Coordinated controlling a large UAV swarm requires significant spectrum resources due to the need for bandwidth allocation per UAV, posing a challenge in resource-limited environments. Over-the-air (OTA) control has emerged as a spectrum-efficient approach, leveraging electromagnetic superposition to form control signals at a base station (BS). However, existing OTA controllers lack sufficient optimization variables to meet UAV swarm control objectives and fail to integrate control with other BS functions like sensing. This work proposes an integrated sensing and OTA control framework (ISAC-OTA) for UAV swarm. The BS performs OTA signal construction (uplink) and dispatch (downlink) while simultaneously sensing objects. Two uplink post-processing methods are developed: a control-centric approach generating closed-form control signals via a feedback-looped OTA control problem, and a sensing-centric method mitigating transmission-induced interference for accurate object sensing. For the downlink, a non-convex problem is formulated and solved to minimize control signal dispatch (transmission) error while maintaining a minimum sensing signal-to-interference-plus-noise ratio (SINR). Simulation results show that the proposed ISAC-OTA controller achieves control performance comparable to the ideal optimal control algorithm while maintaining high sensing accuracy, despite OTA transmission interference. Moreover, it eliminates the need for per-UAV bandwidth allocation, showcasing a spectrum-efficient method for cooperative control in future wireless systems.

To mitigate various challenges in the edge-cloud ecosystem, such as global monitoring, flow control, and policy modification of legacy networking paradigms, software-defined networks (SDN) have evolved as a major technology. However, the dependency on a single centralized controller is challenging due to the scalability and resilience issues. Thus, deploying multiple controllers becomes inevitable to process the data with maximum throughput and minimum delay. Controller placement problem (CPP) is a major issue that needs to be addressed by designing efficient solutions. To address the CPP, two parameters, i) number of controllers and ii) location of controllers, need to be handled optimally. Thus, an Optimal COntroller Placement Scheme (COPS) using the multi-objective evolutionary approach for SDN is proposed in this paper. The results prove its effectiveness in terms of various evaluation parameters.

As the scale of data continues to grow exponentially, managing resource allocation and energy consumption in big data systems becomes increasingly complex and critical. Moreover, with big data systems, energy efficiency is more important daily. In cloud environments, it can be the determining factor between reduced costs and lowered environmental damage. This paper presents a deep learning-based framework for accurately predicting instant energy consumption in real-time and detecting anomalies of different sizes in big data clusters. We use SmartMonit to gather task execution and real-time infrastructure data. A Feedforward Neural Network (FNN) predicts energy consumption from CPU utilisation, memory usage, and task profiling research. The system will track any deviation from predicted consumption with root cause analysis (RCA) if there are significant anomalies. We also integrate an Autoencoder to identify straggler tasks and inefficient resource utilisation. Userdefined functions are next applied to examine these anomalies and try to detect the underlying reasons, like distributed data processing, locality of computation exploitation, or resource waste. Given the scale and heterogeneity of big data workloads, the system's ability to dynamically adjust and optimise resource usage is essential for handling complex processing tasks. The experimental results prove that the proposed system effectively enhances resource allocation and decreases wasted energy.

6G applications rely on data-intensive AI models for network optimization. These demand a scalable and energyefficient framework to handle massive device networks with stringent latency requirements which current solutions struggle to support. Although reinforcement learning (RL) and split learning have matured to provide commercial solutions elsewhere. Current solutions in 6G have not used them systematically to achieve the sustainability goals. In this paper, we propose a three-layer framework that minimizes energy consumption of the communication system capable of handling large number of devices. The proposed solution uses RL agents at the edge layer to mathematically model the system and communicate to fog layer for aggregation. The aggregated feature maps are further communicated to cloud layer for global model training. We use split learning for communication and training, the learning at each device are communicated for global model creation effectively. Each edge device improves the overall RL model where system matures quickly consuming minimal energy. The proposed framework's efficacy has been tested extensively for accuracy and scalability, in terms of energy consumption, latency and memory utilizations. The simulation results validate the claims of maturity in models across edge, fog and cloud levels.

Microgrids, marked by substantial renewable energy integration, have garnered significant attention. Traditional optimization methods face challenges in handling the unpredictability of renewable energy, market prices, and loads. Reinforcement learning (RL) offers a solution by learning from historical data. However, Offline-RL models often encounter adaptability challenges in new environments, while traditional Online-RL faces stability issues in certain scenarios. To address these concerns, this article proposes a preference-based deep deterministic policy gradient (PDDPG) algorithm. It guides the online learning process using expert experience based on expert rules and offline-RL to enhance the model's performance. Moreover, unlike studies that assume perfect communication and ignore random communication failures, the proposed real-time microgrid energy management system tackles communication challenges by incorporating a communication detection and data supplement system (CDDSS), especially during extreme weather conditions. The results indicate that the incorporation of CDDSS, as opposed to zero value supplementation, previous moment data, and conventional predictive models, results in a remarkable reduction of microgrid losses by 88.3%, 80.5%, and 53.4%, respectively.

Reconfigurable intelligent Surfaces (RIS) and half-duplex decoded and forwarded (DF) relays can collaborate to optimize wireless signal propagation in communication systems. Users typically have different rate demands and are clustered into groups in practice based on their requirements, where the former results in the trade-off between maximizing the rate and satisfying fine-grained rate demands, while the latter causes a trade-off between inter-group competition and intra-group cooperation when maximizing the sum rate. However, traditional approaches often overlook the joint optimization encompassing both of these trade-offs, disregarding potential optimal solutions and leaving some users even consistently at low date rates. To address this issue, we propose a novel joint optimization model for a RIS- and DF-assisted multiple-input single-output (MISO) system where a base station (BS) is with multiple antennas transmits data by multiple RISs and DF relays to serve grouped users with fine-grained rate demands. We design a new loss function to not only optimize the sum rate of all groups but also adjust the satisfaction ratio of fine-grained rate demands by modifying the penalty parameter. We further propose a two-phase graph neural network (GNN) based approach that inputs channel state information (CSI) to simultaneously and autonomously learn efficient phase shifts, beamforming, and relay selection. The experimental results demonstrate that the proposed method significantly improves system performance.

In drone swarms, where multiple drones collaborate closely to achieve shared objectives within constrained spatial domains, the intricacies of these interrelated actions can lead to potential issues. Despite rigorous pre-deployment planning, the inherent probability of complications persists. These compli-cations stem from onboard computational resources, hardware failures, and network communication disruptions. While the malfunction of an individual drone may seem inconsequential, it can escalate into a substantial predicament when it disrupts the seamless coordination of the entire swarm. Therefore, the need to proactively monitor drones for predictive failure analysis and the subsequent examination of failed drones to mitigate future occurrences becomes imperative. This paper introduces a comprehensive framework for systematically collecting and processing data within drone swarms. The framework gathers critical information about onboard characteristics and commu-nication metrics. These data points are subjected to advanced analysis using Complex Bayesian Networks to probabilistically uncover complex and hidden relationships between random features. The results demonstrate exceptional accuracy, with influences ranging from 99 % to 79 %, that ensures the reliability and effectiveness of the predictive capabilities in enhancing drone safety and network performance.

Virtual power plants (VPPs) are increasingly utilized to efficiently coordinate and manage the increasing number of distributed energy resources (DERs) within power grids. Traditionally, VPP models have prioritized commercial or financial objectives, often overlooking the technical limitations inherent in the distribution system. A technical VPP (TVPP) operational framework is proposed in this work to enhance the scheduling efficiency of various DERs participating in a day-ahead energy market while considering grid management limitations. This paper presents the formulation of the optimal operation of TVPP within a reconfigurable distribution network as a non-linear optimization problem. An incentive-based demand response model has been incorporated into the TVPP scheduling operation to mitigate energy reliance on the utility grid during peak demand periods. The proposed work is analyzed using a TVPP that aggregates solar and wind energy sources, energy storage systems, and consumers with flexible loads, all connected at various nodes of a 33-bus radial distribution network. The outcome of the TVPP scheduling operation confirms the reduction in power loss, bus voltage variation, and grid power variance by 29.18%, 26.54%, and 36.80%, respectively.

With the increasing integration of renewable energy sources into the power grid, accurate and reliable ultra-short-term forecasting of wind power is critical for optimizing grid stability and energy efficiency, especially for a highly dynamic and variable environment. This paper combines Stacked Sparse Autoencoders (SSAE) with a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BLSTM) architecture to address this challenge, which forms a novel deep learning framework, namely hybrid Stacked Sparse Autoencoder and Convolutional neural network-Bidirectional CNN-BLSTM with advanced Feature selection (SSACBF). The process starts with rigorous data preprocessing and key variable selection through a three-step approach based on expert and statistical methods. The framework employs a stacked sparse multi-layer CNN autoencoder to distil inputs into a robust feature set capturing complex temporal dependencies. These features are then processed by a CNN-BLSTM model, which leverages CNN layers for spatial-temporal nuances and BLSTM layers to simultaneously learn from past and future data. The approach significantly outperforms existing models in accuracy and efficiency, demonstrating potential for real-time applications in wind farm operational planning and energy management systems.

Abstract Joint sensing and communications systems have gained significant research interest by merging sensing capabilities with communication functionalities. However, few works have examined the case of multiple users. This work investigates a dual‐user joint sensing and communications system, focusing on the interference between the users that explores the optimal performance trade‐offs through a time‐division approach. Bi‐static radar setting is considered. Two typical strategies under this approach are studied: one in which both users follow the same order of communications and then sensing, and the other in which the tasks are performed in opposite order at two users. In each strategy, the sum rate and the detection probability are evaluated and optimized. The results show that the opposite order strategy offers superior performance to the same order strategy, and they also quantify their performance difference. This research highlights the potential benefits of time‐division strategies and multiple users in joint sensing and communications systems.

The production of electric vehicle battery packs with ever-increasing energy densities has accelerated the electrification of the world’s automotive industry. With increased attention on the electric vehicle markets, it is vital to increase the safety of these vehicles which now hold higher hazardous potential. This paper aims to explore the field of pack-level thermal runaway mechanisms and evaluate potential mitigation strategies. Most available literature concentrates on the micromanagement of thermal runaway whereas this paper takes a more holistic approach. Thermal simulations for analysing thermal runaway of modules in differing locations are run to characterise the behaviour of a thermal runaway event at pack-level. Results suggest that the propagation of thermal runaway is consistently severe in a cooling plate cooled battery pack as the cooling plate acts as a channel for high temperatures. Additionally, thermal insulation added to contain the rapid increase in temperature unfortunately results in wider spread higher temperatures.

Digital twins is an increasingly valuable technology for realising smart cities worldwide. Visualising this technology using mixed reality creates unprecedented opportunities to easily access relevant data and information. In this paper, a digital twins-based system is designed to visualise information from a city’s street lighting system. Data is obtained in two ways: from measured parameters of a miniature model street light in real-time, and from real Durham street lighting. Machine learning is used to maximise the efficiency of purchasing electricity from the grid, and to forecast appropriate adaptive street light brightness levels based on city’s traffic flow and solar irradiance. An application designed in Unity Pro is deployed on a Microsoft HoloLens 2, and it allows the user to view the processed data and control the model street light. It was found that the application performed as desired, displaying information such as voltage, current, carbon emission, electricity price, battery state of charge and LED mode, while enabling control over the model street light. Moreover, the Deep Q-Network machine learning algorithm successfully scheduled to buy electricity at times of low price and low carbon intensity, while the Long Short-Term Memory algorithm accurately forecasted traffic flow with mean Root-Mean-Square Error and Mean Absolute Percentage Error values of 12.0% and 20.0% respectively.

Intelligent Transportation System (ITS) could facilitate communications among various road entities to improve the driver’s safety and driving experience. These communications are called Vehicle-to-Everything (V2X) communications that can be supported by LTE-V2X protocols. Due to frequent changes of network topology in V2X, the source node (e.g., a vehicle) may have to choose a Device-to-Device(D2D) relay node to forward its packet to the destination node. In this paper, we propose a new method for choosing an optimal D2D relay node. The proposed method considers Quality of Service (QoS) requirements for selecting D2D relay nodes. It employs an Analytic Hierarchy Process (AHP) for making decisions. The decision criteria are linked with channel capacity, link stability and end-to-end delay. A number of simulations were performed considering various network scenarios to evaluate the performance of the proposed method. Simulation results show that the proposed method improves Packet Dropping Rate (PDR) by 30% and delivery ratio by 23% in comparison with the existing methods.

This paper studies the long-standing problem of outage-constrained robust downlink beamforming in multi-user multi-antenna wireless communications systems. State of the art solutions have very high computational complexity which poses a major challenge to meet the latency requirement in the future communications systems, e.g., the targeted 1 ms end-to-end latency in 5G. By transforming the robust beamforming problem into a deep learning problem, we propose a new unsupervised data augmentation based deep neural network (DNN) method to address the outage-constrained robust beamforming problem with uncertain channel state information at the transmitter. Simulation results demonstrate that our proposed data augmentation based DNN method for the robust beamforming problem is capable to satisfy the required outage probability, and more importantly, compared to the benchmark Bernstein-Type Inequality (BTI) method, it is less conservative, more power efficient and several orders of magnitude faster.

Electric vehicles (EVs) are being introduced by different manufacturers, thanks to their environment-friendly perspective to alleviate CO2pollution. In this paper, the proposed EV charging management scheme enables pre-empted charging service for heterogeneous EVs (depends on different charging capabilities, brands, etc.). Particularly, the anticipated EVs' charging reservations information, including their arrival time and expected charging time at charging stations (CSs), are brought for planning CS-selection (where to charge). Along with applying ubiquitous cellular network communication to deliver (delay tolerant) EVs' charging reservations, we further study the feasibility of applying opportunistic vehicle-to-vehicle (V2V) communication with delay/disruption tolerant networking (DTN) nature, due primarily to its flexibility and cost-efficiency in vehicular ad hoc networks (VANETs). Evaluation results under the realistic Helsinki city scenario show that applying the V2V-based charging reservation is promisingly cost-efficient in terms of communication overhead, while achieving a comparable charging performance to apply cellular network communication.

In the Fog Radio Access Networks (F-RANs), the local storage and computing capability of Fog Access Points (FAPs) provide new communication resources to address the latency and computing constraints for delay-sensitive applications. To achieve the ultra-low latency, a novel joint user association and channel allocation scheme is proposed in this paper, where the FAPs are clustered from a user-centric perspective. The delay performance is improved regarding both the control signaling procedure and the data transmission procedure. Specifically, the multiple access interference (MAI) between users is analyzed, where the closed-form expression for the effective rate of a typical user with multiple FAP connections and arbitrary interfering users is obtained. With the consideration of MAI, the proposed distributed joint user association and channel allocation algorithm provides a guaranteed delay violation probability. Moreover, the distributed algorithm can be conducted on individual FAPs, whose calculation is simplified by look-up tables. Simulation results show that the proposed algorithm is capable of providing statistical delay performance guarantee including both average delay and delay bound violation probability, which demonstrates its superiority in supporting delay-sensitive applications in F-RANs.

An intelligent transportation system (ITS) is one of the main systems which have been developed to achieve safe traffic and efficient transportation. It enables the vehicles to establish connections with other road entities and infrastructure units using vehicle-to-everything (V2X) communications. As a consequence, all road entities become exposed to either internal or external attacks. Internal attacks cannot be detected by traditional security schemes. In this article, a recommendation-based trust model for V2X communications is proposed to defend against internal attacks. Four types of malicious attacks are analyzed. In addition, we conduct various experiments with different percentage of malicious nodes to measure the performance of the proposed model. In comparison with the existing model, the proposed model shows an improvement in network throughput and the detection rate for all types of considered malicious behaviors. Our model improves the packet dropping rate (PDR) with 36% when the percentage of malicious nodes is around 87.5%.

The growing number of electric vehicles (EV) in the automotive market is leading to ever sharper spikes in consumer power demand. Smart EV charging techniques seek to adjust EV charging load to compensate for supply-demand mismatch. However, all schemes are vulnerable to communications inefficiencies. This paper models implications of communications-driven latency in smart EV charging relevant to secondary voltage control in the distribution network. EV charging load and driving pattern data are gathered from verified statistical studies. A smart charging scheme is proposed enabling high EV penetration with no peak load increase and minimal infrastructure additions. Further, it is applicable to all flexible loads and permits power allocation via a number of possible algorithms. A communications structure for this scheme is then developed, and system performance under ideal and practical communications constraints are studied.

Due to the increasing of population, the number of hospital visits by patients are increasing which puts a pressure on hospitals. Nowadays, the need for taking care of patients while they are at home is essential. Internet of Things (IoT) has been widely used in different areas such as healthcare and smart homes. IoT will assist in minimizing the hospital burden of frequent patients' visits. Applying IoT in healthcare will improve the efficiency and effectiveness, bring economic benefits, and reduce human exertions. It is well known that the best health monitoring system is able to detect abnormalities and able to make diagnosis without human exertion. However, this kind of system is dealing with health conditions which are essential and sensitive that require high accuracy to be reliable. This paper presents an Electrocardiogram (ECG) monitoring framework that overcome the accuracy limitation. Signal processing and feature extraction are applied. For the diagnosis purpose a classification stage is made in two ways; threshold values and machine learning to increase the accuracy. Experiment results reveal that the proposed model is more accurate in the diagnosis of heart diseases than other researches which makes it more confident to rely on from health experts point of view.

In this paper, we present the experimental measurement and evaluation of Transmission Control Protocol (TCP) performance over Internet Protocol (IP) using a real, heterogeneous network environment, incorporating at least one leg of satellite and land mobile link that, together, make an Integrated Satellite-Terrestrial Network (ISTN) testbed for our investigation and performance analysis. Originally, the TCP algorithm was developed for short latency and low link error network environments and has become a de-facto standard protocol for the reliable delivery of IP traffic over the Internet, which, in reality, is a heterogeneous network environment nowadays. Using the real latency figures measured with our testbed systems, we numerically analyse the performance of a standard TCP scheme and compare it with the newly developed TCP Hybla algorithm that claims to address performance degradation due to long round-trip-time (RTT) and high wireless link error channels such as Geostationary Satellite Links. The overall performance was compared with the achievable throughput of each of the two TCP algorithms and available bandwidth of the real testbed system. TCP Hybla performed better even with changing real values of RTT obtained from a real hybrid ISTN environment with a Geostationary Satellite link as the testbed.

In this work, a precise method for measuring end-to-end (E2E) latency in satellite Internet Protocol (IP) networks is proposed. Latency (i.e., time delay) is considered a key parameter that affects the quality of service (QoS) and the performance of communication systems. This is more pronounced in the IP over Satellite. Metrics such as throughput and bandwidth performance of communication systems are dependent on latency, which also has a direct impact on other QoS metrics, such as Internet packet transfer delay and delay variation (or jitter). The upper limits of QoS objective performance metrics are defined by E2E latency for different QoS traffic classes in this environment. Therefore, there is a need to develop efficient methods for the accurate measurement of E2E latency in a satellite IP environment. Two case study scenarios were developed for satellite heterogeneous networks to measure the latency in a satellite IP network. Two geostationary satellite network services were used to compare the performance of the different scenarios and networks. The results demonstrate that at least 50% of the E2E latency is due to the processing and transmitting of IP packets over the satellite in both scenarios. Inconsistent latency behaviour was also observed from daily results at different times of the day, which may degrade performance of jitter sensitive applications.

As vehicular communication becomes a widespread phenomenon, there will be an increase in spectrum scarcity. Cognitive radio provides an effective solution but requires a robust sensing mechanism that entails a large overhead; this additional sensing data could be detrimental to a system already lacking in bandwidth. This paper proposes novel ways of limiting sensing overhead by improving upon current methods which use cooperative mechanisms and adjustable double thresholds (DTHs). Based on a sliding variable, the proposed thresholds can react to changes in the environment, providing the required primary user detection and false alarm probabilities while limiting the number of vehicles reporting sensing data. Three new DTHs have been proposed: detection-based DTH, decision-based DTH, and independent-threshold DTH. Each has unique properties that make it suited for different environments. Simulations were run on all proposed thresholds to test their validity and endurance under environmental changes. The results indicate that the DTHs would greatly benefit high-contention, dense vehicular networks.

Demand side management (DSM) plays an important role in smart grid for paving the way to a low-carbon future. In this paper, a hierarchical day-ahead DSM model is proposed, where renewable energy sources are integrated. The proposed model consists of three layers: the utility in the upper layer, the demand response (DR) aggregator in the middle layer, and customers in the lower layer. The utility seeks to minimize the operation cost and give part of the revenue to the DR aggregator as a bonus. The DR aggregator acts as an intermediary, receiving bonus from the utility and giving compensation to customers for modifying their energy usage pattern. The aim of the DR aggregator is to maximize its net benefit. Customers desire to maximize the social welfare, i.e., the received compensation minus the dissatisfactory level. To achieve these objectives, a multiobjective problem is formulated. An artificial immune algorithm is used to solve this problem, leading to a Pareto optimal set. Using a selection criterion, a Pareto optimal solution can be selected, which does not favour any particular participant to ensure the overall fairness. Simulation results confirm the feasibility of the proposed method: The utility can reduce the operation cost and the peak to average ratio; the DR aggregator can make a profit for providing DSM services; and customers can reduce their bill.

To reduce the burden on data communication in smart girds, household level distributed energy management systems have become increasingly vital due to their capability of distributed intelligence and scheduling devices. This paper studies the optimal management of storage and electric vehicles at a household level when subject to financial constraints. A model using a real-time pricing structure is used to minimise the final consumer cost, whilst responding to power consumption limits set by the supplier. Implementation of the limits and pricing structure allow the supplier to better balance changes and discrepancies in both demand values and generation values. Using real data, models for solar generation, household load demand, and the pricing structure are proposed and integrated into the overall model for the household system. The model for the household system optimises the power taken from the grid and the power stored for the lowest end cost to the user. A series of laboratory evaluations are run to compare the effects of the electric vehicle, solar generation and limits on the household, and considerations are made to the financial and practical implications of these effects. Evaluation results show important benefits from soft limiting household consumption. This allows a more robust and efficient smart grid system that creates better communication between the supplier and the consumer.

Exponential growth in power consumption of wireless communication devices and lack of progress in battery capacity are increasing pressure for more energy efficient (EE) wireless networks. This paper presents an algorithm for optimum EE time allocation for two cooperative relay selection schemes: opportunistic decode-and- forward (ODF) and opportunistic energy efficiency (OEE) with and without rate constraint. By dynamically optimising transmission time between source and relay it is possible to simultaneously improve EE and minimise capacity loss. Simulation in a multi-user scenario with randomly distributed number and location of cooperative nodes demonstrates the algorithm's effectiveness for improving network performance and applicability to both dynamic and static networks. Results imply a unique globally optimum time and power allocation dependent on relay position.

This paper presents a novel carbon emission flow (CEF) model to assess and analyze the carbon emission of each component in power networks. Through the use of information about CEF, demand side management (DSM) and supply side management (SSM) are combined to reduce the emission. Three levels of load curtailment and three strategies of renewable energy sources (RES) utilization are proposed. The IEEE 30-bus system is used to validate the framework of CEF, involving the UK actual daily data of electricity and RES. Simulation results confirm the feasibility of the proposed model and approaches. In the case of DSM, the higher penetration of DSM can result in a higher emission reduction. In the case of SSM, the proposed largest emission substitution strategy can achieve the best performance. In addition, winter day shows a better carbon reduction than summer day in both cases.

Voltage stability is a long standing issue in power systems. Due to the requirements of on-line monitoring and high computation efficiency, L-index is used as voltage stability metric in this paper. We propose a novel L-index sensitivity based control algorithm for voltage stability enhancement. The proposed method uses both outputs of wind generators and additional reactive power compensators as control variables. The sensitivities between L-index and control variables are introduced. Based on these sensitivities, the control algorithm can minimise all the control efforts, while satisfying the predetermined L-index value. This paper then verifies the proposed voltage stability enhancement method using real load and wind generation data in the IEEE 14 bus system. The simulation results prove the effectiveness of proposed methodology in enhancement of voltage stability.

This paper proposes a unified framework for the effective rate analysis over arbitrary correlated and not necessarily identical multiple-input single-output (MISO) fading channels, which uses the moment generating function (MGF) based approach and H transform representation. The proposed framework has the potential to simplify the cumbersome analysis procedure compared with the probability density function-based approach. Moreover, the effective rates over two specific fading scenarios are investigated, namely, independent but not necessarily identical distributed (i.n.i.d.) MISO hyper Fox's H fading channels and arbitrary correlated generalized K fading channels. The exact analytical representations for these two scenarios are also presented. By substituting corresponding parameters, the effective rates in various practical fading scenarios, such as Rayleigh, Nakagami-m, Weibull/Gamma, and generalized K fading channels, are readily available. In addition, asymptotic approximations are provided for the proposed H transform and MGF-based approach as well as for the effective rate over i.n.i.d. MISO hyper Fox's H fading channels. Simulations under various fading scenarios are also presented, which support the validity of the proposed method.

The huge amount of smart meters and growing frequent data readings have become a big challenge on data acquisition and processing in smart grid advanced metering infrastructure systems. This requires a distributed communication architecture in which multiple distributed meter data management systems (MDMSs) are deployed and meter data are processed locally. In this paper, we present the network model for supporting this distributed communication architecture and propose to use large-scale antenna array at the distributed MDMSs to further improve communication performance. We provide performance assessment for this architecture in terms of system throughput and cost efficiency. Specifically, we derive a closed-form asymptotic approximation to the system throughput, which exhibits a very good accuracy compared with simulation results. Based on this tight approximation, we have defined cost efficiency, which takes the deployment cost of distributed MDMSs into account. Our results demonstrate the significant advantages of the distributed architecture over the transitional centralized one in terms of communication performance and scalability. By carefully selecting the number of distributed MDMSs, the distributed communication architecture is also cost efficient.

Wireless power transfer (WPT) could revolutionize global transportation and accelerate growth in the Electric Vehicle (EV) market, offering an attractive alternative to cabled charging. Coil misalignment is inevitable due to driver parking behaviour and has a detrimental effect on power transfer efficiency (PTE). This paper proposes a novel coil design and adaptive hardware to improve PTE in magnetic resonant coupling WPT and mitigate coil misalignment, a crucial roadblock in its acceptance. The new design was verified using ADS, providing a good match to theoretical analysis. Custom designed receiver and transmitter circuitry was used to simulate vehicle and parking bay conditions and obtain PTE data in a small-scale setup. Experimental results showed that PTE can be improved by 30% at the array's centre, and an impressive 90% when misaligned by 3/4 of the arrays radius. The proposed novel coil array achieves overall higher PTE compared to the benchmark single coil design.

Wireless power transfer (WPT) technologies have been widely used in many areas, e.g., the charging of electric toothbrush, mobile phones, and electric vehicles. This paper introduces fundamental principles of three WPT technologies, i.e., inductive coupling-based WPT, magnetic resonant coupling-based WPT, and electromagnetic radiation-based WPT, together with discussions of their strengths and weaknesses. Main research themes are then presented, i.e., improving the transmission efficiency and distance, and designing multiple transmitters/receivers. The state-of-the-art techniques are reviewed and categorised. Several WPT applications are described. Open research challenges are then presented with a brief discussion of potential roadmap.

As a promising interference management technique, interference alignment (IA) has many applications, such as in cognitive radio (CR) networks. In CR networks, due to the coexistence of the secondary users (SUs) and the primary users (PUs), the signal-to-interference-plus-noise-ratio (SINR) at the PUs may decrease dramatically, leading to degraded performance of the PUs. In this paper, a novel IA algorithm based on frequency scheduling is proposed to guarantee the performance of PUs while sharing the spectrum with the SUs. In the algorithm, we divide SUs into multiple clusters, each of which forms an individual IA-CR network while guaranteeing the performance of the PUs. Thus a double-win game is established such that the PUs achieve performance gain with the aid of SUs while the SUs obtain more spectral opportunities. Simulation results are presented to verify the effectiveness of the proposed IA algorithm and its suitability for spectrum sharing in CR networks.

A virtual‐multiple‐input–multiple‐output (MIMO) wireless system using the receiver‐side cooperation with the compress‐and‐forward (CF) protocol, is an alternative to a point‐to‐point MIMO system, when a single receiver is not equipped with multiple antennas. It is evident that the practicality of CF cooperation will be greatly enhanced if an efficient source coding technique can be used at the relay. It is even more desirable that CF cooperation should not be unduly sensitive to carrier frequency offsets (CFOs). This study presents a practical study of these two issues. Firstly, codebook designs of the Voronoi vector quantisation (VQ) and the tree‐structure VQ (TSVQ) to enable CF cooperation at the relay are described. A comparison in terms of the codebook design and encoding complexity is analysed. It is shown that the TSVQ is much simpler to design and operate, and can achieve a favourable performance‐complexity tradeoff. Furthermore, this study demonstrates that CFO can lead to significant performance degradation for the virtual‐MIMO system. To overcome this, it is proposed to maintain clock synchronisation and jointly estimate the CFO between the relay and the destination. This approach is shown to provide a significant performance improvement.

Interference alignment (IA) is a promising technique that can effectively eliminate the interference in wireless networks. However, in traditional IA schemes, the signal to interference plus noise ratio (SINR) may significantly degrade, and the quality of service (QoS) may be unacceptable. In this paper, a novel IA scheme based on antenna switching (AS-IA) is proposed to improve the SINR of the received signal while guaranteeing the QoS in IA wireless networks. In the proposed scheme, some of the antennas are replaced by reconfigurable ones that can switch among preset modes, and the best channel coefficients are selected. Furthermore, to reduce the computational complexity, a sequential antenna switching IA (SAS-IA) scheme is proposed with only one antenna switching in each time slot, and the communication proceeds during the process of searching for the optimal solution. To further improve the performance of the SAS-IA scheme under imperfect channel state information (CSI), a filtering SAS-IA scheme is proposed through averaging the estimated CSI during the iterations of the distributed IA algorithm. Simulation results are presented to show the effectiveness and efficiency of the proposed schemes in improving the QoS of IA wireless networks.

This paper presents a green data transmission approach to enhance the energy efficiency of power line communications (PLC) by jointly utilizing signal detection and resource allocation techniques. Due to the awareness of the interference as enabled by the signal detection function, the proposed PLC system can adaptively adjust the transmission parameters. Furthermore, given a power budget, a performance optimization algorithm is proposed that maximizes the energy efficiency of PLC by optimally choosing the signal detection duration and the transmit power. Simulation results show that the proposed system can not only mitigate the effects of interference, but also considerably improve the energy efficiency of PLC when compared with the existing PLC systems.

Rapidly rising energy costs and increasingly rigid environmental standards have led to an emerging trend of addressing “energy efficiency” aspect of wireless communication technologies. Cognitive radio can play an important role in improving energy efficiency in wireless networks. In this paper, we propose an energy-efficient and time-saving one-bit cooperative spectrum sensing scheme, which has two stages. If the signal-to-noise ratio (SNR) is high or no primary user exists, only one stage of coarse spectrum sensing is needed, by which the sensing time and energy are saved. Otherwise, the second stage of fine spectrum sensing will be performed to increase the spectrum sensing accuracy. Furthermore, only one-bit decision is sent by each secondary user to minimize the overhead. Plenty of simulation is performed, and the results show that the sensing time and energy consumption are both reduced significantly in the proposed scheme.

Interference alignment (IA) is a promising technique that can eliminate the interference in multi-user communication networks effectively. However, it requires highly accurate and real-time channel state information (CSI) at both transmitters and receivers. In practical systems, it is difficult to obtain the perfect knowledge of a dynamic channel due to channel estimation errors, communication latency and capacity constraints. Particularly, transmitters in IA systems usually get imperfect CSI fed back from receivers with a delay, which will greatly affect the performance of IA. In this paper, the performance of IA with delayed CSI is studied, and the decrease of the total network capacity due to the delayed CSI is analyzed. To mitigate the influence of the delayed CSI, an IA scheme based on channel prediction is proposed using two easy-to-implement and practical channel predictors, minimum mean square estimate (MMSE) and weighted least squares error (WLSE) predictors. The CSI of the next time instant is predicted using the present and past CSI. Simulation results are presented to show the effectiveness of the channel prediction IA schemes with the delayed channel knowledge.

Compressive spectrum sensing techniques present many advantages over traditional spectrum sensing approaches, e.g., low sampling rate, and reduced energy consumption. However, when the spectral sparsity level is unknown, there are two significant challenges. They are: 1) how to choose an appropriate number of measurements, and 2) when to terminate the greedy recovery algorithm. In this paper, a compressive autonomous sensing (CASe) framework is presented that gradually acquires the wideband signal using sub-Nyquist rate. Further, a sparsity-aware recovery algorithm is proposed to reconstruct the full spectrum while solving the problem of under-fitting or over-fitting. Simulation results show that the proposed system can not only reconstruct the spectrum using the appropriate number of measurements, but also considerably improve the recovery performance when compared with the existing approaches.

A cooperative virtual multiple-input multiple-output (MIMO) system using two transmit antennas that implements bit-interleaved coded modulation (BICM) transmission and compress-and-forward (CF) relay cooperation among two receiving nodes is presented here. To perform CF cooperation, we propose to use standard source-coding techniques for virtual MIMO detection, based on the analysis of its expected rate bound and the tightness of the bound. Since the relay and the destination are closely spaced, the authors first assume an error-free conference link between them, to focus on investigating the achievable gain from the CF cooperation. Then the system throughput expression and upper bounds on the system error probabilities over block fading channels are derived. The results show that the relay enables the proposed cooperative virtual-MIMO system to achieve almost ideal MIMO performance with low source-coding rates. Furthermore, when we consider a non-ideal cooperation link for practical considerations, a channel-aware adaptive CF scheme is proposed, so that the relay could always adapt its source-coding rate to meet the data rate on the non-ideal link. Owing to the short-range communication and the proposed scheme, the impact of the non-ideal link is too slight to impair the system performance significantly.

A significant challenge of cognitive radio (CR) is to perform wideband spectrum sensing in a fading environment. In this paper, a novel multi-rate sub-Nyquist spectrum detection(MSSD) system is introduced for cooperative wideband spectrum sensing in a distributed CR network. Using only a few sub- Nyquist samples, MSSD is able to sense the wideband spectrum without full spectrum recovery. Specifically, given the low spectral occupancy, sub-Nyquist sampling is performed in each sampling channel and a test statistic is formed by using sub-Nyquist samples from multiple sampling channels. Furthermore, the use of different sub-Nyquist sampling rates is proposed to improve the system detection performance, and the performance of MSSD over both non-fading and Rayleigh fading channels is analyzed. Numerical results show that MSSD can considerably improve the wideband spectrum sensing performance in a fading scenario, with a relatively low implementation complexity and a low computational complexity.

Spectrum sensing plays a paramount role in cognitive radio (CR), which is widely agreed to be the most promising method for alleviating the symptom of RF spectral scarcity. In this paper, we first analyze the effects of multipath/shadowing on a CR system. Furthermore, we build models for the primary user system and the CR user system separately. For the former model, we develop the possible conversation zone to represent the CR cooperative detectable zone. For the latter model, we construct a novel centralized network, called the spider-net sensing network (SNSN), to implement controllable sensing resolution and reliable sensing while avoiding harmful interference from other CR users. Finally, the numerical results show that our proposed SNSN outperforms one without a grid structure using the same combining scheme.