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Quantum key distribution (QKD) provides a secure method to exchange encrypted information between two parties in a quantum communication infrastructure (QCI). The primary challenge in deploying a QCI is the cost of using optical fibers and trusted repeater nodes (TRNs). Practical systems combine quantum and classical channels on the same fiber to reduce the cost of fibers dedicated to QKD. In such a system with quantum-classical coexistence, the optimal distribution of QKD requests with minimal deployment cost and power usage on the multiplexed links is challenging due to the diverse key rate demands of the requests, number of classical and quantum channels, guard band spacing between classical and quantum channels, and secret key rate of the quantum channels that decreases with distance. To address these challenges, in this work, we propose a Steiner tree-based approach for constructing a QCI that connects all quantum nodes with minimum TRNs. In addition, we propose a genetic algorithm-based solution to optimally distribute the end-to-end QKD requests over the QCI. We also determine feasible optical bypass routes to reduce the overall energy consumption in the network further. The proposed approach reduces the QCI deployment cost by 19.42% compared to the benchmark MST-Baseline. Also, on average, TAQNet with optical bypass achieves 4.69 kbit per Joule more energy efficiency compared to the nonbypass approach.

In this paper, optimal linear precoding for the multibeam geostationary earth orbit (GEO) satellite with the multi-user (MU) multiple-input-multiple-output (MIMO) downlink scenario is addressed. Multiple-user interference is one of the major issues faced by the satellites serving the multiple users operating at the common time-frequency resource block in the downlink channel. To mitigate this issue, the optimal linear precoders are implemented at the gateways (GWs). The precoding computation is performed by utilizing the channel state information obtained at user terminals (UTs). The optimal linear precoders are derived considering beamformer update and power control with an iterative per-antenna power optimization algorithm with a limited required number of iterations. The efficacy of the proposed algorithm is validated using the In-Lab experiment for 16 × 16 precoding with multi-beam satellite for transmitting and receiving the precoded data with digital video broadcasting satellite-second generation extension (DVB-S2X) standard for the GW and the UTs. The software defined radio platforms are employed for emulating the GWs, UTs, and satellite links. The validation is supported by comparing the proposed optimal linear precoder with full frequency reuse (FFR), and minimum mean square error (MMSE) schemes. The experimental results demonstrate that with the optimal linear precoders it is possible to successfully cancel the inter-user interference in the simulated satellite FFR link. Thus, optimal linear precoding brings gains in terms of enhanced signal-to-noise-and-interference ratio, and increased system throughput and spectral efficiency.

This study examines a cooperative non-orthogonal multiple access (NOMA) network utilizing an energy-constrained unmanned aerial vehicle (UAV) relay (UAVR) to expand coverage and improve network throughput. In order to provide energy to the UAVR, we consider the hybrid simultaneous wireless information and power transmission (SWIPT) method, which allows the UAVR to harvest energy from the source (i.e., sink node) signal. Herein, a hybrid time switching (TS)-based and power splitting (PS)-based relaying scheme is applied to improve the UAVR’s energy harvesting (EH) efficiency and the system performance. Given this context, we derive the closed-form expression of the outage probability (OP) for the sensors to evaluate the network performance. Based on the achieved analytical results, we apply the bat algorithm optimization (BAO) method to determine the optimal working point (as a fraction of received power and power allocation coefficients, and the 3-D positions of the UAVR) for the system such that the OP is minimized. The numerical analysis indicates that BAO is effective in both exploring and exploiting solutions, making it a suitable choice for similar non-convex optimization problems in continuous search spaces for cooperative NOMA IoT networks.

Reconfigurable Intelligent Surfaces (RIS) are revolutionizing wireless communications by enhancing signal quality and extending network coverage, particularly in remotely located Internet-of-Things (IoT) applications. However, integrating multiple RIS units into Simultaneous Wireless Information and Power Transfer (SWIPT) systems presents optimization challenges for energy harvesting and data transfer. This paper aims to optimize the performance of RIS-assisted SWIPT-IoT systems, focusing on maximizing three distinct objectives of data rate, energy harvesting (EH) and energy efficiency (EE), under Time-Switching (TS) and Power-Splitting (PS) protocols. The Karush-Kuhn-Tucker (KKT) conditions, alternating optimization-based algorithm and fractional programming based Dinkelbach Transform is proposed to jointly optimize the TS/PS ratio and transmit power. Numerical simulations are conducted to evaluate system performance highlight rate-energy trade-off across different scenarios, considering factors such as RIS angular placement, surfaces count, element count, separation distance, EH and data rate demands. Additionally, the imperfect Channel State Information (CSI) has been analyzed for the proposed algorithm. These findings offer practical insights into deploying multiple RIS units for enhanced SWIPT-IoT performance in real-world settings.

The integration of Positioning, Navigation, and Timing (PNT) services within the 5G non-terrestrial networks (NTN) infrastructure is necessary to eliminate the need for a GNSS receiver in the user terminal. Using the positioning reference signal (PRS) in an NTN scenario presents significant challenges, such as interference analysis from the transmission of multiple PRS signals. This study provides a stochastic model for the interference generated by PRS transmissions in a 5G NTN scenario. This model has been derived empirically from a Monte Carlo simulator designed specifically for this purpose, showing that the distribution that best fits the interference is a Generalized Extreme Value (GEV) distribution. The parameters of this distribution are also modeled based on the PRS configuration. Therefore, a designer can use this model to evaluate the probability of encountering certain levels of interference.

The 3rd Generation Partnership Project (3GPP) aims to integrate non-terrestrial networks (NTNs) with terrestrial 5G networks. In this context, prototyping and testing are essential to demonstrate the readiness of new 5G-NTN releases. However, commercial 5G protocol stack implementations are usually costly and closed solutions, which makes it challenging for universities and nonprofit research institutions to adapt and customize them for 5G-NTN testing and development purposes. Software-defined radio testbeds that utilize open-source software implementations are an attractive alternative. In this article, we describe our protocol stack adaptation of an open-source implementation for 5G-NTN based on OpenAirInterface (OAI), which runs on commodity hardware. We evaluate the performance of our 5G-NTN in various layers in real-world environments with a transparent geostationary satellite. We validate our testbed with a variety of Internet applications. These include streaming video, web browsing, and voice over IP. The results indicate that the current implementation utilizing the 5 MHz bandwidth is reliable over several hours and achieves both downlink and uplink speeds of 3.6 Mb/s while maintaining a typical geostationary latency of approximately 530 ms. The tests demonstrate that our 5G-NTN testbed is an reliable alternative to costly commercial implementations.

Reconfigurable intelligent surface (RIS) has appeared as a revolutionary candidate technology for future sixth-generation (6G) networks, but most works have relied on single-connected reflective RISs, which are mathematically described by diagonal phase shift matrices. In this work, based on the recently presented research on beyond diagonal (BD) RIS unifying different RIS models and architectures towards enhanced advantages such as a greater coverage, we study the impact of channel aging due to user equipment (UE) movement. Especially, we evaluate how channel aging diminishes the system performance of multiple sector BD-RIS systems. Through a robust design, concerning the average sum-rate maximisation problem, we jointly design the BD-matrix and transmit precoder under the channel aging conditions. Numerical results show how channel aging affects performance with respect to fundamental system parameters and shed light on how general aging can be compensated.

Terrestrial networks form the fundamental infrastructure of modern communication systems, serving more than 4 billion users globally. However, terrestrial networks are facing a wide range of challenges, from coverage and reliability to interference and congestion. As the demands of the 6G era are expected to be much higher, it is crucial to address these challenges to ensure a robust and efficient communication infrastructure for the future. To address these problems, Non-terrestrial Network (NTN) has emerged to be a promising solution. NTNs are communication networks that leverage airborne (e.g., unmanned aerial vehicles) and spaceborne vehicles (e.g., satellites) to facilitate ultra-reliable communications and connectivity with high data rates and low latency over expansive regions. This article aims to provide a comprehensive survey on the utilization of network slicing, Artificial Intelligence/Machine Learning (AI/ML), and Open Radio Access Network (ORAN) to address diverse challenges of NTNs from the perspectives of both academia and industry. Particularly, we first provide an in-depth tutorial on NTN and the key enabling technologies including network slicing, AI/ML, and ORAN. Then, we provide a comprehensive survey on how network slicing and AI/ML have been leveraged to overcome the challenges that NTNs are facing. Moreover, we present how ORAN can be utilized for NTNs. Finally, we highlight important challenges, open issues, and future research directions of NTN in the 6G era.

This paper provides a comprehensive overview of the evolution of Machine Learning (ML), from traditional to advanced, in its application and integration into unmanned aerial vehicle (UAV) communication frameworks and practical applications. The manuscript starts with an overview of the existing research on UAV communication and introduces the most traditional ML techniques. It then discusses UAVs as versatile actors in mobile networks, assuming different roles from airborne user equipment (UE) to base stations (BS). UAV have demonstrated considerable potential in addressing the evolving challenges of next-generation mobile networks, such as enhancing coverage and facilitating temporary hotspots but pose new hurdles including optimal positioning, trajectory optimization, and energy efficiency. We therefore conduct a comprehensive review of advanced ML strategies, ranging from federated learning, transfer and meta-learning to explainable AI, to address those challenges. Finally, the use of state-of-the-art ML algorithms in these capabilities is explored and their potential extension to cloud and/or edge computing based network architectures is highlighted.

An important part of designing non-geostationary satellite orbit (NGSO) constellations is to ensure that they can co-exist with the legacy geostationary satellite orbit (GSO) systems in terms of interference levels. According to the current radio regulations defined by the International Telecommunication Union (ITU), NGSO systems shall not cause unacceptable interference to GSO systems. However, the traditional interference avoidance methods considered for the NGSO systems impact the coverage and overall service quality of their users. Embracing the trends of equipping NGSO satellites with phased array antennas, this paper investigates downlink beamforming strategies to improve NGSO users’ service and mitigate the interference caused at GSO systems, ensuring seamless co-channel operation for NGSO and GSO network systems. Our proposed aggregate interference-constrained (AIC) beamforming optimization, limits the co-channel interference at GSO ground stations while minimizing the NGSO satellites’ transmitted power. The AIC beamformer is designed under average channel state information (aCSI) to consider a practical scenario in NGSO satellite communications where acquiring perfect instantaneous channel information is challenging due to satellite mobility and long propagation roundtrip. Furthermore, for a more general scenario, this study considers robust AIC beamforming to account for the uncertainty in GSO ground stations’ location when suppressing interference. Numerical results evidence the benefits of our proposed method ensuring service quality and providing the lowest probability for unaccepted interference levels at GSO ground stations among other benchmarks.

This article explores the performance of quantum communication systems in the presence of noise and focuses on finding the optimal encoding for maximizing the classical communication rate, approaching the classical capacity in some scenarios. Instead of theoretically bounding the ultimate capacity of the channel, we adopt a signal processing perspective to estimate the achievable performance of a physically available but otherwise unknown quantum channel. By employing a variational algorithm to estimate the trace distance between quantum states, we numerically determine the optimal encoding protocol for the amplitude damping and Pauli channels. Our simulations demonstrate the convergence and accuracy of the method with a few iterations, confirming that optimal conditions for binary quantum communication systems can be variationally determined with minimal computation. Furthermore, since the channel knowledge is not required at the transmitter or at the receiver, these results can be employed in arbitrary quantum communication systems, including satellite-based communication systems, a particularly relevant platform for the quantum Internet.

Recent advancements in onboard satellite communication have enhanced the capability of dynamically modifying the radiation pattern of a Direct Radiating Array (DRA). This is crucial not only for conventional communication satellites like Geostationary Orbit (GEO) but also for those in lower orbits such as Low Earth Orbit (LEO). Key design factors include the number of beams, beamwidth, Effective Isotropic Radiated Power (EIRP), and Side Lobe Level (SLL) for each beam. However, a challenge arises in multibeam scenarios when trying to simultaneously meet requirements for the aforementioned design factors which are reflected as uneven power distribution. This leads to over-saturation, especially in centrally located antenna elements due to the activation times per beam, commonly referred to as activation instances. In response to this challenge, this paper presents a method to balance the activation instances across antenna elements for each required beam. Our focus is on beams operating at 19 GHz on a CubeSat positioned 500 km above the Earth’s surface. We introduce a Genetic Algorithm (GA)-based algorithm to optimize the beamforming coefficients by modulating the amplitude component of the weight matrix for each antenna element. A key constraint of this algorithm is a limit on activation instances per element, which avoids over-saturation in the Radio Frequency (RF) chain. Additionally, the algorithm accommodates beam requirements such as beamwidth, SLL, pointing direction, and total available power. With the previous key design factors, the algorithm will optimize the required genes to address the desired beam characteristics and constraints. We tested the algorithm’s effectiveness in three scenarios using an $8\times 8$ DRA patch antenna with circular polarization, arranged in a triangular lattice. The results demonstrate that our algorithm not only meets the required beam pattern specifications but also ensures a uniform activation distribution across the antenna array.

Quantum technologies are increasingly recognized as groundbreaking advancements set to redefine the landscape of computing, communications, and sensing by leveraging quantum phenomena, like entanglement and teleportation. Quantum technologies offer an interesting set of advantages such as unconditional security, large communications capacity, unparalleled computational speed, and ultra-precise sensing capabilities. However, their global deployment faces challenges related to communication ranges and geographical boundaries. Non-terrestrial networks (NTNs) have emerged as a potential remedy for these challenges through providing free-space quantum links to circumvent the exponential losses inherent in fiber optics. Through the utilization of free-space optical (FSO) and Li-Fi links, NTNs provide an effective method for transmitting quantum states over extensive distances. This paper delves into the dynamic interplay between quantum technologies and NTNs to unveil their synergistic potential. We also investigate their integration challenges and the potential solutions to foster a symbiotic convergence of quantum and NTN functionalities while identifying avenues for enhanced interoperability. Specifically, quantum communication over NTNs imposes challenges in communication channel reliability, network flexibility, and scalability. To enhance channel reliability, multicarrier transmission techniques and spatial diversity strategies, including quantum MIMO (q-MIMO) transmission schemes, can be employed. Introducing the software defined networking (SDN) model facilitates flexible network configurations. In NTN-based quantum infrastructure, technologies like trusted relays, measurement device independent (MDI), and quantum repeaters can address scalability concerns efficiently. This paper not only offers useful insights into the mutual advantages but also presents future research directions, aiming to inspire additional studies and advance this interdisciplinary collaboration.

The emergence of the AI era signifies a shift in the future landscape of global communication networks, wherein robots are expected to play a more prominent role compared to humans. The establishment of a novel paradigm for the development of next-generation 6G communication is of utmost importance for semantics task-oriented empowered communications. The goal of semantic communication lies in the integration of collaborative efforts between the intelligence of the transmission source and the joint design of source coding and channel coding. This characteristic represents a significant benefit of joint source-channel coding (JSCC), as it enables the generation of source alphabets with diverse lengths and achieves a code rate of unity. Therefore, we leverage not only quasi-cyclic (QC) characteristics to facilitate the utilization of flexible structural hardware design but also Unequal Error Protection (UEP) to ensure the recovery of semantic importance. In this study, the feasibility of using a semantic encoder/decoder that is aware of UEP can be explored based on the existing JSCC system. This approach is aimed at protecting the significance of semantic task-oriented information. Additionally, the deployment of a JSCC system can be facilitated by employing QC-Low-Density Parity-Check (LDPC) codes on a reconfigurable device. The QC-LDPC layered decoding technique, which has been specifically optimized for hardware parallelism and tailored for channel decoding applications, can be suitably adapted to accommodate the JSCC system. The performance of the proposed system is evaluated by conducting BER measurements using both floating-point and 6-bit quantization. This is done to assess the extent of performance deterioration in a fair manner. The fixed-point system is synthesized and subsequently used as a semantic feature transmission and reception system across a noisy channel, with the aim of presenting a prototype for semantic communications. This study concludes with some insights and potential research avenues for the JSCC prototype in the context of future communication.

Synchronizing the local oscillators in multibeam satellites with the objective of coherent communications is still an open challenge. It has to be addressed to implement full-frequency reuse approaches, such as precoding techniques using the already deployed multibeam satellites. This article addresses the required phase synchronization to enable precoding techniques in multibeam satellite systems. It contains the detailed design of a frequency and phase compensation loop based on the proportional-integral controller, which deals with the phase drift introduced by the hardware components. Specifically, the phase noise of the local oscillators used for up and down conversion at each system element (gateway, satellite, and user terminals). The implementation of the two-state phase noise model used to emulate this phase drift is included in the article. Besides, a comparative analysis of several methods to combine the frequency and phase measurements obtained from the user terminals is also included. Finally, the performance of the proposed closed-loop synchronization method is validated through simulations using our in-house developed MIMO end-to-end satellite emulator based on SDR platforms.

A satellite internet provider (e.g., Starlink, OneWeb, O3b mPOWR), despite possessing the capability to provide internet services to on-ground users in a global scale, can dramatically change the way space missions are designed and operated in the foreseeable future. Assuming a scenario where space mission satellites can access the internet via a space internet system, the satellite can be connected to the network permanently (24 × 7) and act as mere terminal independently from its location. The ability to communicate with the satellite on-demand has the potential to improve aspects such as real-time tasking, outage minimization, operation cost, and dependency on the ground. This paper performs a feasibility study on the concept of connecting space missions to the network through commercial mega-constellations. This study includes a review of existing and near-future space internet systems, identification of candidate space missions for the aforementioned concept, a necessary adaptation of existing Commercial off-the-shelf (COTS) terminals to be plugged into space mission satellites, assessment of communication performance, and investigation of the legal aspects of the radio frequency (RF) spectrum usage. The paper evidences that the concept is practically possible to implement in the near future. Among the studied space internet systems (i.e., Starlink, OneWeb, O3b mPOWER), O3b mPOWER stands out as the most suitable system allowing permanent coverage of low earth orbit (LEO) space missions with data rates that can reach up to 21 Mbps per satellite. Although the concept is very promising and can be implemented in the near future, our investigations show that some regulatory aspects regarding the RF usage should be solved for future exploitation of connecting space missions through NGSO (Non-Geostationary Satellite Orbit) constellations.

This paper addresses the problem of scheduling the uplink bandwidth of Low Earth Orbit (LEO) satellites among multiple Internet of Things (IoT) slices with diverse Quality of Service (QoS) requirements. The scheduling process involves twofold decisions—the amount of bandwidth allocated and the allocation duration. Resource scheduling for satellite IoT services is challenging because of limited bandwidth availability during a satellite pass, especially for LEO satellites. Another challenge is to compute a fair allocation schedule for IoT services with different latency demands, packet transmission frequency, required data volume, and the number of IoT devices. To address these challenges, we propose a fairness-aware inter-slice scheduler for satellite IoT services in this work. The proposed scheduler computes service priority based on respective traffic demands. We propose two algorithms for the scheduler based on weighted greedy and Simulated Annealing (SA), respectively. The weighted greedy algorithm schedules the services greedily based on the priority order. The SA algorithm enhances the greedy solution by ensuring the allocated bandwidth is proportional to the respective priority values. The simulation results show that the proposed SA algorithm achieves up to 21.11% more proportional fairness than the Simulated Annealing and Monte Carlo (SA-MC) benchmark scheme.

In this paper, we investigate the demodulation and decoding of the symbol-level precoded (SLP) signal using the conventional logarithmic likelihood ratio (LLR) and low-density parity check (LDPC) decoding algorithms operating in real-time on the software-defined radio (SDR) receiver. Its conventional forward error correction (FEC) is designed assuming symmetric additive white Gaussian noise (AWGN). While SLP generates asymmetric constellation point clouds it is unclear if it can achieve similar FEC gains in an end-to-end coded setup. The receiver is implemented based on the DVB-S2X standard. It estimates multi-beam interference-limited channel coefficients by using the orthogonal pilots embedded into the DVB-S2X frames to facilitate precoding at the transmitter. After the received frames are fully synchronized the payload symbols are decoded in two stages: soft decision using the LLR and hard decision using the LDPC decoders. We benchmark the bit error rate (BER) and frame error rate (FER) of the complete decoding chain while using the zero-forcing (ZF) and the selected SLP channel-interference canceling techniques. The conducted in-lab experiments show that the SLP can improve BER and FER performance in the conventional receiving and decoding design for symmetric constellations while achieving its original optimization goals.

When fully implemented, sixth generation (6G) wireless systems will constitute intelligent wireless networks that enable not only ubiquitous communication but also high-accuracy localization services. They will be the driving force behind this transformation by introducing a new set of characteristics and service capabilities in which location will coexist with communication while sharing available resources. To that purpose, this survey investigates the envisioned applications and use cases of localization in future 6G wireless systems, while analyzing the impact of the major technology enablers. Afterwards, system models for millimeter wave, terahertz and visible light positioning that take into account both line-of-sight (LOS) and non-LOS channels are presented, while localization key performance indicators are revisited alongside mathematical definitions. Moreover, a detailed review of the state of the art conventional and learning-based localization techniques is conducted. Furthermore, the localization problem is formulated, the wireless system design is considered and the optimization of both is investigated. Finally, insights that arise from the presented analysis are summarized and used to highlight the most important future directions for localization in 6G wireless systems.

Intelligent reflecting surfaces (IRS) have emerged as a promising technology to enhance the performance of wireless communication systems. By actively manipulating the wireless propagation environment, IRS enables efficient signal transmission and reception. In recent years, the integration of IRS with full-duplex (FD) communication has garnered significant attention due to its potential to further improve spectral and energy efficiencies. IRS-assisted FD systems combine the benefits of both IRS and FD technologies, providing a powerful solution for the next generation of cellular systems. In this manuscript, we present a novel approach to jointly optimize active and passive beamforming in a multiple-input-multiple-output (MIMO) FD system assisted by an IRS for weighted sum rate (WSR) maximization. Given the inherent difficulty in obtaining perfect channel state information (CSI) in practical scenarios, we consider imperfect CSI and propose a statistically robust beamforming strategy to maximize the ergodic WSR. Additionally, we analyze the achievable WSR for an IRS-assisted MIMO FD system under imperfect CSI by deriving both the lower and upper bounds. To tackle the problem of ergodic WSR maximization, we employ the concept of expected weighted minimum mean squared error (EWMMSE), which exploits the information of the expected error covariance matrices and ensures convergence to a local optimum. We evaluate the effectiveness of our proposed design through extensive simulations. The results demonstrate that our robust approach yields significant performance improvements compared to the simplistic beamforming approach that disregards CSI errors, while also outperforming the robust half-duplex (HD) system considerably.

The number of non-geostationary orbit (NGSO) satellite constellations is growing persistently. Accordingly, co-channel interference with geostationary orbit (GSO) networks will inevitably affect the operation of NGSO and GSO satellites in the years to come. Towards protecting the well-established GSO systems from the interference produced by the newly launched NGSO satellite constellations, the international telecommunication union (ITU) has established regulations and evaluation metrics for the aggregated emitted power flux-densities from NGSO systems. In conformity with these regulations, the NGSO operators should avoid disruptive emissions, which could lead to acute service interruptions. To reduce the downlink interference levels and alleviate harmful service disconnectivity of satellite constellations, we propose a joint power and tilt control strategy to maximize user demand satisfaction. Our proposed method ensures that the equivalent power flux-density (EPFD) from satellite constellation satisfies the ITU regulatory limits while the radio resource management for the user service is optimized via power control and tilting. Simulation results analyzing EPFD levels and geographical dependencies and the performance of optimized LEO transmissions are presented, showing the benefit of the proposed technique in terms of service continuity and spectrum coexistence.

Onboard satellite communication systems generate and manage coverage beams over the Earth. Depending on data traffic requirements, the number of beams, side lobe levels, nulls, and EIRP, their beamwidth must be efficiently generated and managed. Therefore, this paper describes an approach for beam pattern synthesis applied to geostationary satellite communication systems. The beam pattern synthesis can generate beams with a beamwidth variation from 0.45° to 1.5°, which can be controlled independently for the two principal cuts. In addition, other requirements have been considered, e.g., latitude, and longitude, required EIRP, minimum and maximum side love levels for the two principal cuts, and nulling direction. The output of the synthesizer is a weight matrix with beamforming coefficients of the required beam. The direct radiating array in this contribution utilizes an open-ended waveguide antenna as unit cell elements with a period of $0.875\lambda _{0}$ designed to work in left-hand circular polarization in the frequency band from 17.7 to 20.1 GHz. Since this design is intended for high-data rates applications, the minimum beamwidth requirements are very narrow. Therefore, $36\times 36$ sub-arrays of $4\times 4$ unit cells with a period of $3.5\lambda $ are considered to accomplish the beamwidth requirements while maintaining reduced computational and time resources for the weight matrix calculation compared to the conventional counterpart of $144\times 144$ unit cells. The results show that the algorithm, which uses the surrogate optimizer, can compute the weight matrix and synthesize the beam with a slight deviation from the input data.

Grant-free non-orthogonal multiple access (GF-NOMA) has emerged as a promising access technology for the fifth generation and beyond wireless networks that enable ultra-reliable and low-latency communications (URLLC) to ensure low access latency and high connectivity density. Furthermore, designing energy-efficient (EE) resource allocation strategies is a crucial aspect of future cellular system development. Taking these goals into account, this paper proposes an EE sub-channel and power allocation strategy for URLLC-enabled GF-NOMA (URLLC-GF-NOMA) systems based on multi-agent (MA) deep reinforcement learning (MADRL). In particular, the URLLC-GF-NOMA methods using MA dueling double deep Q network (MA3DQN), MA double deep Q network (MA2DQN), and MA deep Q network (MADQN) techniques are designed to enable users to select the most appropriate sub-channel and transmission power for their communications. The aim is to build an efficient MADRL-based solution, ensuring rapid convergence with small signaling overhead, to maximize the network EE while fulfilling the URLLC requirements of all users. Simulation results show that the MADQN and MA2DQN methods, which have lower complexity than MA3DQN, are more appropriate for the URLLC-GF-NOMA systems under consideration. Moreover, our proposed methods exhibit superior convergence characteristics, a reduction in signaling overhead, and enhanced EE performance compared to other benchmark strategies.

In this paper we demonstrate end-to-end precoded multi-user multiple-input single-output communications over a live GEO satellite link. Precoded communications enable full frequency reuse schemes in satellite communications to achieve broader service availability and higher spectrum efficiency than with the conventional four-color and two-color reuse approaches. In this scope, we develop a novel over-the-air test-bed for end-to-end precoding validations. We were able to demonstrate precoded communications over an actual geostationary orbit multi-beam satellite using the DVB-S2X standard-compliant state-of-the-art gateway and user terminals. The developed system is capable of fully end-to-end real-time communications over a satellite link and demonstrates novel channel measurements and channel compensation techniques for differential frequency and phase tracking. It is shown, that by successfully canceling inter-user interference in the actual satellite link, the precoding improves the received SINR and increases the total system goodput in aggressive full frequency reuse scenarios.

Terahertz (THz) band is expected to be one of the key enabling technologies of the sixth generation (6G) wireless networks because of its abundant available bandwidth and very narrow beamwidth. Due to high frequency operations, electrically small array apertures are employed, and the signal wavefront becomes spherical in the near-field. Therefore, near-field signal model should be considered for channel acquisition in THz systems. Unlike prior works which mostly ignore the impact of near-field beam-squint (NB) and consider either narrowband scenario or far-field models, this paper introduces both a model-based and a model-free techniques for wideband THz channel estimation in the presence of NB. The model-based approach is based on orthogonal matching pursuit (OMP) algorithm, for which we design an NB-aware dictionary. The key idea is to exploit the angular and range deviations due to the NB. We then employ the OMP algorithm, which accounts for the deviations thereby ipso facto mitigating the effect of NB. We further introduce a federated learning (FL)-based approach as a model-free solution for channel estimation in a multi-user scenario to achieve reduced complexity and training overhead. Through numerical simulations, we demonstrate the effectiveness of the proposed channel estimation techniques for wideband THz systems in comparison with the existing state-of-the-art techniques.

For the demonstration of ultra-wideband bandwidth and pencil-beamforming, the terahertz (THz)-band has been envisioned as one of the key enabling technologies for the sixth generation networks. However, the acquisition of the THz channel entails several unique challenges such as severe path loss and beam-split. Prior works usually employ ultra-massive arrays and additional hardware components comprised of time-delayers to compensate for these loses. In order to provide a cost-effective solution, this paper introduces a sparse-Bayesian-learning (SBL) technique for joint channel and beam-split estimation. Specifically, we first model the beam-split as an array perturbation inspired from array signal processing. Next, a low-complexity approach is developed by exploiting the line-of-sight-dominant feature of THz channel to reduce the computational complexity involved in the proposed SBL technique for channel estimation (SBCE). Additionally, based on federated-learning, we implement a model-free technique to the proposed model-based SBCE solution. Further to that, we examine the near-field considerations of THz channel, and introduce the range-dependent near-field beam-split. The theoretical performance bounds, i.e., Cramér-Rao lower bounds, are derived both for near- and far-field parameters, e.g., user directions, beam-split and ranges. Numerical simulations demonstrate that SBCE outperforms the existing approaches and exhibits lower hardware cost.

Linear and symbol-level precoding in satellite communications have received increasing research attention thanks to their ability to tackle inter-beam interference, allowing the use of spectral resources more efficiently. However, there are still challenges and open questions regarding the implementation of practical precoding systems taking the phase uncertainties in estimating the channel state information into account. This work assesses the impact of phase variations and uncertainties inherent to the satellite communication system operating a precoded forward link. Specifically, we address the inability to measure at the user terminal, the absolute phase rotation introduced by the channel, and the transponder local oscillator phase noise effects on the precoding operations considering the use of frequency division multiplexing in the forward-uplink transmission. We formally demonstrate that the system performance for linear and non-linear precoding operations is not affected by the uncertainty in the phase measurements at the user terminal. Additionally, we show that using a single frequency reference for all the local oscillators at the transponder does not avoid the phase variations related to the frequency division multiplexing in the forward-uplink. This work demonstrates that these phase variations would not affect the system performance for an ideal zero-delay precoding loop. However, this is not feasible in practical scenarios, where the phase noise of the frequency reference at the transponder and the loop delay determine the impact on the system performance. We validate our results by simulations considering three frequency references with different stability levels in a typical geosynchronous orbit (GEO) satellite system. Our results suggest that practical implementations of multiuser-MISO precoding systems must include a differential phase synchronization loop to compensate for this performance degradation.

Communication system design has been traditionally guided by task-agnostic principles, which aim at efficiently transmitting as many correct bits as possible through a given channel. However, in the era of cyber-physical systems, the effectiveness of communications is not dictated simply by the bit rate, but most importantly by the efficient completion of the task in hand, e.g., controlling remotely a robot, automating a production line or collaboratively sensing through a drone swarm. In parallel, it is projected that by 2023, half of the worldwide network connections will be among machines rather than humans. In this context, it is crucial to establish a new paradigm for designing communication strategies for multi-agent cyber-physical systems. This is a daunting task, since it requires a combination of principles from information, communication, control theories and computer science in order to formalize a general framework for task-oriented communication designs. In this direction, this paper reviews and structures the relevant theoretical work across a wide range of scientific communities. Subsequently, it proposes a general conceptual framework for task-oriented communication design, along with its specializations according to targeted use cases. Furthermore, it provides a survey of relevant contributions in dominant applications, such as industrial internet of things, multi-unmanned aerial vehicle (UAV) systems, autonomous vehicles, distributed learning systems, smart manufacturing plants, 5G and beyond self-organizing networks, and tactile internet. Finally, this paper also highlights the most important open research topics from both the theoretical framework and application points of view.

Traffic modeling and prediction is a vital task for designing efficient resource allocation strategies in telecommunication networks. This is challenging because network traffic data exhibits complex nonlinear spatiotemporal interactions. Moreover, the data can have missing values when traffic statistic collection is unavailable in certain nodes. In this paper, we introduce a graph Gaussian Process (GP) model for this challenging problem. The GP is a Bayesian non-parametric model and highly flexible in capturing complex patterns in the data. Additionally, it provides uncertainty information which can be exploited for robust resource allocation problems. The developed graph GP model is almost free of hyper-parameter tuning, can accurately capture short-term and long-term temporal patterns and can infer missing values by learning spatiotemporal interactions among the nodes in the network. Subsequently, we approximate the intractable posterior distribution using Variational Bayes (VB) algorithm which can be efficiently implemented. Finally, we evaluate the accuracy of the proposed model for predicting the data traffic using two real-world network datasets. Our simulation results shows that the proposed model can achieve better prediction accuracy with respect to the state-of-the-art approaches.

The combination of radio-frequency (RF) communication and underwater optical wireless communication (UOWC) plays a vital role in the underwater Internet of Things (UIoT). This correspondence proposes a dual-hop hybrid satellite underwater system that exploits non-orthogonal multiple access (NOMA) as a spectrum-efficient access technique. The RF link from the satellite to the relay on an oil platform is presumptively subject to a Shadowed-Rician (SR) fading, while the UOWC channels from the relay to the underwater destinations are suggested to follow Exponential-Generalized Gamma (EGG) distributions. The reliability of the system is characterized in terms of both underwater destinations and system outage probabilities (OPs). We derive new closed-form expressions for the OPs under imperfect successive interference cancellation (SIC) conditions. Furthermore, the asymptotic OP and the diversity order (DO) are obtained to learn more about the system’s performance. The results are verified through an extensive representative Monte-Carlo simulation. Also, we investigate the performance against the turbulence of the salty water, air bubbles level (BL), temperature gradients (TG), shadowing parameters, and satellite pointing errors due to satellite motion, even if the beam is pointed at the center of the directive antenna relay, the beam will randomly oscillate. Finally, we contrast our approach with the conventional orthogonal multiple access (OMA) scheme to demonstrate its superiority.

Beam hopping (BH) and carrier aggregation (CA) are two promising technologies for the next generation satellite communication systems to achieve several orders of magnitude increase in system capacity and to significantly improve the spectral efficiency. While BH allows a great flexibility in adapting the offered capacity to the heterogeneous demand, CA further enhances the user quality-of-service (QoS) by allowing it to pool resources from multiple adjacent beams. In this paper, we consider a multi-beam high throughput satellite (HTS) system that employs BH in conjunction with CA to capitalize on the mutual interplay between both techniques. Particularly, an innovative joint BH-CA scheme is proposed and analyzed in this work to utilize their individual competencies. This includes designing an efficient joint time-space beam illumination pattern for BH and multi-user aggregation strategy for CA. Through this, user-carrier assignment, transponder filling-rates, beams hopping pattern, and illumination duration are all simultaneously optimized by formulating a joint optimization problem as a multi-objective mixed integer linear programming problem (MINLP). Simulation results are provided to corroborate our analysis, demonstrate the design tradeoffs, and point out the potentials of the proposed joint BH-CA concept. Advantages of our BH-CA scheme versus the conventional BH method without employing CA are investigated and presented under the same system circumstances.

In recent years, dynamic traffic demand requisites have driven the satellite communication service providers to implement reconfigurable demand-driven features to align the delivered throughput with the temporal and geographical variations of the traffic demand. Also, in current interference-limited High Throughput Satellite (HTS) systems, the resulting inter-beam co-channel interference can be mitigated by carefully performing precoding and user scheduling. Unfortunately, the conventional user scheduling algorithms fail to provide demand satisfaction for dynamic traffic demand requisites. Hence, in this paper, we focus on the user scheduling design for precoded satellite systems where both co-channel interference and user demands are taken into account. In particular, we first classify the sectors in each beam according to the interference they may cause to neighboring beams. Next, we formulate the scheduling problem such as the activation of neighboring beam sectors is avoided while proportionally dwelling on the sectors based on their traffic demands. The supporting numerical results for different demand distribution profiles validate the effectiveness of proposed interference-aware demand-based user scheduling over conventional scheduling techniques.

Various applications for inter-machine communications are on the rise. Whether it is for autonomous driving vehicles or the Internet of everything, machines are more connected than ever to improve their performance in fulfilling a given task. While in traditional communications the goal has often been to reconstruct the underlying message, under the emerging task-oriented paradigm, the goal of communication is to enable the receiving end to make more informed decisions or more precise estimates/computations. Motivated by these recent developments, in this paper, we perform an indirect design of the communications in a multi-agent system (MAS) in which agents cooperate to maximize the averaged sum of discounted one-stage rewards of a collaborative task. Due to the bit-budgeted communications between the agents, each agent should efficiently represent its local observation and communicate an abstracted version of the observations to improve the collaborative task performance. We first show that this problem can be approximated as a form of data-quantization problem which we call task-oriented data compression (TODC). We then introduce the state-aggregation for information compression algorithm (SAIC) to solve the formulated TODC problem. It is shown that SAIC is able to achieve near-optimal performance in terms of the achieved sum of discounted rewards. The proposed algorithm is applied to a geometric consensus problem and its performance is compared with several benchmarks. Numerical experiments confirm the promise of this indirect design approach for task-oriented multi-agent communications.

The scarce spectrum and power resources, the inter-beam interference, together with the high traffic demand, pose new major challenges for the next generation of Very High Throughput Satellite (VHTS) systems. Accordingly, future satellites are expected to employ advanced resource/interference management techniques to achieve high system spectrum efficiency and low power consumption while ensuring user demand satisfaction. This paper proposes a novel demand and interference aware adaptive resource management for geostationary (GEO) VHTS systems. For this, we formulate a multi-objective optimization problem to minimize the total transmit power consumption and system bandwidth usage while matching the offered capacity with the demand per beam. In this context, we consider resource management for a system with full-precoding, i.e., all beams are precoded; without precoding, i.e., no precoding is applied to any beam; and with partial precoding, i.e., only some beams are precoded. The nature of the problem is non-convex and we solve it by jointly using the Dinkelbach and Successive Convex Approximation (SCA) methods. The simulation results show that the proposed method outperforms the benchmark schemes. Specifically, we show that the proposed method requires low resource consumption, low computational time, and simultaneously achieves a high demand satisfaction.

Cohesive Distributed Satellite Systems (CDSSs) is a key enabling technology for the future of remote sensing and communication missions. However, they have to meet strict synchronization requirements before their use is generalized. When clock or local oscillator signals are generated locally at each of the distributed nodes, achieving exact synchronization in absolute phase, frequency, and time is a complex problem. In addition, satellite systems have significant resource constraints, especially for small satellites, which are envisioned to be part of the future CDSSs. Thus, the development of precise, robust, and resource-efficient synchronization techniques is essential for the advancement of future CDSSs. In this context, this survey aims to summarize and categorize the most relevant results on synchronization techniques for Distributed Satellite Systems (DSSs). First, some important architecture and system concepts are defined. Then, the synchronization methods reported in the literature are reviewed and categorized. This article also provides an extensive list of applications and examples of synchronization techniques for DSSs in addition to the most significant advances in other operations closely related to synchronization, such as inter-satellite ranging and relative position. The survey also provides a discussion on emerging data-driven synchronization techniques based on Machine Learning (ML). Finally, a compilation of current research activities and potential research topics is proposed, identifying problems and open challenges that can be useful for researchers in the field.

This paper considers Low Earth Orbit (LEO) satellite- and cache-assisted unmanned aerial vehicle (UAV) communications for content delivery in terrestrial networks, which shows great potential for next-generation systems to provide ubiquitous connectivity and high capacity. Specifically, caching is provided by the UAV to reduce backhaul congestion, and the LEO satellite supports the UAV’s backhaul link. In this context, we aim to maximize the minimum achievable throughput per ground user (GU) by jointly optimizing cache placement, the UAV’s resource allocation, and trajectory while cache capacity and flight time are limited. The formulated problem is challenging to solve directly due to its non-convexity and combinatorial nature. To find a solution, the problem is decomposed into three sub-problems: (1) cache placement optimization with fixed UAV resources and trajectory, followed by (2) the UAV resources optimization with fixed cache placement vector and trajectory, and finally, (3) we optimize the UAV trajectory with fixed cache placement and UAV resources. Based on the solutions of sub-problems, an efficient alternating algorithm is proposed utilizing the block coordinate descent (BCD) and successive convex approximation (SCA) methods. Simulation results show that the max-min throughput and total achievable throughput enhancement can be achieved by applying our proposed algorithm instead of other benchmark schemes.

Edge-caching is an effective solution to cope with the unprecedented data traffic growth by storing contents in the vicinity of end-users. In this paper, we formulate a hierarchical caching policy where the end-users and cellular base station (BS) are equipped with limited cache capacity with the objective of minimizing the total data traffic load in the network. The caching policy is a nonlinear combinatorial programming problem and difficult to solve. To tackle the issue, we design a heuristic algorithm as an approximate solution which can be solved efficiently. Moreover, to proactively serve the users, it is of high importance to extract useful information from data requests and predict user interest about contents. In practice, the data often contain implicit feedback from users which is quite noisy and complicates the reliable prediction of user interest. In this regard, we introduce a Bayesian Poisson matrix factorization model which utilizes the available side information about contents to effectively filter out the noise in the data and provide accurate prediction. Subsequently, we design an efficient Markov chain Monte Carlo (MCMC) method to perform the posterior approximation. Finally, a real-world dataset is applied to the proposed proactive caching-prediction scheme and our results show significant improvement over several commonly-used methods. For example, when the BS and the users have caches with storage of 25% and 10% of the total contents size respectively, our approach yields around 8% improvement with respect to the state-of-the-art approach in terms of caching performance.

Ambient backscatter communication (AmBC) is an emerging technology that has the potential to offer spectral- and energy-efficient solutions for the next generation wireless communications networks, especially for the Internet of Things (IoT). Intelligent reflecting surfaces (IRSs) are also perceived to be an integral part of the beyond 5G systems to complement the traditional relaying scheme. To this end, this paper proposes a novel system design that enables the co-existence of a backscattering secondary system with the legacy primary system. This co-existence is primarily driven by leveraging the AmBC technique in IRS-assisted unmanned aerial vehicle (UAV) networks. More specifically, an aerial-IRS mounted on a UAV is considered to be employed for cooperatively relaying the transmitted signal from a terrestrial primary source node to a user equipment on the ground. Meanwhile, capitalizing on the AmBC technology, a backscatter capable terrestrial secondary node transmits its information to a terrestrial secondary receiver by modulating and backscattering the ambient relayed radio frequency signals from the UAV-IRS. We comprehensively analyze the performance of the proposed design framework with co-existing systems by deriving the outage probability and ergodic spectral efficiency expressions. Moreover, we also investigate the asymptotic behavior of outage performance in high transmit power regimes for both primary and secondary systems. Importantly, we analyze the performance of the primary system by considering two different scenarios, i.e., optimal phase shifts design and random phase shifting at IRS. Finally, based on the analytical performance assessment, we present numerical results to provide various useful insights and also provide simulation results to corroborate the derived theoretical results.

Abstract Physical layer security (PLS) has been proposed to afford an extra layer of security on top of the conventional cryptographic techniques. Unlike the conventional complexity-based cryptographic techniques at the upper layers, physical layer security exploits the characteristics of wireless channels, e.g., fading, noise, interference, etc., to enhance wireless security. It is proved that secure transmission can benefit from fading channels. Accordingly, numerous researchers have explored what fading can offer for physical layer security, especially the investigation of physical layer security over wiretap fading channels. Therefore, this paper aims at reviewing the existing and ongoing research works on this topic. More specifically, we present a classification of research works in terms of the four categories of fading models: (i) small-scale, (ii) large-scale, (iii) composite, and (iv) cascaded. To elaborate these fading models with a generic and flexible tool, three promising candidates, including the mixture gamma (MG), mixture of Gaussian (MoG), and Fox’s H-function distributions, are comprehensively examined and compared. Their advantages and limitations are further demonstrated via security performance metrics, which are designed as vivid indicators to measure how perfect secrecy is ensured. Two clusters of secrecy metrics, namely (i) secrecy outage probability (SOP), and the lower bound of SOP; and (ii) the probability of nonzero secrecy capacity (PNZ), the intercept probability, average secrecy capacity (ASC), and ergodic secrecy capacity, are displayed and, respectively, deployed in passive and active eavesdropping scenarios. Apart from those, revisiting the secrecy enhancement techniques based on Wyner’s wiretap model, the on-off transmission scheme, jamming approach, antenna selection, and security region are discussed.

Abstract Hybrid processing in millimeter wave (mmWave) communication has been proposed as a solution to reduce the cost and energy consumption by reducing the number of radio‐frequency (RF) chains. However, the impact of the inevitable residual transceiver hardware impairments (RTHIs), including the residual additive transceiver hardware impairments (RATHIs) and the amplified thermal noise (ATN), has not been sufficiently studied in mmWave hybrid processing. In this work, the hybrid precoder and combiner are designed, which include both digital and analog processing by taking into account the RATHIs and the ATN. In particular, a thorough study is provided to shed light on the degradation of the spectral efficiency (SE) of the practical system. The outcomes show the steady degradation of the performance by the ATN across all SNR values, which becomes increasingly critical for higher values of its variance. Furthermore, it is shown that RATHIs result in degradation of the system only in the high SNR regime. Hence, their impact in mmWave system operating at low SNRs might be negligible. Moreover, an increase concerning the number of streams differentiates the impact between the transmit and receive RATHIs with the latter having a more severe effect.

Abstract In unmanned aerial vehicle (UAV)-assisted networks, UAV acts as an aerial base station which acquires the requested data via backhaul link and then serves ground users (GUs) through an access network. In this paper, we investigate an energy minimization problem with a limited power supply for both backhaul and access links. The difficulties for solving such a non-convex and combinatorial problem lie at the high computational complexity/time. In solution development, we consider the approaches from both actor-critic deep reinforcement learning (AC-DRL) and optimization perspectives. First, two offline non-learning algorithms, i.e., an optimal and a heuristic algorithms, based on piecewise linear approximation and relaxation are developed as benchmarks. Second, toward real-time decision-making, we improve the conventional AC-DRL and propose two learning schemes: AC-based user group scheduling and backhaul power allocation (ACGP), and joint AC-based user group scheduling and optimization-based backhaul power allocation (ACGOP). Numerical results show that the computation time of both ACGP and ACGOP is reduced tenfold to hundredfold compared to the offline approaches, and ACGOP is better than ACGP in energy savings. The results also verify the superiority of proposed learning solutions in terms of guaranteeing the feasibility and minimizing the system energy compared to the conventional AC-DRL.

This paper proposes and validates a low-complexity FPGA design for symbol-level precoding (SLP) in multiuser multiple-input single-output (MISO) downlink communication systems. In the optimal case, the symbol-level precoded transmit signal is obtained as the solution to an optimization problem tailored for a given set of users' data symbols. This symbol-by-symbol design, however, imposes excessive computational complexity on the system. To alleviate this issue, we aim to reduce the per-symbol complexity of the SLP scheme by developing an approximate yet computationally-efficient closed-form solution. The proposed solution allows us to achieve a high symbol throughput in real-time implementations. To develop the FPGA design, we express the proposed solution in an algorithmic way and translate it to hardware description language (HDL). We then optimize the processing to accelerate the performance and generate the corresponding intellectual property (IP) core. We provide the synthesis report for the generated IP core, including performance and resource utilization estimates and interface descriptions. To validate our design, we simulate an uncoded transmission over a downlink multiuser channel using the LabVIEW software, where the SLP IP core is implemented as a clock-driven logic (CDL) unit. Our simulation results show that a throughput of 100 Mega symbols per second per user can be achieved via the proposed SLP design. We further use the MATLAB software to produce numerical results for the conventional zero-forcing (ZF) and the optimal SLP techniques as benchmarks for comparison. Thereby, it is shown that the proposed FPGA implementation of SLP offers an improvement of up to 50 percent in power efficiency compared to the ZF precoding. Remarkably, it enjoys the same per-symbol complexity order as that of the ZF technique. We also evaluate the loss of the real-time SLP design, introduced by the algebraic approximations and arithmetic inaccuracies, with respect to the optimal scheme.

This paper begins with the characterization of the sum of M versions of cascaded α-μ (equivalently, N * (α-μ)) distributed random variates (RVs), and later on, explores the feasibility and applicability of the obtained results in the recent proposed reconfigurable intelligent surface (RIS)-assisted communications. More specifically, the sum of cascaded α-μ distributed RVs is exactly characterized by the probability density function (PDF) expressed in terms of the programmable multivariate Fox's H-function, and approximated with the mixture of Gaussian (MoG) distribution. The approximation accuracy is further validated by the Kolmogorov-Smirnov (KS) goodness of fit test. Based on the obtained statistical results, a promising application, i.e., RIS-assisted communication, is considered and evaluated from the effective rate perspective. The exact, asymptotic, and approximated effective rate are derived with closed-form expressions. Finally, our analytical results are further validated using the Monte-Carlo simulation, their accuracy is confirmed with the analytical error indicator. One can conclude that (i) the exact effective rate expression is complex in the manner of mathematical representation; (ii) the MoG-based effective rate expression is simple and provides a highly tight approximation to the exact expression, besides it indeed acts as a highly tight upper bound for the exact effective rate expression; and (iii) the MoG-based solution outperforms the multivariate Fox's H-function based approach with reducing complexity.

This work discusses the optimal reconfigurable intelligent surface placement in highly-directional millimeter wave links. In particular, we present a novel system model that takes into account the relationship between the transmission beam footprint at the RIS plane and the RIS size. Subsequently, based on the model we derive the end-to-end expression of the received signal power and, furthermore, provide approximate closed-form expressions in the case that the RIS size is either much smaller or at least equal to the transmission beam footprint. Moreover, building upon the expressions, we derive the optimal RIS placement that maximizes the end-to-end signal-to-noise ratio. Finally, we substantiate the analytical findings by means of simulations, which reveal important trends regarding the optimal RIS placement according to the system parameters.

Content caching at base stations is an effective solution to cope with the unprecedented data traffic growth by prefetching contents near to end-users. To proactively servicing users, it is of high importance to extract predictive information from data requests. In this paper, we propose an accurate content request prediction algorithm for improving the performance of edge caching systems. In particular, we develop a Bayesian dynamical model through which a complex nonlinear latent temporal trend structure in the content requests can be accurately tracked and predicted. The dynamical model also leverages tensor train decomposition to capture content-location interactions to further enhance the accuracy of predictions. To infer the model’s parameters, we derive an approximation of the posterior distribution based on variational Bayes (VB) method with an embedded Kalman smoother algorithm. Based on the predictions of the proposed model, we design a cost-efficient proactive cooperative caching policy which adaptively utilizes network resources and optimizes the content delivery. The advantage of the proposed caching scheme is demonstrated via numerical results using two real-world datasets, which show that the developed Bayesian dynamical model substantially outperforms reference methods that ignore the temporal trends and content-location interactions.

Phase, frequency, and time synchronization are crucial requirements for many applications, such as multistatic remote sensing and communication systems. Moreover, the synchronization solution becomes even more challenging when the nodes are orbiting or flying on airborne or spaceborne platforms. This article compares the available technologies used for the synchronization and coordination of nodes in distributed remote sensing applications. Additionally, this article proposes a general system model and identifies preliminary guidelines and critical elements for implementing the synchronization mechanisms exploiting the intersatellite communication link. The distributed phase synchronization loop introduced in this work deals with the self-interference in a full-duplex point to point scenario by transmitting two carriers at each node. All carriers appear with different frequency offsets around a central frequency, called the application central-frequency or the beamforming frequency. This article includes a detailed analysis of the proposed algorithm and the required simulations to verify its performance for different phase noise, additive white Gaussian noise, and Doppler shift scenarios.

The current broadband coverage area requisites and the expected user demand is satisfied by the state of the art satellite industry by using multiple spot beams of high throughput satellites with fixed multi-beam pattern and footprint planning. However, in recent years, new mobile broadband users with dynamic traffic demand are requesting for services in remote geographical locations such as air (aeroplanes) and water (ships). Furthermore, the expected demand varies with time and geographical location of the users. Hence, a practical approach to meet such heterogeneous demand is to plan adaptive beams to the satellites equipped with beamforming capabilities. In this paper, we study the state of the art fixed multi-beam pattern and footprint plan and show its drawbacks to support the non-uniformly distributed user terminals and varying traffic demands. To end this, we propose an adaptive multi-beam pattern and footprint plan where we design spot beams with flexible size and position based on the spatial clustering of the users in order to increase the flexibility of the high throughput satellite system. Numerical simulations demonstrate the high system performance of the proposed methodology.

In this work, we consider secure communications in wireless multi-user (MU) multiple-input single-output (MISO) systems with channel coding in the presence of a multi-antenna eavesdropper (Eve), who is a legit user trying to eavesdrop other users. In this setting, we exploit machine learning (ML) tools to design soft and hard decoding schemes by using precoded pilot symbols as training data. The proposed ML frameworks allow an Eve to determine the transmitted message with high accuracy. We thereby show that MU-MISO systems are vulnerable to such eavesdropping attacks even when relatively secure transmission techniques are employed, such as symbol-level precoding (SLP). To counteract this attack, we propose two novel SLP-based schemes that increase the bit-error rate at Eve by impeding the learning process. We design these two security-enhanced schemes to meet different requirements regarding runtime, security, and power consumption. Simulation results validate both the ML-based eavesdropping attacks as well as the countermeasures, and show that the gain in security is achieved without affecting the decoding performance at the intended users.

Non-terrestrial Networks (NTNs) have become an appealing concept over the last few years and they are foreseen as a cornerstone for the next generations of mobile communication systems. Despite opening up new market opportunities and use cases for the future, the novel impairments caused by the signal propagation over the NTN channel, compromises several procedures of the current cellular standards. One of the first and most important procedures impacted is the random access (RA) procedure, which is mainly utilized for achieving uplink synchronization among users in several standards, such as the fourth and fifth generation of mobile communication (4 & 5G) and narrowband internet of things (NB-IoT). In this work, we analyse the challenges imposed by the considerably increased delay in the communication link on the RA procedure and propose new solutions to overcome those challenges. A trade-off analysis of various solutions is provided taking into account also the already existing ones in the literature. In order to broaden the scope of applicability, we keep the analysis general targeting 4G, 5G and NB-IoT systems since the RA procedure is quasi-identical among these technologies. Last but not least, we go one step further and validate our techniques in an experimental setup, consisting of a user and a base station implemented in open air interface (OAI), and an NTN channel implemented in hardware that emulates the signal propagation delay. The laboratory test-bed built in this work, not only enables us to validate various solutions, but also plays a crucial role in identifying novel challenges not previously treated in the literature. Finally, an important key performance indicator (KPI) of the RA procedure over NTN is shown, which is the time that a single user requires to establish a connection with the base station.

We consider a two-way half-duplex decode-and-forward (DF) relaying system with multiple pairs of single-antenna users assisted by a cell-free (CF) massive multiple-input multiple-output (mMIMO) architecture with multiple-antenna access points (APs). Under the practical constraint of imperfect channel state information (CSI), we derive the achievable sum spectral efficiency (SE) for a finite number of APs with maximum ratio (MR) linear processing for both reception and transmission in closed-form. Notably, the proposed CF mMIMO relaying architecture, exploiting the spatial diversity, and providing better coverage, outperforms the conventional collocated mMIMO deployment. Moreover, we shed light on the power-scaling laws maintaining a specific SE as the number of APs grows. A thorough examination of the interplay between the transmit powers per pilot symbol and user/APs takes place, and useful conclusions are extracted. Finally, differently to the common approach for power control in CF mMIMO systems, we design a power allocation scheme maximizing the sum SE.