks can provide context-aware services customized to that user. Objects and the dynamic nature of indoor locations result in lots of multipath and non-line-of-sight (NLOS) propagations. In this work, we carry out a novel experimental investigation to improve indoor localization using a grid approach with channel state information (CSI) fingerprinting and artificial intelligence (AI)/ machine learning (ML) methods for determining the location of a mobile device. Experiments are conducted in a standard indoor setting. This paper compares a method for indoor positioning based on received signal strength identifier (RSSI), phase, and CSI using ML to show how the accuracy of indoor localization can be improved. Compared to heuristic approaches like DOA estimation, the precision of ML is superior.

Intelligent transportation systems require a reliable exchange of information between network terminals in different vehicular communication environments. Making effective use of the dedicated spectrum is crucial to maximizing communication performance. This requires optimising the modulation order according to different channel conditions. This paper proposes a lightweight spectrum awareness methodology that uses wideband spectrum monitoring and deep learning-based modulation classification techniques to optimise the modulation order. We introduce a channel quality indicator block in which the classifier’s accuracy of detection is used as a forward indicator for the choice of the best modulation type for transmission. By using a 3D stochastic vehicular channel, we evaluate the classification performance at different channel parameter settings, including, speed, variance, and signal-to-noise ratio in urban and rural areas. The experimental analyses demonstrate the capability of the proposed approach to supporting a high detection probability for acceptable false decision-making ≤ 20%.

Recently, the use of unmanned aerial vehicles (UAVs) for wireless communications has attracted much research attention. However, most applications of UAVs for wireless communication provisioning are not feasible as researchers fail to consider some vital aspects of their deployment, especially the energy requirements of both the UAV and communication system. The considerable energy consumption overhead involved in flying or hovering UAVs makes them less appealing for green wireless communications. Therefore, in this work, we examine the feasibility of an alternative energy-efficient deployment scheme where UAVs can be made to land-on designated locations, also known as landing stations (LSs). The idea of LS makes the UAV-based wireless communication more durable and advantageous, since the total energy consumption is reduced by minimizing the flying/hovering energy consumption, which, in turn, enables diverse set of applications including emergency and pop-up networking. We evaluate the impact of the separation distance between these LSs and the Optimal Hovering Position (OHP) on the network performance. Specifically, we develop mathematical frameworks to model the relationship between UAV power consumption, coverage probability, throughput, and separation distance. Numerical results reveal that a significant energy reduction can be achieved when the LS concept is exploited with a slight compromise in coverage probability and throughput. However, the choice of a suitable LS location depends on the users’ service requirements, transmit power, and frequency band utilized.

Recent data leak scandals, together with the under-utilization of collected data (estimated that around 90% of data never leaves a device’s local storage), limits the applicability and potential of novel data driven applications. Thus, novel ways to treat data, in which users are guaranteed control, usability, and privacy over their generated data are needed. In this paper we propose a novel privacy-preserving blockchain framework for a data sharing marketplace. The proposed framework allows users (such as sensors and devices) that generate data to store it in external servers, while the blockchain is utilized to record buy and sell transactions between parties, as well as perform access control by generating access sequences whenever trades are performed. A novel perspective over data ownership is presented, in which whoever generates the data has completed ownership and control over it and the blockchain transactions are only utilized to guarantee temporary access to it. The proposed blockchain framework also supports different types of data and provides, via the distributed and openness of the framework, quality, timeliness and similarity control over the data stored in the marketplace. In this context, different types of applications that can benefit from this framework are presented and open problems are discussed.

Smart energy performance monitoring and optimisation at the supplier and consumer levels is essential to realising smart cities. In order to implement a more sustainable energy management plan, it is crucial to conduct a better energy forecast. The next-generation smart meters can also be used to measure, record, and report energy consumption data, which can be used to train machine learning (ML) models for predicting energy needs. However, sharing energy consumption information to perform centralised learning may compromise data privacy and make it vulnerable to misuse, in addition to incurring high transmission overhead on communication resources. This study addresses these issues by utilising federated learning (FL), an emerging technique that performs ML model training at the user/substation level, where data resides. We introduce FedraTrees, a new, lightweight FL framework that benefits from the outstanding features of ensemble learning. Furthermore, we developed a delta-based FL stopping algorithm to monitor FL training and stop it when it does not need to continue. The simulation results demonstrate that FedraTrees outperforms the most popular federated averaging (FedAvg) framework and the baseline Persistence model for providing accurate energy forecasting patterns while taking only 2% of the computation time and 13% of the communication rounds compared to FedAvg, saving considerable amounts of computation and communication resources.

Abstract Using reconfigurable intelligent surfaces (RISs) to improve the coverage and the data rate of future wireless networks is a viable option. These surfaces are constituted of a significant number of passive and nearly passive components that interact with incident signals in a smart way, such as by reflecting them, to increase the wireless system's performance as a result of which the notion of a smart radio environment comes to fruition. In this survey, a study review of RIS‐assisted wireless communication is supplied starting with the principles of RIS which include the hardware architecture, the control mechanisms, and the discussions of previously held views about the channel model and pathloss; then the performance analysis considering different performance parameters, analytical approaches and metrics are presented to describe the RIS‐assisted wireless network performance improvements. Despite its enormous promise, RIS confronts new hurdles in integrating into wireless networks efficiently due to its passive nature. Consequently, the channel estimation for, both full and nearly passive RIS and the RIS deployments are compared under various wireless communication models and for single and multi‐users. Lastly, the challenges and potential future study areas for the RIS aided wireless communication systems are proposed.

The increasing demand for considering multisensor data fusion technology has drawn attention for precise human activity recognition (HAR) over standalone technology due to its reliability and robustness. This article presents a framework that fuses data from multiple sensing systems and applies neuromorphic computing to sense and classify human activities. The data is collected by utilizing inertial measurement unit (IMU) sensors, software-defined radios, and radars, and feature extraction and selection are performed on the data. For each of the actions, such as sitting and standing, an activity matrix is generated, which is then fed into a discrete Hopfield neural network as a binary feature pattern for one-shot learning. Following the Hopfield network neurons’ feedback output, the conformity to the standard activity feature pattern is also determined. Following the Hopfield network neurons’ feedback output, the training of neurons is completed after two steps under the Hebbian learning law, and the conformity to the standard activity feature pattern is also determined. According to the probabilistic statistics on inference predictions, the proposed method, that is the neuromorphic computing of the three data fused framework, achieved the box plot for the highest lower quartile output of 95.34%, while the confusion matrix classification accuracy of the two activities was 98.98%. The results have shown that neuromorphic computing is most capable of multisensor data-fusion-based HAR. Furthermore, the proposed method can be enhanced by incorporating additional hardware signal processing in the system to enable the flexible integration of human activity data.

Intelligent transportation systems are an emerging technology that facilitates real-time vehicle-to-everything communication. Hence, securing and authenticating data packets for intra- and inter-vehicle communication are fundamental security services in vehicular ad-hoc networks (VANETs). However, public-key cryptography (PKC) is commonly used in signature-based authentication, which consumes significant computation resources and communication bandwidth for signatures generation and verification, and key distribution. Therefore, physical layer-based secret key extraction has emerged as an effective candidate for key agreement, exploiting the randomness and reciprocity features of wireless channels. However, the imperfect channel reciprocity generates discrepancies in the extracted key, and existing reconciliation algorithms suffer from significant communication costs and security issues. In this paper, PKC-based authentication is used for initial legitimacy detection and exchanging authenticated probing packets. Accordingly, we propose a blockchain-based reconciliation technique that allows the trusted third party (TTP) to publish the correction sequence of the mismatched bits through a transaction using a smart contract. The smart contract functions enable the TTP to map the transaction address to vehicle-related information and allow vehicles to obtain the transaction contents securely. The obtained shared key is then used for symmetric key cryptography (SKC)-based authentication for subsequent transmissions, saving significant computation and communication costs. The correctness and security robustness of the scheme are proved using Burrows–Abadi–Needham (BAN)-logic and Automated Validation of Internet Security Protocols and Applications (AVISPA) simulator. We also discussed the scheme’s resistance to typical attacks. The scheme’s performance in terms of packet delay and loss ratio is evaluated using the network simulator (OMNeT++). Finally, the computation analysis shows that the scheme saves ∼99% of the time required to verify 1000 messages compared to existing PKC-based schemes.

Sign language is utilized by deaf-mute to communicate through hand movements, body postures, and facial emotions. The motions in sign language comprise a range of distinct hand and finger articulations that are occasionally synchronized with the head, face, and body. Automatic sign language recognition (SLR) is a highly challenging area and still remains in its infancy compared with speech recognition after almost three decades of research. Current wearable and vision-based systems for SLR are intrusive and suffer from the limitations of ambient lighting and privacy concerns. To the best of our knowledge, our work proposes the first contactless British sign language (BSL) recognition system using radar and deep learning (DL) algorithms. Our proposed system extracts the 2-D spatiotemporal features from the radar data and applies the state-of-the-art DL models to classify spatiotemporal features from BSL signs to different verbs and emotions, such as Help, Drink, Eat, Happy, Hate, and Sad. We collected and annotated a large-scale benchmark BSL dataset covering 15 different types of BSL signs. Our proposed system demonstrates highest classification performance with a multiclass accuracy of up to 90.07% at a distance of 141 cm from the subject using the VGGNet model.

The upsurge in wireless devices and real-time service demands force the move to a higher frequency spectrum. Millimetre-wave (mmWave) and terahertz (THz) bands combined with the beamforming technology offer significant performance enhancements for future wireless networks. Unfortunately, shrinking cell coverage and severe penetration loss experienced at higher spectrum render mobility management a critical issue in high-frequency wireless networks, especially optimizing beam blockages and frequent handover (HO). Mobility management challenges have become prevalent in city centres and urban areas. To address this, we propose a novel mechanism driven by exploiting wireless signals and on-road surveillance systems to intelligently predict possible blockages in advance and perform timely HO. This paper employs computer vision (CV) to determine obstacles and users’ location and speed. In addition,this study introduces a new HO event, called block event (BLK), defined by the presence of a blocking object and a user moving towards the blocked area. Moreover, the multivariate regression technique predicts the remaining time until the user reaches the blocked area, hence determining best HO decision. Compared to conventional wireless networks without blockage prediction, simulation results show that our BLK detection and proactive HO algorithm achieves 40% improvement in maintaining user connectivity and the required quality of experience (QoE).

The outbreak of the COVID-19 pandemic has caused unprecedented disruption to education and progressed remote teaching as a predominant model for delivering educational content. However, the online teaching and learning model has its challenges, such as the lack of technological tools to quantity the student attention and engagement with the learning content. This paper focuses on developing an e-learning framework for capturing and analysing the students’ attention during remote teaching sessions and subsequently profiling their learning behaviour leveraging eye-tracking data. Our proposed eye-tracking solution deploys a webcam to capture and track raw gaze points that grant the user the freedom of natural head movement and scalability compared to conventional eye-tracking approaches. We derived various gaze metrics in conjunction with state-of the-art machine learning (ML) models like logistic regression, support vector machine and polynomial regression to classify the student attention with an accuracy above 91%. Furthermore, our findings can help in the early detection and diagnosis of attention deficit hyperactivity disorder (ADHD) among students, thus supporting their learning journeys by creating an adaptive learning environment tailored to their needs.

Cooperative autonomous driving has emerged as an appealing paradigm to expand the perception range of vehicles and improve driving safety by sharing local sensing data and driving intentions. However, the constrained communication resource and unstable link quality seriously restrict the coordination and reliability of driving decisions. The distributed consensus mechanism is a potential approach to address the problem. This paper proposes a fast and efficient vehicular consensus framework to improve the coordination and reliability of driving decisions in delay-sensitive applications. We first design a Raft empowered two-hop consensus mechanism with dynamic negotiation. Moreover, we theoretically analyze the performance of the mechanism in terms of successful consensus ratio, latency, and link quality by leveraging Jensen's inequality and binomial distribution. In addition, an adaptive joint design algorithm for consensus process and communication is put forward to minimize the consensus delay while satisfying the requirements of vehicular resources and coordination degree. Simulation results demonstrate that our proposed scheme can improve the reliability of critical decisions by 15.4% compared with existing approaches.

Industry 4.0 and 5.0 are paradigms of digitalization and intelligentization. The huge available bandwidth and the least spectral interference in the millimeter-wave (mmWave) band can pave the way for a wide range of new industrial automation capabilities. Sophisticated industrial applications such as industrial Internet of Things, time-sensitive networking (TSN), intelligent logistics, product tracking, remote visual monitoring and surveillance, image-guided automated assembly and automatic fault detection require high bandwidth, reliability and low latency which can be ensured using the mmWave band. In this paper, we address the physical layer (PHY) requirements of industrial communication from the viewpoint of Industry 4.0 and beyond, while highlighting the key performance indicators. This paper proposes a 60 GHz mmWave antenna system with high reliability and low latency, making it ideally suited for industrial IoT and communication. The proposed novel antenna system is designed to cover the entire 9 GHz bandwidth of the 60 GHz standard spectrum (57 to 66 GHz) with a single element peak gain of 8.2 dBi. It provides high gain and efficiency across all four channels of 60 GHz communication from 57.24 GHz to 65.88 GHz, each channel with 2.16 GHz of bandwidth. Moreover, the simulated achieved beamforming gain of the proposed antenna system reaches 16.1 dBi, which satisfies the high gain requirement for 60 GHz multi-gigabit industrial communication. The proposed antenna system is a promising physical layer candidate suitable for communication standards such as WiGig IEEE802.11ay, IEEE802.11ad, IEEE802.15.3c, ECMA-387 and WirelessHD to ensure multi-gigabit wireless communication at 60 GHz ISM band for factory automation and industrial applications.

Although the interconnection of all things based on 5G and AI has become an incremental trend in all walks of life, its centralized design has many challenges and drawbacks when applied to industrial and life scenarios. In the field of autonomous driving (a.k.a., auto-driving), the centralized vehicle-to-everything (V2X) system depends heavily on the stability of the central node, and there is seldom a mechanism to guarantee the security, stability and timeliness of information in highly dynamic auto-driving scenarios. In this paper, we first design and implement the AIR-RAFT system that supports wireless distributed consensus for IoT. AIR-RAFT is a complete embedded system based on the Raft consensus algorithm and can be potentially installed on auto-driving vehicles. It can not only achieve wireless consensus to ensure the consistency and security of V2X data but also can synchronize the actions among the vehicles in a distributed manner though all cars are not trusted each other. In addition, we originally propose the “selective edge decision layer” for the AIR-RAFT system which can share part of the decision privilege in auto-driving cars. In practical performance evaluations, the AIR-RAFT based auto-driving vehicles stably achieve multi-node (3–7) wireless data consensus and actions synchronization that maintain good working stability within 350 m centered on the leader.

In recent years, due to the increasing concern about data privacy security, federated learning, whose clients only synchronize the model rather than the personal data, has developed rapidly. However, the traditional federated learning system still has a high dependence on the central server, an unguaranteed enthusiasm of clients and reliability of the central server, and extremely high consumption of communication resources. Therefore, we propose Clustered Hierarchical Distributed Federated Learning to solve the above problems. We motivate the participation of clients by clustering and solve the dependence on the central server through distributed architecture. We apply a hierarchical segmented gossip protocol and feedback mechanism for in-cluster model exchange and gossip protocol for communication between clusters to make full use of bandwidth and have good training convergence. Experimental results demonstrate that our method has better performance with less communication resource consumption.

To preserve participants' privacy, Federated Learning (FL) has been proposed to let participants collaboratively train a global model by sharing their training gradients instead of their raw data. However, several studies have shown that con-ventional FL is insufficient to protect privacy from adversaries, as even from gradients, useful information can still be recovered. To obtain stronger privacy protection, Differential Privacy (DP) has been proposed on the server's side and the clients' side. Although adding artificial noise to the raw data can enhance users' privacy, the accuracy performance of the FL is inevitably degraded. In addition, although the communication overhead caused by the FL is much smaller than that of centralized learning, it still becomes a bottleneck of the learning performance and utilization efficiency due to its frequent parameters exchange. To tackle these problems, we propose a new FL framework via applying DP both locally and centrally in order to strengthen the protection of par-ticipants' privacy. To improve the accuracy performance of the model, we also apply sparse gradients and Momentum Gradient Descent on the server's side and the clients' side. Moreover, using sparse gradients can reduce the total communication costs. We provide the experiments to evaluate our proposed framework and the results show that our framework not only outperforms other DP-based FL frameworks in terms of the model accuracy but also provides a more powerful privacy guarantee. Besides, our framework can save up to 90% of communication costs while achieving the best accuracy performance.

In recent years, research has focused on exploiting the inherent physical (PHY) characteristics of wireless channels to discriminate between different spatially separated network terminals, mitigating the significant costs of signature-based techniques. In this paper, the legitimacy of the corresponding terminal is firstly verified at the protocol stack’s upper layers, and then the re-authentication process is performed at the PHY-layer. In the latter, a unique PHY-layer signature is created for each transmission based on the spatially and temporally correlated channel attributes within the coherence time interval. As part of the verification process, the PHY-layer signature can be used as a message authentication code to prove the packet’s authenticity. Extensive simulation has shown the capability of the proposed scheme to support high detection probability at small signal-to-noise ratios. In addition, security evaluation is conducted against passive and active attacks. Computation and communication comparisons are performed to demonstrate that the proposed scheme provides superior performance compared to conventional cryptographic approaches.

Heterogeneity is a key feature of 5G and beyond networks for Internet of things applications in various fields. Regarded as the leading low power wide area network, long range wide area network (LoRaWAN) is expected to accomplish 5G's massive machine-type communications target by integrating it into 5G network. In this paper, we design and implement a LoRaWAN-5G integrated network with a collaborative Radio Access Network and a converged core network. We built a 5G-based LoRaWAN gateway that communicates with 5G new radio. To the best of our knowledge, this is the first LoRaWAN gateway that uses 5G network as its backhaul. Moreover, the LoRaWAN servers are deployed within the core network of the 5G testbed, enhancing the security and privacy of LoRaWAN data. This hybrid network has been deployed to monitor the heating system of rooms in James Watt South Building at the University of Glasgow, demonstrating the stability, high flexibility and low deployment cost of the network.

The increasing demand for throughput, ultra-low latency, ultra-high reliability, and ubiquitous coverage have made researchers explore several novel solutions to set the basis for future generations of wireless communications. These demands, however, will consume a significant amount of resources, particularly in the case of cell-free millimetre-wave (mm-Wave) massive multiple input multiple output systems (MIMO), which is the promising approach for future wireless generations. In this paper, we propose a novel and low-complexity matching approach to dynamically activate a set of radio frequency (RF) chains based on the Hungarian algorithm to maximize the total energy efficiency in the uplink of the cell-free mm-Wave massive MIMO systems. Simulation results demonstrate that our proposed scheme achieves up to 13.5%, 20% and 58.7% energy efficiency improvement compared to state-of-the-art adaptive RF chains activation (ARFA), random access point activation and fixed activation scheme when all RF chains at each AP are switched on, respectively. In addition, compared to the ARFA scheme, the proposed matching scheme achieves a complexity reduction ratio of up to 189.6%.

Internet of vehicles (IoV) has been developed as a promising technology to improve road safety. However, resource management can be challenging in a congested traffic environment, which can affect the energy efficiency (EE) and spectrum efficiency (SE) in IoV networks. In this paper, we present a novel intelligent resource allocation approach based on deep reinforcement learning to maximize the weighted composite efficiency that incorporates the EE and SE metric subject to latency and reliability constraints of vehicle-to-vehicle (V2V) users. We employ Thompson sampling with double deep Q network to transform the objective function. Moreover, we present a probability-based learning approach to meet the quality of service requirements and to increase the learning ability of the proposed model. The simulation results indicate that the proposed approach maximizes the composite efficiency while satisfying the latency and reliability constraints of V2V users.

In a LoRaWAN network one of the main reasons of packet outage is the destructive interference that is caused by colliding packets. As the network operates with an ALOHA-like channel access setup, there is no easy way of preventing two or more devices transmitting at the same time, possibly generating interference to each other. Different methods are proposed in literature that can be used to decrease this chance. However, most of them require extensive use of downlink messages coupled with involved algorithms at the network side, often for only a marginal improvement in performance. In this paper we analyse some ways to optimise the Packet Delivery Ratio (PDR) of a LoRaWAN network that can be used when setting up a node or a group of nodes, do not involve downlink and can operate without knowledge of other devices in the same network. These are shown to provide a small boost in performance of maximum 10%, which is akin to that of more complex, downlink-dependant schemes, while decreasing the set up complexity considerably.

Narrow Band Internet of Things (NB-IoT) is one of the drivers of industry 4.0 and the study of its wireless behavior in Indoor industrial environments has become important. This is because of the unfavorable conditions poised to wireless propagation as a result of the presence of heavy-duty equipment and the physical structure of industrial buildings. In this paper, an indoor industry was modeled to present some of the reflective characteristics and its effects on wireless propagation, particularly large-scale fading. The results obtained which include propagation paths, path loss, and impulse response showed how the environment affected the wireless transmission of NB-IoT. However, to mitigate this challenge, a collaborative scheme is introduced to improve the transmission among the affected NB-IoT terminals. The proposed scheme resulted in a collective improvement in path loss value by 30.44%.

In the last decade, a sharp surge in the number of user proximity wireless devices (UPWDs) has been observed. This has increased the level of electromagnetic field (EMF) exposure of the users substantially and hence, the possible physiological effects. Ambient backscatter communications (ABC) has appeared to be a promising solution to reduce the power consumption of UPWDs by converting ambient radio frequency (RF) signals into useful signals while non-orthogonal multiple access (NOMA) is a compelling multiplexing scheme for enhanced spectral efficiency. This paper utilises a novel combination of ABC and NOMA to reduce the EMF in the uplink of wireless communication systems. This contemporary approach of EMF-aware resource optimization is based on k-medoids and Silhouette analysis. To curtail the uplink EMF, a power allocation strategy is also derived by converting a non-convex problem to a convex one and solving accordingly. The numerical results exhibit that the proposed ABC, NOMA, and unsupervised learning based scheme achieves a reduction in the EMF by at least 75% in comparison to the existing solutions.

Securing data exchange between intercommunicating terminals, e.g., vehicle-to-everything, constitutes a technological challenge that needs to be addressed. Security solutions must be computationally efficient and flexible enough to be implemented in any wireless propagation environment. Recently, physical layer security has gained popularity, which exploits the randomness of wireless channel responses for extracting high entropy secret cryptographic keys. The current state-of-the-art relies on the independently varying channel sources of randomness, e.g., received signal strength (RSS) and phase. However, the limited capability of RSS-based extraction techniques has motivated researchers to investigate alternative approaches. Although phase-based approaches have emerged in many studies, optimising the extraction performance by adapting the algorithm to the non-reciprocal components of static and dynamic channels remains a challenge. In this paper, we propose an adaptive multilevel quantisation approach that adjusts the size of the quantisation region to the channel responses’ non-reciprocity parameters, thus optimising the trade-off between the bit generation rate (BGR) and the bit mismatch rate (BMR). The probability of error has been theoretically formulated. Accordingly, the order of the quantisation process is adapted for acceptable mismatching probability. Moreover, simulation analysis is conducted to prove the ability of the proposed approach to provide flexible adaptation of the quantisation order at different signal-to-noise ratios (SNRs), achieving fast secret bit generation rates 1. 1$\sim$2.85bits/packet at SNRs of 10$\sim$25 dB for acceptable BMR $\leq 0.1$.

The spectrum efficiency can be greatly enhanced by the deployment of machine-to-machine (M2M) communications through cellular networks. Existing resource allocation approaches allocate maximum resource blocks (RBs) for cellular user equipments (CUEs). However, M2M user equipments (MUEs) share the same frequency among themselves within the same tier. This results in generating co-tier interference, which may deteriorate the MUE’s quality-of-service (QoS). To tackle this problem and improve the user experience, in this paper, we propose a novel resource utilization policy, which exploits reinforcement learning (RL) algorithm considering the pointer network (PN). In particular, we design an optimization problem that determines the optimal frequency and power allocation needed to maximize the achievable rate performance of all M2M pairs and CUEs in the network subject to the co-tier interference and QoS constraints. The proposed scheme enables the user equipment (UE) to autonomously select an available channel and optimal power to maximize the network capacity and spectrum efficiency while minimizing co-tier interference. Moreover, the proposed scheme is compared with traditional spectrum allocation schemes. Simulation results demonstrate the superiority of the proposed scheme than that of the traditional schemes. Moreover, the convergence of the proposed scheme is investigated which reduces the computational complexity (CC).

As promising candidate frequency bands, millimeter wave (mmWave) and terahertz (THz) communications can provide ultrahigh transmission rate to enable vehicle-to-everything (V2X) networks for connected autonomous vehicles (CAVs). However, beam alignment is extremely challenging in mmWave/THz communications due to its narrow beam width and fast mobility of CAV. In this article, we propose a new joint communication and control algorithm for beam alignment, where the mutual positive effect of communications and motion control of CAV on each other is discussed. Specifically, we first provide a framework to show the interaction between motion control of CAV and beam alignment of transmission from base station (BS) to CAV. Then, we analyze the effect of CAV control on beam alignment in communications, where a theorem is obtained to show the closed-form expression of their relationship. Finally, we discuss the CAV control design affected by beam alignment. Simulation results show remarkable performance of the proposed method.

Cache-enabled device-to-device (D2D) communication is a potential approach to tackle the resource shortage problem. However, public concerns of data privacy and system security still remain, which thus arises an urgent need for a reliable caching scheme. Fortunately, federated learning (FL) with a distributed paradigm provides an effective way to privacy issue by training a high-quality global model without any raw data exchanges. Besides the privacy issue, blockchain can be further introduced into the FL framework to resist the malicious attacks occurred in D2D caching networks. In this study, we propose a double-layer blockchain-based deep reinforcement FL (BDRFL) scheme to ensure privacy-preserved and caching-efficient D2D networks. In BDRFL, a double-layer blockchain is utilized to further enhance data security. Simulation results first verify the convergence of the BDRFL-based algorithm, and then demonstrate that the download latency of the BDRFL-based caching scheme can be significantly reduced under different types of attacks when compared to some existing caching policies.

A novel approach is presented in this work for context-aware connectivity and processing optimization of Internet of Things (IoT) networks. Different from the state-of-the-art approaches, the proposed approach simultaneously selects the best connectivity and processing unit (e.g., device, fog, and cloud) along with the percentage of data to be offloaded by jointly optimizing energy consumption, response time, security, and monetary cost. The proposed scheme employs a reinforcement learning algorithm and manages to achieve significant gains compared to deterministic solutions. In particular, the requirements of IoT devices in terms of response time and security are taken as inputs along with the remaining battery level of the devices, and the developed algorithm returns an optimized policy. The results obtained show that only our method is able to meet the holistic multiobjective optimization criteria, albeit, the benchmark approaches may achieve better results on a particular metric at the cost of failing to reach the other targets. Thus, the proposed approach is a device-centric and context-aware solution that accounts for the monetary and battery constraints.

In this paper, we model, analyze and optimize the multi-user and multi-order-reflection (MUMOR) intelligent reflecting surface (IRS) networks. We first derive a complete MUMOR IRS network model applicable for the arbitrary times of reflections, size and number of IRSs/reflectors. The optimal condition for achieving sum rate upper bound with one IRS in a closed-form function and the analytical condition to achieve interference-free transmission are derived, respectively. Leveraging this optimal condition, we obtain the MUMOR sum rate upper bound of the IRS network with different network topologies, where the linear graph (LG), complete graph (CG) and null graph (NG) topologies are considered. Simulation results verify our theories and derivations and demonstrate that the sum rate upper bounds of different network topologies are under a$K$-fold improvement given$K$-piece IRS.

Faster-than-Nyquist signaling (FTNS) has already been shown to increase the communication capacity on certain channels such as additive white Gaussian noise and block flat multiple-input multiple-output (MIMO) Rayleigh fading channels. The following issues, however, remain unresolved: 1) whether FTNS enables a capacity increase in generalized MIMO Rayleigh fading channels that are selective in time, frequency, and space; and 2) how channel selectivities affect the capacity and if present, the FTN capacity gain. To address the issues, this paper firstly investigates the ergodic capacity of MIMO-FTN transmission over triply-selective fading channels. We derive a low-complexity approximate capacity formula and also show how it degenerates in other channel models, such as doubly-selective single-input single-output fading channels, which can be considered as the special cases of triply-selective fading channels. The capacity evaluation results obtained under different channel conditions show that: 1) MIMO-FTN outperforms MIMO-Nyquist in terms of capacity; 2) the FTN gains are nearly consistent, while the FTN gains obtained in the frequency-selective fading channels are slightly higher than those obtained in the flat fading channels.

Intelligent Reflecting Surfaces (IRS) show a revolutionary potential for wireless communications. In this paper, a single IRS is used to achieve distributed multi-user beamforming and interference-free transmission. We first establish the IRS assisted multi-user system model and formulate an optimization problem called multi-user linearly constrained minimum variance (MU-LCMV) beamformer, under the criterion of minimizing the overall received signal power subject to a certain level of power response (e.g., unit power response) at desired signal directions and arbitrary low power response (e.g., zero power response) at the interference directions. A closed-form amplitude-unconstrained phase-continuous (AUPC) solution is derived first, then an amplitude-constrained phase-continuous (ACPC) solution is obtained by using sequential quadratic programming (SQP). Given the solutions, the IRS beam pattern shows that to achieve multi-user ($N$pairs of transceivers,$N > 1$) transmission through a single surface, up to$N-1$redundant beams are generated, significantly affecting power efficiency. The directions of the redundant beams are mathematically derived. The effect of mutual coupling on IRS is also analyzed to show the characteristic of side lobes. Simulation results verify the existence and accuracy of the redundant beam directions. This work can potentially enhance state-of-the-art wireless communication systems ranging from transceiver design, system and architecture design, network deployment and self-organizing-network operations.

The application of multiple-input multiple-output (MIMO) over orthogonal time frequency space (OTFS) modulation is envisioned to provide high-data-rate wireless transmission in high-mobility environments. However, in these communication scenarios, the multiple-dimensional interference, which can generate from space, delay and Doppler domains, challenges the channel equalization and symbol detection at the MIMO-OTFS receiver. To tackle this issue, we propose a time-space domain channel equalizer, relying on the mathematical least squares minimum residual algorithm, to remove the channel distortion on data symbols. The proposed channel equalizer adopts a recursion method to achieve symbol estimates, which can realize fast convergence by leveraging the sparsity of MIMO-OTFS channel matrix. Instead of directly remapping the equalized OTFS symbols into data bits, we develop an enhanced data detection (EDD) scheme to iteratively demodulate the superposed multi-antenna signal. The EDD can not only realize the linear-complexity interference cancellation, but also efficiently reap the spatial and multi-path diversities of MIMO-OTFS channel. The simulations show the proposed channel equalization and EDD algorithms enable the MIMO-OTFS receiver to robustly demodulate multi-stream 256-ary quadrature amplitude modulation symbols, under a maximum velocity of 550 km/h at 5.9 GHz carrier frequency.

Cache-enabled device-to-device (D2D) communication has been widely deemed as a promising approach to tackle the unprecedented growth of wireless traffic demands. Recently, tremendous efforts have been put into designing an efficient caching policy to provide users better quality of service. However, public concerns of data privacy still remain in D2D cache sharing networks, which thus arises an urgent need for a privacy-preserved caching scheme. In this study, we propose a double-layer blockchain-based federated learning (DBFL) scheme with the aim of minimizing the download latency for all users in a privacy-preserving manner. Specifically, in the sublayer, the devices within the same coverage area run a federated learning (FL) to train the caching scheme model for each area separately without exchange of local data. The model parameters for each area are recorded in sublayer chains with Raft consensus mechanism. Meanwhile, in the main layer, a mainchain based on practical Byzantine fault tolerance (PBFT) mechanism is used to resist faults and attacks, thus securing the reliability of FL updates. Only the reliable area models authorized by the mainchain are utilized to update the global model in the main layer. Numerical results show the convergence, as well as the gain of download latency of the proposed DBFL caching scheme when compared with several traditional schemes.

In real-time wireless control systems, the freshness of information is crucial since performance highly depends on timely exchange of information between the plant and the controller. In this study, the age metric, Age of Control Process (AoCP), is proposed for real-time wireless control systems which is different from traditional Age of Information (AoI). The First Generate First Serve (FGFS) M/M/1/1 → M/M/1/1 tandem queue model is considered to present a closed form expression for computation of AoCP and testbed environment is employed for the analysis of AoCP measure. Our experiment results show that FGFS M/M/1/1 → M/M/1/1 queuing model is suitable to represent real-time control systems. In addition, the proposed definition provides closer results to the experiment results when compared with the traditional AoI definition. To the best of our knowledge, this is the first study that conducts information freshness measurements on real-time control testbed.

The design of sharding aims to solve the scalability challenge in a blockchain network. Typically, by splitting the whole blockchain network into smaller shards, the transaction throughput can be significantly improved. However, distributing fewer attesting nodes for transactions in a shard may cause higher security risks. This paper analyzes the security level of transaction verification in different types of shards and transactions. The analyzed result indicates that the size of shards and validating nodes number may influence the transaction security in shards. And the random distribution of attesting nodes can reduce such influence and improve the reliability of consensus in shards.

Millimeter-wave (mm-wave) communication, which has already been a part of the fifth generation of mobile communication networks (5G), would result in ultra dense small cell deployments due to its limited coverage characteristics. To enable seamless handovers between indoor and outdoor environments, a mobility prediction of an indoor user is studied by deploying Markov chains. Based on the effect of external factors on the user's mobility, a simulation scenario is created to model the trajectory of an indoor user w.r.t the most visited areas before leaving the indoor environment. Based on that, a method for initializing the transition matrix of Markov chains is proposed, via Q-learning. The proposed solution is compared to a standard online learning Markov chain model in terms of different mobility models and learning rates. Results show that the proposed solution is always able to outperform the standard method in terms of prediction accuracy.

Abstract The continuous traffic increase of mobile communication systems has the collateral effect of higher energy consumption, affecting battery lifetime in the user equipment (UE). An effective solution for energy saving is to implement a discontinuous reception (DRX) mode. However, guaranteeing a desired quality of experience (QoE) while simultaneously saving energy is a challenge; but undoubtedly both energy efficiency and the QoE have been essential aspects for the provision of real‐time services, such as voice over Internet protocol (VoIP), voice over LTE, and mobile broadband in 4G networks and beyond. This paper focuses on human voice communications and proposes a Gaussian process regression algorithm that is capable of recognizing patterns of silence and predicts its duration in human conversations, with a prediction error as low as 1.87%. The proposed machine learning mechanism saves energy by switching OFF/ON the radio frequency interface, in order to extend the UE autonomy without harming QoE. Simulation results validate the effectiveness of the proposed mechanism compared with the related literature, showing improvements in energy savings of more than 30% while ensuring a desired QoE level with low computational cost.

The convergence of artificial intelligence (AI) and the Internet of Things (IoT) promotes energy-efficient communication in smart homes. Quality-of-Service (QoS) optimization during video streaming through wireless micro medical devices (WMMDs) in smart healthcare homes is the main purpose of this research. This article contributes in four distinct ways. First, to propose a novel lazy video transmission algorithm (LVTA). Second, a novel video transmission rate control algorithm (VTRCA) is proposed. Third, a novel cloud-based video transmission framework is developed. Fourth, the relationship between buffer size and performance indicators, i.e., peak-to-mean ratio (PMR), energy (i.e., encoding and transmission), and standard deviation, is investigated while comparing LVTA, VTRCA, and baseline approaches. The experimental results demonstrate that the reduction in encoding (32% and 35.4%) and transmission (37% and 39%) energy drains, PMR (5 and 4), and standard deviation (3 and 4 dB) for VTRCA and LVTA, respectively, is greater than that obtained by baseline during video streaming through WMMD.

The outbreak of the coronavirus disease 2019 (COVID-19) pandemic has exposed an urgent need for effective contact tracing solutions through mobile phone applications to prevent the infection from spreading further. However, due to the nature of contact tracing, public concern on privacy issues has been a bottleneck to the existing solutions, which is significantly affecting the uptake of contact tracing applications across the globe. In this article, we present a blockchain-enabled privacy-preserving contact tracing scheme: BeepTrace, where we propose to adopt blockchain bridging the user/patient and the authorized solvers to desensitize the user ID and location information. Compared with recently proposed contact tracing solutions, our approach shows higher security and privacy with the additional advantages of being battery friendly and globally accessible. Results show viability in terms of the required resource at both server and mobile phone perspectives. Through breaking the privacy concerns of the public, the proposed BeepTrace solution can provide a timely framework for authorities, companies, software developers, and researchers to fast develop and deploy effective digital contact tracing applications, to conquer the COVID-19 pandemic soon. Meanwhile, the open initiative of BeepTrace allows worldwide collaborations, integrate existing tracing and positioning solutions with the help of blockchain technology.

In industrial internet of things (IIoT), ultra-reliable and low-latency communication (URLLC) is proposed to guarantee the requirement of real-time wireless control systems in worst case, so as to maintain the system working in all cases. However, it is extremely challenging to maintain URLLC throughout the whole control process due to the scarcity of wireless resource. This paper develops an autonomous device-to-device (D2D) communication scheme by jointly considering reliability in URLLC and control requirement. In the proposed scheme, we consider the actual control requirement, i.e., control convergence rate, into communication design, where we find that it can be converted into a constraint on communication reliability. Then, the communication reliability constraint comes from control aspect, instead of URLLC, which leads to that the system does not need to guarantee worst case in URLLC. Second, the sensors autonomously decide whether to be activated with optimal probabilities to participate in the control process, which can maintain the communication reliability requirement with significantly less resource consumption. Simulation results show remarkable performance gain of our method. For instance, compared with fixed activation probability 40% only considering URLLC, the average power consumption of the proposed method can be reduced by at most about 100%.

Orthogonal time frequency space (OTFS) has shown to be a promising modulation technology that achieves the robust wireless transmission in high-mobility environments. The high mobility incurred Doppler effect in OTFS system, is represented as a continuous and relatively large band in the Doppler frequency. It yields the equivalent channel responses (ECRs) in the system change significantly within one symbol block, posing a challenge to channel estimation (CE) or tracking. In order to tackle this issue, in this paper, a set of transform-domain basis functions is designed to span a low-dimensional subspace for modeling the OTFS channel. Then, the CE can be performed by estimating a few projection coefficients of ECRs in the developed subspace, with training pilots. According to the individual transmission characteristic of OTFS signal, we propose a corner-inserted pilot pattern, which targets the low pilot overhead and satisfactory CE performance. Moreover, an OTFS signal detector, leveraging the time-domain channel equalization, linear-complexity interference cancellation and delay-Doppler domain maximal ratio combining detection, is developed to retrieve the transmitted data symbols. The simulations show the precisely estimated ECRs enable the detector to ideally demodulate 256-ary quadrature amplitude modulation signaling, under a velocity of 550 km/h at 5.9 GHz carrier frequency.

Orthogonal time frequency space (OTFS) is a two-dimensional modulation scheme realized in the delay-Doppler domain, which targets the robust wireless transmissions in high-mobility environments. In such scenarios, OTFS signal suffers from multipath channel with continuous Doppler spread, which results in significant inter-symbol interference and inter-Doppler interference (IDI). In this article, we analyze the interference generation mechanism, and compare statistical distributions of the IDI in two typical cases, i.e., limited-Doppler-shift channel and continuous-Doppler-spread channel (CoDSC). Focusing on the OTFS signal transmission over the CoDSC, our study firstly indicates that the widespread IDI incurs a computational burden for the element-wise detector like the message passing in the state-of-the-art works. Addressing this challenge, we propose a block-wise OTFS receiver by exploiting the structure and characteristics of the OTFS transmission matrix. In the receiver, we deliberately design an iteration strategy among the least squares minimum residual based channel equalizer, reliability-based symbol detector and interference eliminator, which can realize fast convergence by leveraging the sparsity of channel matrix. The simulations demonstrate that, in the CoDSC, the proposed scheme achieves much less detection error, and meanwhile reduces the computational complexity by an order of magnitude, compared with the state-of-the-art OTFS receivers.

Thanks to its inherent capabilities (such as fairly long radio coverage with extremely low power consumption), long-range wide area network (LoRaWAN) can support a wide spectrum of low-rate use-cases in Industry 4.0. In this article, both plain and energy harvesting (EH) industrial environments are considered to study the performance of LoRa radios for industrial automation. In the first instance, a model is presented to investigate LoRaWAN in Industry 4.0 in terms of battery life, battery replacement cost, and damage penalty. Then, the EH potential, available within an Industry 4.0, is highlighted to demonstrate the impact of harvested energy on the battery life and sensing interval of LoRa motes deployed across a production facility. The key outcome of these investigations is the cost trade-off analysis between battery replacement and damage penalty along different sensing intervals which demonstrates a linear increase in aggregate cost up to £1500 in case of 5 min sensing interval in the plain (nonenergy harvesting) industrial environment while it tends to decrease after a certain interval up to five times lower in EH scenarios. In addition, the carbon emissions due to the presence of LoRa motes and the annual CO2emission savings per node have been recorded up to 3 kg/kWh when fed through renewable energy sources. The analysis presented herein could be of great significance toward a green industry with cost and energy efficiency optimization.

Network slicing (NS) has been identified as one of the most promising architectural technologies for future mobile network systems to meet the extremely diversified service requirements of users. In radio access networks (RAN) slicing, service provisioning for slice users becomes much more complicated than that in traditional mobile networks, as the constraints of both user physical association with base station (BS) and logical association with NS should be considered. In other words, the user-BS-NS three layer association relationship should be addressed in provisioning tailored service for diversified use cases with various quality of service (QoS) requirements. Therefore, service provisioning in RAN slicing becomes an essential yet challenging issue for 5G and beyond systems. In this paper, we propose a unified framework for service provisioning in RAN slicing with aim of maximizing resource utilization while guaranteeing QoS of users. The framework consists of two steps. The first step is to identify a set of slice users whose QoS can be satisfied simultaneously; while the second step performs joint slice association and bandwidth allocation with aim to minimize bandwidth consumption. Numerical results show that in typical scenarios, our proposed service provisioning framework can achieve significant performance gain in terms of the number of serving users and wireless bandwidth utilization compared with traditional schemes.

Practical Byzantine Fault Tolerance (PBFT) consensus mechanism shows a great potential to break the performance bottleneck of the Proof-of-Work (PoW)-based blockchain systems, which typically support only dozens of transactions per second and require minutes to hours for transaction confirmation. However, due to frequent inter-node communications, PBFT mechanism has a poor node scalability and thus it is typically adopted in small networks. To enable PBFT in large systems such as massive Internet of Things (IoT) ecosystems and blockchain, in this article, a scalable multi-layer PBFT-based consensus mechanism is proposed by hierarchically grouping nodes into different layers and limiting the communication within the group. We first propose an optimal double-layer PBFT and show that the communication complexity is significantly reduced. Specifically, we prove that when the nodes are evenly distributed within the sub-groups in the second layer, the communication complexity is minimized. The security threshold is analyzed based on faulty probability determined (FPD) and faulty number determined (FND) models, respectively. We also provide a practical protocol for the proposed double-layer PBFT system. Finally, the results are extended to arbitrary-layer PBFT systems with communication complexity and security analysis. Simulation results verify the effectiveness of the analytical results.

As we move into the third decade of the 21st century, the 2020s, the unprecedented rate of technological disruption and the short-lived nature of the specifics of engineering state-of-the-art require us to carefully evaluate what it takes to be an effective engineer and what this entails for engineering education and their lifelong learning. While it is true that certain basics of engineering will not change, there will be an increased premium for some skills (such as lifelong learning, meta-learning, collaboration, creativity, critical thinking, communication skills, and cultural/global literacy). 21st-century skills are, as such, timeless skills: it is paradoxically the volatile nature of the modern world that has forced us from ephemeral vocational fads back to these permanently valuable skills. In this full research-to-practice paper, after reporting on the skills that policy think tanks and thought leaders deem necessary for the 21st century, we provide a synthesis in which we describe the pulls and pushes that learners and educators will face in the turbulent times of 2020 and beyond, and how they can thrive in the uncertain future through holistic well-rounded engineering education.

Our earlier work has demonstrated that a sufficiently trained recurrent neural network (RNN) can effectively detect base station performance degradations. We encountered a performance limit however: the accuracy gain diminishes as the RNN deepens. In this paper, we investigate the performance limit of a well-trained RNN by visualising its processes and modeling its internal operation. We first illustrate that inputs following a certain probability density undergo transformation in the RNN. By linearising the RNN process, we then develop a linear model to analyse the transformation. Using the model, we not only unveil insights into RNN operational behaviour, but are also able to explain the effect of diminishing gains in deeper RNNs. Finally, we validate our model and demonstrate its ability to accurately predict the performance of a well-trained RNN.

Subject identification has several applications. In transportation companies, knowing who is driving their vehicles might prevent theft or other ill-intended actions. On the other hand, privacy concerns, and the respective legislation, hinder the applicability of several traditional recognition techniques based on invasive technologies, such as video cameras. Moreover, in order to keep the driver's distractions to a minimum, this technologies must be non-disruptive, that is, they must be able to identify the subject seamlessly without them taking any action. In this context, we propose using deep learning applied to smart watch data for recognizing the person driving a vehicle based on a training set. Our proposal relies on the possibility of using transfer learning to avoid long training sessions for new drivers and to deliver a solution which can be deployed in practice. In this paper, we describe the convolutional neural network used in the solution and present results according to a real data-set collected by us, achieving accuracies ranging from 75 to 94%.

Blockchain-enabled wireless network has been recognized as an emerging network architecture to be widely employed into the Internet of Things (IoT) ecosystems for establishing trust and consensus mechanisms without the involvement of a third party. However, the uncertainty and vulnerability of wireless channels among the IoT nodes may pose a serious challenge to facilitate the deployment of blockchain in wireless networks. In this paper, we first present a generic system model for blockchain enabled wireless networks with multiple communication connections, where the number of communication connections between a client IoT node and the blockchain full nodes can be any arbitrary positive integer to satisfy different security requirements. Based on the proposed spatial-temporal network model, we theoretically calculate the transmission successful probability and the required communication throughput to support a wireless blockchain network. Finally, simulation results validate the accuracy of our theoretical analysis.

The coming fifth-generation (5G) cellular networks encourage to support several innovations and services, some of which will demand Ultra-reliable and Low-latency Communications (URLLC). For instance, URLLC can support real-time control to facilitate several emerging applications, such as robotic arms in industrial applications, and remote surgery for healthcare applications. However, URLLC is expected to be supported without considering the resources usage efficiency in wireless control systems due to the challenging to satisfy Quality of Service (QoS) requirements at the expense of diminishing energy efficiency. In this paper, we analyze uplink energy efficiency in URLLC utilizing multiple antennas in the transmitter and the receiver as well (MIMO) in real-time wireless control systems. We firstly formulate an optimization problem to maximize energy efficiency concerning the effect of the control convergence rate constraint. Then, we develop an exhaustive search method to obtain the maximum energy efficiency. Finally, simulation results are provided to demonstrate the performance of our proposed method.

The foreseen enormous generation of mobile data would result in congestion of the spectrum available. To efficiently use the available spectrum new paradigm named fog computing is a promising solution. In this paper, we developed a fog-IoT network to provide an ε-optimal resource allocation to maximize the overall network throughput. A joint cloudlet selection and power allocation problem is formulated under association and Quality-of-Service (QoS) constraints. The formulated problem falls in class of mixed-integer nonlinear programming (MINLP) problem which is NP-hard generally. We solved our problem by applying a less complex linearization technique that uses the outer approximation algorithm (OAA). Resource allocation and power allocation are efficiently conducted as a result of this optimization, which is less complicated compared to exhaustive search.

As the number of cellular and multimedia users are increasing the current mobile networks are being overloaded and need to be upgraded to new architectural featured 5G networks. Heterogeneous Cloud Radio Access Networks (HCRAN) are one of the dominant candidates for future networks with high data rate, minimized interference and high Energy Efficiency (EE). Due to dense users and base stations placement, the power consumption of H-CRAN is much higher than today's cellular networks. Energy harvesting (EH) is the solution to mitigate the grid power consumption problem in which power is harvested from natural resources like wind, solar, etc. EE of the system can be improved using energy harvesting and efficient resource allocation. In the presented article EE of H-CRAN with energy harvesting aided radio remote heads is explored. Formulated system problem is a mixed-integer non-linear programming (MINLP) problem which has the objective to maximize the H-CRAN system's EE. To optimize the proposed optimization problem Mesh Adaptive Direct Search (MADS) algorithm is explored. EE of H-CRAN system is maximized by resource allocation and power allocation which is efficient in terms of energy consumption. Our results show the objective is achieved with the help of low complexity algorithm and lower consumption of grid energy.

This paper proposes a novel unipolar transceiver for visible light communication (VLC) by using orthogonal waveforms. The main advantage of our proposed scheme over most of the existing unipolar schemes in the literature is that the polarity of the real-valued orthogonal frequency division multiplexing (OFDM) sample determines the pulse shape of the continuous-time signal and thus, the unipolar conversion is performed directly in the analog instead of the digital domain. Therefore, our proposed scheme does not require any direct current (DC) biasing or clipping as it is the case with existing schemes in the literature. The bit error rate (BER) performance of our proposed scheme is analytically derived and its accuracy is verified by using Matlab simulations. Simulation results also substantiate the potential performance gains of our proposed scheme against the state-of-the-art OFDM-based systems in VLC; it indicates that the absence of DC shift and clipping in our scheme supports more reliable communication and outperforms the asymmetrically clipped optical-OFDM (ACO-OFDM), DC optical-OFDM (DCO-OFDM) and unipolar-OFDM (U-OFDM) schemes. For instance, our scheme outperforms ACO-OFDM by at least 3 dB (in terms of signal to noise ratio) at a target BER of 10-4, when considering the same spectral efficiency for both schemes.

Providing higher data rate is a momentous goal for wireless communications systems, while interference is an important obstacle to reach this purpose. To cope with this problem, interference alignment (IA) has been proposed. In this paper, we propose two rank minimization methods to enhance the performance of IA in the presence of uncoordinated interference,i.e., interference that cannot be properly aligned with the rest of the network and thus is a crucial issue. In this scenario, perfect and imperfect channel state information (CSI) cases are considered. Our proposed approaches employ the$l_{2}$and the Schatten-$p$norms to approximate the rank function, due to its non-convexity. Also, we propose a new convex relaxation to expand the feasible set of our optimization problem, providing lower rank solutions compared to other IA methods from the literature. In addition, we propose a modified weighted-sum method to deal with interference in the relay-aided MIMO interference channel, which employs a set of weighting parameters in order to find more solutions.

Escalating cell outages and congestion-treated as anomalies-cost a substantial revenue loss to the cellular operators and severely affect subscriber quality of experience. State-of-the-art literature applies feed-forward deep neural network at core network (CN) for the detection of above problems in a single cell; however, the solution is impractical as it will overload the CN that monitors thousands of cells at a time. Inspired from mobile edge computing and breakthroughs of deep convolutional neural networks (CNNs) in computer vision research, in this article we split the network into several 100-cell regions each monitored by an edge server; and propose a framework that preprocesses raw call detail records having user activities to create an image-like volume, fed to a CNN model. The framework outputs a multilabeled vector identifying anomalous cell(s). Our results suggest that our solution can detect anomalies with up to 96% accuracy, and is scalable and expandable for industrial Internet of Things environment.

Network slicing is identified as a fundamental architectural technology for future mobile networks since it can logically separate networks into multiple slices and provide tailored quality of service (QoS). However, the introduction of network slicing into radio access networks (RAN) can greatly increase user handover complexity in cellular networks. Specifically, both physical resource constraints on base stations (BSs) and logical connection constraints on network slices (NSs) should be considered when making a handover decision. Moreover, various service types call for an intelligent handover scheme to guarantee the diversified QoS requirements. As such, in this article, a multiagent reinforcement LEarning based Smart handover Scheme, named LESS, is proposed, with the purpose of minimizing handover cost while maintaining user QoS. Due to the large action space introduced by multiple users and the data sparsity caused by user mobility, conventional reinforcement learning algorithms cannot be applied directly. To solve these difficulties, LESS exploits the unique characteristics of slicing in designing two algorithms: 1) LESS-DL, a distributed Q-learning algorithm to make handover decisions with reduced action space but without compromising handover performance; 2) LESS-QVU, a modified Q-value update algorithm which exploits slice traffic similarity to improve the accuracy of Q-value evaluation with limited data. Thus, LESS uses LESS-DL to choose the target BS and NS when a handover occurs, while Q-values are updated by using LESS-QVU. The convergence of LESS is theoretically proved in this article. Simulation results show that LESS can significantly improve network performance. In more detail, the number of handovers, handover cost and outage probability are reduced by around 50%, 65%, and 45%, respectively, when compared with traditional methods.

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 the next few years, a considerable innovation concerning the design of the future 5G mobile networks will be a concrete step towards enabling effective high throughput and low latency services. Software Defined Networking (SDN), Network Function Virtualization (NFV) and Self Organizing Network (SON) are considered the enabling technologies to achieve these goals. In this paper, assuming a Control-Data Separation Architecture (CDSA), we propose a flexible SDN/NFV-based SON testbed, for future 5G mobile networks. The main contribution of our work is to cover the need for a CDSA based testbed, enabling the investigation of the NG-SON capabilities for practical implementations. We implement two different testbed setups, a real one and a virtualized one, both based on the FlexRAN and OpenAir-Interface software tools. First, we implement a specific case study, i.e., the RAN entities activation/deactivation procedures. Next, we carry out time measurements, concerning the aforementioned procedures, in order to prove proper testbed functioning. Finally, we validate the C-SON and D-SON capabilities of our testbed, considering the features of the results.

Recent advances in meta-materials offer the prospect of deploying smart surfaces, or intelligent reflecting surfaces (IRS), that can manipulate electromagnetic (EM) channels and expand their achievable capacity. In this paper, we investigate the programmable channel of multi-user multiple input and multiple output (MU-MIMO) transmission and beamforming using meta-surface with multiple elements. We first model the MU-MIMO channel, and the optimal solution is derived based on the proposed multi-user Linearly Constrained Minimum Variance (MU-LCMV) beamformer. The beam pattern is analyzed, which shows that a single set of optimal weights can form multiple interference-free beams with redundant beams to be formed to achieve the multi-stream MIMO transmission in typical configurations. The mathematical relationships of the beams are derived with different surface configurations. Extensive simulations verify the results. This work is fundamental and can potentially enhance any state-of-the-art wireless communication systems ranging from transceiver design, system and architecture design, network deployment, and self-organizing-network operations.

Distributed systems are crucial to the full realization of the Internet of Thing (IoT) ecosystem as it mitigates the challenges of trust, security, and scalability associated with the traditional centralized approach. In this paper, we present an analytical modeling framework for Practical Byzantine Fault Tolerance (PBFT)-a consensus method for blockchain in IoT networks. We define the viable area for the wireless PBFT networks which guarantees the minimum number of replica nodes required for achieving the protocol's safety and liveliness. We also present an analytical framework for obtaining the viable area which we later utilize for power optimization. Results show that significant energy saving can be achieved with the utilization of the viable area concept in wireless PBFT networks. The proposed framework can serve as a theoretical guidance for practical PBFT based wireless blockchain network deployment.

The cell range extension (CRE) has been successfully implemented to bias the user to base station (BS) association policy in a way that achieves load balancing and increases the capacity of heterogeneous networks. The user-centric backhaul (UCB) scheme is a CRE evolution that is both backhaul-aware and user-context-aware -two constraints that are shaping the 5G network development. In this work, we formulate and solve the multi-objective optimisation problem of the UCB user-BS association. We derive analytical expressions of the ergodic throughput resulting from the UCB and, accordingly, identify the optimum association policy. The study demonstrates the gain margins that can be realised with pertinent user-cell association which is aware of the end-to-end network limitations and users requirements.

In the advent of ultra-dense networks with unprecedented complex and heterogeneous infrastructure, the role of automation in network optimization becomes vital for sustaining the target performance. In this work, we address the challenge of identifying and classifying sub-par performing nodes in near-real time through a machine-learning inspection of streaming performance indicators from multiple probe points. We present a novel K-means-based solution for classifying node performance over a sliding time segment and further categorizing the type of failure. The K-means solution first identifies the performance instances of interest. These are then inspected in a second clustering round for automated performance labeling. Next, the labeled data-set is employed to train a Support Vector Machine based classifier that is continuously classifying incoming performance instances from the network. The method is tested using a real network data set comprising call detail records. The results advocate the potential of our method for effectively and accurately identifying and classifying performance degradation in any node in the network.

In this paper, we investigate the resource management for radio access network slicing from user access control and wireless bandwidth allocation perspectives. First, to guarantee users' QoS, we propose two admission control (AC) policies to select admissible users from the perspective of optimizing the QoS and the number of serving users respectively. Then, to optimize the bandwidth utilization for the selected admissible users, we investigate the slice association and bandwidth allocation (SABA) problem and propose network centric and UE centric SABA policies respectively. Numerical results show that in typical scenarios, our proposed AC and SABA policies can significantly outperform traditional policies in terms of wireless bandwidth utilization and number of admissible users.

Network slicing (NS) has been identified as a fundamental technology for future mobile networks to meet extremely diverse communication requirements by providing tailored quality of service (QoS). However, due to the introduction of NS into radio access networks (RAN) forming a UE-BS-NS three-layer association, handoff becomes very complicated and cannot be resolved by conventional policies. In this paper, we propose a multi-agent reinforcement LEarning based Smart handoff policy with data Sharing, named LESS, to reduce handoff cost while maintaining user QoS requirements in RAN slicing. Considering the large action space introduced by multiple users and the data sparsity problem due to user mobility, LESS is designed to have two components: 1) LESS-DL, a modified distributed Q-learning algorithm with small action space to make handoff decisions; 2) LESS-DS, a data sharing mechanism using limited data to improve the accuracy of handoff decisions made by LESS-DL. The proposed LESS mechanism uses LESS-DL to choose both the target base station and NS when a handoff occurs, and then updates the Q-values of each user according to LESS-DS. Numerical results show that in typical scenarios, LESS can significantly reduce the handoff cost when compared with traditional handoff policies without learning.

In this paper, we quantify the effect of residual hardware impairments (RHI) on the error rate performance of a relay-based non-orthogonal multiple access (NOMA) system, where the communication between a source node and multiple users is completed via an amplify-and-forward (AF) relay node. In particular, we focus on the pairwise error probability (PEP) analysis and derive an accurate PEP approximation to characterize the performance of NOMA users under Rayleigh fading channels. The derived PEP expression is then exploited to investigate the diversity gain and the union bound on the bit error rate (BER) of the underlying system. Our results demonstrate that the presence of RHI causes an error floor at high signal-to-noise ratio (SNR) values. This error floor yields a detrimental effect on the achievable diversity order of NOMA users, where it is shown that the diversity order of all users converges to zero.

One of the fundamental goals of mobile networks is to enable uninterrupted access to wireless services without compromising the expected quality of service (QoS). This paper reports a number of significant contributions. First, a novel analytical model is proposed for holistic handover (HO) cost evaluation, that integrates signaling overhead, latency, call dropping, and radio resource wastage. The developed mathematical model is applicable to several cellular architectures, but the focus here is on the Control/Data Separation Architecture (CDSA). Second, data-driven HO prediction is proposed and evaluated as part of the holistic cost, for the first time, through novel application of a recurrent deep learning architecture, specifically, a stacked long-short-term memory (LSTM) model. Finally, simulation results and preliminary analysis reveal different cases where non-predictive and predictive deep neural networks can be effectively utilized, based on HO management requirements. Both analytical and machine learning models are evaluated with a benchmark, real-world dataset measuring human behaviors and interactions. Numerical and comparative simulation results demonstrate the potential of our proposed deep learning-driven HO management framework, as a future benchmark for the mobile networking and machine learning communities.

The radio frequency identification (RFID) has emerged Internet of Things (IoT) into the identification of things. This paper presents, a low-cost smart refrigerator system for future IoT applications. The proposed smart refrigerator is used for automatic billing and restoring of beverage metallic cans. The metallic cans can be restored by generating a product shortage alert message to a nearby retailer. To design a low-cost and low-profile tag antenna for metallic items is very challenging, especially when mass production is required for item-level tagging. Therefore, a novel ultrahigh frequency (UHF) RFID tag antenna is designed for metallic cans by exploiting the metallic structure as the main radiator. Applying characteristics mode analysis, we observed that some characteristic modes associated with the metallic structure could be exploited to radiate more effectively by placing a suitable inductive load. Moreover, a low cost, printed (using conductive ink) small loop integrated with meandered dipole used as an inductive load, which was also connected with RFID chip. The 3-dB bandwidth of the proposed tag covers the whole UHF band ranging from 860 to 960 MHz when embedded with metal cans. The measured read range of the RFID tag is more than 2.5 m in all directions to check the robustness of the proposed solution. To prove the concept, a case study was performed by placing the tagged metallic cans inside a refrigerator for automatic billing, 97.5% tags are read and billed successfully. This paper paves the way for tagging metallic bodies for tracking applications in domains ranging from consumer devices to infotainment solutions, which enlightens a vital aspect for the IoT.

Blockchain has shown a great potential in Internet of Things (IoT) ecosystems for establishing trust and consensus mechanisms without involvement of any third party. Understanding the relationship between communication and blockchain as well as the performance constraints posing on the counterparts can facilitate designing a dedicated blockchain-enabled IoT systems. In this paper, we establish an analytical model for the blockchain-enabled wireless IoT system. By considering spatio-temporal domain Poisson distribution, i.e., node geographical distribution in spatial domain and transaction arrival rate in time domain are both modeled as Poisson point process (PPP), we first derive the distribution of signal-to-interference-plus-noise ratio (SINR), blockchain transaction successful rate as well as overall throughput. Based on the system model and performance analysis, we design an algorithm to determine the optimal full function node deployment for blockchain system under the criterion of maximizing transaction throughput. Finally, the security performance is analyzed in the proposed networks with three typical attacks. Solutions such as physical layer security are presented and discussed to keep the system secure under these attacks. Numerical results validate the accuracy of our theoretical analysis and optimal node deployment algorithm.

With the advent of network densification and the development of other radio interface technologies, the major bottleneck of future cellular networks is shifting from the radio access network to the backhaul. The future networks are expected to handle a wide range of applications and users with different requirements. In order to tackle the problem of downlink user-cell association, and allocate users to the best cell, an intelligent solution based on reinforcement learning is proposed. A distributed solution based on Q-Learning is developed in order to determine the best cell range extension offsets (CREOs) for each small cell (SC) and the best weights of each user requirement to efficiently allocate users to the most appropriate SC, based on both backhaul constraints and user demands. By optimizing both CREOs and user weights, a user-specific allocation can be achieved, resulting in a better overall quality of service. The results show that the proposed algorithm outperforms current solutions by achieving better user satisfaction, mitigating the total number of users in outage, and minimizing user dissatisfaction when satisfaction is not possible.

The smart grid communication plays a pivotal role in coordinating energy generation, energy transmission, and energy distribution. Cellular technology with long-term evolution (LTE)-based standards has been a preference for smart grid communication networks. However, conventional cellular networks could suffer from radio access network (RAN) congestion when many smart grid devices attempt access simultaneously. Heterogeneous cellular networks (HetNets) are proposed as important techniques to solve this problem because HetNets can alleviate the RAN congestion by off-loading access attempt from a macrocell to small cells. In smart grid, real-time data from phasor measurement units (PMUs) has a stringent delay requirement in order to ensure the stability of the grid. In this paper, we propose a joint resource allocation and power control scheme to improve the end-to-end delay in HetNets by taking into account the simultaneous transmission of PMUs. We formulate the optimization problem as a mixed integer problem and adopt a game-theoretic approach and the best response dynamics algorithm to solve the problem. Simulation results show that the proposed scheme can significantly minimize the end-to-end delay compared to first-in first-out scheduling and round-robin scheduling schemes.

In this paper, we establish a comprehensive uplink system model for in-band and guard-band Narrowband Internet of Things (NB-IoT) with arbitrary sample duration in the NB-IoT device. The mathematical expressions of received LTE and NB-IoT signals are derived. Moreover, the close-form interference power on the LTE signal from the adjacent NB-IoT signal is given analytically. The result shows that the sample duration of NB-IoT device has significant impact on its desired signal and on the interference to the LTE user equipment (UE). Numerical results show that the analytical expressions match the simulated ones perfectly, which verifies the effectiveness of proposed system model and derivations. The work in this paper provides a valid guidance for NB-IoT system deployment and co-existence analysis.

The spatially-incoherent radiators in visible light communication (VLC) constrain the optical carrier to be only driven by a real electrical sub-carrier, which cannot be quadrature modulated as in classic RF-based communication systems. This, in turn, severely limits the transmission throughput of VLC systems. To overcome this technical challenge, a novel coherent transmission is proposed. As such, the optical carrier is only treated as a purely amplitude- modulated carrier capable of transmitting the quadrature modulated symbols. The ability of our novel coherent transmission to fully reconstruct the in-phase and quadrature parts is validated through analytical symbol error rate and Matlab simulations. Results also show that the proposed scheme, while remarkable in its simplicity, can improve both the spectral and energy efficiency of VLC systems, i.e. double the spectral efficiency and achieve more than 45% energy efficiency improvement, when compared to its existing counterpart.

We consider an idealistic scenario where the vacation (no-load) period of a typical base station (BS) is known in advance such that its vacation time can be matched with a sleep depth. The latter is the sum of the deactivation latency, actual sleep period and reactivation latency. Noting that the power consumed during the actual sleep period is a function of the deactivation latency, we derive an accurate closed-form expression for the optimal deactivation latency for deterministic BS vacation time. Further, using this expression, we derive the optimal average power consumption for the case where the vacation time follows a known distribution. Numerical results show that significant power consumption savings can be achieved in the sleep mode by selecting the optimal deactivation latency for each vacation period. Furthermore, our results also show that deactivating the BS hardware is sub-optimal for BS vacation less than a particular threshold value.

Understanding of mobile internet traffic patterns and capacity to estimate future traffic, particularly at high spatiotemporal granularity, is crucial for proactive decision making in emerging and future cognizant cellular networks enabled with self-organizing features. It becomes even more important in the world of `Internet of Things' with machines communicating locally. In this paper, internet activity data from a mobile network operator Call Detail Records (CDRs) is analysed at high granularity to study the spatiotemporal variance and traffic patterns. To estimate future traffic at high granularity, a Support Vector Regression (SVR) based traffic model is trained and evaluated for the prediction of maximum, minimum and average internet traffic in the next hour based on the actual traffic in the last hour. Performance of the model is compared with that of the State-of-the-Art (SOTA) deep learning models recently proposed in the literature for the same data, same granularity, and same predicates. It is concluded that this SVR model outperforms the SOTA deep and non-deep learning methods used in the literature.

The explosive growth of connected devices and the increasing number of broadband users have led to an unprecedented growth in traffic demand. To this effect, the next generation wireless systems are envisioned to meet this growth and offer a potential data rate of 10 Gbps or more. In this context, an attractive solution to the current spectrum crunch issue is to exploit the visible light spectrum for the realization of high-speed commutation systems. However, this requires solutions to certain challenges relating to visible light communications (VLC), such as the stringent requirements of VLC-based intensity modulation and direct detection (IM/DD), which require signals to be real and unipolar. The present work proposes a novel power-domain multiplexing based optical asymmetric modulation (OAM) scheme for indoor VLC systems, which is particularly adapted to transmit high-order modulation signals using linear real and unipolar constellations that fit into the restrictions of IM/DD systems. It is shown that the proposed scheme provides improved system performance that outperforms alternative modulation schemes, at no extra complexity.

The Mobile Internet connects billions of smart phones and laptops. With this global connectivity, the stage is set for the emergence of: (a)Tactile Internetto deliver haptic experiences to remote users, and (b) flexible and integratedBackhaul/Fronthaulnetworks to support demands of such applications.

Cell densification is being perceived as the panacea for the imminent capacity crunch. However, high aggregated energy consumption and increased inter-cell interference (ICI) caused by densification, remain the two long-standing problems. We propose a novel network orchestration solution for simultaneously minimizing energy consumption and ICI in ultra-dense 5G networks. The proposed solution builds on a big data analysis of over 10 million CDRs from a real network that shows there exists strong spatio-temporal predictability in real network traffic patterns. Leveraging this, we develop a novel scheme to pro-actively schedule radio resources and small cell sleep cycles yielding substantial energy savings and reduced ICI, without compromising the users QoS. This scheme is derived by formulating a joint Energy Consumption and ICI minimization problem and solving it through a combination of linear binary integer programming, and progressive analysis based heuristic algorithm. Evaluations using: 1) a HetNet deployment designed for Milan city where big data analytics are used on real CDRs data from the Telecom Italia network to model traffic patterns, 2) NS-3 based Monte-Carlo simulations with synthetic Poisson traffic show that, compared to full frequency reuse and always on approach, in best case, the proposed scheme can reduce energy consumption in HetNets to 1/8th while providing same or better QoS.

Wireless technology is the strongest contender for catering for the 5G backhaul (BH) stipulated performance, where optical fiber is unavailable. In the presence of ultra-dense networks, such occurrences are exponentially increasing, and different wireless technologies are investigated for this application. We present the first BH-specific wireless link performance modeling that considers its inherent line-of-sight nature, together with an appropriate representation of the network topology using stochastic geometry. To this end, novel tractable models are obtained to capture the performance of wireless BH links. These are integrated into a multi-hop hybrid BH performance modeling framework and are applied in the analysis of a BH-aware user association optimization problem.

Universal filtered multicarrier (UFMC) is a low complexity promising waveform that provides quasi‐orthogonal property among subcarriers. In addition, it can achieve much better out‐of‐band emission performance than orthogonal frequency division multiplexing (OFDM) system. Authors have proposed a hardware platform to implement a UFMC transmitter in this paper. Highly reduced complexity schemes for IFFT, filtering, and spectrum shifting are realized on actual hardware. This helps to achieve overall architecture of the transmitter at the cost of minimal FPGA resource usage. Hence, the overall design uses only 1038 slice registers, 1154 slice LUTs, and 64 multipliers of Xilinx Virtex‐7 XC7VX330t device. A throughput of 773.5 Msamples/sec at an operational frequency of 364 MHz is achieved. This throughput is adequate for processing 50 Physical Resource Blocks (PRB) of LTE 10 MHz channelization in required time. The presented architecture provides a latency of only 2% of one LTE 10MHz channelization symbol due to the implementation of pipelining at different levels. Although the presented hardware design in its current form meets LTE 10MHz channelization throughput requirements, further increase in throughput is possible due to the scalable nature of the architecture. To the best of our knowledge, this work is first ever FPGA solution for UFMC transmitter presented in the literature.

The Internet of Things (IoT) is spreading much faster than the speed at which the supporting technology is maturing. Today, there are tens of wireless technologies competing for IoT and a myriad of IoT devices with disparate capabilities and constraints. Moreover, each of many verticals employing IoT networks dictates distinctive and differential network qualities. In this work, we present a context‐aware framework that jointly optimises the connectivity and computational speed of the IoT network to deliver the qualities required by each vertical. Based on a smart port application, we identify energy efficiency, security, and response time as essential quality features and consider a wireless realisation of IoT connectivity using short range and long‐range technologies. We propose a reinforcement learning technique and demonstrate significant reduction in energy consumption while meeting the quality requirements of all related applications.

We analyze the area energy efficiency (AEE) of spatial multiplexing (SM) and transmit antenna selection (TAS), considering a realistic power consumption model for small base stations (BSs), which includes the power consumed by the backhaul as well as different interference attenuation levels. Our results show an optimum number of BSs for each technique that maximizes the AEE. Moreover, we also show that TAS has a larger AEE than SM when the demand for system capacity is low, while SM becomes more energy efficient when the demanded capacity is larger. Additionally, when the capacity demand and the area to be covered are fixed, the number of BSs needed to be deployed is smaller for SM than for the other techniques. Finally, the system performance in terms of AEE is shown to be strongly dependent on the amount of interference, which in turn depends on the employed interference-mitigation scheme, and on the employed power consumption model.

Interference is a key bottleneck in wireless communication systems. Interference alignment is a management technique that align interference from other transmitters in the least possibly dimension subspace at each receiver and provides the remaining dimensions for free interference signal. An uncoordinated interference is an example of interference which cannot be aligned coordinately with interference from coordinated part; consequently, the performance of interference alignment approaches are degraded. In this paper, we propose a rank minimization method to enhance the performance of interference alignment in the presence of uncoordinated interference sources. Firstly, to obtain higher multiplexing gain, a new rank minimization based optimization problem is proposed; then, a new class of convex relaxation is introduced which can reduce the optimal value of the problem and obtain lower rank solutions by expanding the feasibility set. Simulation results show that our proposed method can obtain considerably higher multiplexing gain and sum rate than other approaches in the interference alignment framework.

In this paper, a novel low-complexity and spectrally efficient modulation scheme for visible light communication (VLC) is proposed. Our new spatial quadrature modulation (SQM) is designed to efficiently adapt traditional complex modulation schemes to VLC; i.e. converting multi-level quadrature amplitude modulation (M-QAM), to real-unipolar symbols, making it suitable for transmission over light intensity. The proposed SQM relies on the spatial domain to convey the orthogonality and polarity of the complex signals, rather than mapping bits to symbol as in existing spatial modulation (SM) schemes. The detailed symbol error analysis of SQM is derived and the derivation is validated with link level simulation results. Using simulation and derived results, we also provide a performance comparison between the proposed SQM and SM. Simulation results demonstrate that SQM could achieve a better symbol error rate (SER) and/or data rate performance compared to the state of the art in SM; for instance a Eb/Nogain of at least 5 dB at a SER of 10−4.

Heterogeneous networks are a key solution to serving the exponential surge in data volume and higher quality expectations. Nonetheless, such networks require the ubiquitous presence of fiber-to-the-cell to address the performance demands of 5G and fast-spreading small cells. To this end, innovative ways of optimizing the usage of realistic backhaul links are being investigated. In this work, we propose a fuzzy Q-learning-based user-centric backhaul-aware user cell association scheme. The proposed scheme aims at optimizing the user-cell association process in a context-aware and backhaul-aware manner. Complementing the scheme with fuzzy-logic requires 33.3% additional storage memory. On the other hand, it increases the computational efficiency by 60% and improves the users' performance by 12%.

In this paper, we present an analytical framework to model the sleep mode power consumption of a base station (BS) as a function of its sleep depth. The sleep depth is made up of the BS deactivation latency, actual sleep period and activation latency. Numerical results demonstrate a close match between our proposed approach and the actual sleep mode power consumption for selected BS types. As an application of our proposed approach, we analyze the optimal sleep depth of a BS, taking into consideration the increased power consumption during BS activation, which exceeds its no-load power consumption. We also consider the power consumed during BS deactivation, which also exceeds the power consumed when the actual sleep level is attained. From the results, we can observe that the average total power consumption of a BS monotonically decreases with the sleep depth as long as the ratio between the actual sleep period and the transition latency (deactivation plus reactivation latency) exceeds a certain threshold.

The fifth generation of mobile networks (5G) is maturing fast and the target year 2020 is around the corner. However, the realistic backhaul network may not be ready for 5G arrival as it is likely to converge to 5G requirements at a slower pace than the radio counterpart. In this work, we develop a method that identifies pertinent backhaul upgrade stages that are ranked based on their associated cost. First, the User-centric- backhaul (UCB) scheme is employed to reveal the bottlenecks of the incumbent backhaul network, as perceived by users and holistic network. A multi- hop hybrid backhaul modelling framework is then employed to quantify possible rectifications that would deliver the highest improvement at the lowest cost. These are implemented and the results are verified following another usage of UCB. A case study is presented that demonstrates the strength of this method in enabling an effective and cost efficient evolution road map towards the 5G backhaul.

Handover (HO) management is one of the critical challenges in current and future mobile communication systems due to new technologies being deployed at a network level, such as small and femtocells. Because of the smaller sizes of cells, users are expected to perform more frequent HOs, which can increase signaling costs and also decrease user's performance, if a HO is performed poorly. In order to address this issue, predictive HO techniques, such as Markov chains (MC), have been introduced in the literature due to their simplicity and generality. This technique, however, experiences a path dependency problem, specially when a user performs a HO to the same cell, also known as a re-visit. In this paper, the path dependency problem of this kind of predictors is tackled by introducing a new 3D transition matrix, which has an additional dimension representing the orders of HOs, instead of a conventional 2D one. Results show that the proposed algorithm outperforms the classical MC based predictors both in terms of accuracy and HO cost when re-visits are considered.

Predictive algorithms have become very important for handover (HO) management in mobile communications. In this regard, numerous techniques are being applied in order to obtain more accurate and robust methods. Markov chains (MC) are one of the most commonly used predictors since their easy implementation. In this paper, a threshold-based approach is introduced to common MC predictors in order to make predictions more accurate, since the probability is the main actor in a prediction process. The threshold value aims to prevent the predictor from making inaccurate predictions in case the probabilities of two or more states are very close. Results show that the proposed threshold-based method can improve the performance in terms of both prediction accuracy and signaling cost, specially for high randomness degrees, while also decreasing the number of inaccurate predictions.

We evaluate the use of HARQ schemes in the context of vehicle to infrastructure communications considering ultra reliable communications in the short term from a channel capacity stand point. We show that it is not possible to meet strict latency requirements with very high reliability without some diversity strategy and propose a solution to determining an optimal limit on the maximum allowed number of retransmissions using Chase combining and simple HARQ to increase energy efficiency. Results show that using the proposed optimizations leads to spending 5 times less energy when compared to only one retransmission in the context of a benchmark test case for urban scenario. In addition, we present an approximation that relates most system parameters and can predict whether or not the link can be closed, which is valuable for system design.

Frequent handovers (HOs) in dense small cell deployment scenarios could lead to a dramatic increase in signaling overhead. This suggests a paradigm shift toward a signaling conscious cellular architecture with intelligent mobility management. In this direction, a futuristic radio access network with a logical separation between control and data planes has been proposed in research community. It aims to overcome limitations of the conventional architecture by providing high data rate services under the umbrella of a coverage layer in a dual connection mode. This approach enables signaling efficient HO procedures since the control plane remains unchanged when the users move within the footprint of the same umbrella. Considering this configuration, we propose a core-network efficient radio resource control signaling scheme for active state HO and develop an analytical framework to evaluate its signaling load as a function of network density, user mobility, and session characteristics. In addition, we propose an intelligent HO prediction scheme with advance resource preparation in order to minimize the HO signaling latency. Numerical and simulation results show promising gains in terms of reduction in HO latency and signaling load as compared with conventional approaches.

The backhaul network is a critical challenge towards the success of 5G and corresponding difficulties are many-fold, such as network coverage expansion, very high bandwidth, ultra- low latency and energy consumption, at a minimum cost. No single backhaul solution can address all these requirements but, on the other hand, not all of the backhaul links require the same set of stringent requirements. To this end, we propose a novel scheme that capitalises on the diversity in both performance requirements and backhaul capabilities to maximise the system-centric as well as user-centric performance indicators. The user-centric backhaul provisioning scheme uses multiple attribute decision making (MADM) for the user-cell-backhaul association criteria in a way that intelligently associates users with available cells based on corresponding dynamic radio and backhaul conditions while abiding by users' requirements. Radio cells broadcast multiple bias factors, each reflecting a dynamic performance indicator of the end-to-end network performance such as capacity, latency, resilience, energy consumption, etc. A given user would employ these factors to derive a user-centric cell ranking that motivates it to select the cell with radio and backhaul capabilities that conform to the user requirements. Reinforcement learning is used by the radio cell to optimise the bias factors for each performance indicator in a way that maximises the system performance and users' end-to-end quality of experience (QoE). Preliminary results based on a case study show considerable improvement in users QoE when compared to state-of-the-art user-cell association schemes.

The emerging 5G wireless networks will pose extreme requirements such as high throughput and low latency. Caching as a promising technology can effectively decrease latency and provide customized services based on group users behaviour (GUB). In this paper, we carry out the energy efficiency analysis in the cache-enabled hyper cellular networks (HCNs), where the macro cells and small cells (SCs) are deployed heterogeneously with the control and user plane (C/U) split. Benefiting from the assistance of macro cells, a novel access scheme is proposed according to both user interest and fairness of service, where the SCs can turn into semi- sleep mode. Expressions of coverage probability, throughput and energy efficiency (EE) are derived analytically as the functions of key parameters, including the cache ability, search radius and backhaul limitation. Numerical results show that the proposed scheme in HCNs can increase the network coverage probability by more than 200% compared with the single- tier networks. The network EE can be improved by 54% than the nearest access scheme, with larger research radius and higher SC cache capacity under lower traffic load. Our performance study provides insights into the efficient use of cache in the 5G software defined networking (SDN).

Database-aided user association, where users are associated with data base stations (BSs) based on a database which stores their geographical location with signal-to-noise-ratio tagging, will play a vital role in the futuristic cellular architecture with separated control and data planes. However, such approach can lead to inaccurate user-data BS association, as a result of the inaccuracies in the positioning technique, thus leading to sub-optimal performance. In this paper, we investigate the impact of database-aided user association approach on the average spectral efficiency (ASE). We model the data plane base stations using its fluid model equivalent and derive the ASE for the channel model with pathloss only and when shadowing is incorporated. Our results show that the ASE in database-aided networks degrades as the accuracy of the user positioning technique decreases. Hence, system specifications for database-aided networks must take account of inaccuracies in positioning techniques.

With the worldwide evolution of 4G generation and revolution in the information and communications technology(ICT) field to meet the exponential increase of mobile data traffic in the 2020 era, the hybrid satellite and terrestrial network based on the soft defined features is proposed from a perspective of 5G. In this paper, an end-to-end architecture of hybrid satellite and terrestrial network under the control and user Plane (C/U) split concept is studied and the performances are analysed based on stochastic geometry. The relationship between spectral efficiency (SE) and energy efficiency (EE) is investigated, taking consideration of overhead costs, transmission and circuit power, backhaul of gateway (GW), and density of small cells. Numerical results show that, by optimizing the key parameters, the hybrid satellite and terrestrial network can achieve nearly 90% EE gain with only 3% SE loss in relative dense networks, and achieve both higher EE and SE gain (20% and 5% respectively) in sparse networks toward the future 5G green communication networks.

It is our pleasure to write the Editorial for the Special Issue on Evolution and Development of 5G Wireless Communication Systems. Upon conclusion of fourth generation (4G) cellular network standardization tasks a few years ago, the direction of research has started to shift systematically towards fifth generation (5G) communication systems. The difference between 4G and 5G is not limited to the increased throughput and performance. 5G systems are supposed to be flexible to accommodate heterogeneous traffic and devices, and various applications with different quality-of-service (QoS) requirements. Particularly, the goal is to take full benefit of advances in technology including cloud computing, Internet of Things (IoT), ultra-dense networks, massive MIMO, device-to-device communication, pervasive and social computing. In order to meet stringent goals, 5G communication systems build upon the evolution of the existing technologies and the development of the new technologies mentioned above. The Special Issue contains 11 papers, each paper covers the subject from different prospective, and thus, offer readers a holistic view of different research challenges currently under investigation by research communities. The papers can be grouped under following topics: C. Hua et al. present a paper entitled “Wireless backhaul resource allocation and user-centric clustering in ultra-dense wireless networks”. It considers optimization of resource allocation in wireless backhaul links and user-centric clustering in the access links. The objective is to maximise the weighted sum rate of all users under the backhaul resource constraints. An iterative algorithm is proposed to solve the transformed problem based on its special property. Simulation results show that the proposed algorithm outperforms other existing schemes under different network settings. Z. Wang et al. present a paper entitled “Interference pricing in 5G ultra-dense small cell networks: a Stackelberg game approach” which models the scenario as a Stackelberg game, where the macrocell base stations (MBS) act as the leader and all small cell base stations (SCBSs) as followers. Simulation results show the correctness of the analysis and the significant benefits when the power control and channel allocation are jointly considered in the proposed schemes. Z. Kaleem et al. present “Public safety users’ priority-based energy and time-efficient device discovery scheme with contention resolution for ProSe in third generation partnership project long-term evolution-advanced systems”, which proposes a time and energy-efficient contention-resolving device discovery resource allocation (TEECR-DDRA) scheme that has the capability to enhance the success ratio for discovery of D2D users by reducing collisions among users. Moreover, the proposed TEECR-DDRA scheme has the ability to prioritise PS users to meet their QoS and latency requirements. System-level simulations show that the proposed TEECR-DDRA scheme performs remarkably well under D2D network. M. T. Gul et al. present a paper entitled “Merge-and-forward: a cooperative multimedia transmissions protocol using RaptorQ codes”, proposing a cooperative multimedia transmission protocol based on a novel merge-and-forward relaying and the best relay selection (RS) schemes. Moreover, to combat the packet loss for enhanced and reliable video delivery, they adopt application layer forward error correction scheme which is based on the most improved and advanced version of fountain codes (i.e., RaptorQ codes). They evaluate the performance of the proposed scheme in terms of decoding failure probability, decoding overhead, peak signal-to-noise ratio, and mean opinion score. K. Yang et al.'s paper “Edge aware cross-tier base station cooperation in heterogeneous wireless networks with non-uniformly-distributed nodes” investigates the cross-tier base station (BS) cooperation in non-uniform heterogeneous networks where the distribution of pico BSs (PBSs) is modelled as Neyman–Scott cluster process. The authors propose an edge aware cross-tier cooperation scheme to improve the performance of edge hotspot users that have weaker signal-to-interference-plus noise ratio (SINR). Stochastic geometry is utilised to derive the SINR and energy efficiency performance of the proposed scheme, which is compared with other classical schemes such as full cooperation (FC) and traditional non-cooperation scheme. Y. Cai et al. present “Secure transmission in the random cognitive radio networks with secrecy guard zone and artificial noise” which proposes a simple and decentralised secure transmission scheme by jointly incorporating the secrecy guard zone and artificial noise in cognitive radio networks. Numerical results show how the system parameters affect the achievable maximum secrecy throughput, the optimal transmission power and the optimal power allocation between the information-bearing signal and the artificial noise. Y. Sun et al. present “Local altruistic coalition formation game for spectrum sharing and interference management in hyper-dense cloud-RANs” and investigate the spectrum sharing and interference management in hyper-dense cloud radio access networks (C-RANs). The authors formulate this problem as a local altruistic coalition formation game (LACF) with externalities. The authors propose a distributed coalitional formation algorithm based on modified recursive core to obtain the final stable coalition partition. Furthermore, the system stability, convergence and complexity of the proposed algorithm are analysed. W. Chang et al. paper “Effects of non-uniform quantisation on the interference mitigation using multi-cell multiple-input and multiple-output coordinated beamforming” proposes a low complexity cumulative distribution function (CDF)-based non-uniform quantisation method with a limited number of feedback bits for applying more quantisation levels to represent feedback CSI, which occurs with higher probability. The simulation results proved the higher transmission rate, particularly in cases with fewer feedback bits. D. Liu et al.'s paper “Self-organising multiuser matching in cellular networks: a score-based mutually beneficial approach” studies the self-organising user assignment problem for the multi-user cooperation network. Furthermore, the multi-user assignment problem is formulated as a one-to-one matching game, in which idle users and active users rank one another individually based on their own preference. Simulation results show that the proposed distributed algorithm yields well matching performance between source users and relay users, which is close to the optimal centralised results. D. C. Araújo et al.'s paper “Massive MIMO: survey and future research topics” presents an overview of the basic concepts of massive multiple-input multiple-output, with a focus on the challenges and opportunities, based on contemporary research. R. Sun et al. present the paper “Transceiver design for cooperative nonorthogonal multiple access systems with wireless energy transfer”. The paper considers an energy harvesting-based cooperative non-orthogonal multiple access (NOMA) system. Transmitter beamforming, power splitter and receiver filter are jointly designed to maximise rate with the predefined QoS constraint of weaker node and the power constraint of node which simultaneously sends independent signals to a stronger node and weaker node. Since the problem is non-convex, they propose an iterative approach to solve it. Moreover, a zero-forcing based low-complexity solution is also presented. Simulation results demonstrate that, both two proposed schemes have better performance than the direction transmission. All of the papers in Special Issue show that 5G systems can support the specialized use cases which are not supported by the current access systems. In addition, authors investigated the issues related to backhaul for 5G systems and latency reduction. The integration of new technologies with the evolved current systems bring tremendous improvement in 5G systems. Alagan Anpalagan received the B.A.Sc., M.A.Sc., and Ph.D. degrees in electrical engineering from the University of Toronto, Toronto, ON, Canada. In 2001, he joined the Department of Electrical and Computer Engineering, Ryerson University, Toronto, where he was promoted to Full Professor in 2010. He served the department as the Graduate Program Director (2004–2009) and the Interim Electrical Engineering Program Director (2009–2010). He directs a research group working on radio resource management and radio access and networking areas within the WINCORE Lab. During his sabbatical (2010–2011), he was a Visiting Professor with Asian Institute of Technology and a Visiting Researcher with Kyoto University, Kyoto, Japan. His industrial experience includes working at Bell Mobility, Nortel Networks, and IBM Canada. He has coauthored three edited books, namely, Design and Deployment of Small Cell Networks (Cambridge University Press, 2014), Routing in Opportunistic Networks (Springer, 2013), and Handbook on Green Information and Communication Systems (Academic Press, 2012). His current research interests include cognitive radio resource allocation and management, wireless cross-layer design and optimization, cooperative communication, machine-to-machine communication, small cell networks, and green communications technologies. Dr. Anpalagan has served as an Associate Editor of the IEEE Communications Surveys & Tutorials since 2012 and Springer Wireless Personal Communications since 2009. Adnan Shahid received the B.Eng. and the M.Eng. degrees in computer engineering with communication specialization from the University of Engineering and Technology, Taxila, Pakistan in 2006 and 2010, respectively, and the Ph.D degree in information and communication engineering from the Sejong University, South Korea in 2015. He is currently working as a Postdoctoral Researcher at iMinds/IBCN, Department of Information Technology, University of Ghent, Belgium. From Sep 2015 – Jun 2016, he was with the Department of Computer Engineering, Taif University, Saudi Arabia. From Mar 2015 – Aug 2015, he worked as a Postdoc Researcher at Yonsei University, South Korea. From Aug 2012 – Feb 2015, he worked as a PhD research assistant in Sejong University, South Korea. From Mar 2007 – Aug 2012, he served as a Lecturer in electrical engineering department of National University of Computer and Emerging Sciences (NUCES-FAST), Pakistan. He was also the recipient of the prestigious BK 21 plus Postdoc program at Yonsei University, South Korea. He is a member of IEEE and actively involved in various research activities. He is also serving as an Associate Editor at IEEE Access Journal and Annals of Telecommunication Journal. His research interests includes the next generation wireless communication and networks with prime focus on resource management, interference management, cross-layer optimization, self-organizing networks, small cell networks, device to device communications, machine to machine communications, 5G wireless communications, etc. Waleed Ejaz (S’12, M’14, SM‱16) is a Senior Research Associate at the Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada. Prior to this, he was a Post-doctoral fellow at Queen's University, Kingston, Canada. He received his Ph.D. degree in Information and Communication Engineering from Sejong University, Republic of Korea in 2014. He earned his M.Sc. and B.Sc. degrees in Computer Engineering from National University of Sciences & Technology, Islamabad, Pakistan and University of Engineering & Technology, Taxila, Pakistan, respectively. He worked in top engineering universities in Pakistan and Saudi Arabia as a Faculty Member.His current research interests include Internet of Things (IoT), energy harvesting, 5G cellular networks, and mobile cloud computing. He is currently serving as an Associate Editor of the Canadian Journal of Electrical and Computer Engineering and the IEEE ACCESS. In addition, he is handling the special issues in IET Communications, the IEEE ACCESS, and the Journal of Internet Technology. He also completed certificate courses on Teaching and Learning in Higher Education from the Chang School at Ryerson University. Muhammad Ali Imran received his M.Sc. (Distinction) and Ph.D. degrees from Imperial College London, UK, in 2002 and 2007, respectively. He is currently a Reader in the Centre for Communication Systems Research (CCSR) at the University of Surrey, UK. He has a global collaborative research network spanning both academia and key industrial players in the field of wireless communications. He has lead role in a number of multimillion international research projects including the new physical layer work area for 5G innovation centre at Surrey. He has supervised 17 successful PhD graduates and published over 150 peer-reviewed research papers including more than 20 IEEE Journals. His research interests include the derivation of information theoretic performance limits, energy efficient design of cellular system and learning/self-organizing techniques for optimization of cellular system operation. He is a senior member of IEEE and a Fellow of Higher Education Academy (FHEA), UK. Kandeepan Sithamparanathan has a PhD from the University of Technology, Sydney and is currently with the School of Electrical and Computer Engineering at RMIT University. He is also a NICTA Researcher at the NICTA Victoria Research Laboratory (VRL, Melbourne). In the past he had worked with the National ICT Australia (Canberra Research Laboratory) and CREATE-NET (Trento). Kandeepan served as one of the Vice Chairs for the IEEE Technical Committee on Cognitive Networks (TCCN) and has published a book together with Dr Andrea Giorgetti from the University of Bologna, Italy, titled ‘Cognitive Radio Techniques: Spectrum Sensing, Interference Mitigation and Localization’, published by Artech House (Boston). He currently Chairs the IEEE VIC Communication Society Chapter and is a Senior Member of the IEEE. He was awarded as one of the best IEEE Reviewers by the IEEE Communications Society. Kandeepan has published around ninety peer reviewed journal and conference papers. He has chaired several IEEE workshops and other conferences. His research interests are in 5G communications, cognitive radios and signal processing techniques. Yuhua Xu received his B.S. degree in Communications Engineering, and Ph.D. degree in Communications and Information Systems from College of Communications Engineering, PLA University of Science and Technology, in 2006 and 2014 respectively. He has been with College of Communications Engineering, PLA University of Science and Technology since 2012, and currently as an Assistant Professor. His research interests focus on opportunistic spectrum access, learning theory, game theory, and distributed optimization techniques for wireless communications. He has published several papers in international conferences and reputed journals in his research area. He served as Associate Editor for Wiley Transactions on Emerging Telecommunications Technologies and KSII Transactions on Internet and Information Systems. In 2011 and 2012, he was awarded Certificate of Appreciation as Exemplary Reviewer for the IEEE Communications Letters. He was selected to receive the IEEE Signal Processing Society's (SPS) 2015 Young Author Best Paper Award, and the Funds for Distinguished Young Scholars of Jiangsu Province in 2015.

This study proposes a novel adaptive stochastic radio access selection scheme for mobile users in heterogeneous cellular‐wireless local area network (WLAN) systems. In this scheme, a mobile user located in dual coverage area randomly selects WLAN with probability of ω when there is a need for downloading a chunk of data. The value of ω is optimised according to the status of both networks in terms of network load and signal quality of both cellular and WLAN networks. An analytical model based on continuous time Markov chain is proposed to optimise the value of ω and compute the performance of proposed scheme in terms of energy efficiency, throughput, and call blocking probability. Both analytical and simulation results demonstrate the superiority of the proposed scheme compared with the mainstream network selection schemes: namely, WLAN‐first and load balancing .

This study investigates the downlink resource allocation problem in orthogonal frequency division multiple access heterogeneous networks consisting of macrocells and femtocells sharing the same frequency band. The focus is to devise optimised policies for femtocells' access to the shared spectrum, in terms of femtocell transmissions, in order to maximise femto‐users (FUEs) sum data rate while ensuring that certain level of quality of service (QoS) for the macro‐cell users in the vicinity of femtocells is provided. The optimal solution to this problem is obtained by employing the well‐known dual Lagrangian method and the optimal femtocell transmit power and resource allocation solution is derived in detail. However, the optimal solution introduces high computational complexity. To this end, a heuristic solution to the problem is proposed. The algorithms to implement both optimal and efficient suboptimal schemes in a practical system are also given in detail while their complexity is compared. Simulation results show that proposed dynamic resource allocation scheme (a) ensures the macro‐users QoS requirements compared with the Reuse‐1 scheme, where femtocells are allowed to transmit at full power and bandwidth; (b) can maintain FUE data rates at high levels; (c) provides performance close to the optimal solution, while introducing much lower complexity.

The increasing demand for any-time any-where wireless connectivity has posed a formidable ‘1000 × data challenge’ for service providers. With the envisioned 1000 × explosion in mobile data traffic by the end of year 2020, wireless network architecture needs to rapidly evolve. In particular, the evolution trajectory should be charted such that exponential gains can be realised in network wide resource efficiency. This requires a clean slate design for future 5G wireless networks while provisioning interoperability with the legacy deployment. Both operators and technology providers realise that 5G will not merely be a newer version of 4G simply provisioning faster data transfers. These 5G networks are expected to be more dynamic due to heterogeneity in terms of devices, technologies, spectral bands and deployment models. Heterogeneity is indeed the intrinsic and central feature of the evolving networking paradigm. Now several potential solutions have recently been proposed to meet the aforementioned challenges and all address both network architecture and technologies. On the architectural front, concepts such as (i) cloudification & softwarisation of radio access networks; (ii) split-plane deployment; (iii) licensed shared access; (iv) decoupled uplink and downlink transmissions; and (v) information/content centric networking are all being considered as the enabling candidates. In terms of new technologies: (i) mmWave communications; (ii) massive MIMO; (iii) D2D communications; (iv) small cell deployment; and (v) low power IoT communication technologies (such as Bluetooth Low Energy, 802.11.ah WiFi, LoRA, SIGFOX) are all vital design tools for future 5G HetNets. In addition, the so-called concept of ‘tactile internet’, which has a wide spectrum of requirements ranging from ultra-low latency to ultra-high throughput via deployment of HetNets, cannot be realised without significant advances in signal processing algorithms. Thus, the main objective of this Special Section is to provide a platform for the dissemination of important results in those signal processing techniques necessary for enabling large scale, heterogeneous, 5G wireless networks. The first part of this Special Section presents four contributions. In the first paper, Mumtaz et al. present an energy efficient algorithm for D2D users in the presence of other cellular users (CUs). The authors employ Lagrangian duality theory for optimising both the power and rate of the D2D users while guaranteeing an acceptable quality-of-service (QoS) for the CUs. Finally, the solution of the proposed algorithm is then employed to achieve proportional fairness between the D2D and the CU users. The second paper (Butt et al.) reflects a growing interest in the area of green communication. It has recently been accepted that opportunistic exploitation of ambient energy sources is going to be the cornerstone of future wireless networks. The authors discuss relay selection schemes with the objective of minimising outage probability for a network consisting of a single source, multiple relays and a single destination. The relays are powered by radio frequency (RF) signals from the source and the authors present an optimal relay selection strategy to minimise outage probability for the system. Finally, a numerical solution is developed to determine the optimal number of relays. In the third paper, Gurjar et al. examine the significance of wireless channel estimation error on the performance of an analogue network coding (ANC)-based MIMO two-way relay system employing zero-forcing (ZF) transceivers in a Rayleigh fading environment. An analytical framework has been developed to study the overall outage analysis and some interesting (exact) expressions have been derived for special cases such as when the relay is equipped with less than two antennas. Some of the important contributions of this work are: a) exact expressions for the overall outage probability and the ergodic sum-rate have been derived within the context of channel estimation error; b) the authors have shown that system diversity may reduce to zero in the presence of channel estimation error due to imperfect self-interference cancellation; c) they conclude that a low complexity solution can be further derived by exploiting channel estimation error with ZF transmission/reception for an ANC based MIMO two-way relay system. In the final paper, Li et al. propose a hierarchical precoding approach for multi-cell, multi-user systems with any number of base stations and users, which is suitable for any number of data streams. The key feature of this approach is to align the inter-user interferences within the same cell to the room spanned by the inter-cell interferences, by which both the inter-cell and inter-user interferences are cancelled simultaneously. The effectiveness of this proposed method is demonstrated with an extensive set of simulations. In summary, this Special Section presents some important recent advances in D2D and relay assisted communication networks with a special focus on energy efficiency. Moreover, some of the state-of-the-art methods in multiuser MIMO systems have also been studied. For those interested in future 5G wireless networks, these articles will serve as a good springboard to appreciate further developments in this important topic. Finally, we would like to thank (i) all the submitting authors for considering this Special Section as a potential journal in which to publicise their research work; (ii) the reviewers for their high quality evaluations; and (iii) the Editorial team of the IET Signal Processing journal for their professional support. Syed Ali Raza Zaidi is currently University Academic Fellow (Assistant Professor) at the University of Leeds, UK. Prior to this, he was a Research Fellow in SPCOM Research Group at Leeds. He received his B. Eng. degree in information and communication system engineering from the School of Electronics and Electrical Engineering, NUST, Pakistan in 2008. He was awarded the NUST's most prestigious Rector's gold medal for his final year project. From September 2007 till August 2008, he served as a Research Assistant in Wireless Sensor Network Lab on a collaborative research project between NUST, Pakistan and Ajou University, South Korea. In 2008, he was awarded overseas research student scholarship along with Tetley Lupton and Excellence Scholarships to pursue his PhD at the School of Electronics and Electrical Engineering, the University of Leeds, U.K. He was also awarded with COST IC0902, DAAD and Royal Academy of Engineering grants to promote his research. In 2013, he was conferred with the prestigious F.W. Carter Prize for outstanding Doctoral thesis by the University of Leeds. Dr. Ali was a visiting Research Scientist at Qatar Innovations and Mobility Centre from October to December 2013. He has served as an invited reviewer for IEEE flagship journals and conferences. Dr. Ali is also UK Liaison for the European Association for Signal Processing (EURASIP). He is currently serving as an editor for IEEE Communication Letters and Lead Guest Editor for IET Signal Processing Special Section on 5G Wireless Networks. He is also the general secretary for IEEE Technical Committee on 5G Networks. He has published more than 60 papers in leading IEEE journals and conferences and has chaired several IEEE workshops/conferences. His current research interests are in the area of design and implementation of large scale networks for machine-to-machine communication (including robotics and autonomous systems). Des McLernon received his B.Sc in electronic and electrical engineering and his MSc in electronics, both from the Queen's University of Belfast, N. Ireland. He then worked in industry on radar systems research and development with Ferranti Ltd in Edinburgh, Scotland and later joined Imperial College, University of London, where he took his PhD in signal processing. After first lecturing at South Bank University, London, UK, he moved to the School of Electronic and Electrical Engineering, at the University of Leeds, UK, where he is a Reader in Signal Processing. His research interests are broadly within the domain of signal processing for wireless communications (in which area he has published over 285 journal and conference papers). He has supervised over 35 PhD students, given many invited talks in the UK and abroad and is Associate Editor of the IET Signal Processing journal. He has been a member of various international conference TPC's and conference organisation committees - recent conference organisation includes IEEE SPAWC 2010, European Signal Processing Conference (EUSIPCO) 2013, IET Conference on Intelligent Signal Processing (London, 2013/2015) and IEEE Globecom 2014/2015 (2nd /3rd Workshops on Trusted Communications with Physical Layer Security). His current research projects include distributed sensing, PHY layer security, caching and energy efficiency in heterogeneous networks, energy harvesting, robotic and drone communications, intrusion detection in software defined networks, compressive sensing and time-frequency analysis. Muhammad Ali Imran received his M.Sc. (Distinction) and Ph.D. degrees from Imperial College London, UK, in 2002 and 2007, respectively. He is currently a Reader in Communications in the Institute for Communication Systems (ICS - formerly known as CCSR) at the University of Surrey, UK and an adjunct Associate Professor at the University of Oklahoma, USA. He has lead a number of multimillion-funded international research projects encompassing the areas of energy efficiency, fundamental performance limits, sensor networks and self-organising cellular networks. He is also leading the new physical layer work area for 5G innovation centre at Surrey. He has a global collaborative research network spanning both academia and key industrial players in the field of wireless communications. He has supervised 21 successful PhD graduates and published over 200 peer-reviewed research papers including more than 20 IEEE Transaction papers. He has been giving a series of expert tutorials on emerging Green 5G technologies and networks at IEEE flagship conferences such as WCNC, PIMRC and ICC. Recently, he has been appointed as an area Chair for IEEE ComSoc Technical Committee on Backhaul/Fronthaul Networking and Communications (TCBNC). He secured first rank in his B.Sc. and a distinction in his M.Sc. degree along with an award of excellence in recognition of his academic achievements conferred by the President of Pakistan. He has been awarded IEEE ComSoc's Fred Ellersick award 2014 and FEPS Learning and Teaching award 2014 and twice nominated for Tony Jean's Inspirational Teaching award. He is a shortlisted finalist for The Wharton-QS Stars Awards 2014 for innovative teaching and VC's learning and teaching award in University of Surrey. He is a senior member of IEEE and a Senior Fellow of Higher Education Academy (SFHEA), UK. Muhammad Zeeshan Shakir is a Senior Research Fellow at Carleton University, Canada. In recent years, he has been involved in several joint R&D initiatives with Telus, DragonWave, University of Surrey, KAUST, and TAMUQ. His research interests include design and deployment of diverse wireless communication systems, including hyper-dense heterogeneous networks and related 5G technologies. He has published more than 75 technical journal and conference papers and has contributed to seven books, all in reputable venues. He is an author of three research monographs including one authored book. He earned his PhD degree in electronic and electrical engineering from University of Strathclyde, Glasgow, UK in 2010. He is an Associate Technical Editor of IEEE Communications Magazine and has served as a Lead Guest Editor for IEEE Communications and IEEE Wireless Communications Magazines. He has been serving as Chair/Co-chair of several workshops/symposia in IEEE flagship conferences, such as ICC and GlobalSIP. He has been giving a series of expert tutorials on emerging Green 5G technologies and networks at IEEE flagship conferences such as Globecom, ICUWB, PIMRC and ICC. Recently, he has been appointed as a Chair to IEEE ComSoc Technical Committee on Backhaul/Fronthaul Networking and Communications (TCBNC). He is an active member of IEEE, IEEE ComSoc and IEEE Standard Association. Mounir Ghogho received his MSc degree in 1993 and PhD degree in 1997 from the National Polytechnic Institute of Toulouse, France. He was an EPSRC Research Fellow with the University of Strathclyde, Glasgow (Scotland), from September 1997 to November 2001. Since December 2001, he has been a faculty member with the school of Electronic and Electrical Engineering at the University of Leeds, UK, where he currently holds a Chair in Signal Processing and Communications. Since 2010, he has also been a Research Director at the International University of Rabat (Morocco). He was awarded the UK Royal Academy of Engineering Research Fellowship in September 2000. He is one of the recipients of the 2013 IBM Faculty award. He is currently an Associate Editor of the IEEE Signal Processing magazine. He served as an Associate Editor of the IEEE Transactions on Signal Processing from 2005 to 2008, the IEEE Signal Processing Letters from 2001 to 2004, and the Elsevier's Digital Signal Processing journal from 2011 to 2012. He is currently a member of the IEEE Signal Processing Society SAM Technical Committee. He served as a member of the IEEE Signal Processing Society SPCOM Technical Committee from 2005 to 2010 and a member of IEEE Signal Processing Society SPTM Technical Committee from 2006 to 2011. He was the General Chair of the 11th IEEE workshop on Signal Processing for Advanced Wireless Communications (SPAWC2010) and the 21st edition of the European Signal Processing Conference (EUSIPCO 2013), and the Technical co-Chair of the MIMO symposium of IWCMC 2007 and IWCMC 2008. His research interests are in signal processing and communication networks. He has published over 260 journal and conferences papers. He was awarded the UK Royal Academy of Engineering Research Fellowship in September 2000. He is also one of the recipients of the 2013 IBM Faculty award and is the EURASIP Liaison in Morocco.

Principal component analysis and the residual error is an effective anomaly detection technique. In an environment where anomalies are present in the training set, the derived principal components can be skewed by the anomalies. A further aspect of anomaly detection is that data might be distributed across different nodes in a network and their communication to a centralized processing unit is prohibited due to communication cost. Current solutions to distributed anomaly detection rely on a hierarchical network infrastructure to aggregate data or models; however, in this environment, links close to the root of the tree become critical and congested. In this paper, an algorithm is proposed that is more robust in its derivation of the principal components of a training set containing anomalies. A distributed form of the algorithm is then derived where each node in a network can iterate towards the centralized solution by exchanging small matrices with neighboring nodes. Experimental evaluations on both synthetic and real-world data sets demonstrate the superior performance of the proposed approach in comparison to principal component analysis and alternative anomaly detection techniques. In addition, it is shown that in a variety of network infrastructures, the distributed form of the anomaly detection model is able to derive a close approximation of the centralized model.

Abstract This paper investigates the radio resource management (RRM) issues in a heterogeneous macro‐femto network. The objective of femto deployment is to improve coverage, capacity, and experienced quality of service of indoor users. The location and density of user‐deployed femtos is not knowna‐priori. This makes interference management crucial. In particular, with co‐channel allocation (to improve resource utilization efficiency), RRM becomes involved because of both cross‐layer and co‐layer interference. In this paper, we review the resource allocation strategies available in the literature for heterogeneous macro‐femto network. Then, we propose a self‐organized resource allocation (SO‐RA) scheme for an orthogonal frequency division multiple access based macro‐femto network to mitigate co‐layer interference in the downlink transmission. We compare its performance with the existing schemes like Reuse‐1, adaptive frequency reuse (AFR), and AFR with power control (one of our proposed modification to AFR approach) in terms of 10 percentile user throughput and fairness to femto users. The performance of AFR with power control scheme matches closely with Reuse‐1, while the SO‐RA scheme achieves improved throughput and fairness performance. SO‐RA scheme ensures minimum throughput guarantee to all femto users and exhibits better performance than the existing state‐of‐the‐art resource allocation schemes.Copyright © 2014 John Wiley & Sons, Ltd.

Abstract In future heterogeneous cellular networks, cognitive radio compatible with device to device communication technique can be an aid to further enhance system spectral and energy efficiency. The unlicensed smart devices (SDs) are allowed to detect the available licensed spectrum and utilise the spectrum resource which is detected as not being used by the licensed users. In this work, we propose such a system and provide comprehensive analysis of the effect of selection of SDs' frame structure on the energy efficiency, throughput and interference. Moreover, uplink power control strategy is also considered where the licensed users and SDs adapt the transmit power based on the distance from their reference receivers. The optimal frame structure with power control is investigated under high‐signal‐to‐noise ratio (SNR) and low‐SNR network environments. The impact of power control and optimal sensing time and frame length, on the achievable energy efficiency, throughput and interference are illustrated and analysed by simulation results. It has been also shown that the optimal sensing time and frame length which maximizes the energy efficiency of SDs strictly depends on the power control factor employed in the underlying network such that the considered power control strategy may decrease the energy efficiency of SDs under very low‐SNR regime. Copyright © 2016 John Wiley & Sons, Ltd.

In research community, a new radio access network architecture with a logical separation between control plane (CP) and data plane (DP) has been proposed for future cellular systems. It aims to overcome limitations of the conventional architecture by providing high data rate services under the umbrella of a coverage layer in a dual connection mode. This configuration could provide significant savings in signalling overhead. In particular, mobility robustness with minimal handover (HO) signalling is considered as one of the most promising benefits of this architecture. However, the DP mobility remains an issue that needs to be investigated. We consider predictive DP HO management as a solution that could minimise the out-of-band signalling related to the HO procedure. Thus we propose a mobility prediction scheme based on Markov Chains. The developed model predicts the user's trajectory in terms of a HO sequence in order to minimise the interruption time and the associated signalling when the HO is triggered. Depending on the prediction accuracy, numerical results show that the predictive HO management strategy could significantly reduce the signalling cost as compared with the conventional non-predictive mechanism.

Most of the wireless systems such as the long term evolution (LTE) adopt a pilot symbol-aided channel estimation approach for data detection purposes. In this technique, some of the transmission resources are allocated to common pilot signals which constitute a significant overhead in current standards. This can be traced to the worst-case design approach adopted in these systems where the pilot spacing is chosen based on extreme condition assumptions. This suggests extending the set of the parameters that can be adaptively adjusted to include the pilot density. In this paper, we propose an adaptive pilot pattern scheme that depends on estimating the channel correlation. A new system architecture with a logical separation between control and data planes is considered and orthogonal frequency division multiplexing (OFDM) is chosen as the access technique. Simulation results show that the proposed scheme can provide a significant saving of the LTE pilot overhead with a marginal performance penalty.

In this paper, we investigate the optimal inter-frequency small cell discovery (ISCD) periodicity for small cells deployed on carrier frequency other than that of the serving macro cell. We consider that the small cells and user terminals (UTs) positions are modelled according to a homogeneous Poisson Point Process (PPP). We utilize polynomial curve fitting to approximate the percentage of time the typical UT missed small cell offloading opportunity, for a fixed small cell density and fixed UT speed. We then derive analytically, the optimal ISCD periodicity that minimizes the average UT energy consumption (EC). Furthermore, we also derive the optimal ISCD periodicity that maximizes the average energy efficiency (EE), i.e. bit-per-joule capacity. Results show that the EC optimal ISCD periodicity always exceeds the EE optimal ISCD periodicity, with the exception of when the average ergodic rates in both tiers are equal, in which the optimal ISCD periodicity in both cases also becomes equal.

In this paper, we analyse the total network en- ergy efficiency (EE) of cellular-WLAN heterogeneous network (HetNet) that employs cell range expansion (CRE) technique, in order to control the user association to either WLAN or cellular network. To this end, we model the system with OFDM based cellular macro-cells and WiFi access points for a saturated (i.e., full-buffer) downlink scenario, considering practical aspects of each type of access technology. Then we evaluate the EE of network by considering realistic power consumption models for each access network type. We compare the performance of the CRE scheme with two benchmark user association schemes; namely, WLAN-first and Max-RSRP (Reference Signal Receive Power). The results demonstrate that CRE with negative biasing performs best in terms of network EE, while the WLAN-first scheme demonstrates the worst performance. However, the CRE with negative biasing lacks fairness in terms of user throughput, while the WLAN-first scheme shows better fairness. Hence, there is a trade-off between the user fairness and the system EE. We show that by optimising the bias factor of each APs individually, with appropriate utility function, a better balance of this trade-off can be achieved.

The Global energy consumption and carbon footprint related to operating mobile phones in wireless networks is increasing significantly. In order to determine the overall energy consumption of a wireless network, both the operation of the network infrastructure and the devices connected to that network must be considered. Although the larger part of the global energy consumption of wireless networks is consumed at base station sites and access points, a significant part is consumed from the operation of mobile phones. In this paper, system level power consumption models for mobile phones in terms of 2G, 3G and Wi-Fi are presented. The developed model increases the accuracy of the current power profiles by considering different stages of a single transmission, including the variables affecting each stage. The power states of wireless interfaces, maintenance, network attachment/detachment and network resource allocation policies of different network operators are also considered to obtain a complete model that can accurately predict that power consumption at every stage of connectivity. A comparison between power consumption of different radio access technologies is also included to promote energy efficient use of spectrum. Using the system level power models presented in this paper, the contribution of the operation of mobile phones to overall energy consumption of wireless communication networks can be determined.

The next generation networks seem to be too dense compared to the existing one, so a self-control mechanism, which determines the optimal cell size will be essential. In this paper, we present self organized cell size control algorithms, which maintain optimum system throughput and power consumption. Particularly, we investigate three different algorithms that control the cells size, while maintaining the optimum power consumption and block allocation in the network. These algorithms differ in terms of their decision area. The first one is based on a centralized control; the second one is a distributed approach; and the final one is based on a group distributed control. In order to evaluate their performance, these algorithms are tested upon two different simulation environment, which approach real scenarios. Our results indicate that the group distributed algorithm is the best approach for future network, since it has a good performance and about 10 times lower computational complexity when compared with the centralized approach.

High spectral efficiency is at the core of any effort made to achieve the data rates and capacity requirements demanded by next generation mobile technologies. Adaptive Modulation and Coding (AMC) is considered a powerful tool for the efficient usage of spectrum and for the provision of channel adaptive error resilience. In this paper, a novel AMC based transmission scheme is introduced for Long Term Evolution-Advanced (LTE-A) networks to impose adaptive error resilience on High Efficiency Video Coding (HEVC) based video data at the physical layer. A model is proposed to rank REs at the LTE-A transmitter based on channel information. Data are allocated onto ranked REs to maximise the efficiency of the users' allocated bandwidth while maintaining video quality. This is achieved by imposing a stronger Modulation and Coding Scheme (MCS) for data that have a high probability of being distorted, thus spanning the entire allocated bandwidth. Simulation results show that the proposed Unequal Error Protection (UEP) strategy helps to minimise the LTE-A transmitter power by up to 8dB with guaranteed video quality. In effect, this is a reduction of approximately 84% of the transmit power.

This paper investigates the downlink resource allocation problem in Orthogonal Frequency Division Multiple Access (OFDMA) Heterogeneous Networks (HetNets) consisting of macrocells and femtocells sharing the same frequency band. The focus is to devise an optimised policy for femtocells' access to the shared spectrum, in terms of femtocell transmissions, in order to keep femto users sum data rate at high levels while ensuring that certain level of quality of service (QoS) for the macro-cell users in the vicinity of femtocells is provided. Both data and control channel constraints are considered, to insure that not only the macro-cell users' data rate demands are meet, but also a certain level of Bit Error Rate (BER) is ensured for the control channel information. The problem is addressed by our proposed linear binary integer programming heuristic algorithm and the performance is compared with the conventional Reuse-1 scheme. Results show a negligible drop in femtocell performance for our proposed scheme, as a trade-off for ensuring all macro users data rate demands when Reuse-1 scheme can even lead up to 40% outage. Discussion is also presented for the implementation possibility of our proposed in a practical LTE network.

A multiuser system where signals are simultaneously transmitted within the same bandwidth helps in achieving higher capacity. However, such a system is quite vulnerable to co-channel interference, due to presence of multiple users in the system. Signal detection becomes highly challenging when number of transmit antennas exceeds the number of receive antennas,thereby creating an over-loaded wireless system. In this case, Maximum Likelihood (ML) detection offers optimal performance as compared to suboptimal methods. However, ML detection is prohibitively complex especially once the number of transmit antenna increase. This work proposes a receiver architecture tohandle multi user overloaded systems using a combination of Network Coding (NC) and Hybrid Automatic Repeat reQuest (HARQ) protocol to transform an overloaded system into a simplified co-channel system. An overloaded system is created using stacked HARQ retransmissions and NC techniques. This enables application of conventional low complexity detectors in such systems. Monte Carlo Simulations prove that the proposed approach results in significant gains in terms of Error Rates and throughput, while keeping the dropped packet rate quite low at an affordable complexity.

Device-to-device (D2D) communication is being considered an important traffic offloading mechanism for future cellular networks. Coupled with pro-active device caching, it offers huge potential for capacity and coverage enhancements. In order to ensure maximum capacity enhancement, number of nodes for direct communication needs to be identified. In this paper, we derive analytic expression that relates number of D2D nodes (i.e., D2D user density) and average coverage probability of reference D2D receiver. Using stochastic geometry and poisson point process, we introduce retention probability within cooperation region and shortest distance based selection criterion to precisely quantify interference due to D2D pairs in coverage area. The simulation setup and numerical evaluation validates the closed-form expression.

It is generally thought that space‐time block codes (STBCs) can obtain no more than full space diversity. In this study, the authors propose a new construction method of joint orthogonal STBCs based on M ‐dimensional lattice constellations for obtaining space and time diversities simultaneously. By deriving the Chernoff bound of error probability, they prove the exact diversity gain of the proposed code is M times of that in traditional STBCs. This is a valuable scheme as diversity gain is usually the primary factor to determine the ability of anti‐fading. Moreover, the maximum‐likelihood decoder for the proposed code just requires joint decoding of M real symbols, whose complexity is acceptable as M , usually, needs not to be too big. Numerical results show that the proposed code has remarkable improvement of performance compared with some typical STBCs under the comparable low decoding complexity.

Presents corrections made to the paper, “Energy Efficiency-Spectral Efficiency Trade-Off of Transmit Antenna Selection” (Rayel, O.K., et al; IEEE Trans. Commun., vol. 62, no. 12, pp. 4293–4303, Dec. 2014).

Kernel principal component analysis and the reconstruction error is an effective anomaly detection technique for non-linear data sets. In an environment where a phenomenon is generating data that is non-stationary, anomaly detection requires a recomputation of the kernel eigenspace in order to represent the current data distribution. Recomputation is a computationally complex operation and reducing computational complexity is therefore a key challenge. In this paper, we propose an algorithm that is able to accurately remove data from a kernel eigenspace without performing a batch recomputation. Coupled with a kernel eigenspace update, we demonstrate that our technique is able to remove and add data to a kernel eigenspace more accurately than existing techniques. An adaptive version determines an appropriately sized sliding window of data and when a model update is necessary. Experimental evaluations on both synthetic and real-world data sets demonstrate the superior performance of the proposed approach in comparison to alternative incremental KPCA approaches and alternative anomaly detection techniques.

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.

Enquiry-based student-centered learning activities in engineering classrooms may lose its focus and success if the activities in classroom are biased due to the failure in forming mixed-ability oriented teams or groups of students. This paper proposes a technology driven team-building methodology to enhance enquiry-based learning in the conventional engineering classrooms, which is referred to as Heterogeneous Ability-Centered Team Building (H-ACT-B) method. The H-ACT-B method guarantees mixed-ability based team or group formation in classroom to promote effective communication, collaboration and critical thinking based on the programmed evaluation of individual student's aptitude. The proposed team building method is expected to aid the individual student and the team or group to ensure their progress toward achieving the common team task efficiently. Moreover, the study is strongly formulated by considering the useful insights about the current team building practices and methods by conducting survey among the engineering faculty and students.

This paper proposes a low-complexity joint source and relay energy-efficient resource allocation scheme for the two-hop multiple-input-multiple-output (MIMO) amplify-and-forward (AF) system when channel state information is available. We first simplify the multivariate unconstrained energy efficiency (EE)-based problem and derive a convex closed-form approximation of its objective function as well as closed-form expressions of subchannel rates in both the unconstrained and power constraint cases. We then rely on these expressions for designing a low-complexity energy-efficient joint resource allocation algorithm. Our approach has been compared with a generic nonlinear constrained optimization solver and results have indicated the low-complexity and accuracy of our approach. As an application, we have also compared our EE-based approach against the optimal spectral efficiency (SE)-based joint resource allocation approach and results have shown that our EE-based approach provides a good trade-off between power consumption and SE.

This paper presents a novel framework for joint self-optimization of backhaul as well as coverage links spectral efficiency in relay enhanced heterogeneous networks. Considering a realistic heterogeneous network deployment, where some cells contain Relay Station (RS), while others do not, we develop an analytical framework for self-optimisation of macrocell Base Station (BS) antenna tilts. Our framework exploits a unique system level perspective to enable dynamic maximization of system-wide spectral efficiency of the BS-RS backhaul links as well as that of the BS-user coverage links. A distributed and practical self-organising solution is obtained by decomposing the large scale system-wide optimization problem into local small scale optimization problems, by mimicking the operational principles of self-organisation in biological systems. The local problems are non-convex but have very small scale and can be solved via appropriate numerical methods, such as sequential quadratic programming. The performance of developed solution is evaluated through extensive system level simulations for LTE-A type networks and compared against conventional tilting benchmarks. Numerical results show that up to 50% gain in average spectral efficiency is achievable through the proposed solution depending on users geographical distributions.

In this paper, we consider Coordinated Multi-Point (CoMP) in a cloud-based radio access network (RAN), where each coordinating access point compresses its observation using distributed Wyner-Ziv compression and forwards the compressed signal to the receiving processing unit. We map this architecture to the multiple-relay compress-and-forward (CF) problem, derive the achievable rate of such a system and then show that the achievable rate can be maximized by optimizing the distributed compression rate at each individual coordinating point in a joint manner. An iterative optimization algorithm is proposed and numerical results indicate that compared with other coordinating schemes, when the channel between the coordinating points and the processing unit is strong, using distributed compression can effectively improve the spectrum efficiency.

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 this position paper, we identify the potential of an integrated deployment solution for energy efficient cellular networks combining the strengths of two very active research themes: software defined radio access networks (SD-RAN) [1] and decoupled signaling and data transmissions, or beyond cellular green generation (BCG2) architecture, for enhanced energy efficiency [2]. While SD-RAN envisions a decoupled centralized control plane and data forwarding plane for flexible control, the BCG2architecture calls for decoupling coverage from capacity and coverage is provided through always-on low-power signaling node for a larger geographical area; capacity is catered by various on-demand data nodes for maximum energy efficiency. In this paper, we identify that a combined approach bringing in both specifications together can, not only achieve greater benefits, but also facilitates the faster realization of both technologies. We propose the idea and design of a signaling controller which acts as a signaling node to provide always-on coverage, consuming low power, and at the same time also hosts the control plane functions for the SD-RAN through a general purpose processing platform. Phantom cell concept is also a similar idea where a normal macro cell provides interference control to densely deployed small cells [3], although, our initial results show that the integrated architecture has much greater potential of energy savings in comparison to phantom cells.

Automatic detection of cells which are in outage has been identified as one of the key use cases for Self Organizing Networks (SON) for emerging and future generations of cellular systems. A special case of cell outage, referred to as Sleeping Cell (SC) remains particularly challenging to detect in state of the art SON because in this case cell goes into outage or may perform poorly without triggering an alarm for Operation and Maintenance (O&M) entity. Consequently, no SON compensation function can be launched unless SC situation is detected via drive tests or through complaints registered by the affected customers. In this paper, we present a novel solution to address this problem that makes use of minimization of drive test (MDT) measurements recently standardized by 3GPP and NGMN. To overcome the processing complexity challenge, the MDT measurements are projected to a low-dimensional space using multidimensional scaling method. Then we apply state of the art k-nearest neighbor and local outlier factor based anomaly detection models together with pre-processed MDT measurements to profile the network behaviour and to detect SC. Our numerical results show that our proposed solution can automate the SC detection process with 93% accuracy.

This paper brings to light the reasoning and principles shaping the standardization direction in the 3rd Generation Partnership Project (3GPP) regarding provision of low complexity Machine Type Communication (MTC), via Long Term Evolution (LTE) of 3GPP. It elaborates 3GPP's approaches and suggestions towards addressing the physical layer challenges and discusses solutions 3GPP proposed specifically in relation to the low complexity operation of MTC devices and the coverage extension of current cellular networks to challenging scenarios of MTC. Results of comprehensive set of simulations are presented to assess the impact of repetition coding and power boosting on the physical downlink shared channel (PDSCH). In addition, novel ideas are presented to reduce the complexity of channel estimation, one of the most computation intensive blocks in the baseband receive chain. These results all leap towards finding the physical layer design for low cost MTC devices via 3GPP LTE.

Interference forwarding has been shown to be beneficial in the interference channel with a relay as it enlarges the strong interference region, allowing the decoding of the interference at the receivers for larger ranges of the channel gains. In this work we demonstrate the benefit of adding a relay to the cognitive interference channel. We pay special attention to the effect of interference forwarding in this configuration. Two setups are presented. In the first, the interference forwarded by the relay is the primary user's signal, and in the second, this is the cognitive user's signal. We characterise the capacity regions of these two models in the case of strong interference. We show that as opposed to the first setup, in the second setup the capacity region is enlarged, compared to the capacity region of the cognitive interference channel, when the relay does not help the intended receiver.

Optimization in mobile communications has mostly been performed based on spectral efficiency (SE). However, energy efficiency (EE) is fast becoming a key performance indicator, together with SE for designing future networks. In multi-cell communications, other-cell interference (OCI) coming from neighboring cells degrades both the SE and EE performances of the system. This paper presents an energy efficient precoding/power allocation algorithm for the downlink of the multi-cell multi-user multi-antenna channel, based on block diagonalization (BD). Each user employs an interference suppression filter while the precoder uses information about OCI plus noise covariance matrix to cancel interference and perform power allocation in an energy efficient manner. Simulation results show that our energy efficient BD scheme reduces the power consumption of the system and improves the EE in comparison with existing SE-based BD scheme.

With the cell coverage area of current and future mobile networks becoming smaller, heterogeneous small-cell networks (HetSNets), where multiple low-power, low-cost base stations (BSs) complement the existing macrocell infrastructure, are considered constitutive elements of future mobile networks. In this paper, we propose a K-tier HetSNet, where multiple tiers of small-cells are padded between macrocells which in turn expand the network coverage and significantly increase in capacity without compromising the frequency reuse factor. In this context, we derive analytical capacity bounds of the K-tier HetSNets based on the distance of the desired user from its serving BS and all other interfering BSs. It was observed that the upper bound of the capacity becomes tighter as the number of small-cell tiers increases due to the increase in the number of small-cells. Simulation results show the performance of the proposed K-tier HetSNets against the macro-only network in terms of frequency reuse factor, capacity and area spectral efficiency.

We investigate the energy efficiency-spectral efficiency (EE-SE) trade-off of transmit antenna selection/maximum ratio combining (TAS) scheme. A realistic power consumption model (PCM) is considered, and it is shown that using TAS can provide significant energy savings when compared to multiple-input multiple-output (MIMO) in the low to medium SE region, regardless the number of antennas, as well as outperform transmit beamforming scheme (MRT) for the entire SE range. For a fixed number of receive antennas, our results also show that the EE gain of TAS over MIMO becomes even greater as the number of transmit antennas increases. The optimal value of SE that maximizes the EE is obtained analytically, and confirmed by numerical results. Moreover, the influence of receiver correlation is also evaluated and it is shown that considering a non-realistic PCM can lead to mistakes when comparing TAS and MIMO.

Despite years of physical-layer research, the capacity enhancement potential of relays is limited by the additional spectrum required for Base Station (BS)-Relay Station (RS) links. This paper presents a novel distributed solution by exploiting a system level perspective instead. Building on a realistic system model with impromptu RS deployments, we develop an analytical framework for tilt optimization that can dynamically maximize spectral efficiency of both the BS-RS and BS-user links in an online manner. To obtain a distributed self-organizing solution, the large scale system-wide optimization problem is decomposed into local small scale subproblems by applying the design principles of self-organization in biological systems. The local subproblems are non-convex, but having a very small scale, can be solved via standard nonlinear optimization techniques such as sequential quadratic programming. The performance of the developed solution is evaluated through extensive simulations for an LTE-A type system and compared against a number of benchmarks including a centralized solution obtained via brute force, that also gives an upper bound to assess the optimality gap. Results show that the proposed solution can enhance average spectral efficiency by up to 50% compared to fixed tilting, with negligible signaling overheads. The key advantage of the proposed solution is its potential for autonomous and distributed implementation.

This paper examines the uplink of cellular systems employing base station cooperation for joint signal processing. We consider clustered cooperation and investigate effective techniques for managing inter-cluster interference to improve users' performance in terms of both spectral and energy efficiency. We use information theoretic analysis to establish general closed form expressions for the system achievable sum rate and the users' Bit-per-Joule capacity while adopting a realistic user device power consumption model. Two main inter-cluster interference management approaches are identified and studied, i.e., through: 1) spectrum re-use; and 2) users' power control. For the former case, we show that isolating clusters by orthogonal resource allocation is the best strategy. For the latter case, we introduce a mathematically tractable user power control scheme and observe that a green opportunistic transmission strategy can significantly reduce the adverse effects of inter-cluster interference while exploiting the benefits from cooperation. To compare the different approaches in the context of real-world systems and evaluate the effect of key design parameters on the users' energy-spectral efficiency relationship, we fit the analytical expressions into a practical macrocell scenario. Our results demonstrate that significant improvement in terms of both energy and spectral efficiency can be achieved by energy-aware interference management.

There is a need to develop energy-efficient adaptive systems for future telecommunication networks. While traffic varies at different times, the power consumption of the radio access network does not scale with it effectively. To make significant energy savings, a dynamic deployment approach is required to allow the system to operate in an energy-efficient mode with respect to traffic load. By deploying small base stations within the area of a conventional macro station, we are able to reduce energy consumption while maintaining QoS. This paper proposes an energy-efficient dynamic deployment architecture based on fuzzy-logic. The algorithm aids in the decision of the architecture layout deployment. Moreover, by implementing the proposed adaptive energy-efficient algorithm, the network gains flexibility that can increase coverage or throughput throughout the same network by adapting its operation to source its requirements better and change them when new requirements arise.

Energy efficiency (EE) is becoming an important performance indicator for ensuring both the economical and environmental sustainability of the next generation of communication networks. Equally, cooperative communication is an effective way of improving communication system performances. In this paper, we propose a near-optimal energy-efficient joint resource allocation algorithm for multi-hop multiple-input-multiple-output (MIMO) amplify-and-forward (AF) systems. We first show how to simplify the multivariate unconstrained EE-based problem, based on the fact that this problem has a unique optimal solution, and then solve it by means of a low-complexity algorithm. We compare our approach with classic optimization tools in terms of energy efficiency as well as complexity, and results indicate the near-optimality and low-complexity of our approach. As an application, we use our approach to compare the EE of multi-hop MIMO-AF with MIMO systems and our results show that the former outperforms the latter mainly when the direct link quality is poor.

The continuous increase in the energy consumption of wireless networks has led to extensive research and development into energy-efficient communications. Towards this objective, this paper employs a novel technique for maximizing the energy efficiency (EE) of wireless networks, using weighted average EE contours with multiple decoding policies (DPs), where users are prioritized based on different criteria such as channel condition. Moreover, our EE based resource allocation method is extended such that other system targets such as rate-fairness and quality of service (QoS) are satisfied. Results indicate that our EE-based resource allocation scheme achieves the highest EE when DP 2 is employed, i.e. the user with the best channel gain achieves its single user bound, whilst other users experience residual interference. Moreover, both the fairness and QoS constraints increase user satisfaction, in terms of achievable data rate, which comes at the cost of a higher transmit power, and therefore lower EE.

In this paper we compare the energy efficiency of different multiple antenna transmission schemes for long-range wireless networks, assuming a realistic power consumption model. We consider the downlink, between a base station and a mobile station, in which the Alamouti scheme, transmit beamforming, receive diversity, spatial multiplexing, and transmit antenna selection are compared. Our analysis shows that, for different types of base stations, outage probability requirements and spectral efficiencies, the transmit antenna selection scheme is in general the most energy efficient option. Although antenna selection is not the best in terms of outage probability, it becomes the most efficient in terms of overall power consumption as it requires a single radio-frequency chain to obtain spatial diversity.

This paper considers a classic relay channel which consists of a source, a relay and a destination node and investigates the energy-spectral efficiency tradeoff under three different relay protocols: amplify-and-forward; decode-and-forward; and compress-and-forward. We focus on a cellular scenario where a neighbour base station can potentially act as the relay node to help on the transmissions of the source base station to its assigned mobile device. We employ a realistic power model and introduce a framework to evaluate the performance of different communication schemes for various deployments in a practical macrocell scenario. The results of this paper demonstrate that the proposed framework can be applied flexibly in practical scenarios to identify the pragmatic energy-spectral efficiency tradeoffs and choose the most appropriate scheme optimising the overall performance of inter base station relaying communications.

In this paper, the analysis for the average bit error probability (ABEP) of a dual-hop fixed-gain relay network is conducted. To this end, we consider two different scenarios: 1) the second hop (relay- destination link) is subject to composite multipath/shadowing and the first hop (source-relay link) experiences only multipath fading; 2) the first hop is perturbed by the composite multipath/shadowing and the second hop undergoes only multipath fading. We develop new and exact closed-form expressions of the ABEP for the first scenario in terms of the Meijer-G and Lommel functions. Since the exact closed-form expressions for the second scenario are mathematically intractable, we derive a new approximation and bounds. These approximation and bounds are shown to be tight for medium to high average signal-to-noise ratio (SNR) regime. In addition, we also provide new and relatively simpler asymptotic expressions of the ABEP for both the scenarios. It is shown that some physical insights (e.g., diversity order) of the system can readily be obtained by using these asymptotic expressions. All our analytical results are corroborated by the Monte-Carlo simulations.

Owing to limited resources, it is hard to guarantee minimum service levels to all users in conventional cellular systems. Although global cooperation of access points (APs) is considered promising, practical means of enhancing efficiency of cellular systems is by considering distributed or clustered jointly processed APs. The authors present a novel ‘quality of service (QoS) balancing scheme’ to maximise sum rate as well as achieve cell‐based fairness for clustered jointly processed cellular multiple access channel (referred to as CC‐CMAC). Closed‐form cell level QoS balancing function is derived. Maximisation of this function is proved as an NP hard problem. Hence, using power‐frequency granularity, a modified genetic algorithm (GA) is proposed. For inter site distance (ISD) < 500 m, results show that with no fairness considered, the upper bound of the capacity region is achievable. Applying hard fairness restraints on users transmitting in moderately dense AP system, 20% reduction in sum rate contribution increases fairness by upto 10%. The flexible QoS can be applied on a GA‐based centralised dynamic frequency planner architecture.

Energy efficiency (EE) is undoubtedly an important criterion for designing power-limited systems, and yet in a context of global energy saving, its relevance for power-unlimited systems is steadily growing. Equally, resource allocation is a well-known method for improving the performance of cellular systems. In this paper, we propose an EE optimization framework for the downlink of planar cellular systems over frequency-selective channels. Relying on this framework, we design two novel low-complexity resource allocation algorithms for the single-cell and coordinated multi-cell scenarios, which are EE-optimal and EE-suboptimal, respectively. We then utilize our algorithms for comparing the EE performance of the classic non-coordinated, orthogonal and coordinated multi-cell approaches in realistic power and system settings. Our results show that coordination can be a simple and effective method for improving the EE of cellular systems, especially for medium to large cell sizes. Indeed, by using a coordinated rather than a non-coordinated resource allocation approach, the per-sector energy consumption and transmit power can be reduced by up to 15% and more than 90%, respectively.

In this paper, the trade-off between energy efficiency (EE) and spectral efficiency (SE) is analyzed for both the uplink and downlink of the distributed multiple-input multiple-output (DMIMO) system over the Rayleigh fading channel while considering different types of power consumption models (PCMs). A novel tight closed-form approximation of the DMIMO EE-SE trade-off is presented and a detailed analysis is provided for the scenario with practical antenna configurations. Furthermore, generic and accurate low and high-SE approximations of this trade-off are derived for any number of radio access units (RAUs) in both the uplink and downlink channels. Our expressions have been utilized for assessing both the EE gain of DMIMO over co-located MIMO (CMIMO) and the incremental EE gain of DMIMO in the downlink channel. Our results reveal that DMIMO is more energy efficient than CMIMO for cell edge users in both the idealistic and realistic PCMs; whereas in terms of the incremental EE gain, connecting the user terminal to only one RAU is the most energy efficient approach when a realistic PCM is considered.

In this paper, we evaluate the performance of Multicarrier-Low Density Spreading Multiple Access (MC-LDSMA) as a multiple access technique for mobile communication systems. The MC-LDSMA technique is compared with current multiple access techniques, OFDMA and SC-FDMA. The performance is evaluated in terms of cubic metric, block error rate, spectral efficiency and fairness. The aim is to investigate the expected gains of using MC-LDSMA in the uplink for next generation cellular systems. The simulation results of the link and system-level performance evaluation show that MC-LDSMA has significant performance improvements over SC-FDMA and OFDMA. It is shown that using MC-LDSMA can considerably reduce the required transmission power and increase the spectral efficiency and fairness among the users.

Many method has been applied previously to improve the fairness of a wireless communication system. In this paper, we propose using hybrid schemes, where more than one transmission scheme are used in one system, to achieve this objective. These schemes consist of cooperative transmission schemes, maximal ratio transmission and interference alignment, and non-cooperative schemes, orthogonal and non-orthogonal schemes used alongside and in combinations in the same system to improve the fairness. We provide different weight calculation methods to vary the output of the fairness problem. We show the solution of the radio resource allocation problem for the transmission schemes used. Finally, simulation results is provided to show fairness achieved, in terms of Jain's fairness index, by applying the hybrid schemes proposed and the different weight calculation methods at different inter-site distances.

Hybrid systems, where more than one transmission scheme are used within the same cluster, can be used as a way to improve spectral efficiency for the system as a whole and, more importantly, for the cell-edge users. In this paper, we will propose frequency reuse method by grouping the users into two groups, critical and non-critical users. Each user group is served with a transmission scheme, where the most vulnerable users are served by transmission scheme that avoid, make use of, and orthogo-nalise the interference. These schemes include the cooperative maximal ratio transmission and the non-cooperative orthogonal and non-orthogonal schemes. Radio resource allocation is studied and a solution is given for maximal ratio transmission and interference alignment. Simulation results are given, and showing the performance of each scheme when all users are considered critical and one scheme is used. Moreover, results showing the performance of our proposed frequency reuse scheme where different percentage of users considered critical.

Uneven traffic load among the cells increases call blocking rates in some cells and causes low resource utilisation in other cells and thus degrades user satisfaction and overall performance of the cellular system. Various centralised or semi centralised Load Balancing (LB) schemes have been proposed to cope with this time persistent problem, however, a fully distributed Self Organising (SO) LB solution is still needed for the future cellular networks. To this end, we present a novel distributed LB solution based on an analytical framework developed on the principles of nature inspired SO systems. A novel concept of super-cell is proposed to decompose the problem of “system-wide blocking minimization” into the local sub-problems in order to enable a SO distributed solution. Performance of the proposed solution is evaluated through system level simulations for both macro cell and femto cell based systems. Numerical results show that the proposed solution can reduce the blocking in the system close to an Ideal Central Control (ICC) based LB solution. The added advantage of the proposed solution is that it does not require heavy signalling overheads.

Anomaly detection in a Wireless Sensor Network is an important aspect of data analysis in order to facilitate intrusion and event detection. A key challenge is creating optimal classifiers constructed from training sets in which the anomaly rates are varying due to the existence of non-stationary distributions in the data. In this paper we propose an adaptive algorithm that can dynamically adjust the anomaly rate parameter, which can be represented by a model parameter of a one-class quarter-sphere support vector machine. This algorithm operates in an online, iterative manner producing an optimal model for a training set, which is presented sequentially. Our evaluations demonstrate that our algorithm is capable of constructing optimal models for a training set that minimizes the error rate on the classification set compared to a static model, where the anomaly rate is kept stationary.

Energy efficiency (EE) is emerging as a key design criterion for both power limited, i.e. mobile devices, and power-unlimited, i.e. cellular networks, applications. Whereas, resource allocation is a well-known technique for improving the performance of communication systems. In this paper, we design a simple and optimal EE-based resource allocation method for the orthogonal multi-user channel by adapting the transmit power and rate to the channel condition such that the energy-per-bit consumption is minimized. We present our EE framework, i.e. EE metric and node power consumption model, and utilize it for formulating our EE-based optimization problem with or without constraint. In both cases, we derive explicit formulations of the optimal energy-per-bit consumption as well as optimal power and rate for each user. Our results indicate that EE-based allocation can substantially reduce the consumed power and increase the EE in comparison with spectral efficiency-based allocation.

This paper investigates energy efficiency (EE) performance of a virtual multiple-input multiple- output (MIMO) wireless system using the receiver- side cooperation with the compress-and-forward protocol. We derive a linear approximation of EE as a function of spectral efficiency (SE) in the low SE operation regime. In addition, we obtain a closed-form lower bound for EE which is valid for both low and high SE regions. This lower bound can be used for optimizing the power allocation between the transmitter and the relay in order to minimize the overall energy per bit consumption in the system. Both analytical and simulation results demonstrate that the virtual MIMO system using the receiver-side cooperation outperforms the multiple- input single-output (MISO) case in terms of energy efficiency. Finally we show that, with the optimal power allocation, the virtual-MIMO system achieves an EE performance close to that of an ideal MIMO system.

In this paper, we propose a novel closed-form approximation of the Energy Efficiency vs. Spectral Efficiency (EE-SE) trade-off for the uplink/downlink of distributed multipleinput multiple-output (DMIMO) system with two cooperating base stations. Our closed-form expression can be utilized for evaluating the idealistic and realistic EE-SE performances of various antenna configurations as well as assessing how DMIMO compares against MIMO system in terms of EE. Results show a tight match between our closed-form approximation and the Monte-Carlo simulation for both idealistic and realistic EESE trade-off. Our results also show that given a target SE requirement, there exists an optimal antenna setting that maximizes the EE. In addition, DMIMO scheme can offer significant improvement in terms of EE over the MIMO scheme.

In this paper, hybrid relaying schemes are investigated in the two-way relay channel, where the relay node is able to adaptively switch between different forwarding schemes based on the current channel state and its decoding status and thus provides more flexibility as well as improved performance. The analysis is conducted from the energy efficiency perspective for two transmission protocols distinguished by whether exploiting the direct link between two main communicating nodes (the source and destination nodes, and vice versa since it is two way communication) or not. A realistic power model taking circuitry power consumption of all involved nodes into account is employed. The energy efficiency is optimized in terms of consumed energy per bit subject to the Quality of Service (QoS) constraint. Numerical results show that the hybrid schemes are able to achieve the highest energy efficiency due to its capability of adapting to the channel variations and the protocol where the direct link is exploited is more energy efficient.

While many studies have concentrated on providing theoretical analysis for the relay assisted compress-and-forward systems little effort has yet been made to the construction and evaluation of a practical system. In this paper a practical CF system incorporating an error-resilient multilevel Slepian-Wolf decoder is introduced and a novel iterative processing structure which allows information exchanging between the Slepian-Wolf decoder and the forward error correction decoder of the main source message is proposed. In addition, a new quantization scheme is incorporated as well to avoid the complexity of the reconstruction of the relay signal at the final decoder of the destination. The results demonstrate that the iterative structure not only reduces the decoding loss of the Slepian-Wolf decoder, it also improves the decoding performance of the main message from the source.

With the explosion of wireless communications in number of users and data rates, the reduction of network power consumption becomes more and more critical. This is especially true for base stations which represent a dominant share of the total power in cellular networks. In order to study power reduction techniques, a convenient power model is required, providing estimates of the power consumption in different scenarios. This paper proposes such a model, accurate but simple to use. It evaluates the base station power consumption for different types of cells supporting the 3GPP LTE standard. It is flexible enough to enable comparisons between state-of-the-art and advanced configurations, and an easy adaptation to various scenarios. The model is based on a combination of base station components and sub-components as well as power scaling rules as functions of the main system parameters.

Until recently, link adaptation and resource allocation for communication system relied extensively on the spectral efficiency as an optimization criterion. With the emergence of the energy efficiency (EE) as a key system design criterion, resource allocation based on EE is becoming of great interest. In this paper, we propose an optimal EE-based resource allocation method for the scalar broadcast channel (BC-S). We introduce our EE framework, which includes an EE metric as well as a realistic power consumption model for the base station, and utilize this framework for formulating our EE-based optimization problem subject to a power as well as fairness constraints. We then prove the convexity of this problem and compare our EE-based resource allocation method against two other methods, i.e. one based on sum-rate and one based on fairness optimization. Results indicate that our method provides large EE improvement in comparison with the two other methods by significantly reducing the total consumed power. Moreover, they show that near-optimal EE and average fairness can be simultaneously achieved over the BC-S channel.

Wireless cellular communication networks is undergoing a transition from being a simply optional voice communication to becoming a necessity in our everyday lives. In order to ensure uninterrupted high Quality of Experience for subscribers, network operators must ensure 100% reliability of their networks without any discontinuity either for planned maintenance or breakdown. This paper demonstrates self healing capability to the fault recovery process for each cell. It is proposed to compensate cells in failure by neighboring cells optimizing their coverage with antenna reconfiguration and power compensation resulting in filling the coverage gap and improving the QoS for users. The right choice of these reconfigured parameters is determined through a process involving fuzzy logic control and reinforcement learning. Results show an improvement in the network performance for the area under outage as perceived by each user in the system.

Because of its simplicity, amplify-and-forward (AF) is one of the most popular cooperative relaying technique. Relays are used in cooperative communication to improve reliability, coverage or spectral efficiency of cell-edge users. However, relays tend to increase the interferences seen by users of adjacent cells, particularly by the cell-edge users, when used in multi-cell systems. In this paper, we propose a low-complexity precoding scheme to mitigate the effect of other-cell interference (OCI) in cooperative communication. The scheme is designed by taking into account the interference plus noise covariance matrix of each user for mitigating the interference at each receiver by means of precoding at the relay node. Simulation results show the effectiveness of the proposed scheme, both in terms of sum-rate and computational complexity, when compared to other existing OCI-aware precoding algorithms for AF.

In the context of orthogonal frequency division multiplexing (OFDM)-based systems, pilot-based beamforming (BF) exhibits a high degree of sensitivity to the pilot sub-carriers. Increasing the number of reference pilots significantly improves BF performance as well as system performance. However, this increase comes at the cost of data throughput, which inevitably shrinks due to transmission of additional pilots. Hence an approach where reference signals available to the BF process can be increased without transmitting additional pilots can exhibit superior system performance without compromising throughput. Thus, the authors present a novel three-stage iterative turbo beamforming (ITBF) algorithm for an OFDM-based hybrid terrestrial-satellite mobile system, which utilises both pilots and data to perform interference mitigation. Data sub-carriers are utilised as virtual reference signals in the BF process. Results show that when compared to non-iterative conventional BF, the proposed ITBF exhibits bit error rate gain of up to 2.5 dB with only one iteration.

In cognitive radio networks, the licensed frequency bands of the primary users (PUs) are available to the secondary user (SU) provided that they do not cause significant interference to the PUs. In this study, the authors analysed the normalised throughput of the SU with multiple PUs coexisting under any frequency division multiple access communication protocol. The authors consider a cognitive radio transmission where the frame structure consists of sensing and data transmission slots. In order to achieve the maximum normalised throughput of the SU and control the interference level to the legal PUs, the optimal frame length of the SU is found via simulation. In this context, a new analytical formula has been expressed for the achievable normalised throughput of SU with multiple PUs under prefect and imperfect spectrum sensing scenarios. Moreover, the impact of imperfect sensing, variable frame length of SU and the variable PU traffic loads, on the normalised throughput has been critically investigated. It has been shown that the analytical and simulation results are in perfect agreement. The authors analytical results are much useful to determine how to select the frame duration length subject to the parameters of cognitive radio network, such as network traffic load, achievable sensing accuracy and number of coexisting PUs.

Along with spectral efficiency (SE), energy efficiency (EE) is becoming one of the key performance evaluation criteria for communication system. These two criteria, which are conflicting, can be linked through their trade-off. The EE-SE trade-off for the multi-input multi-output (MIMO) Rayleigh fading channel has been accurately approximated in the past but only in the low-SE regime. In this paper, we propose a novel and more generic closed-form approximation of this trade-off which exhibits a greater accuracy for a wider range of SE values and antenna configurations. Our expression has been here utilized for assessing analytically the EE gain of MIMO over single-input single-output (SISO) system for two different types of power consumption models (PCMs): the theoretical PCM, where only the transmit power is considered as consumed power; and a more realistic PCM accounting for the fixed consumed power and amplifier inefficiency. Our analysis unfolds the large mismatch between theoretical and practical MIMO vs. SISO EE gains; the EE gain increases both with the SE and the number of antennas in theory, which indicates that MIMO is a promising EE enabler; whereas it remains small and decreases with the number of transmit antennas when a realistic PCM is considered.

Low density signature orthogonal frequency division multiplexing (LDS-OFDM) is an uplink multi-carrier multiple access scheme that uses low density signatures (LDS) for spreading the symbols in the frequency domain. In this paper, we introduce an effective receiver for the LDS-OFDM scheme. We propose a framework to analyze and design this iterative receiver using extrinsic information transfer (EXIT) charts. Furthermore, a turbo multi-user detector/decoder (MUDD) is proposed for the LDS-OFDM receiver. We show how the turbo MUDD is tuned using EXIT charts analysis. By tuning the turbo-style processing, the turbo MUDD can approach the performance of optimum MUDD with a smaller number of inner iterations. Using the suggested design guidelines in this paper, we show that the proposed structure brings about 2.3 dB performance improvement at a bit error rate (BER) equal to 10-5over conventional LDS-OFDM while keeping the complexity affordable. Simulations for different scenarios also show that the LDS-OFDM outperforms similar well-known multiple access techniques such as multi-carrier code division multiple access (MC-CDMA) and group-orthogonal MC-CDMA.

Energy savings are becoming a global trend, hence the importance of energy efficiency (EE) as an alternative performance evaluation metric. This paper proposes an EE based resource allocation method for the broadcast channel (BC), where a linear power model is used to characterize the power consumed at the base station (BS). Having formulated our EE based optimization problem and objective function, we utilize standard convex optimization techniques to show the concavity of the latter, and thus, the existence of a unique globally optimal energy-efficient rate and power allocation. Our EE based resource allocation framework is also extended to incorporate fairness, and provide a minimum user satisfaction in terms of spectral efficiency (SE). We then derive the generic equation of the EE contours and use them to get insights about the EE-SE trade-off over the BC. The performances of the aforementioned resource allocation schemes are compared for different metrics against the number of users and cell radius. Results indicate that the highest EE improvement is achieved by using the unconstrained optimization scheme, which is obtained by significantly reducing the total transmit power. Moreover, the network EE is shown to increase with the number of users and decrease as the cell radius increases.

Adaptive interference mitigation requires significant resources due to recursive processing. Specific to satellite systems, interference mitigation by employing adaptive beamforming at the gateway or at the satellite both have associated problems. While ground based beamforming reduces the satellite payload complexity, it results in added feeder link bandwidth requirements, higher gateway complexity and suffers from feeder link channel degradations. On the other hand, employing adaptive beamforming onboard the satellite gives more flexibility in case of variation in traffic dynamics and also for changing of beam patterns. However, these advantages come at the cost of additional complexity at the satellite. In pursuit of retaining the benefits of onboard beamforming and to reduce the complexity associated with adaptive processing, we here propose a novel semi-adaptive beamformer for a Hybrid Terrestrial-Satellite Mobile System. The proposed algorithm is a dual form of beamforming that enables adaptive and non-adaptive processing to coexist via a robust gradient based switching mechanism. We present a detailed complexity analysis of the proposed algorithm and derive bounds associated with its power requirements. In the scenarios studied, results show that the proposed algorithm consumes up to 98% less filter computing power as compared to full-adaptive case without compromising on system performance.

In this paper, we derive a generic closed-form approximation (CFA) of the energy efficiency-spectral efficiency (EE-SE) trade-off for the uplink of coordinated multi-point (CoMP) system and demonstrate its accuracy for both idealistic and realistic power consumption models (PCMs). We utilize our CFA to compare CoMP against conventional non-cooperative system with orthogonal multiple access. In the idealistic PCM, CoMP is more energy efficient than non-cooperative system due to a reduction in power consumption; whereas in the realistic PCM, CoMP can also be more energy efficient but due to an improvement in SE and mainly for cell-edge communication and small cell deployment.

Cooperative Transmission can be used in a multicell scenario where base stations are connected to a central processing unit. This cooperation can be used to improve the fairness for users with bad channel conditions-critical users. This paper will look into using cooperative transmission alongside the orthogonal OFDM scheme to improve fairness by careful selection of critical users and a resource allocation and resource division between the two schemes. A solution for power and subcarrier allocations is provided together with a solution for the selection of the critical users. Simulation results are provided to show the fairness achieved by the proposed critical users selection method, resource allocation and the resource division method applied under the stated assumptions.

Mobile services have seen a major upswing driven by the bandwidth hungry applications thus leading to higher data rate requirements on the wireless networks. Spectrum being the most precious resource in the wireless industry is of keen interest. Various spectrum assignment and frequency reuse schemes have been proposed in literature. However in future networks, dynamic schemes that adapt to spatio-temporal variation in the environment are desired. We thus present a hybrid spectrum assignment scheme which adapts its allocation strategies depending on user distribution in the system. Results show that the proposed dynamic spectrum assignment strategy improves spectrum utilization thereby providing a higher data rate for the users.

Cooperative communication is an effective approach for increasing the spectral efficiency and/or the coverage of cellular networks as well as reducing the cost of network deployment. However, it remains to be seen how energy efficient it is. In this paper, we assess the energy efficiency of the conventional Amplifyand- forward (AF) scheme in an in-building relaying scenario. This scenario simplifies the mutual information formulation of the AF system and allows us to express its channel capacity with a simple and accurate closed-form approximation. In addition, a framework for the energy efficiency analysis of AF system is introduced, which includes a power consumption model and an energy efficiency metric, i.e. the bit-per-joule capacity. This framework along with our closed-form approximation are utilized for assessing both the channel and bit-per-joule capacities of the AF system in an in-building scenario. Our results indicate that transmitting with maximum power is not energy efficient and that AF system is more energy efficient than point-to-point communication at low transmit powers and signal-to-noise ratios.

In this paper, the mutual information transfer characteristics of turbo Multiuser Detector (MUD) for a novel air interface scheme, called Low Density Signature Orthogonal Frequency Division Multiplexing (LDS-OFDM) are investigated using Extrinsic Information Transfer (EXIT) charts. LDS-OFDM uses Low Density Signature structure for spreading the data symbols in frequency domain. This technique benefits from frequency diversity besides its ability of supporting parallel data streams more than the number of subcarriers (overloaded condition). The turbo MUD couples the data symbols' detector of LDS scheme with users' FEC (Forward Error Correction) decoders through the message passing principle. The effect of overloading on LDS scheme's performance is evaluated using EXIT chart. The results show that at Eb/N0as low as 0.3, LDS-OFDM can support loads up to 300%.

In this paper, we propose a tight closed-form approximation of the Energy Efficiency vs. Spectral Efficiency (EE-SE) trade-off for the uplink of a linear cellular communication system with base station cooperation and uniformly distributed user terminals. We utilize the doubly-regular property of the channel to obtain a closed form approximation using the Marčenko Pasture law. We demonstrate the accuracy of our expression by comparing it with Monte-Carlo simulation and the EE-SE trade-off expression based on low-power approximation. Results show the great tightness of our expression with Monte-Carlo simulation.We utilize our closed form expression for assessing the EE performance of cooperation for both theoretical and realistic power models. The theoretical power model includes only the transmit power, whereas the realistic power model incorporates the backhaul and signal processing power in addition to the transmit power. Results indicate that for both power models, increasing the number of antennas leads to an improvement in EE performance, whereas, increasing the number of cooperating BSs results in a loss in EE when considering the realistic power model.

In this paper, we propose a novel energy-aware adaptive sectorisation strategy, where the base stations are able to adapt themselves to the temporal traffic variation by switching off some sectors and changing the beam-width of the remaining sectors. An event based user traffic model is established according to Markov-Modulated Poisson Process (MMPP). Adaptation is performed while taking into account the the target Quality of Service (QoS), in terms of blocking probability. In addition, coverage requirement is also considered. This work targets at future cellular systems, in particular LTE systems. The results show that at least 21% energy consumption can be reduced by using the proposed adaptive sectorisation strategy.

Energy efficiency has become increasingly important in wireless communications, with significant environmental and financial benefits. This paper studies the achievable capacity region of a single carrier uplink channel consisting of two transmitters and a single receiver, and uses average energy efficiency contours to find the optimal rate pair based on four different targets: Maximum energy efficiency, a trade-off between maximum energy efficiency and rate fairness, achieving energy efficiency target with maximum sum-rate and achieving energy efficiency target with fairness. In addition to the transmit power, circuit power is also accounted for, with the maximum transmit power constrained to a fixed value. Simulation results demonstrate the achievability of the optimal energy-efficient rate pair within the capacity region, and provide the trade-off for energy efficiency, fairness and maximum sum-rate.

Low Density Signature-Orthogonal Frequency Division Multiplexing (LDS-OFDM) has been introduced recently as an efficient multiple access technique. In this paper, we focus on the subcarrier and power allocation scheme for uplink LDS-OFDM system. Since the resource allocation problem is not convex due to the discrete nature of subcarrier allocation, the complexity of finding the optimal solutions is extremely high. We propose a heuristic subcarrier and power allocation algorithm to maximize the weighted sum-rate. The simulation results show that the proposed algorithm can significantly increase the spectral efficiency of the system. Furthermore, it is shown that LDS-OFDM system can achieve an outage probability much less than that for OFDMA system.

Increasing concern about the energy consumption of cellular networks is driving operators to optimise energy utilisation without sacrificing user experience. In this paper, we consider the capability of spectrum sharing between two or more operators with the objective of achieving energy-efficient operation. Since each operator has a fixed amount of spectrum, they are required to increase the modulation index to achieve a higher data rate for a targeted QoS, which increases the demand on energy. We study the effect of using energy-efficient multilevel quadrature amplitude modulation (MQAM) with the capability of spectrum sharing between operators. Simulations show that using adaptive modulation with spectrum sharing between base stations of different operators can give a more flexible region for the system to operate with a reasonable trade-off.

In order to quantify the energy savings in wireless networks, the power consumption of the entire system needs to be captured and an appropriate energy efficiency evaluation framework must be defined. In this paper, the necessary enhancements over existing performance evaluation frameworks are discussed, such that the energy efficiency of the entire network comprising component, node and network level contributions can be quantified. The most important addendums over existing frameworks include a sophisticated power model for various base station (BS) types, which maps the RF output power radiated at the antenna elements to the total supply power of a BS site. We also consider an approach to quantify the energy efficiency of large geographical areas by using the existing small scale deployment models along with long term traffic models. Finally, the proposed evaluation framework is applied to quantify the energy efficiency of the downlink of a 3GPP LTE radio access network.

Coordinated multi-point (CoMP) architecture has proved to be very effective for improving the user fairness and spectral efficiency of cellular communication system, however, its energy efficiency remains to be evaluated. In this paper, CoMP system is idealized as a distributed antenna system by assuming perfect backhauling and cooperative processing. This simplified model allows us to express the capacity of the idealized CoMP system with a simple and accurate closed-form approximation. In addition, a framework for the energy efficiency analysis of CoMP system is introduced, which includes a power consumption model and an energy efficiency metric, i.e. bit-per-joule capacity. This framework along with our closed-form approximation are utilized for assessing both the channel and bit-per-joule capacities of the idealized CoMP system. Results indicate that multi-base-station cooperation can be energy efficient for cell-edge communication and that the backhauling and cooperative processing power should be kept low. Overall, it has been shown that the potential of improvement of CoMP in terms of bit-per-joule capacity is not as high as in terms of channel capacity due to associated energy cost for cooperative processing and backhauling.

We provide an analytical formula to evaluate the performance of the uplink of planar cellular networks when joint processing is enabled among limited number of base stations in a generalised fading environment. Focusing on user transmission power allocation techniques to mitigate inter-cluster interference we investigate the system's spectral-energy efficiency trade-off. The paper addresses the gains in both cell throughput and transmissions energy efficiency due to the combined strategies of base station cooperation and user power management. We assess the effect of the propagation environment and of the key network design parameters of cooperation cluster size and inter-site distance on the overall performance providing numerical results for a real-world scenario.

With the ever increasing demand for wireless broadband, design of energy efficient systems is paramount. Femtocells, small self installable wireless base stations, are currently being deployed as a solution to the coverage problems faced by mobile operators, particularly in indoor environments. This paper outlines the framework for evaluating the performance of multiple antenna transmit diversity techniques, based on the Alamouti scheme, relevant to minimizing femtocell transmission power. Simulation results for experiments run on appropriate femtocell channel models are provided. The presented material is given in the context of current third generation systems based on the W-CDMA air interface, however the techniques presented can be extended to future OFDM based systems. Appropriate green solutions need to consider issues of implementation complexity and embodiment which are also discussed.

Coordination between two or more multiple access channel (MAC) receivers can enlarge the achievable rate region of the whole system. This paper focuses on coordination by sharing the codebooks of the users between the receivers of MACs. We first define the achievable rate region of the time invariant multiple coordinated MAC (MCMAC) and subsequently derive its achievable rate region. We later express the achievable rate region in terms of the dominating points. We base our numerical analysis on the two-user two-receiver Gaussian coordinated MAC and make comparison with the interference channel, full cooperation and the individual MAC performance analysis. It is observed that this approach though suboptimal is less complex in comparison with full cooperation and that the MCMAC rate region is at least equal to the rate region of the uncoordinated approach. Over several channel states, the rate region of MCMAC exceeds that of the uncoordinated approach.

In this paper, we analyze the Hybrid Automatic Repeat re-Quest (HARQ) protocols used in conjunction with hybrid relaying schemes from the energy efficiency perspective. In cooperative communications, hybrid relaying strategy allows the relay to dynamically switch between different forwarding schemes according to its decoding status and provide either transmit or receive diversity to the users depending on the current channel condition. The energy efficiency analysis takes the circuitry energy consumption of all involved nodes into consideration and the energy efficiency is optimized in terms of energy per bit by manipulating the activation time and the transmission energy.

In this paper, we propose novel Hybrid Automatic Repeat re-Quest (HARQ) strategies used in conjunction with hybrid relaying schemes, named as H2-ARQ-Relaying. The strategies allow the relay to dynamically switch between amplify-and-forward/compress-and-forward and decode-and-forward schemes according to its decoding status. The performance analysis is conducted from both the spectrum and energy efficiency perspectives. The spectrum efficiency of the proposed strategies, in terms of the maximum throughput, is significantly improved compared with their non-hybrid counterparts under the same constraints. The consumed energy per bit is optimized by manipulating the node activation time, the transmission energy and the power allocation between the source and the relay. The circuitry energy consumption of all involved nodes is taken into consideration. Numerical results shed light on how and when the energy efficiency can be improved in cooperative HARQ. For instance, cooperative HARQ is shown to be energy efficient in long distance transmission only. Furthermore, we consider the fact that the compress-and-forward scheme requires instantaneous signal to noise ratios of all three constituent links. However, this requirement can be impractical in some cases. In this regard, we introduce an improved strategy where only partial and affordable channel state information feedback is needed.

In this paper we provide a geometric and mathematical model that can be used to evaluate the user rate distribution for a cellular system under the notion of a hyper-receiver incorporating realistic system parameters. Variable cell sizes and different location-based decoding orders are analysed and the uplink user rate distribution of a cellular system in which each transmitted signal experiences a distance dependent path loss, fast fading and slow fading, is derived. Interestingly enough, among the three different decoding orders examined, the forward one provides the best results both in terms of fairness (larger percentage of users get satisfactory rates) and minimum rate. The difference among the decoding orders becomes even more notable as the cell sizes increase. There has also to be noted that none of these decoding orders affect the sum-rate.

We derive an analytical formula for the sum rate of the uplink of a linear network of cells when clustered joint processing is adopted among the base stations in a generalised fading environment. An inter-cluster interference allowance scheme is considered and various user power allocation profiles are investigated in terms of optimal achievable sum rate to highlight that cell-based power allocation is preferable to cluster-based. The contribution of each base station on the cluster sum rate is investigated and its importance is discussed. Numerical results are produced for a real-world scenario showing how medium density systems are the most viable case for clustered system design by achieving > 80% of the global cooperation capacity.

Efficient use of available spectrum is of concern to future wireless network planners. Although global cooperation at access points (APs) maximizes sum rate, for large networks this assumption is too complex to implement. We partition the wireless network into localized jointly decoded cells implemented as fixed size clusters. This is an alternative model which is more practical and improves efficiency of current systems. Conventionally, frequency allocation using interference avoidance maximizes spectrum usage. However, with clusters, careful allocation of interference needs to be explored. This is done using flexible bin based frequency allocation and applying heuristic tools. This work is the first known attempt to analyze uplink capacity of bin based fixed cluster cellular systems using genetic algorithms. To implement this, we derive an expression for the uplink capacity of bin based fixed clusters. We then input this as a fitness function to a modified simple genetic algorithm to compute a good fit for our bin allocation problem. We deduce that for sparsely distributed APs and large cluster sizes, rates close to that of a joint processor are achievable. Moreover, decreasing AP density for small cluster sizes (inter cell distance greater than 5 km for 7 cell-cluster) has insignificant effect on sum rate performance. However, with a nominal increase in the number of bins available for transmission, for dense system, the per-cell sum rate of a clustered cellular system can reach close to that of a hyper receiver using a genetic algorithm.

Performance of next generation OFDM/OFDMA based Distributed Cellular Network (ODCN) where no cooperation based interference management schemes are used, is dependent on four major factors: 1) spectrum reuse factor, 2) number of sectors per site, 3) number of relay station per site and 4) modulation and coding efficiency achievable through link adaptation. The combined effect of these factors on the overall performance of a Deployment Architecture (DA) has not been studied in a holistic manner. In this paper we provide a framework to characterize the performance of various DA's by deriving two novel performance metrics for 1) spectral efficiency and 2) fairness among users. These metrics are designed to include the effect of all four contributing factors. We evaluate these metrics for a wide set of DA's through extensive system level simulations. The results provide a comparison of various DA's for both cellular and relay enhanced cellular systems in terms of spectral efficiency and fairness they offer and also provide an interesting insight into the tradeoff between the two performance metrics. Numerical results show that, in interference limited regime, DA's with highest spectrum efficiency are not necessarily those that resort to full frequency reuse. In fact, frequency reuse of 3 with 6 sectors per site is spectrally more efficient than that with full frequency reuse and 3 sectors. In case of relay station enhanced ODCN a DA with full frequency reuse, six sectors and 3 relays per site is spectrally more efficient and can yield around 170% higher spectrum efficiency compared to counterpart DA without RS.

Recent research on Frequency Reuse (FR) schemes for OFDM/OFDMA based cellular networks (OCN) suggest that a single fixed FR cannot be optimal to cope with spatiotemporal dynamics of traffic and cellular environments in a spectral and energy efficient way. To address this issue this paper introduces a novel Self Organizing framework for adaptive Frequency Reuse and Deployment (SO-FRD) for future OCN including both cellular (e.g. LTE) and relay enhanced cellular networks (e.g. LTE Advance). In this paper, an optimization problem is first formulated to find optimal frequency reuse factor, number of sectors per site and number of relays per site. The goal is designed as an adaptive utility function which incorporates three major system objectives; 1) spectral efficiency 2) fairness, and 3) energy efficiency. An appropriate metric for each of the three constituent objectives of utility function is then derived. Solution is provided by evaluating these metrics through a combination of analysis and extensive system level simulations for all feasible FRD's. Proposed SO-FRD framework uses this flexible utility function to switch to particular FRD strategy, which is suitable for system's current state according to predefined or self learned performance criterion. The proposed metrics capture the effect of all major optimization parameters like frequency reuse factor, number of sectors and relay per site, and adaptive coding and modulation. Based on the results obtained, interesting insights into the tradeoff among these factors is also provided.

In this paper we consider the uplink of a cellular network partitioned into localized jointly decoded cells. These jointly decoded cells are implemented as fixed size clusters. Such networks have a potential for real world deployments with improved spectral efficiency and user experience. Similar to conventional cellular networks, frequency planning can be considered as an efficient method to control interference between cells belonging to different clusters. Here we consider frequency planning in the form of allocating set of frequency bins to cells within clusters. As service providers are more interested in providing QoS, the considered bin allocation apart from maximizing system throughput, should also allocate cell resources in a fair manner. We propose a new cell based QoS balancing function which helps to maximize sum rate as well as achieve cell based fairness using both coupled and decoupled power allocation schemes. To implement this function, we use SIC in order to derive cell based sum rate. The derived formulation is conditioned for both hard and soft fair1ness constraints. This is then applied as input to a Genetic Algorithm in order to optimize the derived network wide QoS balancing function. Numerical results indicate that under wide range of bandwidth conditions, and in densely located cells employing decoupled power allocation, resources are more fairly allocated than in cells employing coupled power allocation.

Mobile communications are increasingly contributing to global energy consumption. The EARTH (Energy Aware Radio and neTworking tecHnologies) project tackles the important issue of reducing CO2emissions by enhancing the energy efficiency of cellular mobile networks. EARTH is a holistic approach to develop a new generation of energy efficient products, components, deployment strategies and energy-aware network management solutions. In this paper the holistic EARTH approach to energy efficient mobile communication systems is introduced. Performance metrics are studied so to assess the theoretical bounds of energy efficiency and the practical achievable limits. Moreover, various deployment strategies focusing on their potential to reduce energy consumption are studied, whilst providing uncompromised coverage and user experience. This includes heterogeneous networks with a sophisticated mix of different cell sizes, which may be further enhanced by energy efficient relaying and base station cooperation technologies. Finally, scenarios leveraging the capability of advanced terminals to operate on multiple radio access technologies (RAT) are discussed with respect to their energy savings potential.

Multicell joint processing has been shown to efficiently suppress inter-cell interference, while providing a high capacity gain due to spatial multiplexing across distributed Base Stations (BSs). However, the complexity of the optimal joint decoder in the multicell uplink channel grows exponentially with the number of users, making it prohibitive to implement in practice. In this direction, this paper investigates the uplink capacity performance of multicell joint linear minimum mean square error (LMMSE) filtering, followed by single-user decoding. The considered cellular multiple-access channel model assumes both Rayleigh and Rician flat fading, path loss, distributed users and correlated multiple antennas at the base station side. The case of Rayleigh fading is tackled using a free probability approach, while the case of Rician fading is addressed through a deterministic equivalent calculated using non-linear programming techniques. In this context, it is shown that LMMSE can provide high spectral efficiencies in practical macrocellular scenarios.

In this paper, we extend the well-known model for the Gaussian Cellular Multiple Access Channel originally presented by Wyner. The first extension to the model incorporates the distance-dependent path loss (maintaining a close relevance to path loss values in real world cellular systems) experienced by the users distributed in a planar cellular array. The density of base stations and hence the cell sizes are variable. In the context of a Hyper-receiver joint decoder, an expression for the information theoretic capacity is obtained assuming a large number of users in each cell. The model is further extended to incorporate the log-normal shadow fading variations, ensuring that the shadowing models are fairly comparable to the free space model. Using these fair models the effect of the shadow fading standard deviation on the information theoretic capacity of the cellular system is quantified. It is observed that a higher standard deviation results in lower capacity if the mean path loss is appropriately adjusted in order to model the mean loss due to the physical obstacles causing the shadow fading. The results validate that larger cell sizes and a higher standard deviation of shadowing (with appropriately adjusted mean path loss) results in lower spectral efficiency.

In the information-theoretic literature, it has been widely shown that multicell processing is able to provide high capacity gains in the context of cellular systems and that the per-cell sum-rate capacity of multicell processing systems grows linearly with the number of Base Station (BS) receive antennas. However, the majority of results in this area has been produced assuming that the fading coefficients of the MIMO subchannels are totally uncorrelated. In this direction, this paper investigates the ergodic per-cell sum-rate capacity of the MIMO Cellular Multiple-Access Channel under correlated fading and multicell processing. More specifically, the current channel model considers Rayleigh fading, uniformly distributed User Terminals (UTs) over a planar cellular system and power-law path loss. Furthermore, both BSs and Uts are equipped with correlated multiple antennas, which are modelled according to the Kronecker model. The per-cell sum-rate capacity closed form is derived using a Free Probability approach and numerical results are produced by varying the cell density of the system, as well as the level of correlation.

Multicell joint decoding has been proven to greatly enhance the capacity of cellular systems in a range of regimes. However, the complexity of such a joint receiver makes it impossible to implement in practice using current computational capabilities. In this direction, this paper investigates the capacity performance of reduced-complexity communication schemes in order to evaluate their performance with respect to the optimal multicell joint decoding scheme. More specifically, two sub-optimal schemes are considered: 1) intra-cell user orthogonalization combined with optimal multicell joint decoding and 2) intra-cell user orthogonalization combined with linear MMSE filtering and single-user decoding. The employed cellular multiple-access channel model incorporates flat fading, path loss, distributed users and multiple antennas at the Base Station, while both peak and average transmit power constraints are taken into account. In this context, it is shown that linear MMSE filtering combined with multiple BS antennas and intra-cell orthogonalization can still provide a considerable capacity enhancement. Furthermore, FDMA is shown to be more efficient than TDMA as an intra-cell orthogonalization technique.

EARTH is a major new European research project starting in 2010 with 15 partners from 10 countries. Its main technical objective is to achieve a reduction of the overall energy consumption of mobile broadband networks by 50%. In contrast to previous efforts, EARTH regards both network aspects and individual radio components from a holistic point of view. Considering that the signal strength strongly decreases with the distance to the base station, small cells are more energy efficient than large cells. EARTH will develop corresponding deployment strategies as well as management algorithms and protocols on the network level. On the component level, the project focuses on base station optimizations as power amplifiers consume the most energy in the system. A power efficient transceiver will be developed that adapts to changing traffic load for an energy efficient operation in mobile radio systems. With these results EARTH will reduce energy costs and carbon dioxide emissions and will thus enable a sustainable increase of mobile data rates.

In the ergodic capacity literature, the majority of results preserve the assumption of a flat-fading Gaussian channel. However, the actual wireless channels in current communication systems are often wideband and therefore time dispersive and frequency selective. In this direction, we study the ergodic capacity of a wideband MIMO channel. Starting from a tapped delay line model for the time domain, the frequency domain model is derived by employing the Fourier transform. However, in both cases the resulting channel matrix is characterised by non-separable correlation amongst the Gaussian blocks. The asymptotic eigenvalue probability density function and the channel capacity are calculated by specializing a theorem originating in operator-valued free probability. Finally, numerical results are presented to verify the validity of the approach and study the effect of the channel model parameters.

In this paper we study the effect of Base Station (BS) and User Terminal (UT) on the capacity of the Gaussian Cellular Multiple Access Channel. We also build a Monte Carlo simulator using realistic system parameters and distance dependent shadowing correlation estimation models. We are able to properly verify the theoretical analysis and we provide an insight on the maximum achievable capacity for real cellular systems.

In this paper we derive the sum rate of the uplink of a linear network of cells when clustered coordinated processing is adopted among the base stations in a generalised fading environment. Various cluster isolation schemes along with an interference allowance scheme are analysed and compared in terms of achievable sum rate with each other and to the optimum case of a system with central processor. Numerical results are produced for a real-world scenario.

Higher spectral efficiency can be achieved by exploiting the space dimension inherent to any wireless communication system using multiple receiver and multiple transmitter antennas (MIMO). There are several results that provide closed form solutions for acellular system with a single antenna at each base station and each user terminal. Results are also available for the single cell case with MIMO. A cellular system with multiple antennas at the transmitter and the receiver nodes has not been investigated to obtain a closed form solution for the capacity limit. The main information theoretic theorems are not directly applicable to this system because of the form of the channel matrix of such a system. In this paper we extend the well known Wyner's model to a MIMO cellular system. It is observed that the achievable rate is bound by an upper limit and lower limit corresponding to two extreme fading conditions: channel with Rayleigh fading and with no fading. The analytical results are verified using Monte Carlo simulations. The analysis provides the insight that for a cellular system, increasing the number of transmitting antennas is not beneficial to increase the achievable rate, and this is reflected in the results obtained.

In this work we investigate the information theoretic capacity of the uplink of a cellular system. Assuming centralised processing for all base stations, we consider a power-law path loss model along with variable cell size (variable density of Base Stations) and we formulate an average path-loss approximation. Considering a realistic Rician flat fading environment, the analytical result for the per-cell capacity is derived for a large number of users distributed over each cell. We extend this general approach to model the uplink of sectorized cellular system. To this end, we assume that the user terminals are served by perfectly directional receiver antennas, dividing the cell coverage area into perfectly non-interfering sectors. We show how the capacity is increased (due to degrees of freedom gain) in comparison to the single receiving antenna system and we investigate the asymptotic behaviour when the number of sectors grows large. We further extend the analysis to find the capacity when the multiple antennas used for each Base Station are omnidirectional and uncorrelated (power gain on top of degrees of freedom gain). We validate the numerical solutions with Monte Carlo simulations for random fading realizations and we interpret the results for the real-world systems.

In the context of cellular systems, it has been shown that multicell processing can eliminate inter-cell interference and provide high spectral efficiencies with respect to traditional interference-limited implementations. Moreover, it has been proved that the multiplexing sum-rate capacity gain of multicell processing systems is proportional to the number of base station (BS) antennas. These results have been also established for cellular systems, where BSs and user terminals (UTs) are equipped with multiple antennas. Nevertheless, a common simplifying assumption in the literature is the uncorrelated nature of the Rayleigh fading coefficients within the BSUT MIMO links. In this direction, this paper investigates the ergodic multicell-processing sum-rate capacity of the Gaussian MIMO cellular multiple-access channel in a correlated fading environment. More specifically, the multiple antennas of both BSs and UTs are assumed to be correlated according to the Kronecker product model. Furthermore, the current system model considers Rayleigh fading, uniformly distributed UTs over a planar coverage area and power-law path loss. Based on free probabilistic arguments, the empirical eigenvalue distribution of the channel covariance matrix is derived and it is used to calculate both optimal joint decoding and minimum mean square error (MMSE) filtering capacity. In addition, numerical results are presented, where the per-cell sum-rate capacity is evaluated while varying the cell density of the system, as well as the level of fading correlation. In this context, it is shown that the capacity performance is greatly compromised by BS-side correlation, whereas UT-side correlation has a negligible effect on the system's performance. Furthermore, MMSE performance is shown to be greatly suboptimal but more resilient to fading correlation in comparison to optimal decoding.

In the information-theoretic literature, multicell joint processing has been shown to produce high spectral efficiencies. However, the majority of existing results employ simplified models and normalized variables and in addition they consider only the sum-rate capacity, neglecting the individual user rates. In this paper, we investigate a realistic cellular model which incorporates flat fading, path loss and distributed users. Furthermore, the presented results are produced by varying the cell density of the cellular system, while practical values are used for system parameters, such as users per cell, transmitted power, path loss exponent. What is more, we study the effect of sum-rate maximization on the fairness of user rate distribution by comparing channel-dependent and random user orderings within the joint encoding/decoding process.

In this paper, we extend the well-known model for Gaussian Cellular Multiple Access Channel originally presented by Wyner and later extended by Somekh et al. with fading. The extension to the model, incorporates the distance dependent path loss (maintaining a close relevance to path loss values in real world cellular systems) experienced by the users distributed in a planar cellular array. The density of base stations and hence the cell sizes are variable. In the context of Hyper-receiver joint decoder, a closed form expression for the information theoretic capacity is obtained assuming large number of users in each cell. The effect of the path loss factor, on the information theoretic capacity of the cellular system, is quantified and it is observed that higher path loss factor results in lower capacity. The results validate that larger cell sizes result in lower spectral efficiency. The closed form formula derived by the mathematical analysis is also validated by Monte Carlo simulations.

In this paper we derive the information theoretic capacity of the uplink of a cellular system with variable inter site distance and a generalised fading environment. The capacity is shown to be a direct function of the ratio of total received signal power (from within and outside of a cell) to the AWGN noise power, at any BS. This ratio is defined as the rise over thermal (RoT). It is shown that the variation in system parameters like the path loss exponent, number of users, transmit power constraint and the inter site distance, changes the region of operation on a capacity-versus-RoT curve. Results are interpreted for practical channel models and it is shown that RoT provides a useful framework to compare various practical systems.

Applying relaying concept to a universal mobile telecommunications system (UMTS) mobile network is not a new idea, and many systems employing relaying capabilities have been suggested. The power control (PC), which is an important aspect of UMTS, can be applied in such a mobile system, either in a centralised or a distributed fashion. An increase in the system capacity is expected, when utilised in all system links, single-hop (SH) and multi-hop. This is because the PC allocates, to the transmitters, the minimum powers which satisfy the link quality criteria. In this paper we formulate the transmit powers of a relaying system. We then compare, in an example, the power convergence of an iterative PC to the solution provided by the suggested equations. The assumptions, which facilitate rendering the formulations to a linear set of equations, are analysed.

In ad hoc W-CDMA wireless LANs, simultaneous transmissions between transmitter-receiver pairs generate multiple access interference (MAI), which limits the throughput. Reducing MAI increases this throughput limit. This is achieved by scheduling transmissions such that the average number of simultaneous transmissions in each frame is reduced. MAI is minimized by allowing the least interfering links to transmit together. This paper proposes a graph theoretic algorithm that groups all links into a minimum number of subsets, based on the objective of minimizing MAI in each set. Simulations show that the proposed technique achieves around 100% improvement in system capacity over the scheme where all links transmit simultaneously.