With the development of the sixth generation wireless communication, the increasingly scarce spectrum resources limit the further increase in data rate and exacerbate the interference problem among different users and applications. To address this issue, rate-splitting multiple access (RSMA) provides a flexible framework that unifies existing orthogonal and non-orthogonal multiple access schemes. In this work, we analyze the interference scenario of RSMA-based space-terrestrial transmission systems, with multiple satellite users independently and uniformly distributed in the coverage area of the serving satellite. Specifically, the outage performance at the satellite users (resp. terrestrial base station (BS) users) is assessed while considering the interference from the BS users (resp. the satellite and the BSs of other cells). Approximate analytical expressions of the outage probability at each satellite user/BS user are derived, numerically evaluated, and verified through simulation results. The impacts of RSMA power allocation factors, fading parameters, interference severity, and satellite altitude on outage performance are thoroughly analyzed, and the trade-off between outage performance and user fairness is also illustrated.

Programmable wireless environments (PWEs) have emerged as a key paradigm for next-generation communication networks, aiming to transform wireless propagation from an uncontrollable phenomenon into a reconfigurable process that can adapt to diverse service requirements. In this framework, pinching-antenna systems (PASs) have recently been proposed as a promising enabling technology, as they allow the radiation location and effective propagation distance to be adjusted by selectively exciting radiating points along a dielectric waveguide. However, most existing studies on PASs rely on the idealized assumption that pinching-antenna (PA) positions can be continuously adjusted along the waveguide, while realistically only a finite set of pinching locations is available. Motivated by this, this paper analyzes the performance of two-state PASs, where the PA positions are fixed and only their activation state can be controlled. By explicitly accounting for the spatial discreteness of the available pinching points, closed-form analytical expressions for the outage probability and the ergodic achievable data rate are derived. In addition, we introduce the pinching discretization efficiency to quantify the performance gap between discrete and continuous pinching configurations, enabling a direct assessment of the number of PAs required to approximate the ideal continuous case. Finally, numerical results validate the analytical framework and show that near-continuous performance can be achieved with a limited number of PAs, offering useful insights for the design and deployment of PASs in PWEs.

This paper investigates the reliability and security of a hybrid double-reconfigurable intelligent surface (HDRIS) aided wireless-powered communication (WPC) system in the presence of eavesdroppers, where one active/passive RIS (RIS-1) is deployed between the power station and the information user, and the other passive/active RIS (RIS-2) is deployed between the information user and the access point. We propose two modes of HDRIS aided WPC, i.e., HDRIS-I with passive RIS-1 and active RIS-2, and HDRIS-II with active RIS-1 and passive RIS-2. Based on the two modes, we analyze the outage probability (OP) from the perspective of reliability and intercept probability (IP) from the perspective of security, and derive their accurate and asymptotic expressions, respectively. Moreover, a joint metric is proposed, i.e., reliability and security probability (RSP), to reveal the superiority of HDRIS compared to pure double-RIS (PDRIS). The results show that compared to PDRIS, the proposed HDRIS-I and HDRIS-II have better OPs than PDRIS-I with two passive RISs, but worse OPs than PDRIS-II with two active RISs. Both HDRIS-I and HDRIS-II have worse IPs than PDRIS-I, but better IPs than PDRIS-II. Interestingly, in HDRIS-I and HDRIS-II, the diversity gain for legitimate users is proportional to the number of elements of the RISs, while the diversity gain for eavesdroppers is only 1, indicating that HDRIS provide greater benefits for legitimate communications. In Particular, HDRIS-I achieves the best RSP under high transmission power, while HDRIS-II achieves the best RSP under low transmission power, demonstrating the superiority of HDRIS.

With the rapid growth of high-speed rail (HSR) networks, reliable communication is increasingly challenging due to complex terrain and coverage gaps in remote areas. Uncrewed aerial vehicles (UAVs), with their mobility and flexibility, provide aerial relay support to bridge these gaps, enhance signal strength, and improve HSR communication reliability. However, their performance in millimeter-wave (mmWave) systems is significantly degraded by mechanical jitter caused by environmental factors such as wind and turbulence, which adversely affects beam alignment and overall communication quality. To address these challenges, this work introduces a comprehensive analytical framework. We first develop a statistical model that characterizes the relationship between beam gain and jitter intensity. Subsequently, closed-form expressions are derived for the outage probability and ergodic data rate of UAV-assisted HSR mmWave systems under jitter influence, taking into account both co-located (CA) and distributed antenna (DA) configurations. Furthermore, we propose an adaptive beamwidth design that maximizes the average ergodic rate by adjusting the beamwidth according to UAV jitter severity. Numerical simulations verify that this approach significantly improves system capacity and robustness compared with conventional static beamforming, confirming its effectiveness.

As 6G wireless networks seek to enable robust and dynamic programmable wireless environments (PWEs), reconfigurable intelligent surfaces (RISs) have emerged as a cornerstone for controlling electromagnetic wave propagation. However, realizing the potential of RISs for demanding PWE applications depends on precise and real-time user localization, especially in scenarios with random receiver orientations and inherent hardware imperfections. To address this challenge, we propose a novel optical localization framework that integrates conventional ceiling-mounted LEDs with light-emitting reconfigurable intelligent surfaces (LeRISs). By leveraging the spatial diversity offered by the LeRIS architecture, the framework introduces robust signal paths that improve localization accuracy and reduce errors under varying orientations. To this end, we derive a system of equations for received signal strength-based localization that accounts for random receiver orientations and imposes spatial constraints on LED placement, ensuring unique and reliable solutions. Finally, our simulation results demonstrate that the proposed framework achieves precise beam control and high spectral efficiency even for RISs with large number of reflecting elements by tightly coupling the localization process with the beamforming configuration, allowing accurate direction estimation and robust PWE operation.

In this paper, we propose a new concept of self-sustainable active metasurface (SAM), which exploits the dual advantages of energy harvesting in terms of self-sustainability and active metasurface in terms of information transmission, to achieve continuous operation and flexible deployment for reconfigurable intelligent surface (RIS) and simultaneously mitigate its multiplicative fading. SAM can enhance incident signals via power amplifiers and achieve self-sustainability by harvesting ambient energy. We propose three operation schemes to implement energy harvesting and information transmission for SAM, namely, time-switching based SAM (TS-SAM), power-splitting based SAM (PS-SAM), and element-splitting based SAM (ES-SAM). Then, we propose three new metrics, namely, energy-information outage probability (EIOP), energy-information intercept probability (EIIP), and secure energy efficiency ratio (SEER). The accurate and asymptotic EIOP and EIIP as well as accurate SEER for the three proposed schemes are analyzed, respectively. The results show that compared to self-sustainable passive RIS, TS-SAM and ES-SAM have better EIOPs, and PS-SAM has a better EIIP. Among the three schemes, PS-SAM achieves the best EIOP at low RF energy, while TS-SAM and ES-SAM perform better in high-energy scenarios. For EIIP, PS-SAM outperforms the other two schemes. In particular, compared to self-sustainable passive RIS, TS-SAM and ES-SAM have better SEERs, verifying the superiority of the proposed TS-SAM and ES-SAM. Among all inter-node distances, the distance between user and SAM dominates the performance. When the harvested energy and the number of reflecting elements are sufficiently large, the EIOP and EIIP of TS-SAM and ES-SAM are unrelated to the amplification factor of SAM.

This paper introduces the concept of wireless-powered zero-energy reconfigurable intelligent surface (zeRIS), and investigates a wireless-powered zeRIS aided communication system in terms of security, reliability and energy efficiency. In particular, we propose three new wireless-powered zeRIS modes: 1) in mode-I,Nreconfigurable reflecting elements are adjusted to the optimal phase shift design of information user to maximize the reliability of the system; 2) in mode-II,Nreconfigurable reflecting elements are adjusted to the optimal phase shift design of cooperative jamming user to maximize the security of the system; 3) in mode-III,N1andN2(N1+N2=N) reconfigurable reflecting elements are respectively adjusted to the optimal phase shift designs of information user and cooperative jamming user to balance the reliability and security of the system. Then, we propose three new metrics, i.e., joint outage probability (JOP), joint intercept probability (JIP), and secrecy energy efficiency (SEE), and analyze their closed-form expressions in three modes, respectively. The results show that under high transmission power, all the diversity gains of three modes are 1. Among three modes, mode-I achieves the best JOP, while mode-II achieves the best JIP. We exploit two security-reliability trade-off (SRT) metrics, i.e., JOP versus JIP, and normalized joint intercept and outage probability (JIOP), to reveal the SRT performance of the proposed three modes. Interestingly, mode-III achieves the lowest normalized JIOP with increasing time allocation factor, and the highest SEE with increasing transmission power. However, mode-I has the highest SEE with increasing predefined data rate. The optimal zeRIS deployment for mode-I and mode-III is near the PS, while that for mode-II is near the AP.

In response to the increasing number of devices expected in next-generation networks, a shift to over-the-air (OTA) computing has been proposed. By leveraging the superposition of multiple access channels, OTA computing enables efficient resource management by supporting simultaneous uncoded transmission in the time and frequency domains. To advance the integration of OTA computing, our study presents a theoretical analysis that addresses practical issues encountered in current digital communication transceivers, such as transmitter synchronization (sync) errors and intersymbol interference (ISI). To this end, we investigate the theoretical mean squared error (MSE) for OTA transmission under sync errors and ISI, while also exploring methods for minimizing the MSE in OTA transmission. Using alternating optimization, we also derive optimal power policies for both the devices and the base station. In addition, we propose a novel deep neural network (DNN)-based approach to design waveforms that improve OTA transmission performance under sync errors and ISI. To ensure a fair comparison with existing waveforms such as raised cosine (RC) and better-than-raised-cosine (BTRC), we incorporate a custom loss function that integrates energy and bandwidth constraints along with practical design considerations such as waveform symmetry. Simulation results validate our theoretical analysis and demonstrate performance gains of the designed pulse over RC and BTRC waveforms. To facilitate testing of our results without the need to rebuild the DNN structure, we also provide curve-fitting parameters for the selected DNN-based waveforms.

One of the main goals of the upcoming sixth-generation (6G) wireless networks is the ability to support higher network density, while ensuring a high quality of service for each user. In this paper, we introduce distributed uplink rate-splitting multiple access (DU-RSMA), define its basic principles, and provide insights into its advantages. Specifically, a system with two remote radio heads (RRHs) and two users is investigated. To improve the performance of the system, we consider that the RRHs can communicate through a feedback link, and thus they are able to decode the received messages either independently or with the assistance of the other RRH, since the decoded information can be shared through the feedback link. It should be noted that this scheme increases the achievable capacity region compared to the known multiple access schemes, which is also evaluated by a novel metric termed “fill factor”. Both the case of adaptive transmission rates and the case of fixed transmission rates are investigated. To this end, the ergodic rate is investigated to cover the former case, while the outage probability is studied for the latter. Closed-form expressions are derived for both metrics. Finally, the analytical expressions are validated by simulation results, which explicitly show the impact of each parameter on the performance of the system, and prove that the proposed scheme outperforms the corresponding benchmarks.

In this paper, we develop an energy minimization framework for wireless-powered multi-access edge computing (WP-MEC) networks, where two zero-energy reconfigurable intelligent surfaces (zeRISs) are employed to support energy harvesting and task offloading. In the downlink energy harvesting period, the hybrid access point (HAP) provides energy beamforming for multiple zero-energy devices and two zeRISs, where the harvested energy is used to offload tasks in the uplink period. Specifically, an optimization problem is formulated to minimize the energy consumption at the HAP, jointly considering the nonlinear energy harvesting model and cascaded RIS link. Next, an efficient iterative solution is designed to realize joint time allocation, HAP energy beamforming, and reflection coefficients for two-RIS by employing alternating optimization and semidefinite relaxation methods. Numerical results demonstrate the superiority of the algorithm. Compared to the corresponding single RIS setup, the energy consumption of HAP under the proposed two-zeRIS scheme is significantly reduced.

One of the main goals of the upcoming sixthgeneration (6G) wireless networks is the ability to support higher network density, while ensuring a high quality of service for each user. To this end, we introduce distributed uplink ratesplitting multiple access (DU-RSMA), define its basic principles, and provide insights into its benefits. DU-RSMA allows multiple users to efficiently share the same resource block through message splitting and the use of remote radio heads that independently decode incoming messages and exchange the decoded information through a feedback link. It should be noted that this scheme increases the achievable capacity region, compared to the known multiple access schemes, which is also evaluated through a novel metric, termed as “fill factor”. Since adaptive transmission rates are considered, the performance of the proposed scheme is investigated in terms of the ergodic rate of both users, for which we derive closed-form expressions. Simulation results illustrate the performance gains of DU-RSMA over corresponding benchmarks and investigate the effect of system parameters on its performance.

Programmable Wireless Environments (PWEs) leverage Reconfigurable Intelligent Surfaces (RISes) to actively shape electromagnetic (EM) propagation, enabling advanced control over wireless channels. Beyond improved performance in B5G/6G networks, this control also introduces new security capabilities. Exploiting this, we propose RF-Fencing: a service that selectively suppresses EM signals toward eavesdroppers while preserving reliable communication for legitimate users, thereby significantly enhancing network covertness. Building on that, in this paper, we introduce SHIELD, the first RF-Fencing algorithm that partitions the PWE into Signal Suppression Areas (SSAs) and Signal Delivery Areas (SDAs) through on-the-fly merging of RIS configurations. Extensive EM analysis confirms SHIELD’s effectiveness in preventing wardens from intercepting critical information and achieving covert communications with minimal impact on legitimate users. Moreover, SHIELD can serve also as a jamming-mitigation mechanism and is applicable across various frequency bands and RIS designs.

Security is emerging as a critical performance metric for next-generation wireless networks, but conventional multiple-input-multiple-output (MIMO) systems often suffer from severe path loss and are vulnerable to nearby eavesdroppers due to their fixed-antenna configurations. Pinching-antenna systems (PAS) offer a promising alternative, leveraging reconfigurable pinching antennas (PAs) positioned along low-loss dielectric waveguides to enhance channel conditions and dynamically mitigate security threats. In this paper, we propose an artificial noise (AN)-based beamforming scheme for downlink transmissions in PAS, with the goal of maximizing the secrecy rate. A closed-form solution is derived for the single-waveguide scenario, while an alternating optimization approach addresses more complex multiple waveguide setups. Numerical results show that the proposed scheme significantly outperforms conventional MIMO and existing PAS security schemes.

The evolution toward sixth-generation wireless networks introduces the concept of intelligent and reconfigurable environments designed to support advanced services. Achieving this paradigm shift requires addressing the limitations of traditional wireless systems, particularly their inability to effectively counteract path loss or adapt to diverse user scenarios. Pinching antenna systems (PASs) have emerged as a promising solution, enabling dynamic control over path loss by leveraging dielectric waveguides to support low-loss transmission at high frequencies. This work presents an analytical framework for assessing the reliability of PASs through the derivation of closed-form expressions for the outage probability under both free-space and waveguide attenuation. In addition, a rigorous formulation is provided for the optimal positioning of the pinching antennas to maximize signal reception, taking into account the trade-off between waveguide losses and spatial separation. Simulation results validate the impact of waveguide attenuation on performance and show that PASs consistently outperform conventional architectures in terms of outage behavior, confirming their suitability for next-generation wireless networks.

Accurate indoor localization is crucial for enabling 6G applications, such as smart homes, augmented reality, and advanced healthcare systems. Optical wireless systems utilizing Light-Emitting Diodes (LEDs) offer centimeter-level accuracy due to their dominant line-of-sight (LoS) characteristics. However, most existing methods assume fixed and known user orientations, limiting their practical applicability in real-world scenarios with random orientations. In this paper, we propose an LED-based visible light positioning (VLP) scheme that accurately, through closed form equations, localizes users with arbitrary orientations using optical received signal strength (RSS) measurements. The proposed method achieves high localization accuracy, without requiring hardware for orientation measurements. Finally, an analytical expression for the error is derived, while Monte Carlo simulations validate the scheme's performance, highlighting the critical role of the parameters of the system in achieving accurate localization.

One of the main applications expected to be enabled by next-generation networks is computing. The goal-oriented nature of computing allows the use of different implementation techniques, with over-the-air (OTA) computation being one of the main proposed schemes due to its effective resource management and computational efficiency. In this work, we aim at optimizing the waveform of the system in the presence of intersymbol interference (ISI) and sampling error. To this end, we propose a deep neural network (DNN) framework that generates an optimal waveform that minimizes the mean square error (MSE) of the OTA computation system. To ensure that the generated waveform exhibits the same behavior as other common waveforms, weighted energy and spectrum constraints are included in the loss function of the training phase. To better mitigate ISI, the spectrum constraint integrates the roll-off factor of the waveform, allowing for the generation of different waveforms. Simulation results verify that the desired constraints are met and show a significant performance gain over state-of-the-art waveforms.

This paper presents a novel hybrid cross-band radio frequency (RF)-optical system that utilizes coherent RF detection with$M$-QAM modulation and optical intensity modulation and direct detection (IM/DD) to fully utilize the diversity gains of cross-band architectures. By performing joint signal processing in the RF and optical domains, the proposed system achieves significant improvements in reliability, spectral efficiency, and data rates compared to conventional approaches. A key feature of the system is an optimized linear mapping between the RF and optical signals, which ensures seamless signal integration and improved performance. To evaluate the system, we derive the theoretical mutual information (MI) and a tight closed-form approximation for the symbol error probability (SEP) of the proposed modulation scheme. Numerical results validate the theoretical analysis and show that the proposed scheme significantly outperforms state-of-the-art cross-band modulations in terms of MI and SEP. The proposed scheme, with its low-complexity design and superior performance, is a promising candidate for next-generation wireless networks.

In the rapidly advancing field of sixth generation (6G) wireless networks, where the achievement of ultra-high data rates and energy efficiency is crucial, this study introduces the concept of super constellations as a solution to meet these pressing requirements. However, when higher-order modulations are employed, the system becomes increasingly susceptible to Gaussian phase noise (GPN), resulting in significant performance degradation. To address this issue, we propose a novel modulation scheme, called super amplitude phase shift keying (SAPSK), which is specifically designed to improve resilience against GPN. Furthermore, recognizing the critical need for efficient detection methods in high-order constellations, we present a detection framework that is not only capable of mitigating the detrimental effects of GPN, but also provides a foundation for the development of low-complexity detection algorithms. Specifically, we introduce the generalized polar distance detector (GPD-D), a detection metric that approximates maximum likelihood detection (MLD) in GPN-affected channels while introducing structured decision regions. Building on the GPD-D, SAPSK formulates a hexagonal lattice that provides improved energy efficiency and is further supported by a detection algorithm with Ό (1) complexity, ensuring fast and accurate symbol detection. Finally, the superiority of SAPSK is demonstrated through extensive numerical simulations, which show that SAPSK has significant advantages in terms of symbol error probability (SEP) over other conventional modulation schemes.

Optical wireless communication (OWC) systems with multiple light-emitting diodes (LEDs) have recently been benefited from the assistance of optical reflecting intelligent surface (ORIS) to support energy-limited devices via simultaneous lightweight information and power transfer (SLIPT). This article studies the application of rate splitting multiple access (RSMA) for effective interference management and enhancing the data rate of these systems. Regarding the considerable bandwidth of the OWC band and also considerable energy consumption of the multi-LED transmitter, we formulate an energy efficiency (EE) maximization problem to jointly optimize the system variables, including transmit beamforming, LED selection, rate adaptation and ORIS element association, while adhering to the system requirements. Accordingly, we propose a dynamic resource allocation mechanism, leveraging proximal policy optimization (PPO) to accommodate system dynamism and optimize its variables. Concerning the frequent obstruction of OWC Line-of-Sight (LoS) links and consequently swift system reconfiguration, we improve the adaptability and predictability of the PPO agent by integrating Meta-learning technique. Simulations reveal that the proposed Meta-PPO algorithm has superior performance compared to the PPO method in the presence of ORIS with 76% gain. Furthermore, employing an ORIS in the proposed system model improves the performance by 51% compared to a scenario without ORIS.

The emergence of new application scenarios, such as mobile edge computing (MEC) and the industrial internet of things (IIoT), places new demands on next-generation communication systems to support green and sustainable machine-to-machine communications, posing new challenges for wireless network resource allocation. In this paper, the problem of time resource allocation in wireless networks with periodic packet transmission is studied to achieve collision-free transmission. Based on the Diophantine equation, the effect of initial timeslot allocation on transmission collision is analyzed, and the timeslot collision graph (TCG) is constructed. The TCG is then used for timeslot allocation in two wireless communication scenarios. First, a maximum independent set timeslot allocation (MIS-TA) algorithm is proposed for the design of pre-deployed wireless networks. Given multiple packet periods, by finding the maximum independent set in the TCG, we can obtain the number combinations of all devices without transmission collisions and the corresponding timeslot allocation scheme. In addition, for dynamic networks, a dynamic weighted collision graph timeslot allocation (DWCG-TA) scheme is proposed to minimize the number of packet collisions with the given number of devices and transmission periods. We evaluate the DWCG-TA with simulations, which show that it effectively reduces the average transmission collision compared to carrier sense multiple access.

In this paper, we propose a novel approach to minimize the inference delay in semantic segmentation using split learning (SL), tailored to the needs of real-time computer vision (CV) applications for resource-constrained devices. Semantic segmentation is essential for applications such as autonomous vehicles and smart city infrastructure, but faces significant latency challenges due to high computational and communication loads. Traditional centralized processing methods are inefficient in such scenarios, often resulting in unacceptable inference delays. SL offers a promising alternative by partitioning deep neural networks (DNNs) between edge devices and a central server, enabling localized data processing and reducing the amount of data required for transmission. Our contribution includes the joint optimization of bandwidth allocation, cut layer selection of the edge devices’ DNN, and the central server’s processing resource allocation. We investigate both parallel and serial data processing scenarios and propose low-complexity heuristic solutions that maintain near-optimal performance while reducing computational requirements. Numerical results show that our approach effectively reduces inference delay, demonstrating the potential of SL to improve real-time CV applications in dynamic, resource-constrained environments.

The advent of sixth-generation (6G) wireless communication systems represents a transformative leap in global connectivity, moving from traditional internet of things (IoT) frameworks to an advanced artificial intelligence of things (AIoT) paradigm. This evolution presents significant challenges, primarily due to exponential growth in data volume, complexity, and latency requirements. To address these challenges, over-the-air (OTA) computation has emerged as a breakthrough approach by integrating computational processes directly into the communication framework, overcoming the inefficiencies of traditional separate designs. In this study, we explore the integration of OTA computation with reconfigurable intelligent surfaces (RISs) within multi-cell multiple-input multiple-output (MIMO) networks. RIS technology enhances signal propagation, mitigates interference, and optimizes wireless coverage, thereby complementing the OTA computation paradigm’s ability to facilitate real-time data aggregation and processing. Specifically, we propose a novel joint optimization framework aimed at minimizing the mean squared error (MSE) in multi-cell environments. This framework addresses the complexity of beamforming design through an innovative power-iteration-based majorization-minimization approach and a successive alignment technique. In addition, we perform asymptotic analysis to elucidate the performance benefits of large-scale MIMO and RIS configurations. We also consider the fairness of MSE computation throughout the multi-cell system. Finally, numerical simulations validate the effectiveness of the proposed methods and provide additional insights based on the asymptotic analysis.

Optical wireless communication (OWC) systems with multiple light-emitting diodes (LEDs) have recently been explored to support energy-limited devices via simultaneous lightwave information and power transfer (SLIPT). The energy consumption, however, becomes considerable by increasing the number of incorporated LEDs. This paper proposes a joint dimming (JD) scheme that lowers the consumed power of a SLIPT-enabled OWC system by controlling the number of active LEDs. We further enhance the data rate of this system by utilizing rate splitting multiple access (RSMA). More specifically, we formulate a data rate maximization problem to optimize the beamforming design, LED selection and RSMA rate adaptation that guarantees the power budget of the OWC transmitter, as well as the quality-of-service (QoS) and an energy harvesting level for users. We propose a dynamic resource allocation solution based on proximal policy optimization (PPO) reinforcement learning. In simulations, the optimal dimming level is determined to initiate a trade-off between the data rate and power consumption. It is also verified that RSMA significantly improves the data rate.

In this paper, we consider an indoor visible light communication (VLC) system with multiple "white" light emitting diodes serving to form overlapping wireless communication cells. In order to maintain seamless connectivity to mobile users, a handover procedure should be implemented. In particular, practical conditions such as blockages due to obstacles inside the room environment and the mobility of users can affect direct VLC connectivity. The use of reconfigurable intelligent surfaces (RISs) in optical wireless systems allows to exploit non-direct connectivity links, thus providing efficient communication links. In this paper, we present a proactive handover mechanism that exploits the presence of a RIS, in order to redirect the communication links in case of blockages. The proposed approach has been implemented both in hard and soft modes and assessed in terms of achievable data rate and handover latency for a user walking in a given reference room at different user speeds and blockage conditions. Our presented results and comparisons with conventional handover methods (i.e., without RIS) are helpful in showing the superiority of the presented algorithm.

Deep neural networks (DNNs) have demonstrated their efficacy in delivering accurate solutions to a range of optimization problems. However, in the context of wireless communications, the size of these problems may vary across adjacent time slots, due to fast changes in the networks’ architecture, e.g., the number of users. It is essential to note that this time-varying dimensionality of optimization problems in wireless networks necessitates adjustments in the DNN architecture, resulting in different numbers of input and output nodes. To address this challenge, in our paper, optimization problems of varying size are treated as distinct tasks. To tackle these tasks, a multi-task learning (MTL) approach based on modular sharing is proposed. The multi-task approach consists of a DNN, which is used to extract the solutions for all the optimization problems, and a router which manages which nodes and layers of the input and output layer of the DNN to be used during the forward propagation of each task. Consequently, all tasks share common parameters of the DNN, while the DNN dynamically adjusts to the number of nodes of its output and input layers. Numerical results demonstrate the superiority of the suggested approach over zero-padding, which is the current solution for handling resource allocation problems of varying size.

Unmanned aerial vehicles (UAVs) equipped with full-duplex relays (FDRs) are pivotal in overcoming connectivity challenges by dynamically establishing effective communication channels. However, despite their potential in network performance via trajectory optimization, integrating energy consumption models for UAV-mounted FDRs remains unexplored, crucial for trajectory design adhering to existing energy constraints. To this end, we introduce an energy-aware trajectory optimization framework to maximize network performance and user fairness within the UAV’s energy constraints. Specifically, we present a detailed energy consumption model describing the operational needs of UAV-mounted FDRs and formulate a joint time-division multiple access (TDMA) user scheduling-UAV trajectory optimization problem considering the power dynamics of UAV-mounted FDRs. Finally, our simulation results highlight the role of energy awareness in achieving optimal trajectory and scheduling, contributing to UAV-mounted FDRs’ performance in future networks.

One of the principal challenges anticipated for the forthcoming sixth-generation (6G) wireless networks is the imper-ative need to design advanced multiple access techniques capable of enabling massive connectivity. In this direction, rate splitting multiple access (RSMA) has been reported as a promising approach. In this work, we investigate the ergodic rate (ER) performance of an uplink RSMA network which consists of two sources. Specifically, analytical closed-form expressions for the sources' ERs and the system ergodic sum rate (ESR) are derived under the case of perfect successive interference cancellation$(\text{pSIC})$and perfect channel state information (pCSI) as well as under the more realistic scenario of imperfect SIC (ipSIC) and imperfect CSI (ipCSI). Furthermore, an asymptotic analysis for the high signal-to-noise ratio regime is presented, which provides useful insights into the sources' behavior in all considered cases. The accuracy of the provided analytical expressions is validated by simulation results, which not only explore how different system parameters affect the extracted expressions, but also reveal the detrimental impact of ipSIC and ipCSI on system performance.

Semantic communications offer a promising approach to decrease network congestion and improve reliability, leading to more sustainable and energy-efficient wireless networks. However, the design of semantic transceivers constrain their effectiveness. This paper introduces a novel multi-carrier system that combines both semantic and Shannon communications, with a focus on text transmission. We formulate an optimization problem that jointly selects the transmission method and allocates power to reduce the transmission delay. Despite the challenges of solving this non-convex problem, we employ alternating optimization techniques to address it and the closed-form solution of the power allocation is extracted. The simulation results verify that jointly selecting semantic and Shannon communications decreases the transmission delay compared to using only one of the schemes.

In sixth-generation (6G) wireless communication systems, the coexistence of enhanced mobile broadband (eMBB) and massive machine-type communications (mMTC) services requires the investigation of appropriate multiple access schemes. In this direction, this paper delves into the hybrid eMBB-mMTC policy, focusing on the implications of non-orthogonality in contention-based access schemes and combining the strengths of slotted ALOHA and successive interference cancellation to address the challenges of this hybrid policy. Closed-form expressions for the outage probability, which are crucial for deriving the throughput of the sources, are presented and integrated into a comprehensive analysis. Finally, simulation results are used to validate the provided theoretical expressions, highlighting the effects of random source deployment within the hybrid eMBB-mMTC framework and highlighting the potential and challenges of this policy in shaping the future of 6G wireless communication systems.

In the evolving landscape of sixth-generation (6G) wireless networks, unmanned aerial vehicles (UAVs) have emerged as transformative tools for dynamic and adaptive connectivity. However, dynamically adjusting their position to offer favorable communication channels introduces operational challenges in terms of energy consumption, especially when integrating advanced communication technologies like reconfigurable intelligent surfaces (RISs) and full-duplex relays (FDRs). To this end, by recognizing the pivotal role of UAV mobility, the paper introduces an energy-aware trajectory design for UAV-mounted RISs and UAV-mounted FDRs using the decode-and-forward (DF) protocol, aiming to maximize the network’s minimum rate and enhance user fairness, while taking into consideration the available on-board energy. Specifically, this work highlights their distinct energy consumption characteristics and their associated integration challenges by developing appropriate energy consumption models for both UAV-mounted RISs and FDRs that capture the intricate relationship between key factors such as weight, and their operational characteristics. Furthermore, a joint time-division multiple access (TDMA) user scheduling-UAV trajectory optimization problem is formulated, considering the power dynamics of both systems, while assuring that the UAV energy is not depleted mid-air. Finally, simulation results underscore the importance of energy considerations in determining the optimal trajectory and scheduling and provide insights into the performance comparison of UAV-mounted RISs and FDRs in UAV-assisted wireless networks.

Recently, reconfigurable intelligent surfaces (RISs) have emerged as a highly promising technology within the realm of wireless communication systems, as they offer the potential to minimize obstructions, enhance reliability, and establish alternative paths for signal propagation. This article presents the performance of a free space optics (FSOs) system empowered by multiple optical reflecting surfaces (ORSs) over a Gamma-Gamma turbulence-induced fading channel with pointing errors by considering imperfections in channel state information (CSI). The expressions for probability density function (PDF) of the end-to-end FSO channel considering both perfect and imperfect CSI cases are derived. Further, the unified PDF and cumulative distribution function (CDF) of instantaneous signal-to-noise ratio (SNR) are determined under two detection schemes, i.e., intensity modulation/ direct detection and heterodyne detection for both perfect and imperfect CSI cases. Utilizing the derived CDFs, the closed-form expressions for outage probability and average symbol error rate (ASER) of the proposed multiple ORSs system are obtained along with performing asymptotic analysis. Finally, the numerical results indicate that the performance of ORS-assisted FSO systems is significantly degraded by severe turbulence, pointing errors, and imperfect CSI. However, the inclusion of ORSs and increasing their number improves the performance of ORS-assisted FSO systems in the presence of turbulence, pointing errors, and imperfect CSI, compared to FSO systems without ORSs.

This article focuses on the latest research and innovations in fundamental next-generation multiple access (NGMA) techniques and the coexistence with other key technologies for the sixth generation (6G) of wireless networks. In more detail, we first examine multiaccess edge computing (MEC), which is critical to meeting the growing demand for data processing and computational capacity at the edge of the network, as well as network slicing. We then explore over-the-air (OTA) computing, which is considered to be an approach that provides fast and efficient computation of various functions. We also explore semantic communications, identified as an effective way to improve communication systems by focusing on the exchange of meaningful information, thus minimizing unnecessary data and increasing efficiency. The interrelationship between machine learning (ML) and multiple access technologies is also reviewed, with an emphasis on federated learning (FL), federated distillation (FD), split learning (SL), reinforcement learning (RL), and the development of ML-based multiple access protocols. Finally, the concept of digital twinning and its role in network management is discussed, highlighting how virtual replication of physical networks can lead to improvements in network efficiency and reliability.

One of the main challenges that the upcoming sixth-generation (6G) wireless networks will encounter is the necessity to design sophisticated multiple access techniques that besides being capable of supporting massive connectivity, they can also fulfill the heterogeneous requirements of 6G services, namely further-enhanced mobile broadband (feMBB), extremely reliable and low-latency communication, and ultra-massive machine-type communication (umMTC). To this end, this work investigates the coexistence of multiple feMBB and umMTC wireless sources in a network. In order to enhance the achievable connectivity, each orthogonal resource block of the network is assigned to one feMBB and multiple umMTC sources. FeMBB sources are assumed to constantly transmit, while umMTC are considered to access the network in a probabilistic manner. If more than one umMTC sources attempt to access the network in a given resource block, no umMTC transmission is permitted, however, when precisely one umMTC source endeavors to access the medium, rate-splitting multiple access is employed to concurrently serve both feMBB and umMTC transmissions. For such a communication scenario, we derive novel closed-form expressions for sources’ outage probabilities (OPs), ergodic rates (ERs), system throughput, and ergodic sum rate under both the ideal case of perfect channel state information (pCSI) and perfect successive interference cancellation (pSIC) and the more realistic scenario of imperfect CSI (ipCSI) and imperfect SIC (ipSIC). Furthermore, a high signal-to-noise ratio analysis is provided revealing deeper insights for sources’ asymptotic behavior under all considered cases. Simulation results corroborate the accuracy of the derived analytical expressions, investigate the impact of different system parameters on sources’ OP and ER performance, and illustrate the detrimental impact of ipCSI and ipSIC on system performance compared to the ideal case of pCSI and pSIC.

In this work, we address a novel framework for next-generation distributed radio access. In contrast with existing studies, where all remote radio heads (RRHs) in a distributed network are directly connected to a central unit (CU), an alternative architecture for advanced flexibility is proposed. In our setup, only one of the RRHs, namely the$primary$RRH, communicates directly with the CU, while the connectivity between the rest RRHs, namely$secondary$RRHs, and the CU is achieved through the primary RRH via free-space optical links. Assuming that users exploit non-orthogonal multiple access (NOMA) for their transmissions, we introduce two successive interference cancellation (SIC) cooperation schemes, depending on the one-directional or bidirectional communication between the RRHs, as well as, a four-step centralized algorithm for efficient user-RRH association and decoding order operations is proposed. The feasibility of the suggested schemes is adequately demonstrated by deriving analytical expressions for users' outage probabilities and providing valuable insights into the high signal-to-noise ratio regime. Furthermore, the performance of the proposed system under various weather conditions is investigated via simulation and analytical results. The comparison with a benchmark scheme, where all RRHs are directly connected to the CU and cooperate with each other via ideal links, is provided and it is revealed that although the performance of the proposed system model is weather dependent, in most of the practical cases it achieves similar performance with the ideal benchmark.

Semantic communications are considered a promising beyond-Shannon paradigm to reduce network traffic and increase reliability, thus making wireless networks more energy efficient, robust, and sustainable. However, the performance is limited by the efficiency of the semantic transceivers, i.e., the achievable “similarity” between the transmitted and received signals. Under strict similarity conditions, semantic transmission may not be applicable and Shannon communication is mandatory. In this paper, for the first time in the literature, we propose a multi-carrierHybrid Semantic-Shannoncommunication system where, without loss of generality, the case of text transmission is investigated. To this end, a joint semantic-Shannon transmission selection and power allocation optimization problem is formulated, aiming to minimize two transmission delay metrics widely used in the literature, subject to strict similarity thresholds. Despite their non-convexity, both problems are decomposed into a convex and a mixed linear integer programming problem by using alternating optimization, both of which can be solved optimally. Furthermore, to improve the performance of the proposed hybrid schemes, a novel association of text sentences to subcarriers is proposed based on the data size of the sentences and the channel gains of the subcarriers. We show that the proposed association is optimal in terms of transmission delay. Numerical simulations verify the effectiveness of the proposed hybrid semantic-Shannon communication scheme and the derived sentence-to-subcarrier association, and provide useful insights into the design parameters of such systems.

Over-the-air computing (AirComp) has recently attracted considerable attention as an efficient method of data fusion by integrating uncoded communication transmissions with computation thanks to the signal superposition offered by the multiple access channels. However, appropriate processing is required to neutralize the wireless channel effect. As, internet-of-things (IoT) applications through low-cost devices is the main target of AirComp, perfect availability of channel state information (CSI) is not always practical, there is the need to investigate the effect of imperfect CSI on AirComp. Specifically, we present novel closed-form expressions for tight approximations that can be used to design and evaluate AirComp systems. Furthermore, we design a general optimization framework that takes into account both magnitude and phase errors in the CSI. Finally, a pilot retransmission policy is designed, that offers trade-off between resources cost and the gain in the accuracy of the computations. In order to validate its application, a utility function of the cost of retransmission is introduced, namely,Retransmission Policy Cost (RPC), which can incorporate the power or throughput cost opposing to the expected gain of the selected policy. Simulations show the deterioration caused by the imperfect CSI and highlight the added value of the proposed policy under various system conditions.

A primary objective of the forthcoming sixth generation (6G) of wireless networking is to support demanding applications, while ensuring energy efficiency. Programmable wireless environments (PWEs) have emerged as a promising solution, leveraging reconfigurable intelligent surfaces (RISs), to control wireless propagation and deliver exceptional quality-of-service. In this paper, we analyze the performance of a network supported byzero-energy RISs (zeRISs), which harvest energy for their operation and contribute to the realization of PWEs. Specifically, we investigate joint energy-data rate outage probability and the energy efficiency of a zeRIS-assisted communication system by employing three harvest-and-reflect (HaR) methods, i) power splitting, ii) time switching, and iii) element splitting. Furthermore, we consider two zeRIS deployment strategies, namely BS-side zeRIS and UE-side zeRIS. Simulation results validate the provided analysis and examine which HaR method performs better depending on the zeRIS placement. Finally, valuable insights and conclusions for the performance of zeRIS-assisted wireless networks are drawn from the presented results.

In this paper, we propose and evaluate a hybrid visible light communication (VLC)/radio-frequency (RF) network architecture, where a VLC access point serves two user equipments (UEs), which also act as RF relays in order to extend the network’s coverage to a third UE outside the VLC cell. The proposed protocol is inspired by uplink rate-splitting multiple access to efficiently route the messages to the UEs. In more detail, the proposed protocol utilizes the nuances of the specific network architecture to efficiently utilize the wireless resource blocks for both coverage and throughput. The protocol is then optimized by maximizing the minimum achievable rate. Simulation results show that the proposed method achieves superior results compared with a more conventional benchmark scheme, that is also optimized under the same constraints.

Recently, federated learning (FL) has sparked widespread attention as a promising decentralized machine learning approach which provides privacy and low delay. However, communication bottleneck still constitutes an issue, that needs to be resolved for an efficient deployment of FL over wireless networks. In this paper, we aim to minimize the total convergence time of FL, by quantizing the local model parameters prior to uplink transmission. More specifically, the convergence analysis of the FL algorithm with stochastic quantization is firstly presented, which reveals the impact of the quantization error on the convergence rate. Following that, we jointly optimize the computing and communication resources as well as the number of quantization bits, in order to guarantee minimized convergence time, subject to energy and quantization error requirements. The impact of the quantization error on the convergence time is evaluated and the trade-off among model accuracy and timely execution is revealed. Moreover, the proposed method is shown to result in faster convergence compared with baseline schemes. Finally, useful insights for the selection of the quantization error tolerance are provided.

Distributed optimization is ubiquitous in emerging applications, such as robust sensor network control, smart grid management, machine learning, resource slicing, and localization. However, the extensive data exchange among local and central nodes may cause a severe communication bottleneck. To overcome this challenge, over-the-air computing (AirComp) is a promising medium access technology, which exploits the superposition property of the wireless multiple access channel (MAC) and offers significant bandwidth savings. In this work, we propose an AirComp framework for general distributed convex optimization problems. Specifically, a distributed primal-dual (DPD) subgradient method is utilized for the optimization procedure. Under general assumptions, we prove that DPD-AirComp can asymptotically achieve zero expected constraint violation. Therefore, DPD-AirComp ensures the feasibility of the original problem, despite the presence of channel fading and additive noise. Moreover, with proper power control of the users’ signals, the expected non-zero optimality gap can also be mitigated. Two practical applications of the proposed framework are presented, namely, smart grid management and wireless resource allocation. Finally, numerical results confirm DPD-AirComp’s excellent performance, while it is also shown that DPD-AirComp converges an order of magnitude faster compared to two digital orthogonal multiple access schemes, specifically, time-division multiple access (TDMA), and orthogonal frequency-division multiple access (OFDMA).

Optimizing the energy efficiency (EE) of wireless networks is one of the key priorities in the design of beyond 5G mobile technologies. In this pursuit, the use of new frequency bands, in combination with advanced multiple access protocols and cooperative communications strategies, has recently shown promising results. To this end, this paper investigates an indoor wireless network that aggregates communication resources in visible light and radio-frequency (RF) bands, taking advantage of the complementary aspects of the two technologies. More specifically, a non-orthogonal multiple access (NOMA) scheme is introduced for the visible light communication (VLC) downlink, such that cell-edge users experiencing a weak VLC signal enhance their aggregated data rate with the aid of cooperative communications over RF sidelinks (i.e., device-to-device links). The optimal resource allocation strategy over both VLC and RF bands is derived aiming at EE maximization based on the Dinkelbach’s algorithm and successive convex approximation. Additionally, for the sake of flexibility, a weighted EE metric is proposed for the characterization of the aggregated VLC/RF network performance. Simulation results are provided to validate the proposed analysis, revealing the impact of various design and system parameters, such as the weighting factors, quality of service requirements, and channel conditions.

Data collection in massive Internet of Things networks requires novel and flexible methods. Unmanned aerial vehicles (UAVs) are foreseen as a means to collect data rapidly even in remote areas without static telecommunication infrastructure. In this direction, UAV-mounted reconfigurable intelligent surfaces (RISs) aid in reducing the hardware requirements and signal processing complexity at the UAV side, while increasing the network’s energy efficiency and coverage. Hence, in this paper, we propose the utilization of a UAV-mounted RIS for data collection and study the coverage probability in such networks. Additionally, we propose a novel medium access control protocol based on slotted ALOHA and Code Combining to handle the communication of multiple sensors. To account for the crucial energy issue in UAVs, we devise an energy model that considers both the UAV and the RIS weight, as well as the environmental conditions and the UAV’s velocity. Finally, we characterize the performance of the proposed data collection scheme by analyzing the average throughput and the average collected data per flight, while providing useful insights for the design of such networks.

Recently, the use of reconfigurable intelligent surfaces (RIS) has gained popularity and is emerging as a promising technique to provide improved link reliability and enhanced coverage area. In this paper, we propose an optical reflecting surface (ORS)-assisted free space optics (FSO) communication system, which is based on optical space shift keying (OSSK) technique. Specifically, the closed-form expression for probability density function (PDF) of the ORS-assisted FSO channel is derived over Malaga turbulence model. Further, we have obtained the moment generating function (MGF) of the instantaneous signal-to-noise ratio (SNR) of the overall OSSK-based multiple-input multiple-output (MIMO)-FSO system. Using the derived channel statistics, an upper bound expression for the average bit error rate (BER) and a lower bound for the ergodic capacity are derived. Further, the asymptotic BER is utilized to calculate the diversity gain of the system. Numerical results are provided to corroborate the theoretical analysis of the system, along with insightful discussions. It is observed from the numerical results that the atmospheric turbulence and pointing errors have a negligible effect on the performance of the proposed system. Finally, a trade-off is noticed with respect to the average BER performance versus the spectral efficiency of the proposed system.

Coded caching is the distribution of content across a communication system using techniques from coding theory in order to create multicasting opportunities among the users receiving the content. This enables a multiplicative improvement over the classic uncoded caching with respect to the transmission rates required in the delivery phase of the content. Since its introduction, coded caching has eliceted significant research interest as a result of which several different schemes have been proposed over the last few years. This work focuses on the fundamental case of coded caching with uncoded prefetching. In this case, the users’ caches are filled with uncoded content during a prefetching phase in order to best serve the request made by each user during a subsequent delivery phase. This important case has recently received a complete information-theoretic characterization. However, reaching the information-theoretic optimality imposes a significant computational imbalance among the users. To mitigate this imbalance, we perform a complete computational analysis of the two major forms of coded caching with uncoded prefetching, namely centralized and decentralized. Furthermore, we propose a new information-theoretically optimal method for the delivery phase that achieves a significant computational improvement compared to the state of the art.

Handover management in Visible Light Communications (VLC) networks is an open issue due to the need of maintaining alignment between a transmitting Light Emitting Diode (LED) and a receiver PhotoDetector (PD). Mobility strongly affects VLC connectivity links, as well as occlusions that may obstruct the Line-of-Sight (LoS) propagation. In this paper we present a proactive handover solution, which is able to switch connectivity links from a serving VLC “lighting” cell to a candidate one, in case of blockage that affects the VLC link. Differently from conventional handover mechanisms, where a handover is trigged by quality of service metrics or localization information, our technique is dedicated to VLC networks which can be affected by occlusions causing blockages. The proposed approach has been implemented both in hard and soft mode, and assessed in terms of achievable data rate and hand over latency for a user walking in a given reference room at different speeds.

Massive IoT deployments in smart cities pose a significant challenge to the data collection due to the harsh wireless channel conditions of dense urban environments. Aerial reconfigurable intelligent surfaces (RIS) carried by Unmanned Aerial Vehicles (UAVs) can improve the communication thanks to their high mobility that provides line-of-sight propagation. In this paper, we investigate the impact of the aerial RIS in the data collection by deriving the outage probability of the randomly-deployed devices, while taking into account the imperfect channel state and the UAV fluctuations. Furthermore, we study the effects on the network reliability of two hybrid automatic repeat request protocol types, i.e., incremental redundancy and code combining, as well as on the average throughput. Finally, we provide useful insights regarding the RIS characteristics that guarantee the optimal network performance.

The integration of Radio Frequency (RF) and Visible Light Communication (VLC) technologies has been considered an enabler to achieving the Key Performance Indicators (KPIs) in Beyond 5G (B5G). Apart from higher data rates for enhanced Mobile Broadband applications, Ultra-Reliable and Low-Latency Communications and massive Machine-Type Communications must be also supported. This poses notable challenges in the design of a mobile communication system that relies exclusively on the use of licensed RF spectrum. In order to cope with the requirements of B5G services, the complementary benefits that RF and VLC bands have in terms of communication bandwidth, signal propagation characteristics, and ultra-densification feasibility, can be exploited. For this purpose, this paper studies the performance of two integration approaches, namely RF - VLC selection (Layer-3 or network-layer) and RF-VLC aggregation (Layer-2 or MAC-layer). Based on the obtained simulation results, it is possible to conclude that RF - VLC aggregation outperforms RF - VLC selection in terms of data rate performance, especially when ultra-reliable communication services are required to connect a large number of user terminals placed in an indoor scenario.

Over-the-air Computation (AirComp) has attracted significant attention as an efficient way of data fusion by inte-grating uncoded communication transmissions with computation thanks to the superposition offered by the multiple access channels. However, proper pre-processing and post-processing is required to neutralize the wireless channel effect, in order for AirComp to function successfully. Since, internet-of-things (IoT) type of devices with limited capabilities are the target de-mographic of AirComp, having perfect channel state information (CSI) available is not always a practical assumption. In this work, we examine the effect of imperfect CSI on the AirComp system and we design a general optimization framework that takes into account both magnitude and phase errors in CSI. On top of that, a pilot retransmission policy is designed that offers a trade-off between cost of retransmissions and gain in the accuracy of the computations. Simulation results show the deterioration caused by the imperfect CSI and also the value of the proposed policy under various system conditions.

Federated Learning is a promising decentralized machine learning approach, which has the potential to realize the vision of next-generation internet-of-things (NGIoT), by offering intelligent services and meeting the privacy and low latency requirements. By leveraging the combination of edge servers, as intermediate model aggregators, and the central cloud server, as global model aggregator, the concept of Hierarchical Federated Learning (HFL) has recently emerged. In this paper, we aim to minimize the delay of a global HFL round, under user energy requirements. We jointly optimize the computation and communication resources, as well as the user-edge assignment, in order to minimize the overall delay. The formulated non-convex combinatorial problem, is optimally solved by being decomposed into two disjoint subproblems, namely the resource allocation and user-edge assignment. Finally, the simulation results demonstrate the effectiveness of the proposed methods in terms of delay reduction, compared to selected benchmarks, while insights for the networks' behavior are provided.

The Pearson type III and the log Pearson type III distributions have been considered in several scientific fields, as in hydrology and seismology. In this article, we present new results for these distributions and we utilize them, for the first time in the literature, to investigate the statistical behavior of wireless power transfer, which can prolong the lifetime of Internet of Things networks, considering the nonlinear relationship between the received and harvested power, which can be precisely modeled by using the logistic function. Specifically, we present new closed-form expressions for the statistical properties of a general form of the Pearson type III and the log Pearson type III distributions and we utilize them to introduce a new member of the Pearson type III family, the logit Pearson type III distribution, through which the logit gamma and the logit exponential distributions are also defined. Moreover, we derive closed-form expressions for the probability density function, the cumulative distribution function and moments of the distributions of the sum, the log sum, and the logit sum of Pearson type III random variables. Furthermore, taking into account that the Pearson type III family of distributions is closely related to the considered nonlinear energy harvesting model the statistical properties of the distribution of the harvested power are derived, for both single input single output and multiple input single output scenarios with or without channel state information at the transmitter.

The wireless networks beyond the fifth generation (5G) are envisioned to be the platform that will support a vast amount of diversified data-driven applications with stringent requirements in terms of computational accuracy, delay, and energy efficiency. The fulfillment of this objective can be achieved by the convergence of communication and computing networks, enabling the exploitation of edge computing resources and the joint orchestration of the corresponding resources. Mobile edge computing (MEC), which refers to the use of edge serves for offloading tasks from mobile devices, is a particularly promising approach to provide the required computational performance for emerging internet-of-things applications, such as the smart grids, smart industry, healthcare, and smart farming. In this work, we propose the use of an advanced multiple access technique and its joint design with adaptive task offloading, in order to reduce delay and energy consumption. More specifically, the use of generalized hybrid orthogonal/non-orthogonal multiple access (OMA/NOMA) for MEC is introduced, which is theoretically superior to other alternatives from the existing literature. In more detail, the proposed scheme is based on the joint utilization of dynamic user scheduling among OMA/NOMA phases and variable decoding order during the successive interference cancellation in NOMA phase. Also, the system’s orchestration is optimized for both full and partial task offloading. Specifically, in full offloading scenario, the user scheduling, time allocation, and power control are jointly optimized. Regarding partial offloading, the computational resources, i.e., the clock speed of the local processors and the number of offloaded bits, are jointly optimized with the communication resources, taking into account the constraint of the energy that is consumed for both local processing and task offloading, which is particularly challenging due to the non-convex nature of the corresponding optimization problem. All optimization problems are efficiently solved by either using closed-form solutions that provide useful insights or low-complexity algorithms. Finally, simulation results demonstrate the effectiveness of the proposed techniques and provide useful insights on the system’s performance, in terms of average delay and energy consumption.

We introduce a set of multiple access protocols, calledhierarchical multiple access (HiMA), which are based on non-orthogonal multiple access (NOMA) and time-division multiple access (TDMA), optimized for the hierarchical network scenario. The proposed protocols can be efficiently utilized in various network configurations with an hierarchical form, such as relay networks, cloud-radio access networks (C-RANs), and fog-radio access networks (F-RANs). In particular, C-RANs and, more recently, F-RANs are regarded as promising paradigms to fully utilize the edge of the networks. Therefore, the implementation of novel multiple access protocols to properly exploit these configurations is critical for the fifth generation and beyond of wireless access. Furthermore, the resource allocation problem is formulated for each protocol with respect to the timeslot duration and power. As a result two fairness metrics are optimized, namely max-min rate fairness and proportional fairness. Finally, numerical results reveal the effectiveness of the joint design in the hierarchical network and an interesting trade-off is identified between fairness and achievable rate. Interestingly, despite NOMA being a very promising alternative to conventional multiple access schemes, the protocol that is solely based on NOMA does not always outperform the rest.

One of the main challenges in wireless powered networks (WPNs) is the doubly near-far problem, i.e., the twofold degradation of the users' performance due to different path-loss values that affects both the energy harvesting and the information transmission efficiency. To this end, we propose and optimize the application of decentralized power transfer and radio access in WPNs, which is implemented by using multiple remote radio heads (RRHs) with the capability to both transfer energy and receive information. More specifically, the use of non-orthogonal multiple access (NOMA) and time division multiple access (TDMA) is investigated, while two novel schemes are proposed, hereinafter termed as partially and fully asynchronous transmission TDMA (AT-TDMA). According to the proposed schemes, the users harvest energy and transmit information to the RRHs in different portions of time. Furthermore, the sum and minimum throughput among users are jointly maximized by obtaining the Pareto optimal solutions for the scheduling of power transfer and information transmission. To evaluate the performance of the decentralized architecture compared to the centralized one, we solve the aforementioned optimization problem for both architectures, taking also into account the circuit power consumption. Simulations show that the use of multiple RRHs improves spectral efficiency compared to the centralized implementation, since it can tackle more efficiently the doubly near-far problem. In addition, the proposed AT-TDMA schemes increase the achievable data rate.

A hybrid downlink system that simultaneously uses visible light communication (VLC) and radio frequency (RF) is investigated, assuming that only one of the two considered users is capable of receiving information over the optical band. In order to facilitate information transmissions from the VLC access point to the RF user, mixed VLC/RF relaying and simultaneous lightwave information and power transfer (SLIPT) are utilized. Moreover, a cognitive-based resource allocation policy and tractable bounds for the harvested energy are introduced. Furthermore, by taking into account the random location of the VLC and RF user terminals, the closed-form outage probability for the VLC user is given, while for the RF user, the outage probability is derived in terms of infinite-series, which is also approximated by a tractable closed-form expression. In addition, the outage probability of the RF user is minimized by optimizing the direct current (DC) component. Finally, simulations are provided to verify the accuracy of the theoretical analysis and demonstrate the effectiveness of the proposed optimization framework.

In this paper, we investigate simultaneous lightwave information and power transfer (SLIPT) for underwater visible light communication systems. We consider three SLIPT methods namely time switching (TS), power splitting (PS) and time switching-power splitting (TS-PS) where the splitting/switching factors are defined as optimization parameters. For each of these methods, we derive closed-form expressions for the average harvested energy, bit error rate and spectral efficiency in the presence of underwater turbulence modeled by lognormal statistics. Using these expressions, we determine the optimal splitting factors to maximize the harvested energy while satisfying a given bit error rate value and a given threshold spectral efficiency value. Our results reveal that, if not optimized, SLIPT methods under consideration are outperformed by the simple AC-DC separation (ADS) method which provides the largest harvested energy versus spectral efficiency (HE-SE) region. Optimization of splitting/switching factors extends the HE-SE regions; hence, optimized versions of TS, PS and TS-PS methods are able to significantly outperform ADS for most cases. We further investigate the effect of various channel and system parameters such as water type, turbulence level, beam divergence, receiver aperture size on the harvested energy and quantify the improvements in battery lifetime through the use of SLIPT methods.

A new queue-length aware online scheduling scheme is proposed for a buffer-aided wireless-powered communication network (WPCN) with non-orthogonal multiple access (NOMA). The throughput of the considered network is maximized by designing the optimal resource allocation scheme, while preserving the stability of both energy and data queues. The formulated optimization problem is particularly challenging, since it is a long-term mixed-integer optimization problem. In order to solve it efficiently, we first transform the long-term optimization problem into a series of short-term ones at each time slot by taking advantage of the Lyapunov optimization framework, which can be efficiently solved. The analytical expression of the rate allocation reveals that in contrast to the case of WPCN without buffering, the optimal decoding order depends on the length of data buffer. Simulation results show that the proposed scheme outperforms the non-buffering scheme in terms of the long-term time-average sum rate.

In this paper, we investigate a secure relaying network with simultaneous wireless information and power transfer (SWIPT). It is assumed that Alice wants to send confidential information to Bob under the existence of a passive eavesdropper (Eve), while the relay is equipped with a data buffer and an energy storage device. More specifically, we aim at achieving higher secrecy throughput, while retaining the stability of the data and energy queues. To achieve this with acceptable complexity, we transform the original long-term stochastic optimization problem into a series of online subproblems using the framework of Lyapunov optimization. The proposed scheme shows that the optimal time switching factor is 0 or 1, which is different from the conventional secure relaying network with SWIPT. In addition, simulation results verify that the proposed scheme can improve the secrecy throughput compared with the baseline scheme.

Ambient backscatter devices (tags and readers) use existing radio frequency (RF) signals to transmit data. Most prior works consider single-antenna tags, but this paper investigates the case of multiple-antenna tags, which are capable of simultaneous energy harvesting and data transmission. However, the multi-antenna channel between the tag and the reader, and the unpredictable nature of RF signals due to uncontrollable RF sources (e.g., location and transmit power), make signal detection highly challenging. Thus, the detection process becomes a hypothesis testing problem with unknown parameters. Consequently, we design a blind detector based on the generalized likelihood ratio test (GLRT) without using channel state information (CSI), signal power and noise variance. The decision threshold and detection probability of it are also analyzed in detail. Furthermore, to maximize its detection performance, we develop the optimal backscatter antenna selection scheme. Interestingly, we show that the detector performs best when only two backscatter antennas are selected. Finally, extensive simulation results validate the analysis and illustrate the effectiveness of the proposed detector.

Cooperative non-orthogonal multiple access (C-NOMA) and buffering are promising techniques to improve spectrum efficiency in the next generation of wireless networks. In this article, a buffer-aided cooperative NOMA network with direct links is studied. The throughput maximization problem is firstly formulated under the assumption of fixed power allocation and optimally solved by designing a mode selection policy. In order to further improve system throughput, the problem is extended into the case that power allocation and mode selection are jointly optimized. An optimal solution is obtained, while a sub-optimal one is also provided in order to decrease the implementation complexity. Furthermore, considering the case where the buffer has finite size and the users are delay-sensitive, a throughput-delay aware strategy is also presented. Moreover, it is shown that the proposed schemes outperform the baseline one in terms of throughput. Finally, simulations demonstrate that the sub-optimal solution achieves similar performance to the optimal one, while significantly reduces the implementation complexity.

In this paper, we investigate the statistics of the free space optics (FSO) communication channel between a hovering unmanned aerial vehicle (UAV) and a central unit. Two unique characteristics make UAV-based FSO systems significantly different from conventional FSO systems with static transceivers. First, for UAV-based FSO systems, the incident laser beam is not always orthogonal to the receiver lens plane. Second, both position and orientation of the UAV fluctuate over time due to dynamic wind load, inherent random air fluctuations in the atmosphere around the UAV, and internal vibrations of the UAV. On the contrary, for conventional FSO systems, the laser beam is always perpendicular to the receiver lens plane and the relative movement of the transceivers is limited. In this paper, we develop a novel channel model for UAV-based FSO systems by quantifying the corresponding geometric and misalignment losses (GML), while taking into account the non-orthogonality of the laser beam and the random fluctuations of the position and orientation of the UAV. In particular, for diverse weather conditions, we propose different fluctuation models for the position and orientation of the UAV and derive corresponding statistical models for the GML. We further analyze the performance of a UAV-based FSO link in terms of outage probability and ergodic rate and simplify the resulting analytical expressions for the high signal-to-noise ratio (SNR) regime. Finally, simulations validate the accuracy of the presented analysis and provide important insights for system design. For instance, we show that for a given variance of the fluctuations, the beam width should be properly adjusted to minimize the outage probability.

Random access (RA) has recently been revisited and considered as a key technology for the medium access control layer of the Internet of Things applications. Compared to other RA protocols, slotted ALOHA (SA) has the advantages of low complexity and elimination of partially overlapping transmissions, reducing the number of collisions, however it may suffer from congestion as the traffic load and the number of devices increase. To this end, two RA protocols based on SA and uplink non-orthogonal multiple access are proposed and applied to wireless sensor networks and wireless powered sensor networks. More specifically, to reduce the number of collisions and increase the throughput of SA, while maintaining low complexity, two detection techniques are used to mitigate the interference, when two sources transmit information in the same time slot, namely successive interference cancellation (SIC) with optimal decoding order policy and joint decoding (JD). To evaluate the performance of the proposed protocols, the outage probability of SIC and JD is derived, which is used to express the average throughput attained by each protocol in closed-form. Finally, both the analytical results and the simulations verify that the proposed protocols substantially increase the throughput and the number of connected devices compared to SA.

We propose an indoor lightwave downlink wireless communication network with the non-orthogonal multiple access (NOMA) technology, that consists of one visible light communication (VLC) access point (AP) and a pair of randomly located users. Although both users can directly receive information from the AP, the performance of the far user is degraded compared to the near one due to the asymmetrical channel gains. Thus, the user cooperation strategy is proposed to improve the performance of the far user by using mixed VLC/RF relaying technique in parallel with the wireless optical direct link. To efficiently exploit the available heterogeneous links, the concept of cross-band selection combining (CBSC) is introduced, according to which the far user is continuously served by either the mixed VLC/RF or the direct VLC link. Meanwhile, the performance of the proposed scheme is thoroughly investigated and compared to appropriate baselines. To this end, we derive closed-form expressions for the outage probability of each user as well as the system sum throughput. Finally, simulation results are provided to verify the effectiveness of the proposed scheme and the accuracy of the corresponding analysis.

In this paper, we present a novel low cost and low complexity platform for the provisional diagnosis of vessels abnormalities, such as artery stenosis, aneurysms, etc. The proposed system is based on a low cost lightwave wearable device and advanced machine learning techniques to reduce the computational load and improve the accuracy of diagnosis. Specifically, in this work we focus on the image reconstruction in the case of an artery stenosis due to atheromatic plaque, where we briefly present the method and some preliminary results. The proposed platform can automatically provide a provisional diagnosis which can then be followed-up with further detailed and/or established imaging methods (e.g., Doppler ultrasound, Magnetic Resonance Angiography, etc) and treated promptly in order to minimize their likelihood of progression to higher levels of severity. This method may act as an adjunct to existing established screening programmes (e.g., arterial stenosis and aneurysm screening) or be used for new forms of population screening in the future.

Non-orthogonal multiple access (NOMA) has recently attracted the academic and industrial interest, due to offering higher spectral efficiency and connectivity compared to conventional orthogonal multiple access schemes. In this paper, buffer-aided relaying for a downlink NOMA system with direct links is proposed, while in order to take advantage the extra degrees of freedom, appropriate transmission modes are presented. Targeting at the throughput maximization, the corresponding optimization problem is formulated and solved and theoretical expressions are derived for the optimal mode selection policy and maximum throughput. Finally, simulation results illustrate the efficiency of the proposed scheme and its superiority compared with a previously presented baseline scheme.

Visible light communication (VLC) has emerged as a high-capacity connectivity solution for underwater sensor networks. Since water is relatively transparent to blue or green light, visible light lasers or LEDs can be used as transmitters for underwater wireless connectivity with data rates up to hundreds of Mbps. In underwater networks, a critical system design issue is the network lifetime which highly depends on the battery capacity. Since recharging in underwater scenarios is typically very costly and impractical, energy harvesting can be considered as a promising alternative. In this paper, we explore simultaneous lightwave information and power transfer (SLIPT) for VLC-based USNs. We adopt time splitting method where the receiver switches in time between the modes of energy harvesting (EH) and information decoding (ID). We derive a closed-form expression for the average harvested energy over log-normal model underwater turbulence channel. Using this expression, we determine the splitting factor between EH and ID operation modes to maximize the harvested energy while satisfying a given bit error rate value.

Non-orthogonal multiple access (NOMA) has attracted both academic and industrial interest since it has been considered as one of the promising 5G technologies in order to increase connectivity and spectral efficiency. In this paper, we focus on a downlink NOMA network, where a single base station serves a set of users through multiple subchannels. The goal is to jointly optimize energy efficiency (EE) and fairness among users with respect to the subcarrier and power allocation parameters. To achieve this with acceptable complexity, we propose a novel greedy subcarrier assignment scheme. Due to the fractional form of the EE expression and the existence of interference, the power allocation problem is non-convex. To this end, we first transform this into an equivalent subtractive form, which is then solved by using fractional programming with sequential optimization of the power allocation vectors. Simulation results reveal the effectiveness of the proposed scheme in terms of EE and fairness among users compared to baseline schemes. Finally, the proposed algorithms are of fast convergence, low complexity, and insensitive to the initial values.

Non-orthogonal multiple access (NOMA) has attracted both academic and industrial interest since it has been considered as one of the promising 5G technologies in order to increase connectivity and spectral efficiency. In this paper, we focus on a downlink multicarrier (MC) NOMA network, where a single base station serves a set of users through multiple subchannels. The goal is to jointly optimize energy efficiency (EE) and fairness among users with respect to the subcarrier and power allocation parameters. To achieve this with acceptable complexity, a novel greedy subcarrier assignment scheme based on the worst-user first principle is proposed. Due to the fractional form of the EE expression and the existence of interference, the power allocation problem is non-convex and NP-hard. To this end, we first transform this into an equivalent subtractive form, which is then solved by using fractional programming with sequential optimization of the inter/intra-subchannel power allocation vectors. Simulation results reveal the effectiveness of the proposed scheme in terms of EE and fairness among users compared to baseline schemes. Finally, the proposed algorithms are of fast convergence, low complexity, and insensitive to the initial values.

One of the major goals of emerging wireless systems is to prolong the lifetime of wireless communication devices. To this end, this contribution evaluates and optimizes the performance of simultaneous wireless information and power transfer (SWIPT) with an integrated energy and information receiver, which has the advantage of low complexity and energy cost. A tractable expression for the achievable rate is first derived, which is subsequently used to quantify the achievable harvested energy-rate region for the two fundamental SWIPT protocols, namely, power-splitting (PS) and time-switching (TS). In this context, the joint harvested energy-rate outage probability is then defined and minimized for a point-to-point and multicasting system, determining the optimal PS and TS factors for both linear and nonlinear energy harvesting models. In addition, a TS-based broadcasting system is dynamically optimized by maximizing the energy harvested by all users under an achievable rate threshold for each user. The formulated optimization problem is, in fact, particularly challenging due to the non-convex form of the expression for the achievable rate. Yet, an effective solution is ultimately achieved by converting this problem into a convex one. Also, respective computer simulation results corroborate the effectiveness of the proposed framework. Overall, it is shown that the offered results provide meaningful theoretical and practical insights that will be useful in the design and efficient operation of wireless powered systems. Indicatively, unlike the trend in common separated receivers, a region has been identified, where TS outperforms PS.

This paper investigates a hybrid radio frequency (RF)/visible light communication (VLC) ultra-small cell network consisting of multiple optical angle-diversity transmitters, one multi-antenna RF access point (AP) and multiple terminal devices. In the network, the optical transmitters play the primary role and are responsible for delivering information and power over the visible light while the RF AP acts as a complementary power transfer system. Thus, we propose a novel collaborative RF and lightwave resource allocation scheme for hybrid RF/VLC ultra-small cell networks. The proposed scheme aims to maximize the communication quality-of-service provided by the VLC under a constraint of total RF and light energy harvesting performance while keeping illumination constant and ensuring health safety. This scheme leads to the formulation of two optimization problems that correspond to the resource allocation at the optical transmitters and the RF AP. Both problems are optimally solved by appropriate algorithms. Moreover, we propose a closed-form suboptimal solution with high accuracy to tackle the optical transmitters' resource allocation problem, as well as an efficient semi-decentralized method. Finally, simulation results illustrate the achievable performance of the investigated system and the effectiveness of the proposed solutions.

We investigate the performance of mixed free-space optical (FSO)/millimeter-wave (mmWave) relay networks with interference at the destination. The FSO/mmWave channels are assumed to follow Málaga-M/generalized-K fading models with pointing errors in the FSO link. The H-transform theory, wherein integral transforms involve Fox's H-functions as kernels, is embodied to unifying the performance analysis framework that encompasses closed-form expressions for the outage probability, the average bit error rate (BER), and the average capacity. By virtue of some H-transform asymptotic expansions, the high signal-to-interference-plus-noise ratio (SINR) analysis reduces to easy-to-compute expressions for the outage probability and BER, which reveals inside information for the system design. We finally investigate the optimal power allocation strategy that minimizes the outage probability.

Airborne radio access networks (A-RANs) is a particularly promising technology due to its ability to offer fast, cost-efficient, and on-demand enhancement of the existing telecommunication infrastructure. The main challenges of ARANs are the energy sustainability of the aerial platforms (APs) and the establishment of reliable links with the ground nodes. To this direction, we propose a novel approach, which is based on mixed free-space optical (FSO)/radio frequency (RF) relaying protocol and simultaneous lightwave information and power transfer (SLIPT). In this context, we also formulate and optimally solve the max-min fairness problem, which regulates the trade-off between the energy and information transfer to the AP and allocates the available resources to multiple endusers. Finally, the impact of the number of users and weather conditions on system's optimal configuration and performance is investigated through simulations. The offered results provide meaningful theoretical and practical insights on the capabilities of the proposed scheme.

Recently, visible light communication (VLC) networks have emerged as a possible alternative for data access, primarily indoors. The very high data rates, low implementation cost and free from radio frequency (RF) interference property make them particularly attractive for the next generation of indoor networking. Furthermore, non-orthogonal multiple access (NOMA) is a very promising candidate technique for the next generation of wireless networks, mainly due to its increased spectrum efficiency, compared to orthogonal access techniques. In this paper, we investigate, for the first time in existing literature, the practical indoor scenario of a hybrid VLC/RF network, where both VLC and RF subsystems perform NOMA. More specifically, we study the user grouping through the coalitional game theory, where each coalition is assigned to a specific access point, VLC or RF. Note that due to NOMA's particularities, optimal user grouping is still an open problem of research. Computer simulations illustrate the accuracy of the analysis and reveal the effectiveness of the proposed scheme compared to the standard opportunistic one, as well as its robustness with respect to the number of users.

We consider a drone-based communication network, where several drones hover above an area and serve as mobile remote radio heads for a large number of mobile users. We assume that the drones employ free space optical (FSO) links for fronthauling of the users' data to a central unit. The main focus of this paper is to quantify the geometric loss of the FSO channel arising from random fluctuation of the position and orientation of the drones. In particular, we derive upper and lower bounds, corresponding approximate expressions, and a closed-form statistical model for the geometric loss. Simulation results validate our derivations and quantify the FSO channel quality as a function of the drone's instability, i.e., the variation of its position and orientation.

We consider a free space optical (FSO) backhauling system which consists of two base stations (BSs) and one central unit (CU). We propose to employ non-orthogonal multiple access (NOMA) for FSO backhauling where both BSs transmit at the same time and in the same frequency band to the same photodetector at the CU. We develop a dynamic NOMA scheme which determines the optimal decoding order as a function of the channel state information at the CU and the quality of service requirements of the BSs, such that the outage probabilities of both BSs are jointly minimized. Moreover, we analyze the performance of the proposed NOMA scheme in terms of the outage probability over Gamma-Gamma FSO turbulence channels. We further derive closed-form expressions for the outage probability for the high signal-to-noise ratio regime. Our simulation results confirm the analytical derivations and reveal that the proposed dynamic NOMA scheme significantly outperforms orthogonal transmission and existing NOMA schemes.

Visible light communication (VLC)-also known as light fidelity (Li-Fi)-networks will play an important role in the near future, since they will provide full coverage and improved data rates for indoor wireless applications. In this paper, the coexistence of Li-Fi and Wi-Fi networks is investigated for the multi-user scenario, under the practical assumption that both of them are served by the same backhaul network (e.g., optical fiber). More specifically, we study the resource allocation and coordination problems by maximizing the proportional fairness of all users. To do so, we formulate and solve an optimization problem for the power allocation of the hybrid Li-Fi/Wi-Fi scenario, under the constraint of the common backhaul. Computer simulation results are provided to illustrate the effectiveness of the proposed analysis.

We present the concept of Simultaneous Lightwave Information and Power Transfer (SLIPT) for indoor Internet-of-Things (IoT) applications. Specifically, we propose novel and fundamental SLIPT strategies, which can be implemented through Visible Light or Infrared communication systems, equipped with a simple solar panel-based receiver. These strategies are performed at the transmitter or at the receiver, or at both sides, named Adjusting transmission, Adjusting reception, and Coordinated adjustment of transmission and reception, correspondingly. Furthermore, we deal with the fundamental trade-off between harvested energy and quality-of-service (QoS), by maximizing the harvested energy, while achieving the required user's QoS. To this end, two optimization problems are formulated and optimally solved. Computer simulations validate the optimum solutions and reveal that the proposed strategies considerably increase the harvested energy, compared to SLIPT with fixed policies.

We study a wireless-powered uplink communication system with non-orthogonal multiple access (NOMA), consisting of one base station and multiple energy harvesting users. We focus on data rates optimization and fairness increase. We show that the formulated optimization problems can be optimally and efficiently solved by either linear programming methods or convex optimization, which means that the proposed scheme can be easily implemented in practical applications. Simulation results illustrate that the proposed scheme outperforms the baseline orthogonal multiple access scheme, while they reveal the dependence between sum-throughput, minimum data rate, and harvested energy.

Efficient demand side management through dynamic power pricing is an important application in the smart grids. However, in the absence of a detailed user consumption model, it is difficult to set an optimal power price. In this paper, we propose to efficiently capture the user consumption behavior through a user-dependent acceptance price. Each rational user will decide its own acceptance price based on its desire to get served. Then, we model the selfish interaction between operator and users as a Stackelberg game, where the operator aims to maximize its profit, while the individual users try to pay the lowest price and be served in time. After each user selfishly declares its own acceptance price, the operator sets an optimal power price, based on the user feedback and taking into account the random output of the renewable power sources. Simulation results confirm that the operator can maximize its profit and the users get served in time, while the proposed scheme leads to the optimal usage of the renewable power production.

In this paper, we investigate joint relay selection and optimal power allocation, as a means to maximize the achievable rate of an underlay cooperative cognitive radio with carrier aggregation, taking into account the availability of multiple carrier components in two different bands and primary users (PUs) with specific average outage probability requirements. For the acquisition of the interference thresholds, which are set by the PUs on the secondary user (SU), we incorporate a minimum feedback strategy into the problem formulation, based on the minimization of the PUs outage probabilities. The resulting non-convex optimization problem is transformed into a convex one and optimally solved using dual decomposition and an efficient iterative method with closed-form power policies. Simulation results illustrate that the proposed configuration exploits the available degrees of freedom in an efficient way which maximizes the SU throughput while the average outage probability of the PUs is kept at acceptable levels.

A direct-conversion architecture can offer highly integrated low-cost hardware solutions to communication transceivers. However, it has been demonstrated that radio frequency impairments, such as amplifier nonlinearities, phase noise, and in-phase/quadrature-phase imbalances (IQI), can lead to a severe degradation in the performance and fairness. To this end, we study the power allocation (PA) problem in an orthogonal frequency-division multiple access system, when the served user equipment (UE) suffers from different levels of IQI. Additionally, we present a novel low-complexity solution with directly calculated PA policies, given the Lagrange multiplier, which mitigates the impact of IQI and achieves fairness in terms of capacity for the served UE, by maximizing the minimum achievable capacity of the UE. The effectiveness of the offered solution is validated through simulation results, which reveal that it can drastically increase the minimum achievable UE's capacity.

We study a wireless-powered uplink communication system with non-orthogonal multiple access (NOMA), consisting of one base station and multiple energy harvesting users. More specifically, we focus on the individual data rate optimization and fairness improvement and we show that the formulated problems can be optimally and efficiently solved by either linear programming or convex optimization. In the provided analysis, two types of decoding order strategies are considered, namely fixed decoding order and time sharing. Furthermore, we propose an efficient greedy algorithm, which is suitable for the practical implementation of the time-sharing strategy. The simulation results illustrate that the proposed scheme outperforms the baseline orthogonal multiple access scheme. More specifically, it is shown that the NOMA offers a considerable improvement in throughput, fairness, and energy efficiency. Also, the dependence among system throughput, minimum individual data rate, and harvested energy is revealed, as well as an interesting tradeoff between rates and energy efficiency. Finally, the convergence speed of the proposed greedy algorithm is evaluated, and it is shown that the required number of iterations is linear with respect to the number of users.

-We study a cloud network with M distributed receiving antennas and L users, which transmit their messages towards a centralized decoder (CD), where M ≥ L. We consider that the cloud network applies the Compute-and-Forward (C&F) protocol, where L antennas/relays are selected to decode integer equations of the transmitted messages. In this work, we focus on the best relay selection and the optimization of the Physical-Layer Network Coding (PNC) at the relays, aiming at the throughput maximization of the network. Existing literature optimizes PNC with respect to the maximization of the minimum rate among users. The proposed strategy maximizes the sum rate of the users allowing non-symmetric rates, while the optimal solution is explored with the aid of the Pareto frontier. The problem of relay selection is matched to a coalition formation game, where the relays and the CD cooperate to maximize their profit. Efficient coalition formation algorithms are proposed, which perform joint relay selection and PNC optimization. Simulation results show that a considerable improvement is achieved compared to existing results, both in terms of the network sum rate and the players' profits.

Motivated by the cooperative game theory, we propose an efficient physical layer network coding technique for compute-and-forward (C&F) relaying systems, which jointly optimizes the sum and the minimum transmission rate, while minimizing the total transmitting power. Specifically, we show that relay selection in C&F networks leads to an increase of both rates. We also propose an algorithmic approach for the rates' further optimization and we illustrate that its implementation can offer the same or even greater gain than adding an extra relay. Furthermore, the tradeoff between the minimum and the sum rate is explained by utilizing the concept of Pareto frontier. Simulations and numerical results show that a combination of relay selection and the proposed algorithms offers a considerable increase in the sum rate for the same transmitting power, without reducing the system's Quality of Service.

Reducing the power consumption of base transceiver stations (BTSs) in mobile communications networks is typically achieved through energy saving techniques, where they can also be combined with local power generators to create a hybrid power system (HPS). Such a system has reduced power consumption and operational cost, without taking the advantage of real-time energy management. In this paper, we introduce the smart HPS that can facilitate energy consumption scheduling (ECS) via an intelligent connection to the power grid. In doing so, we first develop sensor control and communication systems with an embedded smart ECS unit for the HPS. Then, we propose a real-time energy management algorithm to reduce the operational cost of BTS, according to real-time pricing and estimating demand and supply. The numerical results presented show a significant reduction in the BTS operational costs. We also develop a techno-economic and sizing analysis to describe the total cost of the smart HPS taking the real-time energy pricing into account. Since the lifetime of the operating system can be quite long, our results show that there is possibility to make a profit.