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We investigate the benefits of integrating unmanned aerial vehicles (UAVs) with reconfigurable intelligent surface (RIS) elements to passively relay information sampled by Internet of Things devices (IoTDs) to the base station (BS). In order to maintain the freshness of relayed information, an optimization problem with the objective of minimizing the expected sum Age-of-Information (AoI) is formulated to optimize the altitude of the UAV, the communication schedule, and phases-shift of RIS elements. In the absence of prior knowledge of the activation pattern of the IoTDs, proximal policy optimization algorithm is developed to solve this mixed-integer non-convex optimization problem. Numerical results show that our proposed algorithm outperforms all others in terms of AoI.

With the growing market of Electric Vehicles (EV), the procurement of their charging infrastructure plays a crucial role in their adoption. Within the revolution of Internet of Things, the EV charging infrastructure is getting on board with the introduction of smart Electric Vehicle Charging Stations (EVCS), a myriad set of communication protocols, and different entities. We provide in this article an overview of this infrastructure detailing the participating entities and the communication protocols. Further, we contextualize the current deployment of EVCSs through the use of available public data. In the light of such a survey, we identify two key concerns, the lack of standardization and multiple points of failures, which renders the current deployment of EV charging infrastructure vulnerable to an array of different attacks. Moreover, we propose a novel attack scenario that exploits the unique characteristics of the EVCSs and their protocol (such as high power wattage and support for reverse power flow) to cause disturbances to the power grid. We investigate three different attack variations; sudden surge in power demand, sudden surge in power supply, and a switching attack. To support our claims, we showcase using a real-world example how an adversary can compromise an EVCS and create a traffic bottleneck by tampering with the charging schedules of EVs. Further, we perform a simulation-based study of the impact of our proposed attack variations on the WSCC 9 bus system. Our simulations show that an adversary can cause devastating effects on the power grid, which might result in blackout and cascading failure by comprising a small number of EVCSs.

Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless communication (OWC) technology that is considered as a promising solution for high-speed indoor connectivity. Unlike in conventional radio frequency wireless systems, the OWC channel is not isotropic, meaning that the device orientation affects the channel gain significantly. However, due to the lack of proper channel models for LiFi systems, many studies have assumed that the receiver is vertically upward and randomly located within the coverage area, which is not a realistic assumption from a practical point of view. In this paper, novel realistic and measurement-based channel models for indoor LiFi systems are proposed. Precisely, the statistics of the channel gain are derived for the case of randomly oriented stationary and mobile users. For stationary users, two channel models are proposed, namely, the modified truncated Laplace (MTL) model and the modified Beta (MB) model. For mobile users, two channel models are proposed, namely, the sum of modified truncated Gaussian (SMTG) model and the sum of modified Beta (SMB) model. Based on the derived models, the impact of random orientation and spatial distribution of users is investigated, where we show that the aforementioned factors can strongly affect the channel gain and the system performance.

In this letter, we investigate the amalgamation between the reconfigurable intelligent surface (RIS) technology and the joint transmission coordinated multipoint (JT-CoMP) in order to enhance the performance of a cell-edge user equipment (UE) in a two-user non-orthogonal multiple access (NOMA) group without deteriorating the performance of the NOMA cell-center UE. The RIS is adopted to construct a strong combined channel gain at the cell-edge UE, while JT-CoMP is used to mitigate the effects of inter-cell interference (ICI). In this proposed framework, we derive first a closed-form expression for the ergodic rate of the cell-edge UE, and then we evaluate the network spectral efficiency. We validate the derived expression through Monte-Carlo simulations, where we demonstrate the efficacy of the proposed framework compared to other multiple access techniques proposed in the literature.

Smart grids have been gradually replacing the traditional power grids since the last decade. Such transformation is linked to adding a large number of smart meters and other sources of information extraction units. This provides various opportunities associated with the collected big data. Hence, the triumph of the smart grid energy paradigm depends on the factor of big data analytics. This includes the effective acquisition, transmission, processing, visualization, interpretation, and utilization of big data. The paper provides deep insights into various big data technologies and discusses big data analytics in the context of the smart grid. The paper also presents the challenges and opportunities brought by the advent of machine learning and big data from smart grids.

Machine learning algorithms have been intensively applied to perform load forecasting to obtain better accuracies as compared to traditional statistical methods. However, with the huge increase in data size, sophisticated models have to be created which require big data platforms. Optimal and effective use of the available computational resources can be attained by maximizing the effective utilization of the cluster nodes. Parallel computing is demanded to allow for optimal resource utilization in dealing with smart grid big data. In this paper, a master-slave parallel computing paradigm is utilized and experimented with for load forecasting in a multi-AMI environment. The paper proposes a concurrent job scheduling algorithm in a multi-energy data source environment using Apache Spark. An efficient resource utilization strategy is proposed for submitting multiple Spark jobs to reduce job completion time. The optimal value of clustering is used in this paper to cluster the data into groups to be able to reduce the computational time additionally. Multiple tree-based machine learning algorithms are tested with parallel computation to evaluate the performance with tunable parameters on a real-world dataset. One thousand distribution transformers’ real data from Spain for three years are used to demonstrate the performance of the proposed methodology with a trade-off between accuracy and processing time.

Different aggregation levels of the electric grid’s big data can be helpful to develop highly accurate deep learning models for Short-term Load Forecasting (STLF) in electrical networks. Whilst different models are proposed for STLF, they are based on small historical datasets and are not scalable to process large amounts of big data as energy consumption data grow exponentially in large electric distribution networks. This paper proposes a novel hybrid clustering-based deep learning approach for STLF at the distribution transformers’ level with enhanced scalability. It investigates the gain in training time and the performance in terms of accuracy when clustering-based deep learning modeling is employed for STLF. A k-Medoid based algorithm is employed for clustering whereas the forecasting models are generated for different clusters of load profiles. The clustering of the distribution transformers is based on the similarity in energy consumption profile. This approach reduces the training time since it minimizes the number of models required for many distribution transformers. The developed deep neural network consists of six layers and employs Adam optimization using the TensorFlow framework. The STLF is a day-ahead hourly horizon forecasting. The accuracy of the proposed modeling is tested on a 1,000-transformer substation subset of the Spanish distribution electrical network data containing more than 24 million load records. The results reveal that the proposed model has superior performance when compared to the state-of-the-art STLF methodologies. The proposed approach delivers an improvement of around 44% in training time while maintaining accuracy using single-core processing as compared to non-clustering models.

For the utility to plan the resources accurately and balance the electricity supply and demand, accurate and timely forecasting is required. The proliferation of smart meters in the grids has resulted in an explosion of energy datasets. Processing such data is challenging and usually takes a longer time than the requirement of a short-term load forecast. The paper addresses this concern by utilizing parallel computing capabilities to minimize the execution time while maintaining highly accurate load forecasting models. In this paper, a thousand smart meter energy datasets are analyzed to perform day ahead, hourly short-term load forecast (STLF). The paper utilizes multi-processing to enhance the overall execution time of the forecasting models by submitting simultaneous jobs to all the processors available. The paper demonstrates the efficacy of the proposed approach through the choice of machine learning (ML) models, execution time, and scalability. The proposed approach is validated on real energy consumption data collected at distribution transformers' level in Spanish Electrical Grid. Decision trees have outperformed the other models accomplishing a tradeoff between model accuracy and execution time. The methodology takes only 4 minutes to train 1,000 transformers for an hourly day-ahead forecast of (~24 million records) utilizing 32 processors.

Forecasting of energy consumption in Smart Buildings (SB) and using the extracted information to plan and operate power generation are crucial elements of the Smart Grid (SG) energy management. Prediction of electrical loads and scheduling the generation resources to match the demand enable the utility to mitigate the energy generation cost. Different methodologies have been employed to predict energy consumption at different levels of distribution and transmission systems. In this paper, a novel hybrid deep learning model is proposed to predict energy consumption in smart buildings. The proposed framework consists of two stages, namely, data cleaning, and model building. The data cleaning phase applies pre-processing techniques to the raw data and adds additional features of lag values. In the model-building phase, the hybrid model is trained on the processed data. The hybrid deep learning (DL) model is based on the stacking of fully connected layers, and unidirectional Long Short Term Memory (LSTMs) on bi-directional LSTMs. The proposed model is designed to capture the temporal dependencies of energy consumption on dependent features and to be effective in terms of computational complexity, training time, and forecasting accuracy. The proposed model is evaluated on two benchmark energy consumption datasets yielding superior performance in terms of accuracy when compared with widely used hybrid models such as Convolutional (Conv) Neural Network-LSTM, ConvLSTM, LSTM encoder-decoder model, stacking models, etc. A mean absolute percentage error (MAPE) of 2.00% for case study 1 and a MAPE of 3.71% for case study 2 is obtained for the proposed forecasting DL model in comparison with LSTM-based models that yielded 7.80% MAPE and 5.099% MAPE for two datasets respectively. The proposed model has also been applied for multi-step week-ahead daily forecasting with an improvement of 8.368% and 20.99% in MAPE against the LSTM-based model for the utilized energy consumption datasets respectively.

Smart grids are re-engineering the electricity transmission and distribution system throughout the world. It is an amalgam of increased digital information with the electrical power grids. Managing the data generated from the grid efficiently is the key to successful knowledge extraction from the smart grid big data. Most of the scientific advancements are becoming data-driven and becoming an interesting area of research for data scientists. It is challenging the world computationally enough to develop new storage methods and data processing technologies. Managing big data involves data cleaning, integration of varied data sources, and decision-making applications. This paper focuses on the study of big data management and proposes a management process to help manage the data in the grid. Data management tools and techniques have been leveraged in understanding the sources and data types in the grid. The paper emphasizes the limitations of the existing solutions inclined towards applications of the smart grid big data.

Cooperative non-orthogonal multiple access (C-NOMA) is a novel multiple access technology that is considered as a promising solution for 5G and beyond. The technique has been proposed as a combination between NOMA and cooperative communications, such as device-to-device (D2D) communications. In this letter, we will address some limitations of the state-of-the-art C-NOMA model and promote an enhancement to the contemporary version. The improvement is based on an exhaustive exploitation of the antennas mounted at the users devices. Based on this, we revisit the rate analysis and the performance optimization of C-NOMA systems. Simulation results reveal the robustness of the proposed scheme in the presence of high self-interference (SI) and insightful comparisons with other previously proposed schemes in the literature are provided.

Unmanned aerial vehicles (UAVs) are envisioned to play a key role in intelligent transportation systems to complement the communication infrastructure in future smart cities. UAV-assisted vehicular networking research typically adopts throughput and latency as the main performance metrics. These conventional metrics, however, are not adequate to reflect the freshness of the information, an attribute that has been recently identified as a critical requirement to enable services such as autonomous driving and accident prevention. In this paper, we consider a UAV-assisted single-hop vehicular network, wherein sensors (e.g., LiDARs and cameras) on vehicles generate time sensitive data streams, and UAVs are used to collect and process this data while maintaining a minimum age of information (AoI). We aim to jointly optimize the trajectories of UAVs and find scheduling policies to keep the information fresh under minimum throughput constraints. The formulated optimization problem is shown to be mixed integer non-linear program (MINLP) and generally hard to be solved. Motivated by the success of machine learning (ML) techniques particularly deep learning in solving complex problems with low complexity, we reformulate the trajectories and scheduling policies problem as a Markov decision process (MDP) where the system state space considers the vehicular network dynamics. Then, we develop deep reinforcement learning (DRL) to learn the vehicular environment and its dynamics in order to handle UAVs’ trajectory and scheduling policy. In particular, we leverage Deep Deterministic Policy Gradient (DDPG) for learning the trajectories of the deployed UAVs to efficiently minimize the Expected Weighted Sum AoI (EWSA). Simulations results demonstrate the effectiveness of the proposed design and show the deployed UAVs adapt their velocities during the data collection mission in order to minimize the AoI.

The proliferation of Internet of Things (IoT) has brought an array of different services, from smart health-care, to smart transportation, all the way to smart cities. For a truly connected environment, different sectors need to collaborate. One use case of such overlap is between smart grids and Intelligent Transportation System (ITS) giving rise to Electric Vehicles and their charging infrastructure. Being such a lucrative opportunity for investors and the research community, many efforts have been made toward providing the end-user with an extraordinary Quality of Service (QoS). However, given the current protocols and deployment of the Electric Vehicle (EV) charging infrastructure, some key challenges still need to be addressed. In particular, we identify two main EV challenges: (1) vulnerable charging stations and EVs, and (2) non-optimal charging schedules. With these issues in mind, we evaluate the integration of Blockchain and AI with the EV charging infrastructure. Specifically, we discuss the current AI and Blockchain charging solutions available in the market. In addition, we propose a couple of use cases where both technologies complement each other for a secure, efficient and decentralized charging ecosystem. This article serves as starting point for stakeholders and policymakers to help identify potential directions and implementations of better charging systems for EVs.

The wireless industry is moving towards developing smart cellular architectures that dynamically adjust the use of the network elements according to the service demand, and automating their operations in order to minimize both capital expenditure (CAPEX) and operation expenditure (OPEX). This involves developing efficient and unsupervised radio access network (RAN) planning, which has a direct impact on the system performance and CAPEX. This intelligent cellular planning aims at providing the base stations (BSs) configurations (e.g., coverage, user associations and antenna radiation pattern) that minimize the number of deployed BSs and meet the requirements in terms of coverage and capacity. The cellular planning optimization problem has been shown to be complex and non-scalable. Moreover, most of the existing cellular planning techniques result in an over or under provisioning architecture. Motivated by the above, we propose in this paper a novel and efficient unsupervised planning process. We make use of statistical machine learning (SML) to solve the problem at hand. The core idea of SML is that the planning parameters are treated as random variables. The parameters that maximize the corresponding joint probability distribution, conditioned on observations of users’ positions, are learned or inferred using Gibbs sampling theory and Bayes’ theory. To apply this theory to the planning problem, we make significant efforts to properly formulate the problem to be able to incorporate the constraints into the inference process and extract the planning parameters from the inferred model. Through several numerical examples, we compare the performance of the proposed approach to clustering-based and optimization-based existing planning approaches, and demonstrate the efficacy of our approach. We also demonstrate how our approach can leverage existing cellular infrastructures into the new design.

The agility of unmanned aerial vehicles (UAVs) have been recently harnessed in developing potential solutions that provide seamless coverage for vehicles in areas with poor cellular infrastructure. In this letter, multiple UAVs are deployed to provide the needed cellular coverage to vehicles traveling with random speeds over a given highway segment. This letter minimizes the number of deployed UAVs and optimizes their trajectories to offer prevalent communication coverage to all vehicles crossing the highway segment while saving energy consumption of the UAVs. Due to varying traffic conditions on the highway, a reinforcement learning approach is utilized to govern the number of needed UAVs and their trajectories to serve the existing and newly arriving vehicles. Numerical results demonstrate the effectiveness of the proposed design and show that during the mission time, a minimum number of UAVs adapt their velocities in order to cover the vehicles.

We study in this paper the secrecy performance of the multiple-input multiple-output (MIMO) visible light communication (VLC) wiretap channel. The underlying system model comprises three nodes: one transmitter, equipped with multiple fixtures of LEDs, one legitimate receiver and one eavesdropper, each equipped with multiple photo-diodes (PDs). The VLC channel is modeled as a real-valued amplitude-constrained Gaussian channel and the eavesdropper is assumed to be randomly located in the coverage area. We propose a low-complexity precoding scheme that aims at enhancing the secrecy performance of the system. Specifically, assuming discrete input signaling, we derive an average achievable secrecy rate for the underlying system in a closed-form, and the derived expression is a function of the precoding matrix and the input distribution using stochastic geometry. Then, we propose a low-complexity design of the precoding matrix based on the generalized singular value decomposition (GSVD) of the channel matrices of the system. We examine the resulting average achievable secrecy rate using the truncated discrete generalized normal (TDGN) distribution, which is the best-known discrete distribution available in the literature. Finally, we validate the proposed scheme through extensive simulations and we demonstrate its superiority when compared to other schemes reported in the literature.

The global evolution of wireless technologies and intelligent sensing devices are transforming the realization of smart cities. Among the myriad of use cases, there is a need to support applications whereby low-resource IoT devices need to upload their sensor data to a remote control centre by target hard deadlines; otherwise, the data becomes outdated and loses its value, for example, in emergency or industrial control scenarios. In addition, the IoT devices can be either located in remote areas with limited wireless coverage or in dense areas with relatively low quality of service. This motivates the utilization of UAVs to offload traffic from existing wireless networks by collecting data from time-constrained IoT devices with performance guarantees. To this end, we jointly optimize the trajectory of a UAV and the radio resource allocation to maximize the number of served IoT devices, where each device has its own target data upload deadline. The formulated optimization problem is shown to be mixed integer non-convex and generally NP-hard. To solve it, we first propose the high-complexity branch, reduce and bound (BRB) algorithm to find the global optimal solution for relatively small scale scenarios. Then, we develop an effective sub-optimal algorithm based on successive convex approximation in order to obtain results for larger networks. Next, we propose an extension algorithm to further minimize the UAV’s flight distance for cases where the initial and final UAV locations are known a priori. We demonstrate the favourable characteristics of the algorithms via extensive simulations and analysis as a function of various system parameters, with benchmarking against two greedy algorithms based on distance and deadline metrics.

Along with the dramatic increase in the number of IoT devices, different IoT services with heterogeneous QoS requirements are evolving with the aim of making the current society smarter and more connected. In order to deliver such services to the end users, the network infrastructure has to accommodate the tremendous workload generated by the smart devices and their heterogeneous and stringent latency and reliability requirements. This would only be possible with the emergence of ultra reliable low latency communications (uRLLC) promised by 5G. Mobile Edge Computing (MEC) has emerged as an enabling technology to help with the realization of such services by bringing the remote computing and storage capabilities of the cloud closer to the users. However, integrating uRLLC with MEC would require the network operator to efficiently map the generated workloads to MEC nodes along with resolving the trade-off between the latency and reliability requirements. Thus, we study in this paper the problem of Workload Assignment (WA) and formulate it as a Mixed Integer Program (MIP) to decide on the assignment of the workloads to the available MEC nodes. Due to the complexity of the WA problem, we decompose the problem into two subproblems; Reliability Aware Candidate Selection (RACS) and Latency Aware Workload Assignment (LAWA-MIP). We evaluate the performance of the decomposition approach and propose a more scalable approach; Tabu meta-heuristic (WA-Tabu). Through extensive numerical evaluation, we analyze the performance and show the efficiency of our proposed approach under different system parameters.

Quadratic cost function such as Mean Squared Error (MSE) has been a widely used objective function for training deep neural networks to develop energy forecasting models in Smart Grids. In this work, Penalizing Underestimation Logarithmic Squared Error (PULSE), a novel objective function is proposed with the aim of reducing the tendency of deep learning models to underestimate the target variable. Stacked Long Short-Term Memory (LSTM) networks are adopted on the time series load demand data to investigate the performance of the proposed cost function against the widely used MSE cost function. The evaluation is performed using open-source real-world electricity load diagrams dataset covering a period of three years. The performance of the proposed scheme is examined with deep learning models through several experiments. The results demonstrate that the proposed scheme is able to eliminate the tendency to underestimate and provides competitively accurate load demand forecasting results. The results are additionally compared against the state-of-the-art machine learning models developed in the literature. The proposed cost function maintains the RMSE around 4*10-2 kWh which is also the RMSE for deep learning models with MSE cost function and delivers 25% improvement in MAPE while also eliminating the underestimation of load demand.

In this paper, we investigate the dynamic power allocation for a visible light communication (VLC) cellular system consisting of two coordinating attocells, each equipped with one access-point (AP). The coordinated multipoint (CoMP) between the two cells is introduced to assist users experiencing high inter-cell-interference (ICI). Specifically, the coordinated zero-forcing (ZF) precoding is used to cancel the ICI at the users located near the centers of the cells, whereas the joint transmission (JT) is employed to eliminate the ICI at the users located at the edge of both cells and to improve their receptions as well. Furthermore, two multiple access techniques are invoked within each cell, namely, non-orthogonal-multiple-access (NOMA) and cooperative non-orthogonal-multiple-access (C-NOMA). Hence, two multiple access techniques are proposed for the considered multi-user multi-cell system, namely, the CoMP-assisted NOMA scheme and the CoMP-assisted C-NOMA scheme. For each scheme, two power allocation frameworks are formulated each as an optimization problem, where the objective of the former is maximizing the network sum data rate while guaranteeing a certain quality-of-service (QoS) for each user, whereas the goal of the latter is to maximize the minimum data rate among all coexisting users. The formulated optimization problems are not convex, and hence, difficult to be solved directly unless using heuristic methods, which comes at the expense of high computational complexity.

This paper proposes a novel framework to emerge the reconfigurable intelligent surface (RIS) in cellular networks wherein enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC) services coexist. In order to avoid the violation in the URLLC latency requirements, the framework proposes RIS phase shift matrix that enhances the URLLC reliability is proactively designed at the beginning of the time slot. The system model consists of a single base station (BS) and a single RIS which deployed to enhance the channel environments of the eMBB and the URLLC users. To allocates the eMBB users, we formulate a time-slot basis eMBB allocation problem which has the goal of maximizing the eMBB sum-rate by jointly optimizing the power allocation at the BS and the RIS phase shift matrix while satisfying the eMBB rate constraint. Since the formulated problem is a non-convex problem which hard to be solved directly, we adopt the alternating optimization approach to decompose the eMBB allocation problem optimization problem into a power allocation and a RIS phase shift matrix sub-problems. Then, the URLLC allocation problem is formulated as a multi-objective problem with the goal of maximizing the URLLC admitted packets and minimizing the eMBB rate loss by jointly optimizing the power and frequency allocation. Then, we proposed a heuristic algorithm to allocate the URLLC load. The proposed algorithm has a low time complexity which makes it a efficient method for multiplexing URLLC and eMBB traffics. Finally, simulation results show that using only 60 RIS elements, we observe that the proposed scheme achieves around 99.99% URLLC packets admission rate compared to 95.6% when there is no RIS, while also achieving up to 70% enhancement on the eMBB rates.

Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless communication (OWC) technology that is considered as a promising solution for high-speed indoor connectivity aimed for future sixth generation (6G) wireless networks. In the LiFi physical layer, the majority of the power allocation problems for mobile users investigated and reported in the literature are non-convex. These problems may be solved using dual decomposition techniques or heuristics that require iterative algorithms, and often, cannot be computed in real time due to the high computational load. In this paper, a proactive power allocation (PPA) approach that can alleviate the aforementioned issues is proposed. The core of the PPA approach is two cascaded neural networks consisting of one convolution neural network (CNN) and one long-short-term-memory (LSTM) network that are jointly capable of predicting posterior positions and orientations of mobile users following random trajectories in indoor environments. Afterwards, the predicted parameters are fed into the expression of the channel coefficients of the mobile users. Finally, the resulting predicted channel coefficients are exploited for deriving near-optimal power allocation schemes prior to the intended service time, which enables near-optimal and real-time service for mobile LiFi users.

Ultra-Reliable and Low Latency Communications (URLLC) is one of the essential services in 5G networks and beyond. The coexistence of URLLC alongside other service classes, namely, enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC), calls for developing spectrally efficient multiplexing techniques. In this work, we study the problem of scheduling URLLC traffic in a downlink system with the presence of eMBB traffic class. Based on the superposition/puncturing scheme, a resource allocation problem is formulated with the objective to minimize the eMBB data rate loss while satisfying eMBB and URLLC quality of service (QoS) constraints. The resulting problem is formulated as a mixed integer non-linear programming (MINLP) which is generally NP hard and hence complex to solve. Hence, we derive its feasibility region as well as the optimal solutions for the power and spectral resource allocation. Subsequently, we propose a low complexity algorithm to serve URLLC traffic. Simulation results show that the proposed algorithm achieves higher reliability for URLLC and higher eMBB data rate compared to the puncturing schemes. The results also show that the eMBB QoS requirements, which are represented by the eMBB rate loss threshold, has a negative effect on the URLLC reliability for high URLLC load. Therefore, the eMBB rate and the eMBB loss threshold should be jointly optimized considering QoS of both eMBB and URLLC. Index Terms—eMBB, multiplexing, puncturing, superposition, URLLC, 6G.

Next-generation cellular systems are anticipated to support 100 times higher data rates (ultra-high rate) compared with the fourth generation (4G) of cellular systems. It is, therefore, necessary to develop novel spectrally efficient uplink/downlink techniques. Multiple techniques have been proposed, including the so-called non-orthogonal multiple access (NOMA) technique. However, the spectral efficiency gains achieved by NOMA over OMA techniques have been shown to be modest. Recently, we proposed a spectrally efficient technique for the downlink channel, which involves exploiting similarities among users’ short bit blocks, where we showed that spectral efficiency gains of up to three times that of OMA schemes can be achieved. However, the technique cannot be extended to the uplink scenario because users are not aware of each other’s bit block. To this end, we propose in this paper a spectrally efficient scheme for the uplink channel, where we exploit the similarity between the short bit blocks of the uplink and downlink sequences corresponding to one user. The downlink bit sequences are those received by a user from the base station (BS). It is assumed that the BS keeps track of the bit sequences transmitted on the downlink channel to different users. The uplink and downlink bit sequences, which are assumed to be uncorrelated, are divided into bit blocks of short lengths, and then, the similarity between those blocks is extracted. Once each user determines its similarity index (i.e., the number of similar bit blocks) between its own bit sequence and its respective downlink bit sequence, this information is communicated with the BS, which will, in turn, select the user with the largest similarity index to transmit during that resource block. The same process repeats every resource block where the user with the maximum similarity index is always selected. We propose a simple overhead exchange algorithm that facilitates the exchange of the information on the similarity indexes between the users and the BS, where we assume that this exchange of information is done through a control channel. The performance of the proposed scheme and the overhead exchange algorithm is investigated analytically and by Monte Carlo simulations. Among the parameters that we incorporate into the analysis are the user density, the length of bit blocks used to check the similarity index, and the channel correlation. We show that spectral efficiency gains of approximately two times that of OMA schemes can be achieved.

This letter jointly investigates the trajectory and radio resource optimization for multiple unmanned aerial vehicles (UAVs) to fully deliver critical data in vehicular networks during disaster situations. We aim to minimize the number of deployed UAVs to fully serve all vehicles. The formulated problem is generally NP-hard. To solve it, we employ a sequence of convex approximates. Then, we develop an efficient algorithm to sequentially solve this problem. Our numerical results demonstrate the effectiveness of our proposed design and show that during the mission time, the UAVs adapt their velocities in order to fulfill the requirement of each vehicle.

The proliferation of smart connected Internet of Things (IoT) devices is bringing tremendous challenges in meeting the performance requirement of their supported real-time applications due to their limited resources in terms of computing, storage, and battery life. In addition, the considerable amount of data they generate brings extra burden to the existing wireless network infrastructure. By enabling distributed computing and storage capabilities at the edge of the network, multi-access edge computing (MEC) serves delay sensitive, computationally intensive applications. Managing the heterogeneity of the workload generated by IoT devices, especially in terms of computing and delay requirements, while being cognizant of the cost to network operators, requires an efficient dimensioning of the MEC-enabled network infrastructure. Hence, in this paper, we study and formulate the problem of MEC resource provisioning and workload assignment for IoT services (RPWA) as a mixed integer program to jointly decide on the number and the location of edge servers and applications to deploy, in addition to the workload assignment. Given its complexity, we propose a decomposition approach to solve it which consists of decomposing RPWA into the delay aware load assignment sub-problem and the mobile edge servers dimensioning sub-problem. We analyze the effectiveness of the proposed algorithm through extensive simulations and highlight valuable performance trends and trade-offs as a function of various system parameters.

In this paper, we tackle the problem of joint angle and delays estimation (JADE) of multiple reflections of a known signal impinging on multiple receiving antennae. Based on the importance sampling (IS) concept, we propose a new non-iterative maximum likelihood (ML) estimator that enjoys guaranteed global optimality and enhanced high-resolution capabilities for both single- and multi-carrier models. The new ML approach succeeds in transforming the original multi-dimensional optimization problem into multiple two-dimensional ones thereby resulting in huge computational savings. Moreover, it does not suffer from the off-grid problems that are inherent to most existing JADE techniques. By exploiting the sparsity feature of a carefully designed pseudo-pdf that is intrinsic to the new estimator, we also propose a novel approach that enables the accurate detection of the unknown number of paths over a wide range of practical signal-to-noise ratios (SNRs). Computer simulations show the distinct advantage of the new ML estimator over state-of-the art JADE techniques both in the single- and multi-carrier scenarios. Most remarkably, they suggest that the proposed IS-based ML JADE is statistically efficient as it almost reaches the Camer-Rao lower bound (CRLB) even in the adverse conditions of low SNR levels. Using real-world channel measurements collected from four access points (APs) with IEEE 802.11ac standard’s setup parameters in an indoor environment, we also show that the proposed ML estimator achieves a localization performance below 15 cm accuracy.

This paper investigates the secrecy performance of the multiple-input single-output visible light communication (VLC) wiretap channel. The considered system model comprises three nodes: a transmitter (Alice) equipped with multiple fixtures of LEDs, a legitimate receiver (Bob), and an eavesdropper (Eve), each equipped with one photo-diode. The VLC channel is modeled as a real-valued amplitude-constrained Gaussian channel. Eve is assumed to be randomly located in the same area as Bob. Due to this, artificial noise-based beamforming is adopted as a transmission strategy in order to degrade Eve’s signal-to-noise ratio. Assuming discrete input signaling, we derive an achievable secrecy rate in a closed-form expression as a function of the beamforming vectors and the input distribution. We investigate the average secrecy performance of the system using stochastic geometry to account for the location randomness of Eve. We also adopt the truncated discrete generalized normal (TDGN) as a discrete input distribution. We present several examples through which we confirm the accuracy of the analytical results via Monte Carlo simulations. The results also demonstrate that the TDGN distribution, albeit being not optimal, yields performance close to the secrecy capacity.

This paper investigates the downlink transmission of reconfigurable intelligent surface (RIS)-aided cooperative non-orthogonal-multiple-access (C-NOMA), where both half-duplex (HD) and full-duplex (FD) relaying modes are considered. The system model consists of one base station (BS), two users and one RIS. The goal is to minimize the total transmit power at both the BS and at the user-cooperating relay for each relaying mode by jointly optimizing the power allocation coefficients at the BS, the transmit power coefficient at the relay user, and the passive beamforming at the RIS, subject to power budget constraints, the successive interference cancellation constraint and the minimum required quality-of-service at both cellular users. To address the high-coupled optimization variables, an efficient algorithm is proposed by invoking an alternating optimization approach that decomposes the original problem into a power allocation sub-problem and a passive beamforming sub-problem, which are solved alternately. For the power allocation sub-problem, the optimal closed-form expressions for the power allocation coefficients are derived. Meanwhile, with the aid of difference-of-convex rank-one representation and successive convex approximation, an efficient solution for the passive beamforming is obtained. The simulation results validate the accuracy of the derived power control closed-form expressions and demonstrate the gain in the total transmit power brought by integrating the RIS in C-NOMA networks.

This paper studies the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC) services in a cellular network that is assisted by a reconfigurable intelligent surface (RIS). The system model consists of one base station (BS) and one RIS that is deployed to enhance the performance of both eMBB and URLLC in terms of the achievable data rate and reliability, respectively. We formulate two optimization problems, a time slot basis eMBB allocation problem and a mini-time slot basis URLLC allocation problem. The eMBB allocation problem aims at maximizing the eMBB sum rate by jointly optimizing the power allocation at the BS and the RIS phase-shift matrix while satisfying the eMBB rate constraint. On the other hand, the URLLC allocation problem is formulated as a multi-objective problem with the goal of maximizing the URLLC admitted packets and minimizing the eMBB rate loss. This is achieved by jointly optimizing the power and frequency allocations along with the RIS phase-shift matrix. In order to avoid the violation in the URLLC latency requirements, we propose a novel framework in which the RIS phase-shift matrix that enhances the URLLC reliability is proactively designed at the beginning of the time slot. For the sake of solving the URLLC allocation problem, two algorithms are proposed, namely, an optimization-based URLLC allocation algorithm and a heuristic algorithm. The simulation results show that the heuristic algorithm has a low time complexity, which makes it practical for real-time and efficient multiplexing between eMBB and URLLC traffic. In addition, using only 60 RIS elements, we observe that the proposed scheme achieves around 99.99\% URLLC packets admission rate compared to 95.6\% when there is no RIS, while also achieving up to 70\% enhancement on the eMBB sum rate.

In this paper machine learning (ML) models have been developed for the application of big data load forecasting using parallel computation. The load forecasting models’ performance is directly linked to system execution capacity, memory, thread count, balancing the load, and available resources. This paper is focused on two main challenges. The first challenge is to reduce the execution time of the ML models and the second one is to choose the suitable tree-based model for effective load forecasting. The paper conducts a comprehensive evaluation of the load forecasting using real-world data on energy consumption. Comprehensive results are obtained to show that the performance of random search to tune the ML models exhibits competitive performances whilst not losing the accuracy of the models and gaining a competitive advantage on the run time.

This paper investigates the performance of cooperative non-orthogonal multiple access (C-NOMA) in a cellular downlink system. The system model consists of a base station (BS) serving multiple users, where users that have the capability of full-duplex (FD) communications can assist the transmissions between the BS and users with poor channel quality through device-to-device (D2D) communications. To maximize the achievable sum rate of the whole system while guaranteeing a certain quality of service (QoS) for all users, we formulate and solve a novel optimization problem that jointly determines the optimal D2D user pairing and the optimal power control scheme. The formulated problem is a mixed-integer non-linear program (MINLP), which has extremely high complexity. To overcome this issue, a two-step policy is proposed to solve the problem in polynomial time. First, we derive a closed-form expression of the optimal power control scheme that maximizes the sum rate of a given pair of users with a required QoS. Then, using the derived closed-form in the first step, we employ the Hungarian algorithm as the pairing policy in multi-user settings. Our simulation results show that the proposed scheme prevails some previously proposed heuristic approach for the given problem.

Due to the dramatic increase in high data rate services and in order to meet the demands of the fifth-generation (5G) networks, researchers from both academia and industry are exploring advanced transmission techniques, new network architectures and new frequency spectrum such as the visible light and the millimeter wave (mmWave) spectra. Visible light communication (VLC) particularly is an emerging technology that has been introduced as a promising solution for 5G and beyond, owing to the large unexploited spectrum, which translates to significantly high data rates. Although VLC systems are more immune against interference and less susceptible to security vulnerabilities since light does not penetrate through walls, security issues arise naturally in VLC channels due to their open and broadcasting nature, compared to fiber-optic systems. In addition, since VLC is considered to be an enabling technology for 5G, and security is one of the 5G fundamental requirements, security issues should be carefully addressed and resolved in the VLC context. On the other hand, due to the success of physical layer security (PLS) in improving the security of radio-frequency (RF) wireless networks, extending such PLS techniques to VLC systems has been of great interest. Only two survey papers on security in VLC have been published in the literature. However, a comparative and unified survey on PLS for VLC from information theoretic and signal processing point of views is still missing. This paper covers almost all aspects of PLS for VLC, including different channel models, input distributions, network configurations, precoding/signaling strategies, and secrecy capacity and information rates. Furthermore, we propose a number of timely and open research directions for PLS-VLC systems, including the application of measurement-based indoor and outdoor channel models, incorporating user mobility and device orientation into the channel model, and combining VLC and RF systems to realize the potential of such technologies.

This paper investigates the performance of cooperative non-orthogonal multiple access (C-NOMA) in a cellular downlink system. The system model consists of a base station (BS) serving multiple users, where users with good channel quality can assist the transmissions between the BS and users with poor channel quality through either half-duplex (HD) or full-duplex (FD) device-to-device (D2D) communications. We formulate and solve a novel optimization problem that jointly determines the optimal D2D user pairing and the optimal power control scheme, where the objective is maximizing the achievable sum rate of the whole system while guaranteeing a certain quality of service (QoS) for all users. The formulated problem is a mixed-integer non-linear program (MINLP) which is generally NPhard. To overcome this issue, we reconstruct the original problem into a bi-level optimization problem that can be decomposed into two sub-problems to be solved independently. The outer problem is a linear assignment problem which can be efficiently handled by the well-known Hungarian method. The inner problem is still a non-convex optimization problem for which finding the optimal solution is challenging. However, we derive the optimal power control policies for both the HD and the FD schemes in closedform expressions, which makes the computational complexity of the inner problems polynomial for every possible pairing configurations. These findings solve ultimately the original MILNP in a timely manner that makes it suitable for real-time and low latency applications. Our simulation results show that the proposed framework outperforms a variety of proposed schemes in the literature and that it can obtain the optimal pairing and power control policies for a network with 100 users in a negligible computational time.

Ultra Reliable and Low Latency Communications (URLLC) is deemed to be an essential service in 5G systems and beyond to accommodate a wide range of emerging applications with stringent latency and reliability requirements. Coexistence of URLLC alongside other service categories calls for developing spectrally efficient multiplexing techniques. Specifically, coupling URLLC and conventional enhanced Mobile BroadBand (eMBB) through superposition/puncturing naturally arises as a promising option due to the tolerance of the latter in terms of latency and reliability. The idea here is to transmit URLLC packets over resources occupied by ongoing eMBB transmissions while minimizing the impact on the eMBB transmissions. In this paper, we propose a novel downlink URLLC-eMBB multiplexing technique that exploits possible similarities among URLLC and eMBB symbols, with the objective of reducing the size of the punctured eMBB symbols. We propose that the base station scans the eMBB traffic' symbol sequences and punctures those that have the highest symbol similarity with that of the URLLC users to be served. As the eMBB and URLLC may use different constellation sizes, we introduce the concept of symbol region similarity to accommodate the different constellations. We assess the performance of the proposed scheme analytically, where we derive closed-form expressions for the symbol error rate (SER) of the eMBB and URLLC services. {We also derive an expression for the eMBB loss function due to puncturing in terms of the eMBB SER}. We demonstrate through numerical and simulation results the efficacy of the proposed scheme where we show that 1) the eMBB spectral efficiency is improved by puncturing fewer symbols, 2) the SER and reliability performance of eMBB are improved, and 3) the URLLC data is accommodated within the specified delay constraint while maintaining its reliability.

Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless communication (OWC) technology that is considered as a promising solution for high-speed indoor connectivity. In this paper, the joint estimation of user 3D position and user equipment (UE) orientation in indoor LiFi systems with unknown emission power is investigated. Existing solutions for this problem assume either ideal LiFi system settings or perfect knowledge of the UE states, rendering them unsuitable for realistic LiFi systems. In addition, these solutions consider the non-line-of-sight (NLOS) links of the LiFi channel gain as a source of deterioration for the estimation performance instead of harnessing these components in improving the position and the orientation estimation performance. This is mainly due to the lack of appropriate estimation techniques that can extract the position and orientation information hidden in these components. In this paper, and against the above limitations, the UE is assumed to be connected with at least one access point (AP), i.e., at least one active LiFi link. Fingerprinting is employed as an estimation technique and the received signal-to-noise ratio (SNR) is used as an estimation metric, where both the line-of-sight (LOS) and NLOS components of the LiFi channel are considered. Motivated by the success of deep learning techniques in solving several complex estimation and prediction problems, we employ two deep artificial neural network (ANN) models, one based on the multilayer perceptron (MLP) and the second on the convolutional neural network (CNN), that can map efficiently the instantaneous received SNR with the user 3D position and the UE orientation. Through numerous examples, we investigate the performance of the proposed schemes in terms of the average estimation error, precision, computational time, and the bit error rate. We also compare this performance to that of the k-nearest neighbours (KNN) scheme, which is widely used in solving wireless localization problems. It is demonstrated that the proposed schemes achieve significant gains and are superior to the KNN scheme.

Unmanned aerial vehicles (UAVs) have recently been envisaged as an enabling technology of 5G. UAVs act as an intermediate relay node to facilitate uninterrupted, high quality communication between information sources and their destination. However, UAV energy management has been a major issue of consideration due to limited power supply, affecting flight duration. Thus, we introduce in this paper a unified energy management framework by resorting to wireless power transfer (WPT), simultaneous wireless information and power transfer (SWIPT) and self-interference (SI) energy harvesting (EH) schemes, in cooperative relay communications. In our new technique, UAVs are deployed as relays equipped with a decode and forward protocol and EH capability operating in a full-duplex (FD) mode. The UAV assists information transmission between a terrestrial base station and a user. The UAV's transmission capability is powered exclusively by the energy harvested from WPT, radio frequency signal transmitted from the source via time-switching SWIPT protocol and SI exploitation. We improve the overall system throughput by the use of FD based UAV-assisted cooperative system. In this proposed system, we formulate two optimization problems to minimize end-to-end outage probability, subject to UAV's power profile and trajectory for a DF relay scheme, respectively. The KKT conditions have been used to obtain closed-form solutions for the two formulated problems. Numerical simulation results validate all the theoretical results. We demonstrate that the performance of our proposed unified EH scheme outperforms that of existing techniques in the literature. Ministry of Human Resource Development Scopus

We study, in this paper, the secrecy performance of a multi-user (MU) multiple-input single-output visible light communication broadcast channel with confidential messages. The underlying system model comprises $K +1$ nodes: a transmitter (Alice) equipped with $N$ fixtures of LEDs and $K$ spatially dispersed users, each equipped with a single photo-diode. The MU channel is modeled as deterministic and real-valued and assumed to be perfectly known to Alice, since all users are assumed to be active. We consider typical secrecy performance measures, namely, the max–min fairness, the harmonic mean, the proportional fairness, and the weighted fairness. For each performance measure, we derive an achievable secrecy rate for the system as a function of the precoding matrix. As such, we propose algorithms that yield the best precoding matrix for the derived secrecy rates, where we analyze their convergence and computational complexity. In contrast, what has been considered in the literature so far is zero-forcing (ZF) precoding, which is suboptimal. We present several numerical examples through which we demonstrate the substantial improvements in the secrecy performance achieved by the proposed techniques compared with those achieved by the conventional ZF. However, this comes at a slight increase in the complexity of the proposed techniques compared with ZF.

The notion of non-orthogonal multiple access (NOMA) for 5G essentially relies on the availability of the channel state information at the transmitter (CSIT). Such knowledge is used to judiciously allocate power among users to make their signals separable at their respective receivers while employing successive interference cancellation (SIC). Feeding back the CSI from the users to the BS (transmitter) is obviously bandwidth consuming. Reducing such an overhead is of great importance and has been of interest in recent years. Furthermore, existing NOMA techniques become inapplicable when the CSI is unavailable at the BS. In this case, the BS has only the option of allocating power among users blindly, including equal power splitting, which has been shown to yield poor performance in terms of outage probability and error probability. This motivates us to develop a NOMA scheme that does not require CSI knowledge at the BS. We make use of a nonlinear interference alignment technique that we have proposed recently, namely, interference dissolution, to develop the proposed NOMA scheme, which allows the BS to communicate with two users simultaneously while keeping signals perfectly separable at their respective receivers. We develop the proposed scheme for multiple-input single-output and single-input single-output downlink channels. We analyze the proposed technique analytically in terms of the achievable degrees-of-freedom and achievable rate per user. We show that the proposed NOMA scheme outperforms existing NOMA techniques in terms of the outage probability and error probability.

This paper investigates the performance of coop- erative non-orthogonal multiple access (C-NOMA) in cellulardownlink systems. The system model consists of a base station(BS) that needs to serve multiple users within a region of service.A subset of the users, especially those located close to thecell edge, undergo severe fading and suffer from poor channelquality and low achievable rates. To overcome this problem, C-NOMA is proposed as the system design methodology, in whichusers that have the capability of full-duplex (FD) communicationcan assist the transmissions between the BS and users withpoor channel quality through device-to-device (D2D) communi-cations. To harness both the multiplexing gain from NOMA andthe diversity gain from FD-D2D communications, we formulateand solve a novel optimization problem that jointly deter-mines D2D user pairing and power allocation. The formulatedproblem is a mixed-integer non-linear program (MINLP) withprohibitively high complexity. To overcome this issue, a two-steppolicy is proposed to solve the problem in polynomial time. Oursimulation results show that with reasonable assumptions, theproposed scheme always outperforms some existing schemesin the literature, and that, under undesirable conditions, e.g.,poor D2D channel conditions or imperfect self-interference (SI)cancellation, the proposed scheme is reduced to conventionalNOMA.