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Dynamic metasurface antennas (DMAs) represent a novel transceiver array architecture for extremely large-scale (XL) communications, offering the advantages of reduced power consumption and lower hardware costs compared to conventional arrays. This paper focuses on near-field channel estimation for XL-DMAs. We begin by analyzing the near-field characteristics of uniform planar arrays (UPAs) and introducing the Oblong Approx. model. This model decouples elevation-azimuth (EL-AZ) parameters for XL-DMAs, providing an effective means to characterize the near-field effect. It offers simpler mathematical expressions than the second-order Taylor expansion model, all while maintaining negligible model errors for oblong-shaped arrays. Building on the Oblong Approx. model, we propose an EL-AZ-decoupled estimation framework that involves near- and far-field parameter estimation for AZ/EL and EL/AZ directions, respectively. The former is formulated as a distributed compressive sensing problem, addressed using the proposed off-grid distributed orthogonal least squares algorithm, while the latter involves a straightforward parallelizable search. Crucially, we illustrate the viability of decoupled EL-AZ estimation for near-field UPAs, exhibiting commendable performance and linear complexity correlated with the number of metasurface elements. Moreover, we design an measurement matrix optimization method with the Lorentzian constraint on DMAs and highlight the estimation performance degradation resulting from this constraint.

A dual-robust design of beamforming is investigated in an integrated sensing and communication (ISAC) system.Existing research on robust ISAC waveform design, while proposing solutions to imperfect channel state information (CSI), generally depends on prior knowledge of the target's approximate location to design waveforms. This approach, however, limits the precision in sensing the target's exact location. In this paper, considering both CSI imperfection and target location uncertainty, a novel framework of joint robust optimization is proposed by maximizing the weighted sum of worst-case data rate and beampattern gain. To address this challenging problem, we propose an efficient two-layer iteration algorithm based on S-Procedure and convex hull. Finally, numerical results verify the effectiveness and performance improvement of our dual-robust algorithm, as well as the trade-off between communication and sensing performance.

This paper investigates flexible beamforming design in an integrated sensing and communication (ISAC) network with movable antennas (MAs). A bistatic radar system is integrated into a multi-user multiple-input-single-output (MU-MISO) system, with the base station (BS) equipped with MAs. This enables array response reconfiguration by adjusting the positions of antennas. Thus, a joint beamforming and antenna position optimization problem, namely flexible beamforming, is proposed to maximize communication rate and sensing mutual information (MI). The fractional programming (FP) method is adopted to transform the non-convex objective function, and we alternatively update the beamforming matrix and antenna positions. Karush-Kuhn-Tucker (KKT) conditions are employed to derive the close-form solution of the beamforming matrix, while we propose an efficient search-based projected gradient ascent (SPGA) method to update the antenna positions. Simulation results demonstrate that MAs significantly enhance the ISAC performance when employing our proposed algorithm, achieving a 59.8% performance gain compared to fixed uniform arrays.

Quantum networks (QNs) relying on free-space optical (FSO) quantum channels can support quantum applications in environments wherein establishing an optical fiber infrastructure is challenging and costly. However, FSO-based QNs require a clear line-of-sight (LoS) between users, which is challenging due to blockages and natural obstacles. In this paper, a reconfigurable intelligent surface (RIS)-assisted FSO-based QN is proposed as a cost-efficient framework providing a virtual LoS between users for entanglement distribution. A novel modeling of the quantum noise and losses experienced by quantum states over FSO channels defined by atmospheric losses, turbulence, and pointing errors is derived. Then, the joint optimization of entanglement distribution and RIS placement problem is formulated, under heterogeneous entanglement rate and fidelity constraints. This problem is solved using a simulated annealing metaheuristic algorithm. Simulation results show that the proposed framework effectively meets the minimum fidelity requirements of all users' quantum applications. This is in stark contrast to baseline algorithms that lead to a drop of at least 83% in users' end-to-end fidelities. The proposed framework also achieves a 64% enhancement in the fairness level between users compared to baseline rate maximizing frameworks. Finally, the weather conditions, e.g., rain, are observed to have a more significant effect than pointing errors and turbulence., Comment: 13 pages, 7 figures, 1 table

Although multi-access edge computing (MEC) has allowed for computation offloading at the network edge, weak wireless signals in the radio access network caused by obstacles and high network load are still preventing efficient edge computation offloading, especially for user requests with stringent latency and reliability requirements. Intelligent reflective surfaces (IRS) have recently emerged as a technology capable of enhancing the quality of the signals in the radio access network, where passive reflecting elements can be tuned to improve the uplink or downlink signals. Harnessing the IRS's potential in enhancing the performance of edge computation offloading, in this paper, we study the optimized use of a system of multi-IRS along with the design of the offloading (to an edge with multi MECs) and resource allocation parameters for the purpose of minimizing the devices' energy consumption considering 5G services with stringent latency and reliability requirements. After presenting our non-convex mathematical problem, we propose a suboptimal solution based on alternating optimization where we divide the problem into sub-problems which are then solved separately. Specifically, the offloading decision is solved through a matching game algorithm, and then the IRS phase shifts and resource allocation optimizations are solved in an alternating fashion using the Difference of Convex approach. The obtained results demonstrate the gains both in energy and network resources and highlight the IRS's influence on the design of the MEC parameters.

This work presents an investigation and assessment framework, which, supported by realistic data, aims at provisioning operators with in-depth insights into the consumer-perceived Quality-of-Experience (QoE) at public Electric Vehicle (EV) charging infrastructures. Motivated by the unprecedented EV market growth, it is suspected that the existing charging infrastructure will soon be no longer capable of sustaining the rapidly growing charging demands; let alone that the currently adopted ad hoc infrastructure expansion strategies seem to be far from contributing any quality service sustainability solutions that tangibly reduce (ultimately mitigate) the severity of this problem. Without suitable QoE metrics, operators, today, face remarkable difficulty in assessing the performance of EV Charging Stations (EVCSs) in this regard. This paper aims at filling this gap through the formulation of novel and original critical QoE performance metrics that provide operators with visibility into the per-EVCS operational dynamics and allow for the optimization of these stations' respective utilization. Such metrics shall then be used as inputs to a Machine Learning model finely tailored and trained using recent real-world data sets for the purpose of forecasting future long-term EVCS loads. This will, in turn, allow for making informed optimal EV charging infrastructure expansions that will be capable of reliably coping with the rising EV charging demands and maintaining acceptable QoE levels. The model's accuracy has been tested and extensive simulations are conducted to evaluate the achieved performance in terms of the above listed metrics and show the suitability of the recommended infrastructure expansions., Comment: Accepted for Publication in IEEE Transactions on Intelligent Transportation Systems

Due to growing environmental concerns, the world's governments have been encouraging the shift of the transportation sector towards the adoption of Electric Vehicles (EVs). As a result, EV numbers have been growing exponentially and are expected to continue growing further which will add a large EV charging load to the power grid. To this end, this paper presents an EV-based defense mechanism against Load-Altering (LA) attacks targeting the grid. The developed mechanism utilizes H-infinity controllers and Linear Matrix Inequalities (LMIs) to mitigate LA attacks. After the controller synthesis and presentation of the attack scenarios, we demonstrate the effectiveness and success of our defense mechanism against the three known types of LA attacks. The scenarios include three 800 MW LA attacks against the New England 39-bus grid. The results demonstrate how our EV-based mitigation scheme eliminates the attack impacts and maintains the grid's stability in face of an unknown persisting attack., Comment: Accepted in the 2023 8th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)-ICRAIE 2023. arXiv admin note: substantial text overlap with arXiv:2308.07526

In this letter, a weighted minimum mean square error (WMMSE) empowered integrated sensing and communication (ISAC) system is investigated. One transmitting base station and one receiving wireless access point are considered to serve multiple users a sensing target. Based on the theory of mutual-information (MI), communication MI and sensing MI rate are utilized as the performance metrics under the presence of clutters. In particular, we propose an novel MI-based WMMSE-ISAC method by developing a unique transceiver design mechanism to maximize the weighted sensing and communication sum-rate of this system. Such a maximization process is achieved by utilizing the classical method -- WMMSE, aiming to better manage the effect of sensing clutters and the interference among users. Numerical results show the effectiveness of our proposed method, and the performance trade-off between sensing and communication is also validated.

Due to the growing threat of climate change, the worlds governments have been encouraging the adoption of Electric Vehicles (EVs). As a result, EV numbers have been growing exponentially which will introduce a large EV charging load into the power grid. On this basis, we present a scheme to utilize EVs as a defense mechanism to mitigate Load-Altering (LA) attacks against the grid. The developed scheme relies on robust control theory and Linear Matrix Inequalities (LMIs). Our EV-based defense mechanism is formulated as a feedback controller synthesized using H-2 and H-infinity control techniques to eliminate the impact of unknown LA attacks. The controller synthesis considers the grid topology and the uncertainties of the EV connection to the grid. To demonstrate the effectiveness of the proposed mitigation scheme, it is tested against three types of LA attacks on the New England 39-bus grid. We test our mitigation scheme against 800 MW static, switching, and dynamic attacks in the presence of multiple sources of uncertainty that can affect the EV load during deployment. The results demonstrate how the grid remains stable under the LA attacks that would otherwise lead to serious instabilities., Comment: Accepted for publication in Applied Energy

Millimeter wave (mmWave) full-duplex (FD) is a promising technique for improving capacity by maximizing the utilization of both time and the rich mmWave frequency resources. Still, it has restrictions due to FD self-interference (SI) and mmWave's limited coverage. Therefore, this study dives into FD mmWave MIMO with the assistance of reconfigurable intelligent surfaces (RIS) for capacity improvement. First, we demonstrate the angular-domain reciprocity of FD antenna arrays under the far-field planar wavefront assumption. Accordingly, a strategy for joint downlink-uplink (DL-UL) channel estimation is presented. For estimating the SI channel, the direct channel, and the cascaded channel, the Khatri-Rao product-based compressive sensing (KR-CS), distributed CS (D-CS), and two-stage multiple measurement vector-based D-CS (M-D-CS) frameworks are proposed, respectively. Additionally, we propose a passive beamforming optimization solution based on the angular-domain cascaded channel. With hybrid beamforming architectures, a novel hybrid weighted minimum mean squared error method for SI cancellation (H-WMMSE-SIC) is proposed. Simulations have revealed that joint DL-UL processing significantly improves estimation performance in comparison to separate DL/UL channel estimation. Particularly, when the interference-to-noise ratio is less than 35 dB, our proposed H-WMMSE-SIC offers spectral efficiency performance comparable to fully-digital WMMSE-SIC. Finally, the computational complexity is analyzed for our proposed methods.

In this paper, we investigate an edge-based approach for the detection and localization of coordinated oscillatory load attacks initiated by exploited EV charging stations against the power grid. We rely on the behavioral characteristics of the power grid in the presence of interconnected EVCS while combining cyber and physical layer features to implement deep learning algorithms for the effective detection of oscillatory load attacks at the EVCS. We evaluate the proposed detection approach by building a real-time test bed to synthesize benign and malicious data, which was generated by analyzing real-life EV charging data collected during recent years. The results demonstrate the effectiveness of the implemented approach with the Convolutional Long-Short Term Memory model producing optimal classification accuracy (99.4\%). Moreover, our analysis results shed light on the impact of such detection mechanisms towards building resiliency into different levels of the EV charging ecosystem while allowing power grid operators to localize attacks and take further mitigation measures. Specifically, we managed to decentralize the detection mechanism of oscillatory load attacks and create an effective alternative for operator-centric mechanisms to mitigate multi-operator and MitM oscillatory load attacks against the power grid. Finally, we leverage the created test bed to evaluate a distributed mitigation technique, which can be deployed on public/private charging stations to average out the impact of oscillatory load attacks while allowing the power system to recover smoothly within 1 second with minimal overhead.

Faced with the threat of climate change, the world is rapidly adopting Electric Vehicles (EVs). The EV ecosystem, however, is vulnerable to cyber-attacks putting it and the power grid at risk. In this article, we present a security-oriented real-time Co-simulation Testbed for the EV ecosystem and the power grid., Comment: "\c{opyright} 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works."

Reconfigurable intelligent surface (RIS) is an attractive technology to improve the transmission rate of millimetre-wave (mmWave) communication systems. The previous {research} on RIS technology mainly focused on improving the transmission rate and security rate of the mmWave communication systems. Since the emergence of RIS technology creates the conditions for generating an intelligent radio environment, it also has potential advantages on improving the localization accuracy of the mmWave communication systems. Deployed on walls and objects, RISs are capable of significantly improving communications and positioning coverage by controlling the multi-path reflection. This paper considers the RIS-aided mmWave localization system and proposes a joint beamforming and localization problem. However, since the objective function depends on the unknown UE's position and instantaneous channel state information (CSI), this beamforming and localization technology based on RIS assistance is challenging. To solve this problem, we propose a new joint localization and beamforming optimization (JLBO) algorithm, and give the proof of its convergence. The simulation results show that the RIS can improve the user localization accuracy of the system and the proposed scheme has a significant performance improvement compared with the traditional schemes.

This paper presents a downlink reconfigurable intelligent surface (RIS)-assisted half-duplex (HD) cooperative rate splitting multiple access (C-RSMA) networks. The proposed system model is built up considering one base station (BS), one RIS, and two users. With the goal of minimizing the network energy consumption, a joint framework to optimize the precoding vectors at the BS, common stream split, relaying device transmit power, the time slot allocation, and the passive beamforming at the RIS subject to the power budget constraints at both the BS and the relaying node, the quality of service (QoS) constraints at both users, and common stream rate constraint is proposed. The formulated problem is a non-convex optimization problem due to the high coupling among the optimization variables. To tackle this challenge, an efficient algorithm is presented by invoking the alternating optimization technique, which decomposes the original problem into two sub-problems; namely, sub-problem-1 and sub-problem-2, which are alternatively solved. Specifically, sub-problem-1 is to jointly optimize the precoding vectors, common stream split, and relaying device power. Meanwhile, sub-problem-2 is to optimize the phase shift matrix at the RIS. In order to solve sub-problem-1, an efficient low-complexity solution based on the successive convex approximation (SCA) is proposed. Meanwhile, and with the aid of difference-of-convex rank-one representation and the SCA approach, an efficient solution for the phase shift matrix at the RIS is obtained. The simulation results demonstrate that the proposed RIS-assisted HD C-RSMA achieves a significant gain in minimizing the total energy consumption compared to the RIS-assisted RSMA scheme, RIS-assisted HD cooperative non-orthogonal multiple access (C-NOMA), RIS-assisted NOMA, HD C-RSMA without RIS, and HD C-NOMA without RIS.

This paper investigates the benefits of integrating multiple reconfigurable intelligent surfaces (RISs) in enhancing the timeliness performance of uplink Internet-of-Things (IoT) network, where IoT devices (IoTDs) upload their time-stamped status update information to a base station (BS) using non-orthogonal multiple access (NOMA). Accounting to the potential unreliable wireless channels due to the impurities of the propagation environments, such as deep fading, blockages, etc., multiple RISs are deployed in the considered IoT network to mitigate the propagation-induced impairments, to enhance the quality of the wireless links, and to ensure that the required freshness of information is achieved. In this setup, an optimization problem has been formulated to minimize the average sum Age of Information (AoI) by optimizing the transmit power of the IoTDs, the IoTDs clustering policy, and the RISs configurations. The formulated problem ends up to be a mixed-integer non-convex problem. In order to tackle this challenge, the RISs configurations are first obtained by adopting a semi-definite relaxation (SDR) approach. Then, the joint power allocation and user-clustering problem is solved using the concept of bi-level optimization, where the original problem is decomposed into an outer IoTDs clustering problem and an inner power allocation problem. Optimal closed-form expressions are derived for the inner problem and the Hungarian method is invoked to solve the outer problem. Numerical results demonstrate that our proposed approach achieves lowest AoI compared to the other baseline approaches., Comment: IEEE Global Communications Conference (Globecom), 2022, 6 pages, 4 Figures. arXiv admin note: substantial text overlap with arXiv:2202.13572

Driven by the necessity to combat climate change, Electric Vehicles (EV) are being deployed to take advantage of their ability in reducing emissions generated by the transportation sector. This deployment has left the power grid vulnerable to attacks through the EV infrastructure. This paper is written from an attacker\'s perspective and proposes a dynamic load altering strategy through manipulating EV charging to destabilize the grid. The attack is formulated based on feedback control theory, i.e., designing an attack based on Linear Matrix Inequalities (LMIs). After the stability metric and controller design have been established, we demonstrate our attack method against the Kundur 2 area grid. The attack scenario includes a cap of 200 MW EV load controlled by the attacker. However, the results show that even with this limitation, the attacker would be successful in pushing the grid toward instability and blackout., Comment: "\c{opyright} 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works."

In this paper, we investigate an outdoor and indoor wireless communication network with the assistance of a novel relay-aided double-sided reconfigurable intelligent surface (RIS). A scheduling problem is considered at the outdoor access point (AP) to minimize the sum of age of information (AoI). To serve the indoor users and further enhance the wireless link quality, a novel double-sided RIS with relay is utilized. Since the formulated problem is non-convex with highly-coupled variables, a successive convex approximation (SCA) based alternating optimization (AO) algorithm is proposed to solve it in an iterative manner. Finally, simulation results show the effectiveness and significant performance improvement in terms of AoI of the proposed algorithm compared with other benchmarks.

In this paper, we investigate for the first time the dynamic power allocation and decoding order at the base station (BS) of two-user uplink (UL) cooperative non-orthogonal multiple access (C-NOMA)-based cellular networks. In doing so, we formulate a joint optimization problem aiming at maximizing the minimum user achievable rate, which is a non-convex optimization problem and hard to be directly solved. To tackle this issue, an iterative algorithm based on successive convex approximation (SCA) is proposed. The numerical results reveal that the proposed scheme provides superior performance in comparison with the traditional UL NOMA. In addition, we demonstrated that in UL C-NOMA, decoding the far NOMA user first at the BS provides the best performance.

This paper investigates the benefits of integrating reconfigurable intelligent surface (RIS) on minimizing the average sum age of information (AoI) in uplink non-orthogonal multiple access-based Internet-of-Things (IoT) networks. In this setup, an optimization problem is formulated to optimize the RIS configuration, the transmit power per IoT device and the clustering policy of IoT devices. The formulated problem is a mixed-integer non-convex one, and in order to solve it we obtain first the RIS configuration by adopting a semi-definite relaxation (SDR) approach. Afterwards, the joint power allocation and user-clustering problem is solved using the concept of bi-level optimization and is decomposed into an outer user clustering problem and an inner power allocation problem. Optimal closed-form expressions are derived for the inner problem and the Hungarian method is employed to solve the outer one. Numerical results demonstrate the performance superiority of our approach.

The increasing need for reducing greenhouse gas emissions and the drive for green cities have promoted the use of electric vehicles due to their environmental benefits. In fact, countries have set their own targets and are offering incentives for people to purchase EVs as opposed to traditional gasoline-powered cars. Manufacturers have been hastily deploying charging stations to meet the charging requirements of the EVs on the road. This rapid deployment has contributed to the EV ecosystem's lack of proper security measures, raising multiple questions related to the power grid security and vulnerability. In this paper, we offer a complete examination of the EV ecosystem from the vulnerability to the attacks and finally the solutions. We start by examining the existing vulnerabilities in the EV ecosystem that can be exploited to control the EV charging and launch attacks against the power grid. We then discuss the non-linear nature of the EV charging load and simulate multiple attacks that can be launched against the power grid using these EVs. EV loads have high reactive power demand which can have a larger impact on the grid compared to residential loads. We perform simulations on two power grids and demonstrate that while the grid can recover after a 48 MW attack utilizing traditional residential loads, a smaller 30 MW EV load attack can completely destabilize the system. Finally, we suggest several patches for the existing vulnerabilities and discuss two methods aimed at detecting EV attacks., Comment: International Journal of Electrical Power & Energy Systems, Available online 21 November 2021, 107784, Full citation details will be added when the final version becomes available

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 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.

This paper investigates the channel aging problem of mobile light-fidelity (LiFi) systems. In the LiFi physical layer, the majority of the optimization problems for mobile users are non-convex and require the use of dual decomposition or heuristics techniques. Such techniques are based on iterative algorithms, and often, cause a high processing delay at the physical layer. Hence, the obtained solutions are no longer optimal since the LiFi channels are evolving. In this paper, a proactive-optimization (PO) approach that can alleviate the LiFi channel aging problem is proposed. The core idea is to design a long-short-term-memory (LSTM) network that is capable of predicting posterior positions and orientations of mobile users, which can be then used to predict their channel coefficients. Consequently, the obtained channel coefficients can be exploited to derive near-optimal transmission-schemes prior to the intended service-time, which enables real-time service. Through various simulations, the performance of the designed LSTM model is evaluated in terms of prediction error and time, as well as its application in a practical LiFi optimization problem.

Intelligent Transportation Systems are thriving thanks to a wide range of technological advances, namely 5G communications, Internet of Things, artificial intelligence and edge computing. Central to this is the wide deployment of smart sensing devices and accordingly the large amount of harvested information to be processed for timely decision making. Robust network access is, hence, essential for offloading the collected data before a set deadline, beyond which the data loses its value. In environments where direct communication can be impaired by, for instance, blockages such as in urban cities, unmanned aerial vehicles (UAVs) can be considered as an alternative for providing and enhancing connectivity, particularly when IoT devices (IoTD) are constrained with their resources. Also, to conserve energy, IoTDs are assumed to alternate between their active and passive modes. This paper, therefore, considers a time-constrained data gathering problem from a network of sensing devices and with assistance from a UAV. A Reconfigurable Intelligent Surface (RIS) is deployed to further improve both the connectivity and energy efficiency of the UAV, particularly when multiple devices are served concurrently and experience different channel impairments. This integrated problem brings challenges related to the configuration of the phase shift elements of the RIS, the scheduling of IoTDs transmissions as well as the trajectory of the UAV. First, the problem is formulated with the objective of maximizing the total number of served devices each during its activation period. Owing to its complexity and the incomplete knowledge about the environment, we leverage deep reinforcement learning in our solution; the UAV trajectory planning is modeled as a Markov Decision Process, and Proximal Policy Optimization is invoked to solve it. Next, the RIS configuration is then handled via Block Coordinate Descent.

In this paper, we consider a wireless network consisting of a base station (BS) that is serving multiple real-time traffic streams forwarding information updates to their destinations in order to sustain the freshness of information for time-critical applications. Since the wireless channels may be unreliable due to the impurities of the propagation environments, such as deep fading, blockages, etc., we integrate a reconfigurable intelligent surface (RIS) to the wireless system in order to mitigate the propagation-induced impairments, enhance the quality of the wireless links, and ensure that the required freshness of information is achieved for these real-time applications. For this network set-up, we investigate the joint optimization of the traffic streams scheduling and the RIS phase-shift matrix with the goal of minimizing the long-term average Age of Information (AoI). The formulated optimization problem is a mixed-integer non-convex optimization problem, which is difficult to solve. To circumvent the high-coupled optimization variables, and with the aid of bilevel optimization, we decompose the original problem into an outer traffic stream scheduling problem and an inner RIS phase-shift matrix problem. For the outer problem, owing to its complexity and stochastic nature of packet arrivals, we resort to deep reinforcement learning (DRL) solution where the traffic stream scheduling is modeled as a Markov Decision Process (MDP), and Proximal Policy Optimization (PPO) is invoked to solve it. Whereas, the inner problem that determines the RIS configuration is solved through semi-definite relaxation (SDR). Finally, we show through extensive simulations that our approach evaluates the combined impact of scheduling policy and RIS configuration on the long-term average AoI, where we demonstrate its superiority against other baseline schemes.

Given its ability to control and manipulate wireless environments, reconfigurable intelligent surface (RIS), also known as intelligent reflecting surface (IRS), has emerged as a key enabler technology for the six-generation (6G) cellular networks. In the meantime, vehicular environment radio propagation is negatively influenced by a large set of objects that cause transmission distortion such as high buildings. Therefore, this work is devoted to explore the area of RIS technology integration with vehicular communications while considering the dynamic nature of such communication environment. Specifically, we provide a system model where RoadSide Unit (RSU) leverages RIS to provide indirect wireless transmissions to disconnected areas, known as dark zones. Dark zones are spots within RSU coverage where the communication links are blocked due to the existence of blockages. In details, a discrete RIS is utilized to provide communication links between the RSU and the vehicles passing through out-of-service zones. Therefore, the joint problem of RSU resource scheduling and RIS passive beamforming or phase-shift matrix is formulated as an optimization problem with the objective of maximizing the minimum average bit rate. The formulated problem is mixed integer non-convex program which is difficult to be solved and does not account for the uncertain dynamic environment in vehicular networks. Thereby, we resort to alternative methods based on Deep Reinforcement Learning to determine RSU wireless scheduling and Block Coordinate Descent (BCD) to solve for the phase-shift matrix, \textit{i.e.,} passive beamforming, of the RIS. The Markov Decision Process (MDP) is defined and the complexity of the solution approach is discussed. Our numerical results demonstrate the superiority of our proposed approach over baseline techniques.

Visible light communication (VLC) is an optical wireless communication technology that is considered a promising solution for high-speed indoor connectivity. Unlike the case in conventional radio-frequency wireless systems, the VLC channel is not isotropic, meaning that the device orientation affects the channel gain significantly. In addition, due to the use of optical frequency bands, the presence of different obstacles (e.g., walls, human bodies, furniture) may easily block the VLC links. One solution to overcome these issues is the integration of the intelligent reflective surface (IRS), which is a new and revolutionizing technology that has the potential to significantly improve the performance of wireless networks. IRS is capable of smartly reconfiguring the wireless propagation environment with the use of massive low-cost passive reflecting elements integrated on a planar surface. In this paper, a framework for integrating IRS in indoor VLC systems is presented. We give an overview of IRS, including its advantages, different types and main applications in VLC systems, where we demonstrate the potential of IRS in overcoming the effects of random device orientation and links blockages. We discuss key factors pertaining to the design and integration of IRS in VLC systems, namely, the deployment of IRSs, the channel state information acquisition, the optimization of IRS configuration and the real-time IRS control. We also lay out a number of promising research directions that center around the integration of IRS in indoor VLC systems.

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, the semi-definite relaxation approach is exploited to tackle the passive beamforming sub-problem. 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.

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.

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.

Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless communication (OWC) that is considered 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 LiFi receivers. For stationary users, two channel models are proposed, namely, the modified truncated Laplace (MTL) model and the modified Beta (MB) model. For LiFi 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 LiFi users is investigated, where we show that the aforementioned factors can strongly affect the channel gain and system performance.

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.

Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless communication (OWC) that is considered 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 LiFi receivers. For stationary users, two channel models are proposed, namely, the modified truncated Laplace (MTL) model and the modified Beta (MB) model. For LiFi 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 LiFi users is investigated, where we show that the aforementioned factors can strongly affect the channel gain and system performance.

To establish reliable and long-range millimeter-wave (mmWave) communication, beamforming is deemed to be a promising solution. Although beamforming can be done in the digital and analog domains, both approaches are hindered by several constraints when it comes to mmWave communications. For example, performing fully digital beamforming in mmWave systems involves using many radio frequency (RF) chains, which are expensive and consume high power. This necessitates finding more efficient ways for using fewer RF chains while taking advantage of the large antenna arrays. One way to overcome this challenge is to employ (partially or fully) analog beamforming through proper configuration of phase-shifters. Existing works on mmWave analog beam design either rely on the knowledge of the channel state information (CSI) per antenna within the array, require a large search time (e.g., exhaustive search) or do not guarantee a minimum beamforming gain (e.g., codebook based beamforming). In this paper, we propose a beam design technique that reduces the search time and does not require CSI while guaranteeing a minimum beamforming gain. The key idea derives from observations drawn from real-life measurements. It was observed that for a given propagation environment (e.g., coverage area of a mmWave BS) the azimuthal angles of dominant signals could be more probable from certain angles than others. Thus, pre-collected measurements could used to build a beamforming codebook that regroups the most probable beam designs. We invoke Bayesian learning for measurements clustering. We evaluate the efficacy of the proposed scheme in terms of building the codebook and assessing its performance through real-life measurements. We demonstrate that the training time required by the proposed scheme is only 5% of that of exhaustive search. This crucial gain is obtained while achieving a minimum targeted beamforming gain.

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 spectra. Visible light communication (VLC) particularly is an emerging technology that has been introduced as a promising solution for 5G and beyond. 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.

Among the challenges of realizing the full potential of light-fidelity (LiFi) cellular networks are user mobility, random device orientation and blockage. We study the impact of those challenges on the performance of LiFi in an indoor environment using measurement-based channel models. We adopt spatial modulation (SM), which has been shown to be energy efficient in many applications, including LiFi. We consider two configurations for placing the photodiodes (PDs) on the user equipment (UE). The first one is referred to as the screen receiver (SR) whereby all the PDs are located on one face of the UE, whereas the other one is a multi-directional receiver (MDR), in which the PDs are located on different sides of the UE. The latter configuration was motivated by the fact that SR exhibited poor performance in the presence of random device orientation and blockage. We show that MDR outperforms SR by over $10$ dB at BER of $3.8\times10^{-3}$. Moreover, an adaptive access point (AP) selection scheme for SM is considered where the number of APs are chosen adaptively in an effort to achieve the lowest energy requirement for a target BER and spectral efficiency. The user performance with random orientation and blockage in the whole room is evaluated for sitting and walking activities. For the latter, we invoke the orientation-based random waypoint (ORWP) mobility model. We also study the performance of the underlying system on the uplink channel where the same techniques are used for the downlink channel. Specifically, as the transmitted uplink power is constrained, the energy efficiency of SM is evaluated analytically. It is shown that the multi-directional transmitter (MDT) with adaptive SM is highly energy efficient. As a benchmark, we compare the performance of the proposed framework to that of the conventional spatial multiplexing system, and demonstrate the superiority of the proposed one., Comment: 30 pages, 14 figures, Journal paper

The current power grid is no longer a feasible solution due to ever-increasing user demand of electricity, old infrastructure, and reliability issues and thus require transformation to a better grid a.k.a., smart grid (SG). The key features that distinguish SG from the conventional electrical power grid are its capability to perform two-way communication, demand side management, and real time pricing. Despite all these advantages that SG will bring, there are certain issues which are specific to SG communication system. For instance, network management of current SG systems is complex, time consuming, and done manually. Moreover, SG communication (SGC) system is built on different vendor specific devices and protocols. Therefore, the current SG systems are not protocol independent, thus leading to interoperability issue. Software defined network (SDN) has been proposed to monitor and manage the communication networks globally. This article serves as a comprehensive survey on SDN-based SGC. In this article, we first discuss taxonomy of advantages of SDNbased SGC.We then discuss SDN-based SGC architectures, along with case studies. Our article provides an in-depth discussion on routing schemes for SDN-based SGC. We also provide detailed survey of security and privacy schemes applied to SDN-based SGC. We furthermore present challenges, open issues, and future research directions related to SDN-based SGC., Comment: Accepted

Cooperative relaying is often deployed to enhance the communication reliability (i.e., diversity order) and consequently the end-to-end achievable rate. However, this raises several security concerns when the relays are untrusted since they may have access to the relayed message. In this paper, we study the achievable secrecy diversity order of cooperative networks with untrusted relays. In particular, we consider a network with an N-antenna transmitter (Alice), K single-antenna relays, and a single-antenna destination (Bob). We consider the general scenario where there is no relation between N and K, and therefore K can be larger than N. Alice and Bob are assumed to be far away from each other, and all communication is done through the relays, i.e., there is no direct link. Providing secure communication while enhancing the diversity order has been shown to be very challenging. In fact, it has been shown in the literature that the maximum achievable secrecy diversity order for the adopted system model is one (while using artificial noise jamming). In this paper, we adopt a nonlinear interference alignment scheme that we have proposed recently to transmit the signals from Alice to Bob. We analyze the proposed scheme in terms of the achievable secrecy rate and secrecy diversity order. Assuming Gaussian inputs, we derive an explicit expression for the achievable secrecy rate and show analytically that a secrecy diversity order of up to min(N,K)-1 can be achieved using the proposed technique. We provide several numerical examples to validate the obtained analytical results and demonstrate the superiority of the proposed technique to its counterparts that exist in the literature.

The evolution of smart microgrid and its demand-response characteristics not only will change the paradigms of the century-old electric grid but also will shape the electricity market. In this new market scenario, once always energy consumers, now may act as sellers due to the excess of energy generated from newly deployed distributed generators (DG). The smart microgrid will use the existing electrical transmission network and a pay per use transportation cost without implementing new transmission lines which involve a massive capital investment. In this paper, we propose a novel algorithm to minimize the electricity price with the optimal trading of energy between sellers and buyers of the smart microgrid network. The algorithm is capable of solving the optimal power allocation problem (with optimal transmission cost) for a microgrid network in a polynomial time without modifying the actual marginal costs of power generation. We mathematically formulate the problem as a nonlinear non-convex and decompose the problem to separate the optimal marginal cost model from the electricity allocation model. Then, we develop a divide-and-conquer method to minimize the electricity price by jointly solving the optimal marginal cost model and electricity allocation problems. To evaluate the performance of the solution method, we develop and simulate the model with different marginal cost functions and compare it with a first come first serve electricity allocation method., Comment: 1. This withdraw request is made because of some potential confidential issues with the financial authority for another project