We propose ECPv2, a scalable and theoretically grounded algorithm for global optimization of Lipschitz continuous functions with unknown Lipschitz constants. Building on the Every Call is Precious (ECP) framework, which ensures that each accepted function evaluation is potentially informative, ECPv2 addresses key limitations of ECP, including high computational cost and overly conservative early behavior. ECPv2 introduces three innovations: (i) an adaptive lower bound that prevents vacuous acceptance regions, (ii) a memory mechanism that restricts comparisons to a fixed-size subset of past evaluations, and (iii) a fixed random projection that accelerates distance computations in high dimensions. We theoretically show that ECPv2 retains ECP’s regret guarantees and expands the acceptance region with high probability. Extensive experiments and ablation studies empirically validate these findings. Using principled hyperparameter settings, we evaluate ECPv2 across a wide range of nonconvex optimization problems and find that it consistently matches or outperforms leading optimizers while significantly reducing wall clock time.

In today’s digital age, access to the Internet is essential, yet a significant digital divide exists, particularly in rural areas of developing nations. This paper presents a Delay Tolerant Networking (DTN) framework that utilizes informal public transportation systems, such as minibus taxis, as mobile data mules to enhance connectivity in these underserved regions. We develop a probabilistic model to capture the randomness in vehicle mobility, including travel times and contact durations at bus stops. Key performance metrics are analyzed, including average data transmission rate and Peak Age of Information (PAoI), to assess the effectiveness of the proposed system. An analytical approximation for the Mean PAoI (MPAoI) is derived and validated through simulations. Case studies from real-world datasets in Nouakchott, Accra, and Addis Ababa demonstrate the practical applicability and scalability of our framework. The findings indicate that leveraging existing transportation networks can significantly bridge the digital divide by providing reliable internet-like connectivity to remote areas.

In post-disaster scenarios, the rapid deployment of adequate communication infrastructure is essential to support disaster search, rescue, and recovery operations. To achieve this, uncrewed aerial vehicle (UAV) has emerged as a promising solution for emergency communication due to its low cost and deployment flexibility. However, conventional untethered UAV (U-UAV) is constrained by size, weight, and power (SWaP) limitations, making it incapable of maintaining the operation of a macro base station. To address this limitation, we propose a heterogeneous UAV-based framework that integrates tethered UAV (T-UAV) and U-UAVs, where U-UAVs are utilized to enhance the throughput of cell-edge ground user equipments (G-UEs) and guarantee seamless connectivity during G-UEs’ mobility to safe zones. It is noted that the integrated access and backhaul (IAB) technique is adopted to support the wireless backhaul of U-UAVs. Accordingly, we formulate a two-timescale joint user scheduling and trajectory control optimization problem, aiming to maximize the downlink throughput under asymmetric traffic demands and G-UEs’ mobility. To solve the formulated problem, we proposed a two-timescale multi-agent deep deterministic policy gradient (TTS-MADDPG) algorithm based on the centralized training and distributed execution paradigm. Numerical results show that the proposed algorithm outperforms other benchmarks, including the two-timescale multi-agent proximal policy optimization (TTS-MAPPO) algorithm and MADDPG scheduling method, with robust and higher throughput. Specifically, the proposed algorithm obtains up to 12.2% average throughput gain compared to the MADDPG scheduling method.

Underwater Wireless Optical Communication (UOWC) is a key enabling technology for future space-air-ground-sea integrated networks. However, UOWC faces critical hurdles from spatial randomness and stringent energy constraints. These challenges fundamentally limit network lifetime and sustainability. This paper develops a comprehensive stochastic geometry framework to perform a differential energy analysis of UOWC links.Instead of relying on simplified models, we employ a three-dimensional truncated Poisson point process (TPPP) to accurately capture the anisotropic nature of the underwater environment, specifically the disparity between horizontal spread and vertical depth. It incorporates a Lambertian emission pattern, random receiver positions and orientations, and a realistic channel model with extinction effects. Under this model, we derive a full suite of closed-form expressions for key performance indicators. These include the nearest-neighbor distance distribution, expected received power, signal-to-noise ratio (SNR), and bit error rate (BER). A principal and counter-intuitive finding of our analysis is an “offset-pointing” strategy. This strategy involves intentionally misaligning the receiver by a deterministically optimal angle. This approach maximizes the integrated received power across the aperture, contrary to the conventional pursuit of perfect alignment. We formulate and solve an energy-efficiency optimization problem. Our results demonstrate that this strategy enhances system robustness and yields substantial performance gains. Simulation results validate our analytical models. They show that the optimal offset strategy can reduce the required transmit power by nearly 20% to achieve a target BER. This reduction directly translates into extended network lifetime and higher total data throughput. These findings offer a new design paradigm for deploying robust, cost-effective, and sustainable UOWC networks.

The use of unlicensed spectrum offers a promising solution to spectrum scarcity in densely populated areas and a cost-effective way to connect underserved regions. Recognizing this potential, both academia and industry are actively exploring innovative uses of unlicensed spectrum. This work presents a broad overview of unlicensed spectrum bands below 8 GHz, including TV White Spaces, Civil Broadband Radio Services, Industrial, Scientific, and Medical bands, and the Unlicensed National Information Infrastructure. The paper examines three main aspects: regulations, existing technologies, and applications. Regulations play a central role, as understanding these rules is vital since “unlicensed” does not mean “unregulated”. From a technological perspective, we review current technologies, standards, and products, their features, and related applications. Furthermore, the shared nature of these spectrum bands introduces challenges related to user interference. The communication collisions can be managed through two primary strategies we described: a database-driven approach and coexistence mechanisms at the MAC and PHY layers. This work may serve as a starting point for those interested in the unlicensed spectrum, both in academia and industry.

Reconfigurable intelligent surfaces (RISs) have emerged as a key enabling technology for next-generation (xG) Internet-of-Things (IoT) networks operating in high-frequency bands such as terahertz (THz). Due to the large number of reflecting elements, RIS-assisted communications predominantly occur in the near-field region, where signal propagation is governed by spherical wavefronts. This far-field to near-field transition induces a nonlinear coupling between the channel response and the user position, which significantly complicates channel estimation and localization in RIS-assisted THz systems. Existing approaches typically address these two tasks separately, leading to redundant processing and error propagation between channel and position domains. To overcome these limitations, we propose a unified framework, termed near-field unified channel estimation and localization (NF-UCL), that exploits the intrinsic bijective correspondence between the near-field channel and the user position. We introduce the concept of a near-field channel map and show that its image, termed the near-field channel manifold, exhibits a smooth Riemannian structure. This geometric characterization enables the application of manifold optimization techniques for efficient joint processing. Specifically, we develop an Riemannian conjugate gradient (RCG)-based algorithm that performs channel estimation and localization directly on the near-field channel manifold. Simulation results demonstrate that the proposed NF-UCL framework outperforms conventional methods in terms of channel normalized mean square error (NMSE) and user position root mean square error (RMSE).

In underwater target localization (UTL), the accuracy of distance measurement between anchor nodes and the target is a critical factor that directly affects localization performance. This is especially true for localization methods based on time of arrival (TOA) and time difference of arrival (TDOA), where the acoustic signal propagation time is converted into distance to estimate the target’s position. However, in most existing literature, the distance measurement noise, regardless of whether it is derived from TOA or TDOA, is commonly modeled as Gaussian. In this paper, we propose that the distance noise in UTL is better characterized by a shifted-Gamma distribution. Specifically, we establish a TOA-based UTL model and show that the overall distance error arises from four independent sources: 1) anchor node position error, 2) signal path bending due to refraction, 3) uncertainty in sound velocity, and 4) time synchronization error. Assuming each of these error sources follows a basic Gaussian distribution, we derive the resulting composite distance error, which is then approximated by a shifted-Gamma distribution. Monte Carlo simulation results validate the accuracy of this approximation and reflect its consistency with practical measurement noise. Furthermore, the Cramer–Rao Lower Bound (CRLB) for the UTL accuracy is derived based on the proposed noise model with the derived shifted-Gamma distribution. Across representative geometries and noise ranges, the shifted-Gamma noise model provides tighter Cramér-Rao lower bounds and lower localization error than a Gaussian noise model. The model supports noise-aware estimator design and anchor geometry selection in underwater target localization systems, and improves robustness in environments with strong non-Gaussian disturbances.

The evolution of communication network architectures is steering towards more sustainable, flexible, and lightweight designs, particularly with the advent of sixth-generation (6G) mobile communications. Spectrum-rich optical integrated systems are anticipated to play a crucial role in this transformation, offering significant advantages such as high data rates, reduced interference, and improved energy efficiency. This paper introduces and experimentally demonstrates a novel optical integrated communication, sensing, and power transfer (O-ICSPT) system. The proposed system integrates optical wireless communication, sensing, and wireless power transfer into a multifunctional framework, addressing the limitations of existing systems in terms of flexibility and resource utilization. The experimental setup investigates the effects of bias current, peak-to-peak voltage, and light source wavelength on the performance of each functional module. Experimental results indicate that the O-ICSPT system achieves a maximum data rate of approximately 632.58 Mbps, a best ranging root mean square error approaching 0 m, and a peak energy harvesting capability of about 10.02 mW. These findings underscore the potential of the O-ICSPT system in future 6G integrated communication networks, marking the first experimental validation of such a system.

The growing demand for intelligent, adaptive resource management in next-generation wireless networks has underscored the importance of accurate and scalable wireless traffic prediction. While recent advancements in deep learning and foundation models such as large language models (LLMs) have demonstrated promising forecasting capabilities, they largely overlook the spatial dependencies inherent in city-scale traffic dynamics. In this paper, we propose TIDES (Traffic Intelligence with DeepSeek-Enhanced Spatial-temporal prediction), a novel LLM-based framework that captures spatial-temporal correlations for urban wireless traffic prediction. TIDES first identifies heterogeneous traffic patterns across regions through a clustering mechanism and trains personalized models for each region to balance generalization and specialization. To bridge the domain gap between numerical traffic data and language-based models, we introduce a prompt engineering scheme that embeds statistical traffic features as structured inputs. Furthermore, we design a DeepSeek module that enables spatial alignment via cross-domain attention, allowing the LLM to leverage information from spatially related regions. By fine-tuning only lightweight components while freezing core LLM layers, TIDES achieves efficient adaptation to domain-specific patterns without incurring excessive training overhead. Extensive experiments on real-world cellular traffic datasets demonstrate that TIDES significantly outperforms state-of-the-art baselines in both prediction accuracy and robustness. Our results indicate that integrating spatial awareness into LLM-based predictors is the key to unlocking scalable and intelligent network management in future 6G systems.

Modern wireless systems are expected to support both communication and sensing, forming the foundation of Integrated Sensing and Communication (ISAC). Although many approaches have been proposed, practical and efficient solutions remain an open challenge. This work addresses this issue by constraining transmitted MIMO radar waveforms to finite-alphabet signals, which are better suited for power amplifiers and signal processing hardware. Under this setting, we investigate methods to jointly enhance communication and sensing performance. First, by exploiting the beampattern-invariance property, we reduce the computational complexity of an existing finite-alphabet waveform generation method and address its limitations to further improve performance. Furthermore, we propose beampattern-preserving schemes that embed communication data into radar waveforms without altering the beampattern, enabling practical ISAC implementation. Specifically, two information-encoding methods and a new precoding technique are introduced, together with an extension to multi-user scenarios based on time and code division. Simulation results demonstrate the trade-off between radar and communication performance.

This paper investigates the performance limits of pulse position modulation (PPM) optical communication systems using multi-pixel single-photon detectors (SPDs) under the constraint of detector dead time. A detailed analytical framework is developed by modeling the temporal detection behavior of SPDs as a Markov process, capturing the effects of dead time across consecutive PPM symbols. Closed-form expressions are derived for slot-wise detection probabilities, symbol transition matrices, and symbol error rate (SER) under maximum-likelihood detection. To enhance spectral and energy efficiency, a probabilistic shaping scheme is introduced and optimized using a truncated Blahut-Arimoto algorithm, allowing the transmitter to adapt its symbol distribution to the nonlinear detection characteristics of the SPD array. The proposed model is validated through extensive Monte Carlo simulations, demonstrating excellent agreement with theoretical predictions. Results show that probabilistic shaping significantly improves communication sensitivity, reducing the required number of photons per bit by up to 25% in photon-starved or high-noise regimes.

This paper investigates the joint optimization of trajectory planning and resource allocation for a high-altitude platform stations synthetic aperture radar (HAPs-SAR) system. To support real-time sensing and conserve the limited energy budget of the HAPs, the proposed framework assumes that the acquired radar data are transmitted in real time to a ground base station for SAR image reconstruction. A dynamic trajectory model is developed, and the power consumption associated with radar sensing, data transmission, and circular flight is comprehensively analyzed. In addition, solar energy harvesting is considered to enhance system sustainability. An energy-aware mixed-integer nonlinear programming (MINLP) problem is formulated to maximize radar beam coverage while satisfying operational constraints. To solve this challenging problem, a sub-optimal successive convex approximation (SCA)-based framework is proposed, incorporating iterative optimization and finite search. Simulation results validate the convergence of the proposed algorithm and demonstrate its effectiveness in balancing SAR performance, communication reliability, and energy efficiency. A final SAR imaging simulation on a 9-target lattice scenario further confirms the practical feasibility of the proposed solution.

This paper proposes a movable-antenna-based index modulation (MA-IM) framework that exploits the spatial mobility of a single reconfigurable antenna to create additional information-bearing dimensions for next-generation wireless systems. By discretizing the continuous movable region into a dense set of candidate sampling points and selecting representative anchors for indexing, the proposed framework converts spatial degrees of freedom into a practical modulation resource. Building on this framework, we develop a family of anchor-selection strategies with different levels of channel awareness, including geometry-based, SNR-based, max--min channel-domain, and joint constellation-aware designs. For the resulting MA-IM schemes, joint maximum-likelihood (ML) detectors are derived, along with a low-complexity two-stage detector, and unified analytical upper bounds on the average bit error probability (ABEP) are established based on the joint index--modulation constellation. The results reveal that directly indexing all sampling points is generally unreliable, highlighting the necessity of anchor optimization. The performance of MA-IM is shown to depend on key system parameters, including channel richness, spatial correlation, the number of index states, and the modulation order. In particular, increasing the number of index states and increasing the QAM order affect MA-IM in fundamentally different ways, even under the same transmission rate. Among the proposed schemes, the joint constellation-aware anchor design achieves the best error performance, demonstrating that optimizing channel-domain separation alone is insufficient and that effective MA-IM design must account for the geometry of the joint signal constellation. Simulation results further show that, with properly designed anchors, MA-IM can approach or even outperform same-spectral-efficiency QAM baselines.

Visible light communication (VLC) systems often rely on uniformly distributed constellations, which can lead to suboptimal performance and a reduction in spectral efficiency (SE). To address this limitation, we propose a novel spectrally efficient modulation scheme that leverages probabilistic shaping (PS) to enhance the SE of VLC systems. The proposed scheme is based on the color-shift keying (CSK) modulation format for a quadrichromatic LED (QLED)-based system. We derive both the capacity and transmission rate (TR) for the proposed scheme, and adapt the TR based on the optical signal-to-noise ratio (OSNR) by optimizing the distribution of constellation symbols and forward error correction (FEC) coding rate, thus ensuring optimal system performance under varying channel conditions. Furthermore, we introduce an algorithm to compute the optimal capacity-approaching input distribution. To validate the proposed scheme, a QLED CSK system prototype was developed and experimentally tested. We evaluated the performance of the proposed scheme in terms of SE and frame error rate (FER) under different OSNR levels, and compare it against the conventional uniform-based scheme. The results demonstrated that the proposed scheme achieves a 20% improvement in SE over the uniform-based scheme.

Radio altimeter (RA) accuracy is crucial to aircraft safety in critical scenarios such as low visibility, close proximity to terrain, collision avoidance, and autoland (automatic landing) procedures. Nowadays, there is growing concern that possibly damaging interference from 5G communications systems in C-band may interfere with the operation of RA at 4.2 - 4.4 GHz. Some countries and international organisations have already taken corrective measures. In this work, we study a system where radio altimeter and 5G network exist in the same area. the system is composed of one airplane landing in a straight direction parallel to the runway and 5G base stations (5G-BSs) deployed on the ground. Our ultimate goal is to analyse the interference caused by 5G-BSs and ensure a safe operation environment for the radio altimeter. Using tools from stochastic geometry, we determine the CDF of the interference caused by the 5G-BSs. We use the probability of having an interference greater then a specific threshold as a metric. We want this metric to be as close as possible to zero. Thus, we propose different exclusion zones as a solution to mitigate interference. Then, we analyse the performance of the 5G network to evaluate the impact of deploying exclusion zones on it. We verify our findings using Monte-Carlo simulations and draw multiple useful insights.

Received signal strength (RSS)-based localization techniques rely on the transmit power of nodes, with many existing approaches assuming full knowledge of this parameter. However, transmit power depends on various factors like battery levels, antenna orientation, and component aging. To address this issue, existing techniques typically employ semidefinite programming (SDP), which exhibits significantly high computational complexity. In this paper, we present a cooperative RSS-based localization technique named CR-CA (Convex Relaxation with Centralized Armijo optimization), which jointly estimates nodes’ locations and transmit power. CR-CA transforms the unconstrained maximum likelihood (ML) of the RSS-based localization problem into a constrained optimization problem using a convex approximation of the non-convex and discontinuous objective function. We demonstrate that the relaxed ML objective function possesses a Lipschitz continuous gradient. We solve the relaxed ML problem using the augmented Lagrange multiplier method and provide the theoretical proof of its convergence. Additionally, we derive the Cramer-Rao Lower Bound (CRLB) for RSS-based cooperative localization under scenarios where transmit power is unknown. We conduct extensive simulations and real-world experiments to verify the effectiveness of CR-CA, showcasing its superior accuracy in estimating nodes’ locations and transmit power. Simulations and experiments further validate that CR-CA exhibits linear computational complexity with the number of wireless links, thus making it suitable for large-scale networks.

Non-terrestrial networks (NTNs) are poised to play a critical role in next-generation mobile communications, offering enhanced flexibility, improved line-of-sight (LoS) conditions, and overcoming the limitations of terrestrial networks (TNs). Among NTN platforms, high altitude platform stations (HAPSs) have emerged as a promising solution to provide Internet connectivity to underserved regions, including rural areas, islands, and maritime zones, where traditional infrastructure deployment is costly and challenging to deploy. In this paper, we investigate the feasibility of large-scale HAPS deployment to connect island and maritime users, considering real-world shadowing effects on part of HAPSs caused by the presence of island building clusters. We first analyze the coverage performance of onshore (island) and offshore (remote sea) users, in which the channels between HAPSs and the user follow the shadowed Rician distributions and Rician distributions, respectively. Next, we introduce an evaluation method for nearshore users in a hybrid channel environment with HAPSs, and propose approximations that can reduce computational complexity. Based on the simulation results, we discuss how the distance from the island boundary (i.e. the relative remoteness of maritime users) affects coverage performance under different HAPS densities. We also emphasize the importance of choosing a balanced HAPS density or an advanced HAPS deployment scheme.

Maritime communication demands have surged due to diverse applications such as transoceanic shipping, offshore resource exploration, and emergency rescue. However, current communication systems are unable to meet the diverse data rate demands of maritime applications. In this paper, we propose a space-air-ground-sea integrated network (SAGSIN) that combines onshore base stations, uncrewed aerial vehicles, high-altitude platform stations, and low Earth orbit satellites to provide seamless and reliable communication across vast oceanic regions. Based on stochastic geometry, we model the spatial distribution of network devices on spherical surfaces. The Matérn hard-core point process (MHCPP) is used to model the deployment of both ground and aerial devices, considering the minimum spacing to avoid overdense and collisions. Aerial relays are used to extend the coverage of coastal base stations, with relay selection aimed at maximizing the end-to-end channel capacity. Analytical expressions for the uplink coverage probability are derived by considering the receiver’s decoding threshold. Furthermore, the analysis is extended to the communication probability by incorporating quality of service (QoS) with data rate as the metric. Numerical results validate the accuracy of the proposed model and demonstrate that SAGSIN can effectively extend reliable communication from coastal regions to the deep ocean. Furthermore, dynamic QoS scheduling across heterogeneous links, each with different transmission capabilities, can enhance overall system efficiency to meet diverse QoS demands.

Free-space optical (FSO) satellite communication systems face distinct challenges depending on link topology, as horizontal or inter-satellite links (ISLs) operate above the atmosphere, uplink (UL) channels experience atmospheric turbulence near the transmitter, and downlink (DL) channels suffer primarily from geometric losses. This paper presents a comprehensive analysis of low Earth Orbit (LEO) satellite communication links employing single-photon detectors (SPDs) to address photon-starved conditions under various FSO channel impairments. We develop comprehensive channel models for DL, ISL, and UL that capture the combined effects of atmospheric turbulence, attenuation, pointing errors, angle-of-arrival (AoA) blockage, and geometric spreading. To further enhance performance in high-data-rate scenarios, we investigate multi-pixel SPD arrays and derive analytical expressions for error-rate performance that include detector dead time and dark counts. Our theoretical models are validated through extensive Monte Carlo simulations, showing strong agreement with predicted results. The findings highlight the benefits of spatial diversity in multi-pixel SPDs, demonstrating notable gains over single-pixel configurations. Finally, we provide key insights into optimizing link parameters: receiver aperture, link distance, detection efficiency, modulation order, and beam divergence for robust, high-sensitivity photoncounting FSO satellite communication.

To mitigate inter-satellite interference and payload limits in LEO mega-constellations, satellite clusters, groups of small cooperative satellites have been proposed to improve performance and reduce interference. The typical configuration divides the cluster into a leader satellite with full processing and control capabilities and multiple simpler follower satellites that assist with coverage and throughput. These clusters enhance coverage and throughput, prompting interest in their performance gains and optimal deployment. Given that the spherical stochastic geometry (SG) model has been proven effective for modeling such structures, we establish a performance evaluation framework based on the SG approach for the leader-follower satellite architecture, enabling an assessment of communication performance under different deployment configurations quantitatively. We derive analytical expressions for the outage probability and average data rate to evaluate the communication performance of the satellite system, along with low-complexity approximations. Numerical results demonstrate the performance advantages of the leader-follower architecture over a single leader satellite and explore optimal deployment configurations for the follower satellites.

This paper proposes a novel non-terrestrial networks (NTNs) system that integrates optical intelligent reflecting surfaces (OIRS) and simultaneous transmitting and reflecting Intelligent reflecting surfaces (STAR-IRS) to address critical challenges in next-generation communication networks. The proposed system model features a signal transmitted from the optical ground station (OGS) to the earth station (ES) via an OIRS mounted horizontally on a high altitude platform (HAP). The ES uses an amplify-and-forward (AF) relay with fixed gain for signal relaying, which is then transmitted through a STAR-IRS vertically installed on a building to facilitate communication with both indoor and outdoor users. The FSO link incorporates (multiple-input multiple-output) MIMO technology, and this paper develops a channel model specifically designed for scenarios where the number of OIRS units exceeds one. For the radio-frequency (RF) link, a novel and highly precise approximation method is introduced, offering superior accuracy compared to traditional approaches based on the central limit theorem (CLT). Closed-form analytical expressions for key performance metrics, including outage probability (OP), ergodic capacity and average bit error rate (BER) are derived in terms of the bivariate Fox-H function for this novel five hops system. Asymptotic expressions at high SNR are also presented, providing insights into system diversity order.

In this work, we propose an innovative system that combines high-altitude platforms (HAPs) and optical intelligent reflecting surfaces (OIRS) to address line-of-sight (LOS) challenges in urban environments. Our three-hops system setup includes an optical ground station (OGS), a HAP, an OIRS, and a user. Signals are transmitted from the OGS to the HAP via a free space optical (FSO) link, with the HAP functioning as an amplify-and-forward (AF) relay that redirects signals through an OIRS, effectively bypassing obstacles such as buildings and trees to improve connectivity for non-line-of-sight (NLOS) User. For the OIRS link, we address key channel impairments, including atmospheric turbulence, pointing errors, attenuation, and geometric and misalignment losses (GML). An accurate approximation for the Hoyt-distributed GML model is derived, enabling us to obtain closed-form expressions for outage probability (OP) and various performance metrics, such as average bit error rate (BER) and channel capacity of the OIRS-assisted FSO link. Furthermore, we analyze the end-to-end signal-to-noise ratio (SNR) and derive closed-form expressions for OP and performance metrics. Asymptotic expressions are provided for high-SNR regimes, allowing the system’s diversity order to be calculated.

Prevailing research on random access schemes often overlooks the complexities of variable slot durations in freshness-critical applications, especially for satellite communication. Our work addresses this by developing an age-optimized irregular repetition slotted ALOHA (IRSA) framework design that adaptively integrates finite-length forward error correction (FEC) to enhance information freshness. We initially model the Age of Information (AoI) dynamics by jointly considering satellite link characteristics, grant-free access collisions, and processing delays. Next, we propose a novel approach for estimating the performance of the packet loss rate (PLR) for finite-length IRSA over the Packet Erasure Channel (PEC), capturing the effects of coding and repetition diversity. To further characterize the temporal behavior of information freshness, a Markovian formulation is then derived to obtain the stationary age distribution, from which the average AoI and the age violation probability are computed in closed form. Building upon the formulations, we perform a joint optimization of frame size and code rate to minimize the average AoI, revealing the inherent trade-off between transmission redundancy and latency. Numerical results confirm the accuracy of the proposed analytical framework and show that the proposed adaptive IRSA scheme achieves about 33% AoI reduction compared with conventional the Slotted ALOHA (SA) and static IRSA baselines, validating its effectiveness for satellite-enabled freshness-critical IoT systems.

In this work, we study the design of receivers for uplink multi-user systems, aiming to estimate both the channel and the transmitted symbols. We consider two estimation strategies: 1) a joint estimation approach, where the channel and symbols are estimated simultaneously; and 2) a sequential estimation approach, where the channel is first estimated and then used for symbol detection. For both strategies, we derive the Cramér-Rao Bound (CRB) for symbol estimation to characterize fundamental performance limits. When efficient receivers achieving the CRB exist, these bounds provide accurate lower bounds on the mutual information. In general, however, such receivers may not be available, and we instead use these same CRB-based metrics as practical proxies for achievable throughput. Leveraging tools from random matrix theory (RMT), we analyze the asymptotic behavior of these lower bounds under various asymptotic regimes for both estimation strategies. This analysis enables the derivation of generic power allocation guidelines that asymptotically maximize the proxy metrics. Simulation results confirm the accuracy of the asymptotic expressions and their effectiveness in guiding resource allocation decisions.

Optimizing expensive, non-convex, black-box Lipschitz continuous functions presents significant challenges, particularly when the Lipschitz constant of the underlying function is unknown. Such problems often demand numerous function evaluations to approximate the global optimum, which can be prohibitive in terms of time, energy, or resources. In this work, we introduce Every Call is Precious (ECP), a novel global optimization algorithm that minimizes unpromising evaluations by strategically focusing on potentially optimal regions. Unlike previous approaches, ECP eliminates the need to estimate the Lipschitz constant, thereby avoiding additional function evaluations. ECP guarantees no-regret performance for infinite evaluation budgets and achieves minimax-optimal regret bounds within finite budgets. Extensive ablation studies validate the algorithm’s robustness, while empirical evaluations show that ECP outperforms 10 benchmark algorithms—including Lipschitz, Bayesian, bandits, and evolutionary methods—across 30 multi-dimensional non-convex synthetic and real-world optimization problems, which positions ECP as a competitive approach for global optimization.

This work provides a precise performance analysis of a joint antenna selection and precoding technique for massive multiple-input-single-output (MISO) multi-user systems with limited dynamic range power amplifiers. The proposed precoder is formulated as an optimization problem that aims to minimize distortion error power while incorporating an ℓ1-regularization term to promote sparsity and enable antenna selection. It also includes constraints to ensure that the maximum power at each antenna remains within the permissible range for low-dynamic range power amplifiers. Utilizing the convex-Gaussian min-max theorem framework, we offer a precise characterization of the proposed solution’s performance as the number of users and antennas at the base station simultaneously increases. Through extensive numerical experiments, we evaluate the accuracy of our results and derive valuable insights into the effectiveness of the proposed joint antenna selection and precoding approach.

Even though Terahertz (THz) band is promising for Drone-to-Drone (D2D) communication, there exists significant challenges, primarily high path loss and the sensitivity of directional links to mobility-induced beam misalignment. In this paper, we propose a novel adaptive beamwidth control system for THz D2D communication by activating rows of the designed 8×8 microstrip patch antenna array operating at 272.5 GHz. The proposed row-selective activation mechanism provides dynamic adjustment of the elevation beamwidth by activating different combinations of antenna rows (from 1×8 to 8×8 configurations). The proposed mechanism offers a flexible trade-off in its beam characteristics, utilizes narrow, high-gain beams to maximize performance in stable links, and dynamically switches to wider, more robust beams to ensure connectivity during drone maneuvers. The performance of the reconfigurable array is evaluated through real drone mobility traces. Simulation results reveal that during periods of misalignment, the adaptive beamwidth strategy maintains link capacity that is up to three orders of magnitude higher than fixed-beam configurations, promising a practical solution for resource-constrained THz D2D communication.

The TV white space (TVWS) technology has proven to be effective and feasible in connecting rural and hard-to-reach areas to Internet service. The TVWS wireless systems operate based on geolocation white space databases (WSDB) to protect the primary systems from harmful interference and thus there is a critical need to know the available and usable channels that can be used by the secondary users in a specific geographic area. In this work, we developed a generalized and flexible universal graphical user interface (GUI) tool to evaluate the availability and usability of the TVWS channels and their noise levels in any specific geographical area. The developed tool has many features and capabilities such as scanning the TVWS spectrum for any geographical area in the world and any TVWS frequency band. Moreover, it allows the user to apply widely used terrain-based radio propagation models. It provides flexibility in importing the elevation terrain profile of any region with the desired spatial accuracy and resolution. In addition, various system parameters including regulation rules can be modified in the tool. This tool exports to an external dataset file the output data of the available and usable channels and their noise levels and it also visualizes these data interactively.

Large-scale deployment of Internet of Things (IoT) networks in the industrial, scientific, and medical (ISM) band leads to spectrum congestion and requires multiple gateways to cover wide areas. This will increase cost, complexity, and energy consumption. TV White Spaces (TVWS) provides an abundant spectrum sufficient for low-data-rate IoT applications. This lowfrequency band offers coverage over larger areas due to the ability of wireless signals to penetrate obstacles and terrain. In this paper, we examine the performance of narrowband data communications in TVWS through an outdoor experiment in a suburban area with non-line-of-sight (NLOS) propagation scenarios. We implement a software-defined radio (SDR) testbed and develop a new benchmark module in GNU Radio to perform outdoor experiments for TVWS narrowband data communication between a gateway and wireless nodes at various locations. The results reveal that the system can achieve a throughput of up to 97 Kbps with a packet error rate (PER) and packet loss rate (PLR) under 1% over NLOS paths, making it suitable for low-data rate applications. This work offers valuable insights for designing the physical layer of narrowband white space devices (WSDs). The developed benchmark tool will also greatly assist other researchers in evaluating the performance of SDR-based communication systems.

Many rural and suburban areas still lack broadband Internet access due to economic, technological and geographic challenges. In this paper, we examine the performance of TVWS wireless technology for providing Internet service through practical deployment in a suburban area of the King Abdullah University of Science and Technology (KAUST). We conduct outdoor measurements to measure the various performance metrics, including the received signal strength (RSS), signal-to-interference-plus-noise ratio (SINR), and data rate. In addition, the collected data are used to evaluate and compare the measured pathloss with some widely used pathloss models.

Space-air-ground integrated networks (SAGINs) hold great promise in delivering ubiquitous aerial access, effectively meeting the demands for large-coverage on-demand services. Moreover, in 6G networks, the integration of Communication, Computation, Caching, and Control (4C) enables seamless connectivity, efficient data processing, optimized content delivery, and intelligent decision-making for next-generation services. However, various components like unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), satellites, and terrestrial networks each face distinct limitations. In this demo, we first showcase a SAGIN-4C-6G platform capable of establishing a high-capacity backhaul link to the core network while ensuring stable and continuous coverage. Experiments demonstrate that the proposed platform can deliver high-speed, on-demand air-to-ground (A2G) coverage with wireless backhaul, extending over an area of up to 100 km2. Beyond communication enhancement and optimization control, we also illustrate the potential for computation and caching services by deploying the proposed SAGIN platform.

This paper presents Super-LoRa, a novel approach to enhancing the throughput of LoRa networks by leveraging the inherent robustness of LoRa modulation against interference. By superimposing multiple payload symbols, Super-LoRa significantly increases the data rate while maintaining lower transmitter and receiver complexity. Our solution is evaluated through both simulations and real-world experiments, showing a potential throughput improvement of up to 5× compared to standard LoRa. This advancement positions Super-LoRa as a viable solution for data-intensive IoT applications such as smart cities and precision agriculture, which demand higher data transmission rates.

The emerging field of smart agriculture leverages the power of the Internet of Things (IoT) to revolutionize farming practices. This article investigates the transformative potential of long range (LoRa) technology as a key enabler of long-range wireless communication for agricultural IoT systems. By critically reviewing existing literature, we identify a lacuna in research specifically focused on LoRa’s prospects and challenges from a communication perspective in smart agriculture. We delve into the details of LoRa-based agricultural networks, encompassing network architecture design, physical layer (PHY) considerations tailored to the agricultural environment, and the development of channel modeling techniques that account for unique soil characteristics. This article further explores relaying and routing mechanisms that address the challenges of extending network coverage and optimizing data transmission in vast agricultural landscapes. Transitioning to practical considerations, we discuss sensor deployment strategies and energy management techniques, providing valuable insights for real-world deployments. A comparative analysis of LoRa with other prevalent wireless communication technologies employed in agricultural IoT applications highlights its strengths and weaknesses within this specific context. Furthermore, this article outlines several future research directions to leverage the potential of LoRa-based agriculture 4.0. These include advancements in channel modeling for heterogeneous farming environments, developing novel relay routing algorithms, integrating emerging sensor technologies like hyper-spectral imaging and drone-based sensing, on-device artificial intelligence (AI) models, and sustainable solutions. This survey can serve as a cornerstone for researchers, technologists, and practitioners seeking to understand, implement, and propel smart agriculture initiatives utilizing LoRa technology.

In Internet of Things (IoT) networks, ensuring the timely delivery of information is significantly constrained by the limited energy resources of IoT devices and the signal attenuation experienced in wireless channels. In this paper, we investigate resource management for self-sustaining IoT networks with ambient radio frequency (RF) energy harvesting via a spatio-temporal approach. We consider a hard deadline for packet delivery, and we aim to jointly reduce the age of information (AoI) and the packet drop rate due to the hard deadline for packet delivery or buffer overflow. To achieve that, using tools from deep reinforcement learning (DRL) and stochastic geometry, we propose a joint rate adaptation and power control scheme that accounts for the spatial topology of the network and the temporal attributes at the device level. In particular, stochastic geometry is leveraged to characterize the energy harvesting process and the packet transmission success probability for a given transmit rate and power. Furthermore, the joint rate adaptation and power control policy at the device level is obtained using a deep R-network (DRN), which is a DRL algorithm that utilizes a deep neural network to approximate the R-function (the expected average reward). The performances of the last-come-first-served (LCFS) queuing discipline, the first-come-first-served (FCFS) queuing discipline, and a proposed hybrid queuing discipline are compared. For the proposed hybrid queuing discipline, DRL is used not only for rate adaptation and power control but also for specifying the transmission order of generated packets. The presented numerical results demonstrate that the LCFS queuing discipline improves AoI performance, while the FCFS queuing discipline improves packet drop rate. Also, the proposed hybrid queuing discipline strikes an intricate balance between AoI and packet drop rate, and achieves a good performance in both measures compared to the other queuing disciplines.

This article presents a novel technique for the simultaneous wireless information and power transfer (SWIPT) toward low-power devices, by means of reconfigurable intelligent surfaces (RISs). Our approach is based on a Reflect-and-Amplify protocol meaning that, based on the RIS beam-steering functionality and assuming different reflection coefficients for the RIS elements, it allows to reflect and amplify the impinging wireless signal from the transmitter toward the receiver, which will be able to both collect energy and download data. Specifically, by considering a hybrid RIS comprised of both passive and active elements, the proposed SWIPT approach is defined active/passive switching (APS). The reflection of the wireless beam toward the receiver is accomplished by means of passive RIS elements, while an amplification of the wireless signal is provided through active RIS elements. Such amplification allows the receiver to convert the incoming wave power into a DC voltage, but also causes an increase of power consumption due to active elements. To assess the performance of the proposed SWIPT approach, we derive the closed-form expression of the joint data transmission and energy harvesting outage probability (OP) in case of both a single and multiple users. We compare the proposed technique to other SWIPT approaches from the literature, such as the power splitting and the time switching, under power fairness conditions. Our results validate the benefits of using hybrid RISs for SWIPT systems, as compared to other existing approaches showing reduced performance, also in case of asymptotic analysis for high power and high number of RIS elements.

Wireless underground sensor networks (WUSNs) offer significant social and economic benefits by enabling the monitoring of subterranean entities. However, the communication reliability of WUSNs diminishes in harsh environments where terrestrial network infrastructure is either unavailable or unreliable. To address this challenge, we explore the feasibility of integrating buried massive machine-type communication (mMTC) sensors with non-terrestrial networks (NTNs), including unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), and low Earth orbit (LEO) satellites, to establish underground-to-NTN connectivity for various large-scale underground monitoring applications. To assess the effectiveness of underground-to-NTN connectivity, we develop a Monte Carlo simulator that incorporates a multi-layer underground attenuation model, the 3GPP empirical path loss model for various NTN platforms, and two LoRaWAN modulation schemes, i.e., LoRa and LoRa-frequency hopping spread spectrum (LR-FHSS). Our results evidence that LoRa SF7 is a strong candidate for short-range UAV communication in rural environments, while LR-FHSS modulation proves to be a promising option for HAP and LEO satellite platforms in massive WUSNs scenarios thanks to its adequate link budget and robustness to the interference. Finally, we demonstrate that the success probability of underground-to-NTN connectivity using LoRa and LR-FHSS is significantly affected by factors such as the monitoring environment, the number of devices, burial depth, and the soil’s volumetric water content.

In the era of sixth-generation (6G) wireless communication, numerous applications are expected to be realized, including environmental monitoring, smart agriculture, remote education, security protection, and intelligent transportation systems. These scenarios require large-scale, continuous Internet services in forests, rivers, oceans, and road networks, to name a few, where optical cables are difficult to deploy. High-altitude platform stations (HAPSs) emerge as a promising solution, offering low-latency, high-capacity services while facilitating the establishment of vertical heterogeneous networks (vHetNets) in fiber-less areas. This paper investigates three HAPS-based solutions, where HAPSs can serve wireless devices directly or via gateway (GW) networks: the HAPS-to-device (H2D) scheme, the HAPS-to-GW-to-device (H2G2D) scheme, and the hybrid scheme. Leveraging percolation theory, we study the feasibility of large-scale continuous Internet coverage, where the key performance indicator (KPI) is the percolation probability. We discuss the subcritical and supercritical cases in different coverage schemes, and prove that the phase transition from zero to non-zero percolation probability appears when increasing the HAPS density or GW density. Numerical results verify that the curve of the critical condition of the phase transition exists between the derived lower bound and upper bound, which can help reduce the upfront cost of HAPS-based vHetNet solutions.

Internet of Underwater Things (IoUT) is increasingly gathering attention with the aim of monitoring sea life and deep ocean environment, underwater surveillance as well as maintenance of underwater installments. However, conventional IoUT devices, reliant on battery power, face limitations in lifespan and pose environmental hazards upon disposal. This article introduces a sustainable approach for simultaneous information uplink from the IoUT devices and acoustic energy transfer (AET) to the devices via an autonomous underwater vehicle (AUV), potentially enabling them to operate indefinitely. To tackle the time-sensitivity, we adopt age of information (AoI), and Jain’s fairness index. We develop two deep-reinforcement learning (DRL) algorithms, offering a high-complexity, high-performance frequency division duplex (FDD) solution and a low-complexity, medium-performance time division duplex (TDD) approach. The results elucidate that the proposed FDD and TDD solutions significantly reduce the average AoI and boost the harvested energy as well as data collection fairness compared to baseline approaches.

Understanding the interdependencies among communication performance metrics is crucial for designing efficient vehicle-assisted delay-tolerant networks (DTNs), as these metrics behave differently from those in traditional networks and directly affect data delivery reliability and timeliness. Motivated by this, and unlike most existing works that focus on protocol design, we analyze a vehicle-assisted DTN operated via TV white space (TVWS) from a communications perspective. Specifically, we consider a scenario where cities are connected to the cloud, while remote villages or IoT device clusters rely on vehicles traveling along highways to deliver data packages. Initially, we define and introduce three key performance metrics: uplink/ downlink transmission time, uplink peak age of information (PAoI), and request delay within this vehicle-assisted DTN. To comprehend the behaviors and correlations among these metrics, we first investigate a basic scenario considering only one road: while vehicles moving on a highway from one city to another, delivering data to the remote sites near the highway. Our results reveal correlations between uplink and downlink transmission times, the existence of an optimal transmitted data size per trip to minimize request delay and uplink PAoI, and that the request delay time can be approximated by a Gamma distribution when transmitting large data sizes. Subsequently, we extend the analysis to a more complex scenario involving multiple roads and information exchange between vehicles, with vehicle movement modeled as a 2D random walk. While the communication performance metrics exhibit similar trends to the basic scenario, we capture the effects of distance, vehicle arrival rate, and transmitted data size on data traveling time. Finally, we propose a special case that represents the upper bound of data traveling time within the DTN.

This paper, addressing the integration requirements of radar imaging and communication for High-Altitude Platform Stations (HAPs) platforms, designs a waveform based on linear frequency modulated (LFM) frequency-hopping signals that combines synthetic aperture radar (SAR) and communication functionalities. Specifically, each pulse of an LFM signal is segmented into multiple parts, forming a sequence of sub-pulses. Each sub-pulse can adopt a different carrier frequency, leading to frequency hops between sub-pulses. This design is termed frequency index modulation (FIM), enabling the embedding of communication information into different carrier frequencies for transmission. To further enhance the data transmission rate at the communication end, this paper incorporates quadrature amplitude modulation (QAM) into waveform design. The paper derives the ambiguity function of the proposed waveform and analyzes its Doppler and range resolution, establishing upper and lower bounds for the range resolution. In processing SAR signals, the receiver first removes QAM symbols, and to address phase discontinuities between sub-pulses, a phase compensation algorithm is proposed to achieve coherent processing. For the communication receiver, the user first performs de-chirp processing and then demodulates QAM symbols and FIM index symbols using a two-step maximum likelihood (ML) algorithm. Numerical simulations further confirm the theoretical validity of the proposed approach.

In this paper, we propose distributed hybrid active-passive reconfigurable intelligent surfaces (H-RIS) for Terahertz (THz) wireless communication systems in the presence of hardware impairments and$\alpha -\mu $small-scale fading. At first, the end-to-end channel is characterized by a gamma distribution approximation by means of the Lyapunov-central limit theorem. Based on this, analytic expressions of the outage probability and the ergodic capacity of the system are derived. Also, the scaling-law for the received signal-to-noise ratio (SNR) is analyzed in association with the H-RIS scheme, in comparison with fully active RIS and fully passive RIS counterparts. Next, the transmit power is optimized to achieve maximum energy-efficiency in practical scenarios where only partial channel state information (CSI) is available. Furthermore, impacts of hardware impairments, RIS phase-shift errors, as well as numbers of active and passive RIS elements on the system performance are evaluated. Finally, simulations are provided to validate the accuracy of the theoretical analysis.

The Dual-function radar communication (DFRC) design aims to exploit the tractability and reusability of both radar and communication systems’ components, parameters, and spectrum to achieve an integrated system. In this work, we propose a novel DFRC signal design by leveraging the phenomenon of the MIMO beampattern invariance, which was comprehensively explored in our previous work. It is demonstrated that the flexibility afforded by this beampattern invariance can be used to construct an integrated waveform while maintaining the required beam. A beampattern-preserving unitary transformation of the MIMO weight matrix is proposed to realize a desired information-bearing symbol at the communication receiver. The proposed method has the following promising features: 1) low-complexity, closed-form solution for the transmitted signals; 2) flexibility of modulation scheme; 3) independence from the type of orthonormal MIMO waveforms; 4) independence from the beampattern design; 5) adaptability for multi-target and multi-user scenarios. Using this generic signal design method, a number of modulation schemes are implemented. Furthermore, the impact of this operation on the estimation accuracy of the target parameters is analyzed by computing their respective Cramer-Rao Lower Bounds (CRLBs). Simulation results show that modulation schemes yielding a range of symbol error rate (SER) performances can be implemented while keeping the radar parameter estimation unaffected.

Cell-free massive multiple-input-multiple-output (CF-mMIMO) systems are promising deployment paradigms for next-generation networks. They comprise a large number of base stations (BSs) and simultaneously serve all users over the same time and frequency resources. We analyze the performance of integrating aerial users, such as unmanned aerial vehicles (UAVs), into this novel system. Specifically, we consider a CF-mMIMO network containing both ground and aerial users and study the influence of system parameters on the signal-to-interference-plus-noise ratio (SINR) and rate coverage performance. Additionally, we use tools from stochastic geometry to capture the spatial randomness of users and BSs and compare the performance of the CF-mMIMO system to that of traditional small cell systems. Given the improvement of dedicated antennas, such as up-tilted/down-tilted antennas, on the performance of small cell systems, we include the up-tilted/down-tilted antenna model in this work and analyze the omnidirectional antenna model as a special case. We derive both the exact expressions and dominant-signal-based approximations for SINR and rate coverage. Our numerical results demonstrate that the CF-mMIMO system exhibits better performance at low values of SINR and rate thresholds and higher minimum achievable SINR compared to the small cell system. Furthermore, we observe that users benefit dramatically from increasing altitudes and establishing line-of-sight (LoS) channels with BSs.

Single-photon detector (SPD)-based communication systems, and in particular those employing superconducting nanowire single-photon detectors (SNSPDs), are playing an irreplaceable role in scenarios such as quantum communication and deep-space communication. Due to the high cost of experimental equipment, establishing a theoretical framework to analyze the performance of SPD-based systems has become an effective and low-cost solution. However, due to the unique detection mechanism of SPDs and the decisive impact of dead time, there is currently no analytical framework suitable for evaluating the performance of high-speed communication systems with SPDs. To fill this gap, we propose an analytical framework tailored to SPD-based pulse-position modulation (PPM) systems, based on a Markov detection model, and use this framework to evaluate the system’s symbol error rate and achievable symbol rate. The framework demonstrates significant advantages over simulations and experiments, particularly in its ability to predict theoretical performance limits. Based on this framework, we reveal unique characteristics of the SPD-based PPM system, such as channel asymmetry and detection dependence. In addition, optimization guidelines for three representative system configurations are provided.

This paper investigates a highly spectrally efficient transmission scheme in an integrated satellite and aerial network (ISAN). Specifically, we first propose a novel uplink access framework, where the co-design of communication and over-the-air computation (AirComp) is implemented through layer division multiplexing (LDM) in the aerial network, while the cognitive radio-inspired non-orthogonal multiple access (CR-NOMA) technology is employed in the satellite network. Then, according to the proposed framework, we mathematically formulate a joint optimization problem that aims at maximizing the system achievable sum rate, subject to the constraints of minimal accuracy requirement of AirComp and minimal quality-of-service requirements of communication service. Next, by introducing the inter-network interference-related auxiliary variable, we divide the original optimization problem into two subproblems associated with the optimization of the satellite and aerial networks. To tackle the first subproblem, we propose a beamspace-inspired analog beamforming (BF) method, and derive closed-form expressions for BF vectors and transmit powers to implement the CR-NOMA scheme in the satellite network. Meanwhile, to address the second subproblem, we propose a beamspace-inspired digital BF together with successive convex approximation and alternating optimization approaches, to obtain the BF matrices, transmit power coefficients and AirComp scaling factor, so that the LDM-based communication and computation co-design (CCCD) can be realized in the aerial network. Moreover, for complexity reduction, we propose a beamspace-inspired zero-forcing BF method to calculate the communication BF matrices, and then leverage the orthogonal beam superposition approach to obtain the computation BF matrix, thereby presenting another CCCD scheme. Finally, our simulation results confirm that since the proposed schemes can realize spectrum multiplexing for communication and AirComp services, we achieve higher system spectral efficiency and lower computation error than the benchmarks.

This paper presents a performance analysis of two distinct techniques for antenna selection and precoding in downlink multi-user massive multiple-input single-output systems with limited dynamic range power amplifiers. Both techniques are derived from the original formulation of the regularized-zero forcing precoder, designed as the solution to minimizing a regularized distortion. Based on this, the first technique, called the ℓ1-norm precoder, adopts an ℓ1-norm regularization term to encourage sparse solutions, thereby enabling antenna selection. The second technique, termed the thresholded ℓ1-norm precoder, involves post-processing the precoder solution obtained from the first method by applying an entry-wise thresholding operation. This work conducts a precise performance analysis to compare these two techniques. The analysis leverages the Gaussian min-max theorem which is effective for examining the asymptotic behavior of optimization problems without explicit solutions. While the analysis of the ℓ1-norm precoder follows from the conventional convex Gaussian min-max theorem framework, understanding the thresholded ℓ1-norm precoder is more complex due to the non-linear behavior introduced by the thresholding operation. To address this complexity, we develop a novel Gaussian min-max theorem tailored to these scenarios. We provide precise asymptotic behavior analysis of the precoders, focusing on metrics such as received signal-to-noise and distortion ratio and bit error rate. Our analysis demonstrates that the thresholded ℓ1-norm precoder can offer superior performance when the threshold parameter is carefully selected. Simulations confirm that the asymptotic results are accurate for systems equipped with hundreds of antennas at the base station, serving dozens of user terminals.

This paper focuses on the performance analysis of time-varying fading channels, introducing a new general metric called fade duration. Fade duration measures the time during which a signal remains below a specified threshold within a fixed time interval. To model the signal, we utilize established models for the inphase and quadrature components, employing stochastic differential equations (SDEs) to capture the continuous-time statistical properties of the fading channel. We estimate the complementary cumulative distribution function (CCDF) of the fade duration in different fading environments using Monte Carlo simulations and analyze how various system parameters impact its behavior. To enhance the efficiency of our estimates, we leverage importance sampling (IS), a well-known variance-reduction technique, for accurately estimating the tail of the CCDF. The proposed IS scheme involves solving a high-dimensional controlled partial differential equation. To overcome the curse of dimensionality, we use Markovian projection to develop a novel one-dimensional SDE for signal envelope variations, enhancing the computational feasibility of IS. We present numerical results for the CCDF of fade duration in Rayleigh and Rice environments using our proposed IS estimators.

For next-generation green communication systems, this article proposes an innovative communication system based on frequency-diverse array-multiple-input multiple-output (FDA-MIMO) technology, which aims to achieve high data rates while maintaining low power consumption. This system utilizes frequency offset index realign modulation, multiple-antenna spatial index modulation, and spreading code index modulation techniques. In the proposed generalized code index modulation-aided frequency offset realign multiple-antenna spatial modulation (GCIM-FORMASM) system, the coming bits are divided into five parts: spatial modulation bits by activating multiple transmit antennas, frequency offset index bits of the FDA antennas, including frequency offset combination bits and frequency offset realign bits, spreading code index modulation bits, and modulated symbol bits. Subsequently, this paper utilizes the orthogonal waveforms transmitted by the FDA to design the corresponding transmitter and receiver structures and provide specific expressions for the received signals. Meanwhile, to reduce the decoding complexity of the maximum likelihood (ML) algorithm, we propose a three-stage despreading-based low complexity (DBLC) algorithm leveraging the orthogonality of the spreading codes. Additionally, a closed-form expression for the upper bound of the average bit error probability (ABEP) of the DBLC algorithm has been derived. Analyzing metrics such as energy efficiency and data rate shows that the proposed system features low power consumption and high data transmission rates, which aligns better with the concept of future green communications. The effectiveness of our proposed methods has been validated through comprehensive numerical results.

Stratospheric solar-powered high-altitude platform station (HAPS) can provide line-of-sight (LoS) communications to the ground users in its ultra-wide coverage area. This paper addresses the challenge of HAPS communication system design especially the access link. We propose to divide the ground users into multiple user-groups and serve each group by a high-density dynamically steerable spotbeam, generated by the phased array antennas mounted on HAPS. We employ time-division multiplexing (TDM) to serve different user groups and non-orthogonal multiple access (NOMA) to simultaneously serve all users within a usergroup. We formulate user grouping problem as an equivalent geometric disk cover (GDC) problem and beam optimization problem as a minimum enclosing circle (MEC) problem. We present the optimization framework to jointly design user grouping, user association, beam optimization, and power allocation aiming at sum rate maximization while guaranteeing the quality-of-service (QoS) with limited power budget. System performance is assessed using the key metrics such as signal-to-interference noise ratio (SINR), achievable data rate, average energy efficiency (AEE), average spectral efficiency (ASE), user fairness and outage probability. We observe upto 42% reduction in required groups, 5dB increase in received SINR, 37.5% improvement in energy efficiency, 57.9% rise in spectral efficiency, 22% enhanced user fairness, 65% surge in achievable data rates and ten-folds reduction in outage with the proposed optimization framework over conventional schemes using system-level simulations. Our findings reveal the significance of joint design of system parameters for enhanced performance, optimum energy utilization, and resource allocation.

Free-space optical (FSO) communication is a promising solution to support wireless backhaul links in emerging 6G non-terrestrial networks. At the link level, pointing errors in FSO links can significantly impact capacity, making accurate modeling of these errors essential for both assessing and enhancing communication performance. In this paper, we introduce a novel model for FSO pointing errors in autonomous aerial vehicles (UAVs) that incorporates three-dimensional (3D) jitter, including roll, pitch, and yaw angle jittering. We derive a probability density function for the pointing error angle based on the relative position and posture of the UAV to the ground station. This model is then integrated into a trajectory optimization problem designed to maximize energy efficiency while meeting constraints on speed, acceleration, and elevation angle. Our proposed optimization method significantly improves energy efficiency by adjusting the UAV’s flight trajectory to minimize exposure to directions highly affected by jitter. The simulation results emphasize the importance of using UAV-specific 3D jitter models in achieving accurate performance measurements and effective system optimization in FSO communication networks. Using our generalized model, the optimized trajectories achieve up to 11.8% higher energy efficiency compared to those derived from conventional Gaussian pointing error models.

In this paper, we consider a low earth orbit satellite communication system with a legitimate user and an eavesdropper and investigate its secrecy performance. Considering the beam coverage of the satellites, we propose a system where a friendly jammer satellite transmits artificial noise signals only to eavesdroppers. We obtain the cumulative distribution functions (CDFs) of the signal-to-interference-plus-noise ratios taking into account the Nakagami-𝑚 fading and the positions of ground users and satellites. The CDFs are used to derive the analytical forms of the probability of non-zero secrecy capacity, secrecy capacity outage probability, and average secrecy capacity. In addition, for tractability and insight, asymptotic forms are also investigated at a high-power regime. Finally, we present the numerical results to validate the obtained expressions and show the diversity order of the secrecy capacity.

The integration of non-terrestrial networks (NTNs), which include high altitude platform (HAP) stations and intelligent reflecting surfaces (IRS) into communication infrastructures has become a crucial area of research to address the increasing requirements for connectivity and performance in the post-5G era. This paper presents a comprehensive performance study of a new NTN architecture, which enables communication from the optical ground station (OGS) to end users through the utilization of HAP and terrestrial IRS nodes. In this configuration, the HAP acts as an amplify-and-forward (AF) relay terminal between the free-space optical (FSO) link and the RF links. Specifically, the RF links are modeled using the Shadowed Rician and the generalized Nakagami-m models, where the FSO link is characterized by the Gamma-Gamma distribution with generalized pointing errors. The FSO system operates under either intensity modulation with direct detection or heterodyne detection. Using the mixture Gamma model, we approximate the non-centered chi-square distribution that describes the total fading of the RF link, and we assess the performance of the end-to-end system by analyzing the ergodic capacity, the average bit-error rate (BER), and the outage probability, calculated using the bivariate Fox-H function. We also provide simple asymptotic expressions for the average BER and the outage probability at high signal-to-noise ratio (SNR). Finally, the proposed analysis is validated with numerical and Monte-Carlo simulation results, showing an exact match.

Reconfigurable intelligent surface (RIS)-assisted wideband terahertz (THz) communications are essential for achieving ultra-high data rates in sixth-generation (6G) networks. By adjusting the phase shifts of reflecting elements, RIS can effectively reshape wireless channels to enhance overall performance. However, two major challenges arise in RIS-assisted THz systems: 1) the dual beam split effect, where the large bandwidth causes subcarrier beam directions to diverge at both base station (BS) and RIS; and 2) the near-field effect, where the channel becomes a nonlinear function of both angle and distance. In this paper, we propose a novel beamforming technique, termed RIS-assisted wideband beamforming (RWB), to address these challenges and maximize data rates in RIS-assisted wideband THz systems. The RWB scheme leverages partially-connected true time delays (TTDs) and phase shifters (PSs) to generate frequency-dependent BS transmit beamforming vectors, while utilizing passive reflecting elements to control the frequency-invariant RIS reflect beamforming vector. By jointly optimizing the transmit and reflect beamforming vectors on the Riemannian manifold of unit-modulus phase shifts, RWB effectively mitigates both beam split and near-field effects. Numerical evaluations demonstrate that RWB achieves substantial data rate improvements over conventional wideband beamforming schemes.

We propose a novel combinatorial stochastic-greedy bandit (SGB) algorithm for combinatorial multi-armed bandit problems when no extra information other than the joint reward of the selected set of n arms at each time step t in [T] is observed. SGB adopts an optimized stochastic-explore-then-commit approach and is specifically designed for scenarios with a large set of base arms. Unlike existing methods that explore the entire set of unselected base arms during each selection step, our SGB algorithm samples only an optimized proportion of unselected arms and selects actions from this subset. We prove that our algorithm achieves a (1-1/e)-regret bound of O(n^(1/3) k^(2/3) T^(2/3) log(T)^(2/3)) for monotone stochastic submodular rewards, which outperforms the state-of-the-art in terms of the cardinality constraint k. Furthermore, we empirically evaluate the performance of our algorithm in the context of online constrained social influence maximization. Our results demonstrate that our proposed approach consistently outperforms the other algorithms, increasing the performance gap as k grows.

Federated learning, an emerging machine learning paradigm, enables clients to collaboratively train a model without exchanging local data. Clients participating in the training process significantly impact the convergence rate, learning efficiency, and model generalization. We propose a novel approach, client filtering, to improve model generalization and optimize client participation and training. The proposed method periodically filters available clients to identify a subset that maximizes a combinatorial objective function with an efficient greedy filtering algorithm. Thus, the clients are assessed as a combination rather than individually. We theoretically analyze the convergence of federated learning with client filtering in heterogeneous settings and evaluate its performance across diverse vision and language tasks, including realistic scenarios with time-varying client availability. Our empirical results demonstrate several benefits of our approach, including improved learning efficiency, faster convergence, and up to 10% higher test accuracy than training without client filtering.

The security of low-earth orbit (LEO) satellite communication systems faces challenges due to the high-speed movement characteristic. In this paper, we study the secure transmission for an ISAC-enabled LEO satellite system by considering the sensing function to enhance security. With the assistance of the sensing capability, more precise angle information of potential targets/eavesdroppers (Eve) can be estimated, thereby strengthening the system's performance. However, it is impossible to avoid the errors. To tackle this problem, we consider the channel uncertainty model and maximize the sum secrecy rate by jointly designing the secure transmit beamforming and radar receive filters. Furthermore, the non-convex problem is transformed into a series of convex optimization sub-problems, and the required optimization parameters are obtained through iterative calculation. Simulation results verify the advantages of the proposed scheme in achieving secure transmission of the LEO satellite system.

This paper introduces a federated learning framework tailored for online combinatorial optimization with bandit feedback. In this setting, agents select subsets of arms, observe noisy rewards for these subsets without accessing individual arm information, and can cooperate and share information at specific intervals. Our framework transforms any offline resilient single-agent $(\alpha-\epsilon)$-approximation algorithm—having a complexity of $\tilde{\mathcal{O}}\left(\frac{\psi}{\epsilon^\beta}\right)$, where the logarithm is omitted, for some function $\psi$ and constant $\beta$—into an online multi-agent algorithm with $m$ communicating agents and an $\alpha$-regret of no more than $\tilde{\mathcal{O}}\left(m^{-\frac{1}{3+\beta}} \psi^\frac{1}{3+\beta} T^\frac{2+\beta}{3+\beta}\right)$. Our approach not only eliminates the $\epsilon$ approximation error but also ensures sublinear growth with respect to the time horizon $T$ and demonstrates a linear speedup with an increasing number of communicating agents. Additionally, the algorithm is notably communication-efficient, requiring only a sublinear number of communication rounds, quantified as $\tilde{\mathcal{O}}\left(\psi T^\frac{\beta}{\beta+1}\right)$. Furthermore, the framework has been successfully applied to online stochastic submodular maximization using various offline algorithms, yielding the first results for both single-agent and multi-agent settings and recovering specialized single-agent theoretical guarantees. We empirically validate our approach to a stochastic data summarization problem, illustrating the effectiveness of the proposed framework, even in single-agent scenarios.

In complex environments with large discrete action spaces, effective decision-making is critical in reinforcement learning (RL). Despite the widespread use of value-based RL approaches like Q-learning, they come with a computational burden, necessitating the maximization of a value function over all actions in each iteration. This burden becomes particularly challenging when addressing large-scale problems and using deep neural networks as function approximators. In this paper, we present stochastic value-based RL approaches which, in each iteration, as opposed to optimizing over the entire set of $n$ actions, only consider a variable stochastic set of a sublinear number of actions, possibly as small as $\mathcal{O}(\log(n))$. The presented stochastic value-based RL methods include, among others, Stochastic Q-learning, StochDQN, and StochDDQN, all of which integrate this stochastic approach for both value-function updates and action selection. The theoretical convergence of Stochastic Q-learning is established, while an analysis of stochastic maximization is provided. Moreover, through empirical validation, we illustrate that the various proposed approaches outperform the baseline methods across diverse environments, including different control problems, achieving near-optimal average returns in significantly reduced time.

Free-space optics (FSO) is slated as a promising solution to support extremely high data rates for future satellite communications (SatCom). Nonetheless, FSO transmission is susceptible to atmospheric turbulence impacts. Space-air-ground (SAG) FSO and hybrid single-hop (SH) FSO/radio frequency (RF) transmission systems are suggested to enhance the performance and reliability of FSO-based SatCom systems. They can also be integrated to further improve system performance. Previous work analyzed the performance of the resulting integrated SAG-FSO/SH-FSO/RF transmission system, assuming independent turbulence effects. In this paper, we perform a capacity analysis of the system in the presence of correlated turbulence. A novel analytical expression for the end-to-end ergodic capacity is derived and validated by Monte Carlo simulations. The numerical results demonstrate that, although the correlated turbulence adversely affects its performance, the integrated transmission system can still achieve a considerable capacity gain over existing solutions.

As advancements close the gap between current device capabilities and the requirements for terahertz (THz)-band communications, the demand for terabit-per-second (Tbps) circuits is on the rise. This paper addresses the challenge of achieving Tbps data rates in THz-band communications by focusing on the baseband computation bottleneck. We propose leveraging parallel processing and pseudo-soft information (PSI) across multicarrier THz channels for efficient channel code decoding. We map bits to transmission resources using shorter code-words to enhance parallelizability and reduce complexity. Additionally, we integrate channel state information into PSI to alleviate the processing overhead of soft decoding. Results demonstrate that PSI-aided decoding of 64-bit code-words halves the complexity of 128-bit hard decoding under comparable effective rates, while introducing a 4dB gain at a 10−3block error rate. The proposed scheme approximates soft decoding with significant complexity reduction at a graceful performance cost.

Space-air-ground integrated networks (SAGINs) will play a pivotal role in 6G communication systems. They are considered a promising technology for enhancing network capacity in densely populated urban areas and extending connectivity to rural regions. However, the complex, multilayered, and heterogeneous nature of SAGINs demands an innovative approach to designing their multi-tier associations. In this context, we propose a modeling of the SAGINs association problem using multi-sided matching theory. Our objective is to devise a reliable, asynchronous, and fully distributed approach that associates nodes across the layers to maximize the total end-to-end rate of the assigned agents. To achieve this, our problem is formulated as a multi-sided many-to-one matching game. We introduce a randomized matching algorithm with minimal information exchange. The algorithm is shown to reach an efficient and stable association between nodes in adjacent layers. Simulation results show that our proposed approach yields significant gains compared to both greedy and distance-based algorithms,

Indoor positioning systems (IPS) are gaining higher attention recently due to the increased demand for indoor location aware services. Visible light communication (VLC) is a promising technology to use for IPS. In particular, received signal strength (RSS) based visible light positioning (VLP) systems are gaining high attention due to their low complexity and cost, in addition to higher positioning accuracy compared to their radio frequency (RF) counterparts. One of the main challenges in RSS based VLP systems is encountered when the receiver (the target) is tilted and not placed in parallel with the transmitters (the anchors). RSS based trilateration techniques require a computationally expensive and time-consuming process to solve the nonlinear problem of tilted receivers. Fingerprint based systems generally provide high positioning accuracy with short positioning time, and maybe used to circumvent the need to deal with the high complexity associated with tilted receivers. However, the design of a fingerprinting VLP system for tilted receiver has not been explored yet as far as receivers with a single photodetector (PD) are concerned. In this work, a fingerprint based VLP system for tilted receivers using artificial neural networks (ANN) is proposed, where different types of input features for training the positioning algorithm are studied. We show that using the components of the normal vector to the PD's surface in addition to RSS values provides an excellent positioning accuracy with an average positioning error of 25.41 cm and a remarkably low average positioning time less than$\mathbf{5} \boldsymbol{\mu} \boldsymbol{s}$. In addition, important research directions for future work are discussed.

This study investigates the secrecy performance of a rate-splitting multiple-access system inspired by cognitive radio principles, where the primary user takes on the role of a potential eavesdropper seeking to intercept the secondary user's message. The on-off scheme is utilized to guarantee the transmission of the primary user. We provide closed-form expressions for critical metrics, including the transmission probability of the system under consideration, the secrecy outage probability, and the reliable outage probability of the cognitive user. Monte Carlo simulation results are presented to substantiate our findings and explore the impact of system parameters, such as transmit power and the number of antennas at the base station, on system performance.

Among many envisaged drivers for sixth generation (6G), one is from the United Nation’s Sustainability Development Goals 2030 to eliminate digital inequality. Remote coverage in sparsely populated areas, difficult terrains or emergency scenarios requires on-demand access and flexible deployment with minimal capex and opex. In this context, network-in-a-box (NIB) is an exciting solution which packs the whole wireless network into a single portable and reconfigurable box to support multiple access technologies, such as WiFi, 2G–5G, etc. In this article, we propose low-altitude platform station (LAPS)-based NIBs with stratospheric high-altitude platform station (HAPS) as backhaul. Specifically, backhaul employs nonorthogonal multiple access (NOMA) with superposition coding at the transmitting HAPS and successive interference cancellation (SIC) at the receiving NIBs, whereas the access link (AL) employs superposition coding along with the regularized zero-forcing (RZF) precoding at the NIB in order to elevate the computational overhead from the ground users (GUs). The required number of airborne NIBs to serve a desired coverage area, their optimal placement, user association (UA), beam optimization, and resource allocation are optimized by maximizing the sum rate of the AL while maintaining the quality of service. Our findings reveal the significance of thorough system planning and communication parameters optimization for enhanced system performance and best coverage under limited resources.

In this article, we study a reconfigurable intelligent surfaces (RISs)-assisted Terahertz (THz) wireless systems with hardware impairments, where$\alpha \!-\!\mu $small-scale fading is considered for THz links in accordance with a recent measurement campaign. First, we propose an accurate closed-form approximation of a weighted sum of cascaded nonidentical$\alpha \!-\!\mu $variates based on the Gauss-Laguerre quadrature and a moment-matching method. This approximate approach facilitates analysis of the RIS-THz system over$\alpha \!-\!\mu $fading channels. To demonstrate, we derived closed-form expressions of the outage probability (OP), the ergodic capacity (EC), and the energy efficiency (EE) of the system based on the proposed approximation. Second, we approximately characterize the end-to-end channel of the RIS-THz system when the number of RIS elements is large in scenarios with or without the presence of phase-shift errors. Based on this statistical characterization, the closed-form expressions of the OP, the EC, and the EE of the large-size RIS-THz system are obtained. Furthermore, we devise a low-complexity algorithm that jointly optimizes the transmit power and RIS element activation (i.e., ON/OFF RIS) to maximize the EE in the RIS-THz systems. This algorithm adopts an iterative dynamic programming approach for a maximum subarray problem (i.e., Kadane’s algorithm). Finally, simulations are provided to validate the accuracy of the theoretical analysis as well as demonstrate the efficacy of the devised algorithm.

Millimeter-wave (mmWave) communication is one of the effective technologies for the next generation of wireless communications due to the enormous amount of available spectrum resources. Rate splitting multiple access (RSMA) is a powerful multiple access, interference management, and multiuser strategy for designing future wireless networks. In this work, a multiple-input-single-output mmWave RSMA system is considered wherein a base station serves two users in the presence of a passive eavesdropper. Different eavesdropping scenarios are considered corresponding to the overlapped resolvable paths between the main and the wiretap channels under the considered transmission schemes. The analytical expressions for the secrecy outage probability (SOP) are derived respectively through the Gaussian–Chebyshev quadrature method. Monte Carlo simulation results are presented to validate the correctness of the derived analytical expressions and demonstrate the effects of system parameters on the SOP of the considered mmWave RSMA systems.

In this article, we analyze the Peak Age of Information (PAoI) in unmanned aerial vehicle (UAV)-assisted Internet of Thing (IoT) networks, in which the locations of IoT devices are modeled by a () and UAVs are deployed at the cluster centers to collect the status updates from the devices. Specifically, we consider that IoT devices can either monitor the same physical process or different physical processes and UAVs split their resources, time or bandwidth, to serve the devices to avoid intercluster interference. Using tools from stochastic geometry, we are able to compute the mean activity probability of IoT devices and the conditional success probability of an individual device. We then use tools from queuing theory to compute the PAoI under two load models and two scenarios for devices, respectively. Our numerical results show interesting system insights. We first show that for a low-data arrival rate, increasing the number of correlated devices can improve the PAoI for both load models. Next, we show that even though the time-splitting technique causes higher interference, it has a limited impact on the mean PAoI, and the mean PAoI benefits more from the time-splitting technique. This is because of the nature of UAV communication, especially at places where devices (users) are spatially clustered: shorter transmission distances and better communication channels, comparing the links established by the cluster UAV and serving devices (users) to links established by interferers.

This paper analyzes the uplink performance of IoT devices communicating with LEO satellites. We consider a scenario where IoT devices are deployed in hard-to-reach areas; hence, energy harvesting is the only sustainable solution. Due to the battery-free assumption of IoT devices, we consider a K-tier PB network to provide a sustainable energy supply for the IoT network. We particularly focus on two uplink communication scenarios: (i) direct communication from IoT devices to LEO satellites and (ii) indirect communication via GWs. The system’s performance is evaluated based on the two-stage ’harvest-then-transmit’ strategy, in which the IoT device harvests energy from all available energy sources during the first stage, referred to as the harvesting sub-slot. The harvested energy is then utilized to transmit information to its desired receiver in the second stage, known as the communication sub-slot. More precisely, we derive the joint uplink coverage probability if the following two conditions are satisfied: (i) the transmit power of the typical IoT device is sufficient, and (ii) the received SINR is above a predefined threshold. Furthermore, we investigate the effect of the proposed techniques on performance metrics such as the average number of successful data packet transmissions per day as well as the average amount of data uploaded per day. Finally, we explore the optimal network parameters to improve energy harvesting, communication reliability, and coverage for both direct and indirect scenarios.

The extensive deployment of low earth orbit (LEO) satellites introduces significant security challenges for communication security issues in Internet of Things (IoT) networks. With the rising number of satellites potentially acting as eavesdroppers, integrating physical-layer security (PLS) into satellite communications has become increasingly critical. However, these studies are facing challenges, such as dealing with dynamic topology difficulties, limitations in interference analysis, and the high complexity of performance evaluation. To address these challenges, for the first time, we investigate the PLS strategies in satellite communications using the stochastic geometry (SG) analytical framework. We consider the uplink communication scenario in an LEO-enabled IoT network, where the multitier satellites from different operators, respectively, serve as the legitimate receivers and eavesdroppers. In this scenario, we derive low-complexity analytical expressions for the security performance metrics, namely availability probability, successful communication probability, and secure communication probability. By introducing the power allocation parameters, we incorporate the artificial noise (AN) technique, which is an important PLS strategy, into this the analytical framework, and evaluate the gains it brings to secure transmission. In addition to the AN technique, we also analyse the impact of constellation configuration, physical-layer parameters, and network layer parameters on the aforementioned metrics.

Long Range -Frequency Hopping Spread Spectrum (LR-FHSS) framework is a promising technology to enable Direct-to-Satellite (DtS) IoT systems with extensive coverage and high resistance to interference while maintaining costeffectiveness. However, this system currently implements primitive channel coding in Frequency-Hopping Spread-Spectrum (FH-SS) without considering the characteristics of the interference signal. In this work, we propose an innovative coded frequency-hopping (FH) design that incorporates Segment-Level Coding (SLC) in high order Galois field (GF) with erasure detection to enhance immunity against clustered errors commonly encountered in FH-SS, thereby improving the reliability of DtS communication. Additionally, our design inherits the packet structure from LR-FHSS, enabling specific applicability in realworld scenarios. We have also established an analytical model to validate our proposed design in terms of Packet loss rate (PLR) and energy consumption. The mathematical analyses and simulation of the proposed scheme quantify the effectiveness of this enhancement. The numerical results show that the proposed system can accommodate 20 times more users compared to LR-FHSS at a packet loss rate of 0:001, and it costs only approximately 50% of the energy consumption when achieving equivalent performance.

Modern technology has contributed to the rise of high-dimensional data in various domains such as bio-informatics, chemometrics, and face recognition. In the recent literature, random projections and, in particular, randomly-projected ensembles based on the classical Linear Discriminant Analysis (LDA), have been proposed for classification problems involving such high-dimensional data. In this work, we study the two main classes of randomly-projected LDA ensemble classifiers, namely discriminant averaging and vote averaging. Through asymptotic analysis in a growth regime where the problem dimensions are assumed to grow at constant rates to each other for a fixed ensemble size, we determine the exact mechanism through which the ensemble size affects the classification performance. Furthermore, we investigate whether projection selection truly matters in an ensemble setting, and, ultimately, derive the optimal form of the randomly-projected LDA ensemble. Motivated by these findings, we propose a framework for efficient tuning of the optimal classifier's ensemble size and projection dimension based on an estimator of the classifier probability of misclassification which is consistent under the assumed growth regime. The proposed framework is shown to outperform the existing rule-of-thumb, as well as other methods for parameter tuning, on both real and synthetic data.

In recent years, LEO satellite constellations have become envisioned as a core component of the next-generation wireless communication networks. The successive establishment of mega satellite constellations has triggered further demands for satellite communication advanced features: high reliability and low latency. In this article, we first establish a multi-objective optimization problem that simultaneously maximizes reliability and minimizes latency, then we solve it by two methods. According to the optimal solution, ideal upper bounds for reliability and latency performance of LEO satellite routing can be derived. Next, we design an algorithm for relay satellite subset selection, which can approach the ideal upper bounds in terms of performance. Furthermore, we derive analytical expressions for satellite availability, coverage probability, and latency under the stochastic geometry (SG) framework, and the accuracy is verified by Monte Carlo simulation. In the numerical results, we study the routing performance of three existing mega constellations and the impact of different constellation parameter configurations on performance. By comparing with existing routing strategies, we demonstrate the advantages of our proposed routing strategy and extend the scope of our research.

With the expansive deployment of ground base stations, low Earth orbit (LEO) satellites, and aerial platforms such as unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs), the concept of space-air-ground integrated network (SAGIN) has emerged as a promising architecture for future 6G wireless systems. In general, SAGIN aims to amalgamate terrestrial nodes, aerial platforms, and satellites to enhance global coverage and ensure seamless connectivity. Moreover, beyond mere communication functionality, computing capability is increasingly recognized as a critical attribute of sixth generation (6G) networks. To address this, integrated communication and computing have recently been advocated as a viable approach. Additionally, to overcome the technical challenges of complicated systems such as high mobility, unbalanced traffics, limited resources, and various demands in communication and computing among different network segments, various solutions have been introduced recently. Consequently, this paper offers a comprehensive survey of the technological advances in communication and computing within SAGIN for 6G, including system architecture, network characteristics, general communication, and computing technologies. Subsequently, we summarize the pivotal technologies of SAGIN-enabled 6G, including the physical layer, medium access control (MAC) layer, and network layer. Finally, we explore the technical challenges and future trends in this field.

Nonterrestrial networks (NTN) are pivotal, enabling technologies for achieving global and ubiquitous connectivity in the sixth generation (6G) of wireless systems. On the other hand, nonorthogonal multiple access (NOMA) emerges as a promising candidate for the next-generation multiple access (NGMA), designed to enable massive connectivity and improve spectral efficiency. In this article, we investigate the synergy between NTN and NOMA [power-domain NOMA (PD-NOMA)], which are integral in addressing the connectivity challenges of 6G. First, we present an overview of NTN, detailing their types, unique characteristics, and the challenges they face. Then, we explain the foundational principles of NOMA, such as power allocation and access strategies. We, then, argue the suitability of NOMA as an NGMA technology for 6G and how it addresses the specific challenges associated with NTN. Furthermore, we explore the integration and the interplay of NTN and NOMA with emerging technologies such as millimeter-wave (mmWave), terahertz (THz) frequencies, reconfigurable intelligent surfaces (RISs), and integrated sensing and communication (ISAC) systems. These advancements are critical in meeting the 6G requirements for extremely high data rates and improved spectral efficiency, enhancing the overall functionality of NTN and NOMA. We, then, examine how NTN using NOMA can use different architectural frameworks, including cellular, cell-free (CF), ad hoc, and integrated access and backhaul (IAB) systems, and detailed several use cases where the integration of NTN and NOMA could have a significant impact, such as in vehicular communications, the Internet of Things (IoT), urban air mobility (UAM), and achieving global connectivity. To optimize NTN using NOMA, we present a comprehensive overview of the mathematical optimization algorithms and machine learning tools. Finally, we elaborate on the primary challenges associated with merging NTN with NOMA and propose relevant future research avenues.

This paper explores the evolution of wireless communication networks from utilizing the sub-6 GHz spectrum and the millimeter wave frequency band to incorporating extremely high frequencies like optical and terahertz for 6G and beyond. While these higher frequencies offer broader bandwidths and extreme data rate capabilities, the transition from single-band and heterogeneous networks to multi-band networks (MBNs), where various frequency bands coexist introduces novel challenges in channel modeling, transceiver and antenna design, programmable simulation platforms, standardization, and resource allocation. This paper provides a tutorial overview from the communication design perspective of the various frequency bands, elaborating on the above issues. Then, we introduce and examine typical MBN architectures for future networks and provide a detailed overview of state-of-the-art resource allocation problems for existing MBNs that typically operate on two frequency bands. The considered resource allocation optimization problems and solution techniques are discussed comprehensively. We then identify key performance metrics and constraint sets that should be considered for resource allocation optimization in future MBNs and provide numerical results to depict how various system parameters and user behaviors can influence their performance. Finally, we present several potential research issues as future work for the design and performance optimization of MBNs.

The evolvement of wireless communication services concurs with significant growth in data traffic, thereby inflicting stringent requirements on terrestrial networks. This work invigorates a connectivity solution that integrates aerial and terrestrial communications with a cloud-enabled high-altitude platform station (C-HAPS) to promote an equitable connectivity landscape. The C-HAPS system is connected to terrestrial base-stations and hot-air balloons via a data-sharing fronthauling strategy. The base-stations and hot-air balloons are then grouped into disjoint clusters and coordinately serve both aerial and terrestrial users. The paper focuses on determining the user-to-transmitter scheduling policy and the associated users’ beamforming vectors in the downlink direction of the considered network by maximizing two different objectives: the sum-rate and sum-of-log of the long-term average rate, both subject to limited transmit power and finite fronthaul capacity. The paper uses well-chosen convexification and approximation steps, such as fractional programming and sparse beamforming via re-weighted$\ell _{0}$-norm approximation, to solve the two non-convex discrete and continuous optimization problems using numerical iterative optimization algorithms. The results outline the gain illustrated through equitable access service in crowded and unserved areas and showcase the numerical benefits stemming from the proposed C-HAPS coordination of hot-air balloons and terrestrial base-stations for empowering the digital inclusion framework.

As air-to-ground links tend to exhibit a high probability of being line-of-sight (LoS), unmanned aerial vehicles (UAVs) are widely used to improve the performance of wireless communications. The design of the UAV flight path plays a pivotal role in determining the effectiveness of UAV communication systems. However, the air-to-ground links with a high probability of LoS introduce a heightened risk of eavesdropping, posing a significant security challenge. In this work, we investigate the problem of ensuring secure data acquisition for quadrotor UAV-based communication systems in the presence of multiple location-uncertain terrestrial eavesdroppers. The bandwidth allocation and the three-dimensional trajectory of the UAV are jointly designed to maximize the system’s overall fair secrecy rate. This design also considers the UAV’s energy consumption during flight and aims to ensure fairness among users. Solving this problem poses a challenge since it is a non-convex and involves multiple variables, making it difficult to address using conventional optimization methods. Therefore, a deep reinforcement learning algorithm is developed based on the deep deterministic policy gradient algorithm. Simulation results are given to verify the effectiveness of the proposed algorithm in improving the security of aerial Internet of Things systems.

The deployment of 5G networks is sometimes questioned due to the impact of ElectroMagnetic Field (EMF) generated by Radio Base Station (RBS) on users. The goal of this work is to analyze such issue from a novel perspective, by comparing RBS EMF against exposure generated by 5G smartphones in commercial deployments. The measurement of exposure from 5G is hampered by several implementation aspects, such as dual connectivity between 4G and 5G, spectrum fragmentation, and carrier aggregation. To face such issues, we deploy a novel framework, called5G-EA, tailored to the assessment of smartphone and RBS exposure through an innovative measurement algorithm, able to remotely control a programmable spectrum analyzer. Results, obtained in both outdoor and indoor locations, reveal that smartphone exposure (upon generation of uplink traffic) dominates over the RBS one. Moreover, Line-of-Sight locations experience a reduction of around one order of magnitude on the overall exposure compared to Non-Line-of-Sight ones. In addition, 5G exposure always represents a small share (up to 38%) compared to the total one radiated by the smartphone.

Hyperloop or evacuated-tube transportation is a groundbreaking technology that can reach aircraft-like speeds. Its uncommon configuration of a steel-made tube isolates the moving pod from the outside wireless world. In this work, we propose an inner tube network architecture that can provide the moving pod with a seamless and reliable connection. The proposed network consists of successive access points (APs) and intelligent reflecting surfaces (IRS) strategically positioned along the tube and connected to a control station (CS) through wired links to improve the wireless cell coverage. The subsequent entities of the proposed design are intelligently placed along the movement path, steering the transmitted beam towards the receiver, while a soft handover is achieved between consecutive cells. First, we optimize each IRS's positioning and phase shifts to maximize cell coverage thanks to the IRS scanning abilities while keeping a minimum quality of service. Afterward, we exploit the centralized operation at the CS and design a soft handover scheme for the inner-tube wireless network. The numerical results show that the proposed approach provides good cell coverage and spectral efficiency with different IRS scanning ranges.

The selection of an optimal photodetector area is closely linked to the attainment of higher data rates in optical wireless communication receivers. If the photodetector area is too large, the channel capacity degrades due to lower modulation bandwidth of the detector. A smaller photodetector maximizes the bandwidth, but minimizes the captured signal power and the subsequent signal-to-noise ratio. Therein lies an opportunity in this trade-off to maximize the channel rate by choosing the optimal photodetector area. In this study, we have optimized the photodetector area in order to maximize the channel capacity of a free-space optical link for a diverse set of communication scenarios. We believe that the study in this paper in general—and the closed-form solutions derived in this study in particular—will be helpful to maximize achievable data rates of a wide gamut of optical wireless communication systems: from long range deep space optical links to short range indoor visible light communication systems.

The limited modulation bandwidth of the light emitting diodes (LEDs) presents a challenge in the development of practical high-data-rate visible light communication (VLC) systems. In this paper, a novel adaptive coded probabilistic shaping (PS)-based nonorthogonal multiple access (NOMA) scheme is proposed to improve spectral efficiency (SE) of VLC systems in multiuser uplink communication scenarios. The proposed scheme adapts its rate to the optical signal-to-noise ratio (OSNR) by utilizing non-uniformly distributed discrete constellation symbols and low complexity channel encoder. Furthermore, an alternate optimization algorithm is proposed to determine the optimal channel coding rate, constellation spacing, and probability mass function (PMF) of each user. The extensive numerical results show that the proposed PS-based NOMA scheme closely approaches the capacity of NOMA with fine granularity. Presented results demonstrate the effectiveness of our scheme in improving the SE of VLC systems in multiuser scenarios. For instance, our scheme exhibits substantial SE gains over existing schemes, namely, the pairwise coded modulation (PCM), geometric shaping (GS), and uniform-distribution schemes. These findings highlight the potential of our approach to significantly enhance VLC systems.

Received signal strength (RSS)–based cooperative localization has gained significant attention due to its straightforward system architectures and cost-effectiveness. In this paper, we propose Cooperative Localization Techniques (with Unknown Parameters), referred to as CTUP(s), which consider uncertainty in anchor nodes’ locations and assume the transmit power and path loss exponent (PLE) to be unknown. Unlike prior studies, CTUP(s) address unknowns by estimating these parameters, along with the location of target nodes. The non-convex and non-linear nature of the maximum likelihood (ML) estimator of the problem is addressed through relaxation techniques, employing Taylor series expansion, semidefinite relaxation (SDR), and the epigraph method. The resulting problem is solved using semidefinite second-order cone programming (SDP-SOCP), leveraging the precision of SDP and the simplicity of SOCP. We deployed an extensive network comprising 50 BLE nodes covering an area of 640 m$\times 180$m to gather RSS data. The precise location of the nodes is obtained using real-time kinematics global positioning system (RTK-GPS), which is treated as the ground truth. Furthermore, to replicate real-world scenarios, we recorded the positions of the anchor nodes using a standard GPS, thereby introducing uncertainty into the anchor node locations. Extensive simulation and hardware experimentation demonstrate the superior performance of CTUP compared to existing techniques.

The conventional method of designing the desired beampattern in MIMO systems involves the computation of the waveform covariance and/or weight matrices. In this work, through some simple derivations, it is demonstrated that there are infinitely many covariance matrices that yield the same beampattern. Furthermore, when beam is created using weighted sums of orthogonal waveforms, there are infinitely many weight matrices that also generate the same beampattern. Thus, the conditions for the beampattern invariance are formulated with respect to the covariance and the weight matrices. This theoretical foundation allows the transmitted waveform to be altered, without changing the beampattern and the orthogonal radar waveforms. Methods for the computation of beampattern-invariant covariance and weight matrices are proposed. Consequently, it is demonstrated that there is additional degrees of freedom in the design space of many applications, such as the dual-function radar communication (DFRC), in which information can be embedded in radar transmissions while keeping the radar function intact. Other potential applications are peak-to-average power ratio (PAPR) reduction at the radar transmitter and deceptive jamming avoidance.

In this paper, we consider a novel cellular network for aerial users, which is composed of dedicated base stations (BSs), whose antennas are directed towards aerial users, and traditional terrestrial BSs (TBSs). Besides, the dedicated BSs are deployed on roadside furniture, such as lampposts and traffic lights, to achieve multiple features while occupying less space. Therefore, the locations of dedicated BSs and TBSs are modeled by a Poisson-line-Cox-process (PLCP) and Poisson point process (PPP), respectively. For the proposed network, we first compute the aerial coverage probability and show that the deployment of dedicated BSs improves the coverage probability in both high dense areas and rural areas. We then consider a cellular-connected UAV that has a flying mission and optimize its trajectory to maximize the minimal achievable signal-to-interference-plus-noise ratio (SINR) (Max-Min SINR). To obtain the Max-Min SINR and minimal time trajectory that satisfies the Max-Min SINR, we proposed two algorithms that are practical in large-scale networks. Finally, our results show that the optimal density of dedicated BSs which maximizes Max-Min SINR decreases with the increase of the road densities.

This paper investigates the role of reconfigurable intelligent surface (RIS) for enhanced handover management in 6G networks with overlapping wireless network layers i.e., ground, aerial, and space. A high altitude platform system (HAPS) is equipped with RIS and the comparison of direct and RIS-assisted link performance is presented. To do this, the average bit error rate, the outage probability (OP), and the channel capacity of direct and HAPS-assisted links are obtained in closed form expressions. By estimating the link error probability, a novel handover mechanism exploits the use of RIS, resulting in enhanced connectivity management. Hard and soft handover modes are defined depending on the radio frequency (RF) connectivity by means of link error threshold. The decision for the handover execution is based on selecting the optimal link among multiple available ones from ground, aerial, and space layers. The performance of RIS-assisted handover mechanism has been compared to traditional hard and soft handover procedure, as well as link performance achieved with relay node. Numerical and simulation results reveal that the connectivity of a user equipment can be maintained through RIS-aided links in case of any performance degradation in the direct RF links. Particular scenarios highlight that the use of RIS for handover is preferred to traditional handover procedures, as well as relay nodes.

This article studies the reliability of multi-hop routing in a multi-tier hybrid satellite-terrestrial relay network (HSTRN). We evaluate the reliability of multi-hop routing by introducing interruption probability, which is the probability that no relay device (ground gateway or satellite) is available during a hop. The single-hop interruption probability is derived and extended to the multi-hop interruption probability using a stochastic geometry-based approach. Since the interruption probability in HSTRN highly depends on the priority of selecting communication devices at different tiers, we propose three priority strategies: (i) stationary optimal priority strategy, (ii) single-hop interruption probability inspired strategy, and (iii) density inspired strategy. Among them, the interruption probability under the stationary optimal priority strategy can approach the ideal lower bound. However, when analyzing an HSTRN with a large number of tiers, the stationary optimal priority strategy is computationally expensive. The single-hop interruption probability inspired strategy is expected to be a low-complexity but less reliable alternative to the stationary optimal priority strategy. In numerical results, we study the complementarity between terrestrial devices and satellites. Furthermore, analytical results for reliability are also applicable to the analysis of satellite availability, coverage probability, and ultra-reliable and low latency communications (URLLC) rate. Finally, we extend our original routing strategy into a multi-flow one with dynamic priority strategy.

In this work, a mixed underwater acoustic/optical wireless transmission system under both amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols is proposed. Assuming pointing errors and both heterodyne detection (HD) as well as intensity modulation/direct detection (IM/DD) techniques in the underwater optical link, we deduce the exact analytical formulas for the outage probability (OP), average bit error rate (ABER), and average capacity of the system under consideration. Also, we further derive the corresponding asymptotic expressions to gain intuitive physical insights about the system and channel models under consideration. Additionally, we extend the analysis to a more general multi-sensor system. Finally, we check the analytical results by Monte Carlo simulations.

Joint sensing and communication (JSAC) is regarded as a promising technology for future networks, which can reuse most devices of the systems in sensing and communication (S&C) and reduces the cost in terms of power and spectrum (P&S) critically. The current research considers the P&S allocation of S&C separately and then discusses the performance from different aspects. However, as an integrated system, the allocation strategy of P&S allocation affects the joint performance significantly. In this article, we use tools from stochastic geometry to study the coverage performance considering the trade-off of P&S allocation for JSAC with the principle requirements of small distance resolution (SDR) in sensing and high data rate (HDR) in communication. In particular, we model the locations of user equipment (UE) and base stations (BSs) as two different Poisson Point Processes and allocate P&S at BSs with two independent ratios. The sensing system will detect the surrounding environment and obtain UE positions. After that, an adaptive beamwidth for beamforming technology is applied in communication, which can save energy effectively. First, we introduce the distance resolution in sensing and special channel models in S&C with a high frequency. Then, considering the proposed system model, we separately model the interference in S&C. Further, the joint coverage probability (CP) of JSAC is derived as a function of densities of UE and BSs, required HDR and SDR, and allocation ratios of P&S. Finally, We draw multiple valuable system-level insights from the proposed analysis. For instance, we show that the SDR and HDR are the two main constraints to the maximum achievable CP with optimized allocations of P&S. Furthermore, we show that different densities of BSs should be considered in various scenarios. The revealed relationship between the densities of UE and BSs can be taken as a reference in practical applications.

Reconfigurable intelligent surfaces (RIS) have been advocated as a promising technology to overcome blockage issues in mmWave communications caused by severe propagation absorption and high directivity. This paper investigates the design of finite resolution phase shifters at the RIS to maximize the signal-to-noise ratio of a point-to-point multiple-input single output mmWave communication system. Both the transmitting antennas at the base station and the reflecting elements on the RIS are modeled as uniform planar arrays. The optimization of the discrete RIS design remains a computationally expensive procedure, especially for large reflecting surfaces and high resolution phase shifts. As a solution, we propose in this work a low-complexity suboptimal approach that exploits the structure of mmWave propagation channels. Specifically, the developed algorithms rely on decomposing the reflecting beamforming vectors and the channel path vectors into Kronecker products of factors of uni-modulus vectors. In addition to the computational complexity advantage, the proposed solutions also promise to require only partial information of the cascaded channel rather than the full one, the estimation of which is more practically convenient due to the passive nature of the RIS. To enable the proposed reflecting beamforming designs, we propose a channel estimation technique that invokes the atomic norm minimization framework to estimate the parameters of the channel, namely, the path’s magnitudes and their associated departure and arrival angles. Simulation results confirm the superiority of the proposed reflecting design and channel estimation scheme as compared to other existing techniques.

Millimeter-wave (mmWave) integrated access and backhaul (IAB) has recently received considerable interest for its advantage in reducing the expenses related to the deployment of fiber optics, such as the Terragraph proposed by Meta’s Connectivity Lab. Terragraph networks aim to provide high-speed internet access to dense urban environments. However, due to the vulnerability to blockages and high path loss associated with mmWave frequencies, the proper deployment planning of mmWave networks is required to achieve the desired service quality. By obtaining a stable power supply through its tether connected to the ground, tethered unmanned aerial vehicle (UAV)-mounted base station (BS) can provide reliable communication service with the sacrifice of limited mobility. In this paper, we investigate the potential of incorporating tethered UAVs into Terragraph-like networks. To this end, we propose a novel deep reinforcement learning (DRL) framework that aims to minimize the overall deployment cost by optimizing the number of required UAVs and terrestrial BSs (TBSs), the hovering positions of deployed UAVs, and the multi-hop backhauling topology. Unlike the conventional DRL frameworks that focus on maximizing the expected cumulative or average reward, we formulate the proposed framework based on the max-Bellman optimality equation in order to maximize the maximum reward. Numerical results reveal that the proposed algorithm is able to yield significant reduction in terms of deployment cost. We also use case studies from cities in Asia, Europe, and North America to verify the practical applicability of the proposed framework.

Age of information (AoI) has introduced a new dimension into the design of real-time monitoring networks. However, how to achieve energy-efficient freshness-aware information transmission with energy-constrained Internet of Things (IoT) devices remains a challenge. To solve this challenge, in this paper, we consider a new metric called freshness-aware energy efficiency (FAEE), which is defined as the ratio of AoI improvement to transmission energy consumption. We study a freshness-aware integrated access and backhaul (IAB) network in which multiple IoT devices (IDs) that are accountable for generating and transmitting status updates frequently over time so as to maintain the freshness of information observed at the IAB donor. For this system setup, we first formulate three mixed discrete-continuous optimization problems in order to maximize the long-term average FAEE under different transmission scheduling schemes, including round-robin (RR), sub-channel allocation (SA), and time-resource allocation (TA). By constructing average-reward Markov decision processes (MDPs) with mixed discrete-continuous action space to model these optimization problems, we propose a novel proximal policy optimization (PPO) based deep reinforcement learning (DRL) framework, which is referred to as APO-CD, to learn sub-optimal policies for status update strategy and resource allocation. Extensive simulation results are provided to show the effectiveness of our proposed algorithm and to reveal several design insights of freshness-aware IAB networks.

We investigate the problem of unconstrained combinatorial multi-armed bandits with full-bandit feedback and stochastic rewards for submodular maximization. Previous works investigate the same problem assuming a submodular and monotone reward function. In this work, we study a more general problem, i.e., when the reward function is not necessarily monotone, and the submodularity is assumed only in expectation. We propose Randomized Greedy Learning (RGL) algorithm and theoretically prove that it achieves a $\frac{1}{2}$-regret upper bound of $\tilde{\mathcal{O}}(n T^{\frac{2}{3}})$ for horizon $T$ and number of arms $n$. We also show in experiments that RGL empirically outperforms other full-bandit variants in submodular and non-submodular settings.

Low latency and high throughput are critical features for 5G mobile communication systems and beyond, in which the support of large MIMO is essential. Signal detection in large Multiple-Input Multiple-Output (MIMO) is a paramount component of a communication system since its performance in terms of latency, error rate, and achieved throughput depends on it. In this paper, we demonstrate the ability of our proposed massively parallel non-linear detection approach to support a large number of antennas and sustain high throughput at the extreme low latency of next-generation mobile communication systems. Our proposed method operates on a search tree that models all possible combinations of the transmitted signal. It selects coefficients from different levels and navigates the tree toward the Maximum Likelihood (ML) solution. To maintain the low latency requirement, we leverage the significant computational power of the Graphics Processing Unit (GPU) by expressing operations in terms of matrix-matrix multiplications. The obtained results show the ability of our non-linear detection approach to deal with up to 120 antennas with one-millisecond latency while satisfying good error rate performance at a practical signal-to-noise ratio (SNR).

Satellite networks are playing an important role in realizing global seamless connectivity in beyond 5G and 6G wireless networks. In this paper, we develop a comprehensive analytical framework to assess the performance of hybrid terrestrial/satellite networks in providing rural connectivity. We assume that the terrestrial base stations are equipped with multiple-input-multiple-output (MIMO) technologies and that the user has the option to associate with a base station or a satellite to be served. Using tools from stochastic geometry, we derive tractable expressions for the coverage probability and average data rate and prove the accuracy of the derived expressions through Monte Carlo simulations. The obtained results capture the impact of the satellite constellation size, the terrestrial base station density, and the MIMO configuration parameters.

The recent breakthroughs in electronic and photonic technologies enabled the design and implementation of intelligent reflective surfaces (IRSs) to manipulate electromagnetic waves and control the wireless environment. A promising application of IRSs is their integration with Terahertz (THz) communications. IRSs can cope with the blockage sensitivity of THz propagation by providing alternative line-of-sight (LoS) links to user equipment (UEs) which are initially blocked. However, deploying more IRSs may degrade the network performance as it leads to non-negligible interference levels. In this paper, we use tools from stochastic geometry to investigate the coverage probability of a downlink (DL) indoor THz network assisted by IRSs, which are added to a subset of the existing blockages. The numerical results reveal that there is an optimal density of IRSs that should be deployed to maximize the coverage of UEs in THz networks.

This work studies the signal-to-interference-plus-noise-ratio (SINR) meta distribution for the uplink transmission of a Poisson network with Rayleigh fading by using the dominant interferer-based approximation. The proposed approach relies on computing the mix of exact and mean-field analysis of interference. In particular, it requires the distance distribution of the nearest interferer and the conditional average of the rest of the interference. Using the widely studied fractional path-loss inversion power control and modeling the spatial locations of base stations (BSs) by a Poisson point process (PPP), we obtain the meta distribution based on the proposed method and compare it with the traditional beta approximation, as well as the exact results obtained via Monte-Carlo simulations. Our numerical results validate that the proposed method shows good matching and is time competitive.

Water covers 71% of the Earth’s surface, where the steady increase in oceanic activities has promoted the need for reliable maritime communication technologies. The existing maritime communication systems involve terrestrial, aerial, and space networks. This article presents a holistic overview of the different forms of maritime communications and provides the latest advances in various marine technologies. This article first introduces the different techniques used for maritime communications over the radio frequency (RF) and optical bands. Then, we present the channel models for RF and optical bands, modulation and coding schemes, coverage and capacity, and radio resource management in maritime communications. After that, this article presents some emerging use cases of maritime networks, such as the Internet of Ships and the ship-to-underwater Internet of Things. Finally, we highlight a few exciting open challenges and identify a set of future research directions for maritime communication, including bringing broadband connectivity to the deep sea, using terahertz and visible light signals for on-board applications, and data-driven modeling for radio and optical marine propagation.

The communication system for health-monitoring applications is a substantial research area considering its practical limitations. One of the potential suggested technologies is the Internet of Every Nano Things (IoENT). Through this work, we demonstrate the different aspects of interfacing of molecular communication (MC) with other nano-communication systems, such as human body communication (HBC), acoustic communication (AC), and tera-Hertz (Thz) communication systems for IoENT. Moreover, we also emphasized eclectic possibilities and various issues related to the applications of IoENT. First, we explored each of the nano-communication systems in detail by showing their mathematical frameworks. Subsequently, we are also concerned about different ways to interface MC with other nano-communication systems, one-by-one. In the end, we also discuss various likely challenges and possible future directions, e.g., availability of devices, mobility of devices and synchronization, etc., in the context of interfacing of diverse technologies.

This article studies the end-to-end uplink (UL) and downlink (DL)-outage probability (OP) of an Internet of Things (IoT) network with radio-frequency (RF) energy harvesting (EH) over Nakagami-m fading channels. Power-domain nonorthogonal multiple access (NOMA) is adopted to support both the UL and DL transmissions to increase the network spectral efficiency. The system end-to-end UL and DL outage probabilities are analyzed where exact closed-form expressions are derived. The system performance is explored for various system parameters, such as time allocation for EH, transmission power, fading conditions, number of IoT devices (IoDs), and data rate. The obtained analytical results are corroborated using Monte Carlo simulation for various operating scenarios. The obtained results show that the optimum harvesting time may broadly vary based on the adopted system parameters. Moreover, the results show that the system OP is highly sensitive to the harvesting time where OP may vary drastically if the harvesting time deviates from the optimum. The impact of the perfect successive interference cancelation (SIC) (PSIC) is also evaluated and compared with imperfect SIC (ISIC), and the obtained results show that the OP of the system can be significantly underestimated with the PSIC assumption. Therefore, the commonly used PSIC assumption may cause substantial deviation from the practical case where the detection process experiences ISIC.

Unmanned aerial vehicles (UAVs) have been widely employed to enhance the end-to-end performance of wireless communications since the links between UAVs and terrestrial nodes are Line-of-Sight (LoS) with high probability. However, the broadcast characteristics of signal propagation in LoS links make them vulnerable to being wiretapped by malicious eavesdroppers, which poses a considerable challenge to the security of wireless communications. In this work, we investigate the security of aerial underlay Internet of Things (IoT) systems by jointly designing trajectory and user scheduling. An airborne base station transmits confidential messages to secondary users utilizing the same spectrum as the primary network. An aerial jammer transmits jamming signals to suppress the eavesdropper to enhance secrecy performance. The uncertainty of eavesdropping node locations is considered, and the average secrecy rate of the secondary user is maximized by optimizing multiple users’ scheduling, the UAVs’ trajectory, and transmit power. To solve the nonconvex optimization problem with a mixed multi-integer variable problem, we propose an iterative algorithm based on block coordinate descent and successive convex approximation. Numerical results verify the effectiveness of our proposed algorithm and demonstrate that our scheme is beneficial in improving the secrecy performance of aerial underlay IoT systems.

LoRa backscatter (LB) communication systems can be considered as a potential candidate for ultra-low-power wide-area networks (LPWANs) because of their low cost and low power consumption. In this article, we comprehensively analyze LB modulation from various aspects, i.e., temporal, spectral, and error performance characteristics. First, we propose a signal model for LB signals that accounts for the limited number of loads in the tag. Then, we investigate the spectral properties of LB signals, obtaining a closed-form expression for the power spectrum. Finally, we derived the symbol error rate (SER) of LB with two decoders, i.e., the maximum likelihood (ML) and fast Fourier transform (FFT) decoders, in both additive white Gaussian noise (AWGN) and double Nakagami-m fading channels. The spectral analysis shows that out-of-band emissions for LB satisfy the European Telecommunications Standards Institute (ETSI) regulation only when considering a relatively large number of loads. For the error performance, unlike conventional LoRa, the FFT decoder is not optimal. Nevertheless, the ML decoder can achieve a performance similar to conventional LoRa with a moderate number of loads.

Using drones for communications and transportation is drawing great attention in many practical scenarios, such as package delivery and providing additional wireless coverage. However, the increasing demand for unmanned aerial vehicles (UAVs) from industry and academia will cause aerial traffic conflicts in the future. This, in turn, motivates the idea of this article: multipurpose UAVs, acting as aerial wireless data relays and means of aerial transportation simultaneously, to deliver packages and data at the same time. This article aims to analyze the feasibility of using drones to collect and deliver data from the Internet of Things (IoT) devices to terrestrial base stations (TBSs) while delivering packages from warehouses to residential areas. We propose an algorithm to optimize the trajectory of UAVs to maximize the size of collected/delivered data while minimizing the total round trip time subject to the limited onboard battery of UAVs. Specifically, we use tools from stochastic geometry to model the locations of the IoT clusters and the TBSs and study the system performance with respect to energy efficiency, average size of collected/delivered data, and package delivery time. Our numerical results reveal that multifunctional UAVs have great potential to enhance the efficiency of both communication and transportation networks.

Underwater long-distance wireless communication (ULWC) is a critical challenge in many applications, such as marine environmental monitoring, underwater remote control, and underwater navigation, to name a few. However, very little literature focuses on ULWC, especially where the communication distance ups to thousands of kilometers, which is urgently required in the Underwater Internet of Things (UIoT) in the large-scale and deep sea. In this article, to improve the underwater communication capacity at a level of thousands of kilometers, we propose a three-hop underwater wireless acoustic communication (3H-UWAC) structure based on the sound fixing and ranging (SOFAR) channel. The proposed 3H-UWAC consists of transmitters, relay stations (RSs), and receivers. Different from the existing ULWC, 3H-ULWC can improve energy efficiency with a small vertical directivity angle (VDA). Due to the characteristics of UWAC, the straight-line communication link can be realized in the proposed three hops, and the communication distance can be increased to thousands of kilometers. Respecting the randomness of underwater devices, tools from stochastic geometry are used to model the spatial distributions of transmitters’, receivers’, and RSs’ locations. RSs are set on the SOFAR channel at a known depth. In the first hop, the transmitter sends information to the nearest first RS (NFRS) on the SOFAR plane. In the second hop, the NFRS sends information to the nearest RS, which is called the nearest second RS (NSRS), to the receiver on the SOFAR plane. In the third hop, SNFS sends information to the receiver. All three communication hops can be achieved with a narrow beam width, where the energy efficiency is improved critically. With given densities of transmitters, RSs, and receivers, the coverage probabilities (CPs) of the three hops (transmitter to first RS (FRS), FRS to second RS (SRS), and SRS to receiver) are analyzed, and the final CP from a transmitter to a receiver through the 3H link is derived. Insights about the effects of VDAs at the transmitters, FNRS, and SNRS, as well as the depths of transmitters and receivers, are revealed. A rapid optimization method is proposed based on the analytical results. The accuracy of the analysis is verified by Monte Carlo simulations.

Reliable and energy-efficient wireless transmission is of critical importance to the success of future advanced Internet of Things (IoT). Due to the sporadic nature of IoT transmissions, the energy consumption of the individual IoT transmission session varies dramatically with the instantaneous operating environment as well as the Quality of Service (QoS) requirements. In this article, we analyze and design the energy-efficient relay transmission systems from an individual data transmission session perspective. Specifically, we consider a dual-hop transmission system with a decode-and-forward relay that is solely powered by wireless power transfer from source node. For both time switching and power splitting modes of simultaneous power and information transmission, we analyze and minimize the total energy consumption of the system when transmitting a fixed amount of data, under a piecewise linear energy harvesting (EH) model. Closed-form expressions for optimal transmission parameters are obtained with and without the consideration of latency constraint. Through selected numerical results, we illustrate various design tradeoffs between energy consumption and latency constraint. We show that with optimal transmission parameters, relay transmission with energy transfer can achieve considerable energy saving compared to the direct transmission when the direct link quality is poor and the latency constraint is not stringent. We also show that with optimized parameters, power splitting mode leads to lower energy consumption and smaller transmission duration than time switching mode, at the cost of higher implementation complexity.

Over the past few years, the prevalence of wireless devices has become one of the essential sources of electromagnetic (EM) radiation to the public. Facing with the swift development of wireless communications, people are skeptical about the risks of long-term exposure to EM radiation. As EM exposure is required to be restricted at user terminals, it is inefficient to blindly decrease the transmit power, which leads to limited spectral efficiency and energy efficiency (EE). Recently, rate-splitting multiple access (RSMA) has been proposed as an effective way to provide higher wireless transmission performance, which is a promising technology for future wireless communications. To this end, we propose using RSMA to increase the EE of massive MIMO uplink while limiting the EM exposure of users. In particularly, we investigate the optimization of the transmit covariance matrices and decoding order using statistical channel state information (CSI). The problem is formulated as non-convex mixed integer program, which is in general difficult to handle. We first propose a modified water-filling scheme to obtain the transmit covariance matrices with fixed decoding order. Then, a greedy approach is proposed to obtain the decoding permutation. Numerical results verify the effectiveness of the proposed EM exposure-aware EE maximization scheme for uplink RSMA.

This paper investigates a semi-grant-free (SGF) based transmission strategy to provide a flexible connectivity for various kinds of users in an integrated satellite-aerial-terrestrial network (ISATN). Herein, a high-altitude platform (HAP) termed as a grant-based user (GBU), which serves multiple mobile terminals (MTs) through space division multiple access (SDMA), wants to access a satellite network with multiple earth stations (ESs) termed as grant-free users (GFUs) simultaneously via non-orthogonal multiple access (NOMA) assisted SGF. To this end, we first propose two SGF-based uplink transmission schemes for both perfect channel state information (CSI) and imperfect CSI cases. When perfect CSI is available, a zero-forcing based beamforming (BF) scheme is used in HAP network while an adaptive transmit power allocation (ATPA) approach is adopted for SGF transmission. When only imperfect CSI is available, BF scheme employing the derived channel correlation matrix of HAP-MT link is proposed to achieve SDMA, and a novel ATPA strategy with rate probability constraint is proposed to guarantee quality-of-service of the GBU. Next, we derive the closed-form throughput expressions to evaluate the performance of the considered ISATN with the proposed two SGF-based schemes. Finally, computer simulations are conducted to validate the theoretical performance analysis and show the superiority of the proposed schemes over the related works. Moreover, our numerical results not only demonstrate a satisfactory performance of the proposed SGF-based scheme using imperfect CSI, but also reveal the impact of CSI errors on the system performance.

This paper proposes, and evaluates the benefits of, a hybrid central cloud (CC) and mobile edge computing (MEC) platform, especially introduced to balance the network resources for joint communication and computation. The transmission is further empowered by splitting the users’ messages into private and common parts, to mitigate the interference within the CC and MEC platforms. While several power-hungry, computationally-limited unmanned aerial vehicles (UAVs) are deployed at the cell-edge to boost the CC connectivity and relieve part of its computation burden, the CC connects to the base-stations via capacity-limited fronthauls. The paper then considers the problem of maximizing the weighted sum-rate subject to fronthaul and computation capacity, achievable rates, power, delay, and data-split constraints. Thereby determining the beamforming vectors associated with the private and common messages, the computation allocations, and the data-split factors. Such intricate non-convex optimization problem is tackled using an iterative algorithm that relies on well-chosen discrete relaxation, successive convex approximation, and fractional programming, and can be compellingly implemented in a distributed fashion. The simulations illustrate the proposed algorithm’s capabilities for empowering joint communication and computation, and highlight the pronounced role of rate-splitting and common message decoding in alleviating large-scale interference in hybrid CC/MEC networks.

Reconfigurable intelligent surfaces (RIS) can be crucial in next-generation communication systems. However, designing the RIS phases according to the instantaneous channel state information (CSI) can be challenging in practice due to the short coherent time of the channel. In this regard, we propose a novel algorithm based on the channel statistics of massive multiple input multiple output systems rather than the instantaneous CSI. The beamforming at the base station (BS), power allocation of the users, and phase shifts at the RIS elements are optimized to maximize the minimum signal-to-interference and noise ratio (SINR), guaranteeing fair operation among various users. In particular, we design the RIS phases by leveraging the asymptotic deterministic equivalent of the minimum SINR that depends only on the channel statistics. This significantly reduces the computational complexity and the amount of controlling data between the BS and RIS for updating the phases. This setup is also useful for electromagnetic fields (EMF)-aware systems with constraints on the maximum user’s exposure to EMF. The numerical results show that the proposed algorithms achieve more than 100 % gain in terms of minimum SINR, compared to a system with random RIS phase shifts, when 40 RIS elements, 20 antennas at the BS and 10 users, are considered.

Cognitive satellite and terrestrial network (CSTN) is considered as a promising technology to provide ubiquitous connectivity for various users within wide-coverage. This paper proposes a robust downlink transmission scheme for multiple intelligent reflecting surfaces (IRSs) assisted CSTN. Here, the satellite network adopts multigroup multicast transmission scheme to serve many earth stations, while the terrestrial network exploits space division multiple access and multi-IRS-enhanced non-orthogonal multiple access technology to communicate with many terrestrial users. By assuming that these two networks share the same frequency band having only the angular information based imperfect channel state information of each user, we formulate an optimization problem to minimize the total transmit power subject to the constraints of quality-of-service requirement for each user, per-antenna transmit power budgets of satellite and BS, and unit-modulus requirement for each reflecting element. To tackle this mathematically intractable problem, we then employ angular discretization together with the successive convex approximation method to obtain the active beamforming (BF) vectors of satellite and BS, the passive BF vector of IRS, and the power allocation coefficients. Moreover, we propose a generalized zero forcing BF and alternative optimization to obtain the suboptimal solutions of the optimization problem with low computational complexity. Finally, simulation results are given to demonstrate the effectiveness and superiority of the proposed two schemes over the benchmarks.

This paper proposes a new scheme to enhance the secrecy performance of non-orthogonal multiple access (NOMA)-based coordinated direct relay transmission (CDRT) systems with an untrusted relay. The physical-layer network coding (PNC) and the NOMA schemes are combined to improve spectrum efficiency. Furthermore, inter-user interference and friendly jamming signals are utilized to suppress the eavesdropping ability of the untrusted relay without compromising the acceptance quality of legitimate users. Specifically, the far user in the first slot and the near user in the second slot act as jammers that generate jamming signals to ensure secure transmissions of confidential messages. We investigate the secrecy performance of the NOMA-based CDRT systems with the PNC scheme and derive the closed-form expression for the ergodic secrecy sum rate. The asymptotic analysis at a high signal-to-noise ratio is performed to obtain more insights. Finally, simulation results are presented to demonstrate the proposed scheme’s effectiveness and the theoretical analysis’s correctness.

Millimeter-wave (mmWave) communication has attracted considerable attention from academia and industry, providing multi-gigabits per second rates due to the substantial bandwidth. Rate splitting multiple access (RSMA) is an effective technology that provides a generalized multiple access framework. Regarding the new propagation characteristics of the mmWave, we investigate the outage performance of the mmWave RSMA multiple-input-single-output system with a fixed-located user and a randomly-located user. Based on the spatial correlation of the paired users, the user’s paths are divided into overlapped and non-overlapped paths. Two beamforming schemes are proposed to improve the reliability of the mmWave RSMA system. The common stream is transmitted on the overlapped paths or all the paths. By utilizing stochastic geometry theory, the closed-form expressions of the outage probability (OP) with proposed schemes are derived. To obtain more insights, the expressions for the asymptotic OP are derived. Monte Carlo simulation results are presented to validate the analysis and the effects of the system parameters, such as power allocation coefficients and the number of resolvable paths, on the outage performance are investigated.

This paper proposes a novel approach for computing the meta distribution of the signal-to-interference-plus-noise ratio (SINR) for the downlink transmission in a wireless network with Rayleigh fading. The novel approach relies on an approximation mix of exact and mean-field analysis of interference (dominant interferer-based approximation) to reduce the complexity of analysis and enhance tractability. In particular, the proposed approximation omits the need to compute the first or the second moment of the SINR that is used in the beta approximation typically adopted in the literature but requires of computing the joint distance distributions. We first derive the proposed approximation based on a Poisson point process (PPP) network with a standard path-loss and Rayleigh fading and then illustrate its accuracy and operability in another four widely used point processes: Poisson bipolar network, Matérn cluster process (MCP),$K$-tier PPP and Poisson line Cox process (PLCP). Specifically, we obtain the SINR meta distribution for PLCP networks for the first time. Even though the proposed approximation looks simple but it shows good matching in comparison to the popular beta approximation as well as the Monte-Carlo simulations, which opens the door to adopting this approximation in more advanced network architectures.

The meta distribution of the signal-to-interference-plus-noise ratio (SINR) provides fine-grained information about each link’s performance in a wireless system and the reliability of the whole network. While the UAV-enabled network has been studied extensively, most of the works focus on the spatial average performance, such as coverage probability, while SINR meta distribution has received less attention. In this paper, we use the SINR meta distribution to systematically analyze the improvement and the influence of deploying UAVs on the reliability of a wireless network. We first derive the$b$-th moments of the conditional success probability of the UAV-enabled network and give the approximated expressions derived by Gil-Pelaez theorem and the beta approximation of the meta distribution. Our numerical results show that deploying UAVs in wireless networks in most cases can greatly improve the system reliability, which denotes the fraction of users achieving cellular coverage, especially for the spatially-clustered users. In addition, establishing LoS links is not always beneficial since it also increases the interference. For instance, with the increase of the SINR threshold, the system reliability of a high LoS probability environment decreases dramatically and it is even lower than a low LoS probability environment. We also show that in highrise urban areas, UAVs can help in establishing extremely reliable (very high SINR) links.

Terahertz (THz) band spans the frequencies lying between 0.1 and 10 THz and represents the gap between millimeter waves and Infrared bands. THz will play an influential role in mitigating the spectrum resources shortage to meet the exponential growth of services and wireless devices. Standardization activities are being carried out to regulate the THz band's exploitation under the IEEE 802.15 standardization project. Despite the generous bandwidth, THz communications suffer from high pathloss and attenuation due to Molecular Absorption. As such, THz systems need to use extra power, suitable antennas, and enhanced signal processing and communication techniques to compensate for different signal attenuation sources. In this paper, we propose a new operation mode for the IEEE standard 802.15 to save the power transmission requirement while achieving the required data rate. Hence, less battery or antenna size is needed to support the same communication link quality. To this end, we optimize the power, modulation scheme, and channel allocation to minimize the total transmitted power keeping a minimum quality-of-service. Moreover, the design captures the effect of humidity on the system performance.

Large low earth orbit (LEO) mega-constellation systems have been designed and deployed as a global backbone to provide ubiquitous connectivity across the world. However, due to the high traffic load/congestion arisen from the required numerous information relay and forwarding, it is a challenge for LEO mega-constellation systems to set up long-distance connections between two remote terrestrial users for real-time communications, which requires strict low latency. Other than inter-LEO satellite links (ILSL), introducing third-party relays, such as terrestrial, aerial, and satellite relays, is an alternative way to improve the latency performance for wide-area deliveries of real-time traffic. However, the reliability of the transmitted signal will unavoidably degrade, because of the increased path-loss attributed to long-distance relaying transmissions. Then, to reveal the principle that the third-party relays affect the latency and reliability, in this work, an LEO satellite-terrestrial communication scenario is considered, in which two remote terrestrial users communicate with each other via an LEO mega-constellation system. Analysis models are built up to investigate the end-to-end time delay and outage performance while considering different ILSL, terrestrial, aerial, and satellite relay assisted transmission scenarios. More specifically, by applying geometrical probability theory, exact/approximated closed-form analytical expressions have been derived for average time delay

Mobile edge computing (MEC) can dramatically promote the computation capability and prolong the lifetime of mobile users (MUs) by offloading computation-intensive tasks to edge cloud. In this paper, a spatial-random two-tier heterogeneous network (HetNet) is modelled to feature random node distribution, where the small-cell base stations (SBSs) and the macro base stations (MBSs) are cascaded with servers with different levels of computing and storage capacity. Only a certain type of application services and finite number of offloaded tasks can be cached and processed in the resource-limited edge server. For that setup, we investigate the performance of two offloading strategies corresponding to integrated access and backhaul (IAB)-enabled MEC networks and traditional cellular MEC networks. Using tools from stochastic geometry and queuing theory, we derive the average delay for the two different strategies, in order to better understand the influence of IAB on MEC networks. Simulations results are provided to verify the derived expressions and to reveal various system-level insights.

This paper investigates the performance of high altitude platforms high altitude platforms (HAPS) based free-space optical (FSO) communication links including HAPS-to-ground station (downlink), ground-to-HAPS (uplink) and HAPS-to-HAPS (horizontal link) communications. The effects of attenuation loss, atmospheric turbulence, pointing error and angle-of-arrival (AOA) are taken into account. Also, the application of adaptive optics correction, one of the most effective turbulence mitigation techniques, is analyzed using the Zernike polynomials representation. Analytical expressions are obtained for probability density function (PDF), cumulative distribution function (CDF), Rytov variance, adaptive optics filter function and outage probability mainly in terms of Meijer’s G functions when both no adaptive optics correction is used and adaptive optics correction is applied. Some selected results are presented depending on the various parameters such as the HAPS altitude, the ratio of vertical and horizontal deviations, beam waist, Zenith angle, height of ground station, receiver aperture diameter, channel state threshold and wind speed. The performance improvement with adaptive optics correction is investigated by removing different Zernike modes. We show that (i) the downlink outperforms the uplink, (ii) the performance of the horizontal link sharply increases above a certain altitude, and, (iii) the communication links benefit from the adaptive optics correction up to a certain level in terms of performance improvement.

Due to their flexibility and low cost deployment, unmanned aerial vehicles (UAV) will most likely act as base stations and backhaul relays in the next generation of wireless communication systems. However, these UAVs—in the untethered mode—can only operate for a finite time due to limited energy they carry in their batteries. In free-space optical communications, one solution is to transport both data and energy from the source to the UAV through the laser beam—a concept known as simultaneous lightwave information and power transfer (SLIPT). In this study, we have analyzed the SLIPT scheme for laser-powered decode-and-forward UAV relays in an optical wireless backhaul. The major goal of this study is to optimally allocate the received beam energy between the decoding circuit, the transmitting circuit and the rotor block of the relay in order to maximize a quality-of-service metric such as maximum achievable rate, outage or error probabilities. As expected, we note that the optimal power allocation depends heavily on the source-relay and relay-destination channel conditions. In the final part of this study, we have maximized the operational time of the UAV relay given that the maximum achievable rate stays above a certain threshold in order to meet a minimum quality-of-service requirement.

Installing more base stations (BSs) into the existing cellular infrastructure is an essential way to provide greater network capacity and higher data rates in the 5th-generation cellular networks (5G). However, a non-negligible amount of the population is concerned that such network densification will generate a notable increase in exposure to electric and magnetic fields (EMF) over the territory. In this paper, we analyze the downlink, uplink, and joint downlink&uplink exposure induced by the radiation from BSs and personal user equipment (UE), respectively, in terms of the received power density and exposure index. In our analysis, we consider the EMF restrictions set by the regulatory authorities such as the minimum distance between restricted areas (e.g., schools and hospitals) and BSs, and the maximum permitted exposure. Exploiting tools from stochastic geometry, mathematical expressions for the coverage probability and statistical EMF exposure are derived and validated. Tuning the system parameters such as the BS density and the minimum distance from a BS to restricted areas, we show a trade-off between reducing the population’s exposure to EMF and enhancing the network coverage performance. Then, we formulate optimization problems to maximize the performance of the EMF-aware cellular network while ensuring that the EMF exposure complies with the standard regulation limits with high probability. For instance, the exposure from BSs is two orders of magnitude less than the maximum permissible level when the density of BSs is less than$20 \text {BSs/km}^{2}$.

A new generation of aerial vehicles is hopeful to be the next frontier for the transportation of people and goods, becoming even as important as ground users in the communication systems. To enhance the coverage of aerial users, appropriate adjustments should be made to the existing cellular networks that mainly provide services for ground users by the down-tilted antennas of the terrestrial base stations (BSs). It is promising to up-tilt the antennas of a subset of BSs for serving aerial users through the mainlobe. With this motivation, in this work, we use tools from stochastic geometry to analyze the coverage performance of the adjusted cellular network (consisting of the up-tilted BSs and the down-tilted BSs). Correspondingly, we present exact and approximate expressions of the signal-to-interference ratio (SIR)-based coverage probabilities for users in the sky and on the ground, respectively. Numerical results verify the analysis accuracy and clarify the advantages of up-tilting BS antennas on the communication connectivity of aerial users without the potential adverse impact on the quality of service (QoS) of ground users. Moreover, it is unveiled that there exists an optimal value of the up-tilted/down-tilted BS density ratio for maximizing the coverage probability of the aerial or ground users.

Fluid antenna systems (FAS) are an emerging technology that promises a significant diversity gain even in the smallest spaces. It consists of a freely moving antenna in a small linear space to pick up the strongest received signal. Previous works in the literature provide a simple yet insightful parameterization of the FAS channel that leads to single-integral expressions of the probability of outage and various insights on the achievable performance. Nevertheless, this channel model may not accurately capture the correlation between the FAS ports, given by Jake’s model. This work builds on the state-of-the-art by incorporating more parameters into the channel model to accurately approximate the FAS channel distribution while maintaining analytical tractability. The approximation is performed in two stages. The first stage approximation considerably reduces the number of multi-fold integrals in the probability of outage expression, while the second stage approximation represents it in a single integral form. Numerical results validate our approximations of the FAS channel model and demonstrate a limited performance gain under a more accurate correlation model. Further, our work opens the door for future research to investigate scenarios in which the FAS provides a performance gain compared to the current multiple antenna solutions.

Wireless communications over Terahertz (THz)-band frequencies are vital enablers of ultra-high rate applications and services in sixth-generation (6G) networks. However, THz communications suffer from poor coverage because of inherent THz features such as high penetration losses, significant molecular absorption, and severe path loss. To surmount these critical challenges and fully exploit the THz band, we explore a coexisting radio frequency (RF) and THz finite indoor network in which THz small cells are deployed to provide high data rates, and RF macrocells are deployed to satisfy coverage requirements. Using stochastic geometry tools, we assess the performance of coexisting RF and THz networks and derive tractable analytical expressions for the coverage probability and average achievable rate. The analytical results are validated with Monte-Carlo simulations. Several insights are devised for accurate tuning and optimization of THz system parameters, including the THz bias, and the fraction of THz access points (APs) to deploy. The obtained results recognize a clear coverage/rate trade-off where a high fraction of THz AP improves the rate significantly but may degrade the coverage performance. Furthermore, the location of a user in the finite area highly affects the fraction of THz APs that optimizes its quality of service.

The use of unmanned aerial vehicles (UAVs) is strongly advocated for sixth-generation (6G) networks, as the 6G standard will not be limited to improving broadband services, but will also target the extension of the geographical cellular coverage. In this context, the deployment of UAVs is considered a key solution for seamless connectivity and reliable coverage. That being said, it is important to underline that although UAVs are characterized by their high mobility and their ability to establish line-of-sight (LOS) links, their use is still impeded by several factors such as weather conditions, their limited computing power, and, most importantly, their limited energy. In this work, we are aiming for the novel technology that enables indefinite wireless power transfer for UAVs using laser beams. We propose a novel UAV deployment strategy, based on which we analyze the overall performance of the system in terms of wireless coverage. To this end, we use tractable tools from stochastic geometry to model the complex communication system. We analyze the user’s connectivity profile under different laser charging capabilities and in different type of environments. We show a decrease in the coverage probability by more than 12% in moderate-to-strong turbulence conditions compared to low turbulence conditions. We also show how the connection rate to the aerial network significantly decreases in favor of the terrestrial network for short laser charging ranges. We conclude that laser-powered drones are considered interesting alternatives when placed in LOS with users, in low-to-moderate optical turbulence, and at reasonable ranges from the charging stations.

This paper analyzes the energy efficiency of a novel system model where unmanned aerial vehicles (UAVs) are used to provide coverage for user hotspots (user clusters) and are deployed on charging pads to enhance the flight time. We introduce a new notion of “cluster pairs” to capture the dynamic nature of the users spatial distribution in order to exploit one of the top advantages of UAVs, which is the mobility and relocation flexibility. Using tools from stochastic geometry, we first derive a new distance distribution that is vital for the energy efficiency analysis. Next, we compute the coverage probability under two deployment strategies: (i) one UAV per cluster pair, and (ii) one UAV per cluster. Finally, we compute the energy efficiency for both strategies. Our numerical results reveal which of the two strategies is better for different system parameters. Our work investigates some new aspects of the UAV-enabled communication system such as the dynamic density of users and the advantages or disadvantages of one- or two-UAV deployment strategies per cluster pair. By considering the relationships between the densities of user cluster pairs and the charging pads, it is shown that an optimal cluster pair density exists to maximize the energy efficiency.

In this work, a hybrid free-space optics (FSO)/ teraHertz (THz) based backhaul network is considered to provide high-data-rate reliable communication to the terrestrial mobile users (MUs) operating at millimeter-wave (mmWave) bands. The FSO link is affected by atmospheric turbulence and pointing error impairments. At the FSO receiver, both intensity-modulated direct detection and heterodyne detection techniques are considered. The multi-antenna THz link suffers from high path-loss, small-scale fading, and misalignment error. To minimize the effect of back-and-forth switching, soft switching method is introduced at the access point (AP) to select the signal coming through the hybrid FSO/THz link, and a comparison with hard switching method is presented. Selective decode-and-forward relaying is considered at the AP. In this context, we derive closed-form expressions of the individual link’s outage probability, end-to-end (E2E) outage probability, asymptotic outage probability, ergodic capacity, and generalized average bit-error-rate. Finally, we study the effect of different parameters such as atmospheric turbulence, pointing/misalignment errors, link distance, atmospheric attenuation/path-loss, fading parameters of the THz and access links, and number of antennas on the network performance. Our results indicate that, with a proper switching method, the joint implementation of FSO/THz links improves the rate/reliability of the backhaul links with limited switching overhead.

Nowadays, satellite and aerial platforms are playing an important role in realizing global seamless wireless coverage. In this paper, a cooperative satellite-aerial-terrestrial network (SATN) is considered, in which two kinds of relaying links, satellite and aerial relaying links, are used to assist a group of aerial terminals to forward their information to a remote terrestrial destination (D). Specifically, we model these aerial platforms sharing the same frequency band as a Matérn hard-core point process type-II. Also, a group of aerial jammers at D’s side is modeled as a Poisson point process. To demonstrate the end-to-end (e2e) performance of the two relaying links, the statistical characteristics of the received signal-to-interference are characterized and then a closed-form expression for the outage probability (OP) over the uplink from the aerial source to the satellite/the aerial relay, the downlink from the satellite/the aerial relay to D, and the inter-aerial relay link are derived. Numerical results are presented to verify the proposed analysis models and compare the outage performance of the considered cooperative SATN with the two relay links under numerous scenarios.

Satellite-aerial communication (SAC) is envisioned as a fundamental component of the sixth-generation (6G) wireless networks. Motivated by its importance, we investigate a SAC system including a geostationary satellite (S), a target aircraft (TA), and a set of interfering aircraft (IA). Specifically, TA sends signals to S in the presence of IA interference. Considering the trajectory, hierarchy, and safety distance of the aircraft’s flight routes, we propose a novel three-dimensional stacked Poisson line hardcore point process. That is, we introduce safety distances to the stacked Poisson line Cox process in order to describe the locations of IA in the sky. We also propose two approximations, namely, the equi-dense model and the discretization model, to maintain the tractability of the analysis. To this end, the uplink coverage probability is studied by using the two proposed mathematical models. Moreover, we investigate the coverage probability of the aviation use case with predefined flight altitudes. Finally, numerical results and Monte Carlo simulations are presented to validate the accuracy of the proposed analysis.

Using Unmanned Aerial Vehicles (UAVs) to enhance network coverage has proven a variety of benefits compared to terrestrial counterparts. One of the commonly used mathematical tools to model the locations of the UAVs is stochastic geometry (SG). However, in the existing studies, both users and UAVs are often modeled as homogeneous point processes. In this paper, we consider an inhomogeneous Poisson point process (PPP)-based model for the locations of the users that captures the degradation in the density of active users as we move away from the town center. In addition, we propose the deployment of aerial vehicles following the same inhomogeneity of the users to maximize the performance. In addition, a multi-tier network model is also considered to make better use of the rich space resources. Then, the analytical expressions of the coverage probability for a typical user and the total coverage probability are derived. Finally, we optimize the coverage probability with limitations of the total number of UAVs and the minimum local coverage probability. Finally we give the optimal UAV distribution parameters when the maximum overall coverage probability is reached.

In physical-layer security, secrecy capacity is an important performance metric. This work aims to determine the secrecy capacity for an indoor visible light communication system consisting of a transmitter, a legitimate receiver and an eavesdropping receiver. In such a system, both signal-independent noise and signal-dependent noise are considered. Under nonnegativity and average optical intensity constraints, lower and upper bounds on secrecy capacity are derived by the variational method, the dual expression of the secrecy capacity, and the concept of “the optimal input distribution that escapes to infinity”. By an asymptotic analysis at large optical intensity, there is a small gap between the asymptotic upper and lower bounds. Then, by adding a peak optical intensity constraint, we further analyze the exact and asymptotic secrecy-capacity bounds. For practical considerations, the effects of imperfect channel state information, multi-photodiode eavesdropper, and artificial noise on secrecy performance are also discussed. Finally, the derived secrecy-capacity bounds are verified by numerical results.

Due to its potential to enable global connectivity in remote locations, such as rural areas and islands, Space-Air-Ground networks have become an ambitious solution for terrestrial communication in the sixth generation (6G) wireless communication network. In this paper, we propose a novel structure of Space-Air-Ground-Sea integrated networks (SAGSINs) to study and derive the coverage probability (CP) of users who are annotated as surface stations (SSs) on the far-reaching ocean surface that is far away from the coastline. By incorporating different types of relays such as onshore stations (OSs), tethered balloons (TBs), high altitude platforms (HAPs), and satellites (SATs), communication links between the terrestrial core connected base stations (CCBSs) and SSs are established via one of the four types of relay stations. Considering practical scenarios with a random distribution of SSs, we model the channel using the point-to-area model, which is recommended by ITU (for OSs to SS), the Rician model (for TBs or HAPs to SS), and the Shadowed-Rician model (for SATs to SS). When the SS’s distance from the coastline continues to increase from zero, since different channel models are considered, different relay stations will result in specific received signal strengths at SSs. The most powerful relay station will be chosen as the relay at one time. Hence, as we move away from the coastline, the respective strengths of the different types of relay stations vary, and hence, the association preference (among HAPs, OSs, TBs, and SATs) of the SSs changes leading to a CP value high enough even at locations far away from the coastline into the ocean. We analyze the CP using tools from stochastic geometry. Comparisons of CP between the integrated system with four types of relay stations and the single relay station system (only one type of relay station available) are represented. Numerical results verified by Monte-Carlo simulations reveal insights into the applicability of SAGSINs.

The satellite communication (Satcom) community is exploring the license-free optical spectrum to support the growing traffic demand in a cost-effective manner. However, free-space optical (FSO) communications are vulnerable to atmospheric turbulence, pointing errors, and weather effects. Space-air-ground (SAG) FSO transmission and hybrid single-hop (SH) FSO/radio frequency (RF) transmission are promising solutions to improve the performance of FSO links and can be integrated into a Satcom system. In this work, we carry out a thorough error rate analysis of the resulting integrated SAG-FSO/SH-FSO/RF Satcom system, where Gamma-Gamma and Rician distributions are used to characterize FSO and RF links, respectively. The exact analytical expressions are derived and validated by Monte-Carlo simulations. The numerical results highlight the significant potential of the integrated SAG-FSO/SH-FSO/RF Satcom system over existing solutions.

The tremendous growth of data traffic in mobile communication networks (MCNs) and the associated exponential increase in mobile devices’ numbers necessitate the use of multi-cloud radio access networks (MC-RANs) as a viable solution to cope with the requirements of next-generation MCNs (6G). In MC-RANs, each central processor (CP) manages the signal processing of its own set of base stations (BSs), and so the system performance becomes a function of the joint intra-cloud and inter-cloud interference mitigation techniques. To this end, this paper considers the problem of maximizing the network-wide energy efficiency (EE) subject to user-to-cloud association, fronthaul capacity, maximum transmit power, and achievable rate constraints, so as to determine the joint beamforming vector of each user and the user-to-cloud association strategy. The paper tackles the non-convex and mixed discrete-continuous nature of the problem formulation using fractional programming (FP) and inner-convex approximation (ICA) techniques, as well as l0-norm relaxation heuristics, and shows how the proposed approach can be implemented in a distributed fashion via a reasonable amount of information exchange across the CPs. The paper simulations highlight the appreciable algorithmic efficiency of the proposed approach over state-of-the-art schemes.

Spectrum-sharing backscatter communication (SSBC) systems are among the most prominent technologies for ultralow power and spectrum-efficient communications. In this article, we propose an underlay SSBC system, in which the secondary network is a backscatter communication system. We analyze the performance of the secondary network under a transmit power adaption strategy at the secondary transmitter, which guarantees that the interference caused by the secondary network to the primary receiver is below a predetermined threshold. We first derive a novel analytical expression for the cumulative distribution function (CDF) of the instantaneous signal-to-noise ratio of the secondary network. Capitalizing on the obtained CDF, we derive novel accurate approximate expressions for the ergodic capacity, effective capacity, and average bit-error rate. We further validate our theoretical analysis using the extensive Monte Carlo simulations.

Internet of Underwater Things (IoUT) is a technological revolution that could mark a new era for scientific, industrial, and military underwater applications. To mitigate the hostile underwater channel characteristics, this article considers a multimodal underwater network that hybridizes acoustic and optical wireless communications to achieve an ubiquitous control and high-speed low-latency networking performance, respectively. Since underwater optical wireless communications (UOWCs) suffer from limited range, it requires effective multihop routing solutions. In this regard, we propose a sector-based opportunistic routing (SectOR) protocol. Unlike the traditional unicast routing (TUR) techniques, which send packets to a unique relay, opportunistic routing (OR) targets a set of candidate relays by leveraging the broadcast nature of the UOWC channel. OR improves the packet delivery ratio as the likelihood of having at least one successful packet reception is much higher than that in TUR. Contingent upon the performance characterization of a single-hop link, we obtain a variety of local and global metrics to evaluate the fitness of a candidate set (CS) and develop candidate prioritization techniques for various OR metrics. Since rate$\leftrightarrow $error and range$\leftrightarrow $beamwidth tradeoffs yield different CS diversities, we develop a candidate filtering and searching algorithm to find the optimal sector shaped coverage region by scanning the feasible search space. Moreover, a hybrid acoustic/optic coordination mechanism is considered to avoid duplicate transmission of the relays. Numerical results show that the SectOR protocol can perform even better than optimal unicast routing protocols in well-connected underwater networks.

Radio sensing has become increasingly important, as the demand for “smartness” is drastically increasing. Unlike conventional sensing, radio sensing uses existing radio signals or devices to passively sense the ambient environment for low cost and wide deployment. In this article, a comprehensive overview of radio sensing using the recent fifth-generation (5G) signals is provided. 5G systems have many merits, such as high frequency, large bandwidth, massive antenna array, and dense network, making them ideal for radio sensing. In the overview, basic theories and concepts of 5G radio sensing are first introduced. Then, different state-of-the-art 5G sensing works are discussed based on their applications. These applications show that 5G radio sensing represents a step change in radio sensing. After that, several open challenges in 5G radio sensing are illustrated with relevant insights. These insights manifest that 5G radio sensing has great potentials to explore.

This article investigates a symbiotic ambient backscatter communication (AmBC) system, where for the primary system, a source node T1 transmits information to a destination node T2. Whereas for the backscatter system, by riding on T1’s signal, the backscatter device passively conveys its own information$c(n)$to T1 and T2 via backscattering. For such, the coexistence outage probability (COP) and ergodic capacity (EC) of the AmBC system are characterized for three cases of coexistence constraints, i.e., 1) both T1 and T2 decode$c(n)$(Case I); 2) only T2 decodes$c(n)$(Case II); and 3) only T1 decodes$c(n)$(Case III). It is analytically shown that for sufficiently high transmit signal-to-noise ratio (SNR), the COP obeys the scaling law of$({1}/{\sqrt {P_{s}}})$(with$P_{s}$denoting T1’s transmit power) for Cases I and III, whereas its scaling law is determined by$([{\mathrm {log}(P_{s})}]/{P_{s}})$as well as$({1}/{P_{s}})$for Case II. In addition, it is shown that the restriction condition of decoding$c(n)$at T1 results in a dominating term$({1}/{\sqrt {P_{s}}})$for the COP at high SNR, whereas the restriction condition of decoding$c(n)$at T2 results in an infinitesimal relative to$({1}/{\sqrt {P_{s}}})$. It is also shown that for different cases, the effects of the T1–T2 channel statistics on the COP are significantly different. However, unlike the metric of COP, for the EC, the impacts of decoding constraints of$c(n)$gradually disappear at high SNR and the ECs of the backscatter channels for Cases II and III approach, respectively, toward the counterpart for Case I.

This article investigates the system outage performance of a useful architecture of two-way hybrid visible light communication/radio-frequency (VLC/RF) communication using overlay mode of the cooperative cognitive radio network (CCRN). The demand of high data rate application can be fulfilled using VLC link and communication over a wide area of coverage with high reliability can be achieved through RF link. In the proposed architecture, cooperative communication between two licensed user (LU) nodes is accomplished via an aggregation agent (AA). AA can perform like a relay node and in return, it can access the LU spectrum for two-way communications with the Internet-of-Things (IoT) device. First, closed-form expressions of outage probability of both LU and IoT communication are established. On the basis of these expressions, optimization problems are formulated to achieve minimum outage probability of both LU and IoT network. The impacts of both VLC and RF system parameters on these systems outage probability and throughput are finally shown in simulation results.

Satellite and unmanned aerial vehicle (UAV) integrated networks (SUINs) are considered as a promising method to offer various Internet of Remote Things (IoRT) applications. In this article, we investigate the downlink transmission of SUINs where the satellite-to-UAV link uses the free-space optical (FSO) technology with an equal gain combining (EGC) scheme while the links from UAV to IoRT devices exploit radio frequency (RF) with the space-division multiple access (SDMA) technique. Specifically, considering that only statistical channel state information (CSI) is available, we first formulate an optimization problem to maximize the ergodic sum rate (ESR) of the system, which is constrained by the total transmit power budget and IoRT devices’ rate requirements. Then, a beamforming (BF) scheme based on the alternating direction method of multipliers (ADMM) is proposed to solve the nonconvex problem. Furthermore, a zero-forcing (ZF)-based suboptimal approach is also presented to reduce the implementation complexity. Finally, by assuming that the FSO link and RF links are subject to Gamma–Gamma fading and Nakagami-$m$fading, respectively, we derive closed-form ESR expressions for the considered network with the proposed BF schemes. Simulation results are provided to confirm the accuracy of the theoretical analysis. Moreover, it is revealed that our proposed EGC scheme for FSO communication and BF schemes for RF transmission can both achieve better performance than the existing works.

Underwater acoustic communication networks (UACNs) are considered a key enabler to the Underwater Internet of Things (UIoT). UACN is regarded as essential for various marine applications, such as monitoring, exploration, and trading. However, a large part of existing literature disregards the 3-D nature of the underwater communication system. In this article, we propose a$K$-tier UACN that acts as a gateway that connects the UIoT with the space–air–ground–sea integrated system (SAGSIS). The proposed network architecture consists of several tiers along the vertical direction with adjustable depths. On the horizontal dimension, the best coverage probability (CP) is computed and maximized by optimizing the densities of surface stations (SSs) in each tier. On the vertical dimension, the depth of each tier is also optimized to minimize intertier interference and maximize overall system performance. Using tools from stochastic geometry, the total CP of the proposed$K$-tier network is analyzed. For given spatial distribution of UIoT device’s depth, the best CP can be achieved by optimizing the depths of the transceivers connected to the SSs through a tether. We verify the accuracy of the analysis using Monte Carlo simulations. In addition, we draw multiple useful system-level insights that help optimize the design of underwater 3-D networks based on the given distribution of UIoT device’s depths.

Nonterrestrial communications have emerged as a key enabler for seamless connectivity in the upcoming generation networks. This kind of network can support high data rate communications among aerial platforms (i.e., unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), and satellites) and cellular networks, achieving anywhere and anytime connections. However, there are many practical implementation limitations, especially overload power consumption, high probability of blockage, and dynamic propagation environment. Fortunately, the recent technology reconfigurable intelligent surface (RIS) is expected to be one of the most cost-efficient solutions to address such issues. RIS with low-cost elements can bypass blockages and create multiple line-of-sight (LoS) links and provide controllable communication channels. In this article, we present a comprehensive literature review on the RIS-assisted nonterrestrial networks (RANTNs). First, the framework of the RANTNs is introduced with detailed discussion about distinct properties of RIS in NTNs and the two deployment types of RIS, that is, terrestrial RISs (TRISs), and aerial RISs (ARISs), and the classification of RANTNs, including RIS-assisted air-to-ground (A2G)/ground-to-air (G2A), ARIS-assisted ground-to-ground (G2G), and RIS-assisted air-to-air (A2A) communications. In combination with next-generation communication technologies, the advanced technologies in RANTNs are discussed. Then, we overview the literature related to RANTNs from the perspectives of performance analysis and optimization, followed by the widely used methodologies. Finally, open challenges and future research direction in the context of the RANTNs are highlighted.

This paper studies the temporal characteristics of the intelligent reflecting surface (IRS)-based visible light communication (VLC) channel using radiometric concepts. Throughout this study, we account for the delays experienced by the transmitted power along the continuum of paths originating at the source, passing through the IRS, reaching the detector. Then, we derive the impulse response of multi-element phase-tunable metasurface and orientation-tunable mirror array-based reflector setups for a general setting of source, reflector, and detector dimensions and relative positions. In addition, we derive simpler expressions for the two special cases, namely, the point source and the large-source small-reflector. Moreover, we present the exact expression for the delay spread and derive lower, upper bounds and asymptotic expressions when the number of reflecting elements increases for both reflector types. Finally, we study the impact of several system parameters on the temporal characterization of the two IRS-based VLC systems.

The integration between the satellite network and the terrestrial network will play a key role in the upcoming sixth-generation (6G) of mobile cellular networks thanks to the wide coverage and bandwidth offered by satellite networks. To leverage this integration, we propose a proactive traffic offloading scheme in an integrated multi-satellite terrestrial network (IMSTN) that considers the future networks’ heterogeneity and predicts their variability. Our proposed offloading scheme hinges on traffic prediction to answer the stringent requirements of data-rate, latency and reliability imposed by heterogeneous and coexisting services and traffic namely enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliable low latency communication (URLLC). However, the fulfilment of these requirements during offloading in dynamic IMSTN comes at the expense of significant energy consumption and introduces inherently supplementary latency. Therefore, our offloading scheme aims to balance the fundamental trade-offs first between energy consumption and the achievable data-rate and second between energy consumption and latency while meeting the respective needs of the present traffic. Our findings prove the importance of the cooperation between the multi-satellite network and the terrestrial network conditioned by traffic prediction to enhance the performance of IMTSN in terms of latency and energy consumption.

This paper presents the design and bit error rate (BER) analysis of a phase-independent non-orthogonal multiple access (NOMA) system. The proposed NOMA system can utilize amplitude-coherent detection (ACD) which requires only the channel amplitude for equalization purposes. In what follows, three different designs for realizing the detection of the proposed NOMA are investigated. One is based on the maximum likelihood (ML) principle, while the other two are based on successive interference cancellation (SIC). Closed-form expressions for the BER of all detectors are derived and compared with the BER of the coherent ML detector. The obtained results, which are corroborated by simulations, demonstrate that, in most scenarios, the BER is dominated by multiuser interference rather than the absence of the channel phase information. Consequently, the BER using ML and ACD are comparable for various cases of interest. The paper also shows that the SIC detector is just an alternative approach to realize the ML detector, and hence, both detectors provide the same BER performance.

The relay positions or hop distances in a multi-hop relaying scheme is an important parameter that can be optimized in order to mitigate the angle-of-arrival variance or pointing error in a free-space optical (FSO) backhaul link. In this study, we have optimized the relay positions for amplify-and-forward and decode-and-forward relays in a multi-hop unmanned aerial vehicle-based relaying scheme for FSO. Particularly, we have shown that a significant performance improvement may be achieved by optimizing the outage probability as a function of the hop distance for amplify-and-forward relays when the relays are constrained by a finite power gain. Additionally, we have discovered that for a low signal-to-noise ratio channel, the optimal hop distance of a particular hop for decode-and-forward relays is inversely proportional to angle-of-arrival variance in that hop.

This work investigates the secrecy outage performance of the uplink transmission of a radio-frequency (RF)-free-space optical (FSO) hybrid cooperative satellite-aerial-terrestrial network (SATN). Specifically, in the considered cooperative SATN, a terrestrial source (S) transmits its information to a satellite receiver (D) via the help of a cache-enabled aerial relay (R) terminal with the most popular content caching scheme, while a group of eavesdropping aerial terminals (Eves) trying to overhear the transmitted confidential information. Moreover, RF and FSO transmissions are employed over S-R and R-D links, respectively. Considering the randomness of R, D, and Eves, and employing a stochastic geometry framework, the secrecy outage performance of the cooperative uplink transmission in the considered SATN is investigated and a closed-form analytical expression for the end-to-end secrecy outage probability is derived. Finally, Monte-Carlo simulations are shown to verify the accuracy of our analysis.

This paper derives the asymptotic distribution of the normalized$k$-th maximum order statistics of a sequence of non-central chi-square random variables with non-identical non-centrality parameters. We demonstrate the utility of these results in characterizing the signal-to-noise ratio in three different applications in wireless communication systems where the statistics of the$k$-th maximum channel power over Rician fading links are of interest. Furthermore, we derive simple expressions for the asymptotic outage probability, average throughput, achievable throughput, and average bit error probability. The proposed results are validated via extensive Monte Carlo simulations.

To expedite research progress on terahertz (THz) communications, we analyze the outage performance of THz communication systems by a compound channel model in this paper. Different from existing models, the compound channel model incorporates the effects of spreading loss, molecular absorption loss, shadowing, and multi-path fading via a composite distribution. By using this model, we maintain an equilibrium of the outage performance analysis between mathematical tractability and the fidelity of realistic THz channels. Specifically, by utilizing the compound channel model, outage performance analysis can get rid of sophisticated case-specific channel modeling relying on field measurement and the ray-tracing assessment. To facilitate the application of the proposed channel model, we also design a maximum likelihood estimation (MLE) based channel parameter estimation approach for the compound channel model. The analytical results of outage performance by using the compound channel model are given in closed form and verified by numerical results.

This paper aims to derive expressions of the downlink ergodic user rates in a multi-cell large-scale multi-user multiple-input single-output (MISO) system under the assumption that each base station (BS) employs maximum ratio transmission (MRT) precoding and that single-antenna users in each cell are divided into groups, where channels of users in the same group share common covariance matrices and follow the double scattering channel model. Moreover, both channel estimation errors and pilot contamination effects caused by re-use of pilot sequences in neighboring cells are taken into consideration in this work. The analysis is carried out using statistical tools under the exact and asymptotic regimes in which the number of antennas at BS$N$, the number of users in each cell$K$, and the number of scatterers$S$grow large at the same pace. Furthermore, the obtained exact expressions and deterministic approximations of the ergodic rates are expressed in simplified closed-forms under the special case of multi-keyhole channels to yield useful insights. They reveal that signal-to-noise plus interference ratio (SINR) without user grouping in a multi-keyhole channel is similar to that under standard Rayleigh channel in the asymptotic regime. However, under user grouping, we show that the massive multiple-input multiple-output (MIMO) gains promised by deploying large-scale antenna arrays in multi-cell settings are limited by the number of scatterers even if the number of antennas grows large. Simulation results illustrate the close match provided by the asymptotic analysis for moderate system dimensions and confirm the insights drawn from the theoretical findings.

Conventional radars repeat the transmission of the same waveform after a predefined interval of time called pulse-repetition-interval (PRI). This technique helps estimate the range and Doppler shift of the targets and suppress clutter. However, in dual-function radar communication (DFRC), a different symbol waveform is transmitted after each PRI. Depending on the number of targets, radar receiver output yields several peaks representing different targets’ ranges. Each peak comes with its side-lobes called range-side-lobes (RSL). In DFRC, due to different symbol waveform transmission, peaks and RSLs do not remain coherent, making Doppler shift estimation and clutter suppression challenging tasks. In most of the available literature, iterative receive filters have been designed for DFRC to minimize RSLs and achieve coherent output for different waveforms. However, the proposed receive filter does not guarantee coherent output for more than two waveforms. In contrast, we proposed two novel closed-form algorithms to design receive filters for DFRC that guarantee coherent output response for several waveforms and suppress RSLs. Simulation results demonstrate that the proposed receivers achieve full coherency, and the RSLs are significantly lower than the conventional method. Furthermore, the advantage of achieving coherent output response is shown in target detection and bit-error-rate improvement.

This paper considers a three-dimensional anomalous-diffusive mobile molecular communication (MC) channel. Herein, the communicating devices, i.e., point transmitter (Tx) and passive receiver (Rx) can also move anomalously with the information-carrying molecule (ICM). In order to incorporate the anomalous diffusion phenomenon, the concept of rescaled-Brownian motion is explored. Two different scenarios, named point-to-point and multi-user MC systems, are considered for the analysis. First, considering the point-to-point scenario, the expressions for the channel impulse response (CIR) incorporating static and mobile Tx and Rx are derived. Further, the expressions for the peak time of CIR and the corresponding peak value are also derived. Furthermore, the point-to-point MC system is exploited in terms of bit-error-rate (BER), minimum BER, maximum mutual information, and throughput. A multi-user MC system is considered in the second scenario, and a molecular division multiple access (MDMA) method is used. The system is analyzed in terms of BER and throughput. Further, the system performance is optimized using two schemes known as min-max fairness for error probability and max-min fairness for throughput. Furthermore, we use best-to-best and best-to-worst assignment strategies to allocate different types of ICMs for each Rx. Subsequently, the optimal allocation for the number of ICMs is obtained when specific ICMs are assigned to all users/receivers for a two-user scenario. All the channel metrics are verified through particle-based and Monte-Carlo simulations.

Visible light communication (VLC) is a promising technology for 6th-generation (6G) networks because of its attractive feature such as a wide unlicensed spectrum. In this paper, a novel adaptive coded spatial modulation scheme with probabilistic shaping (PS) is proposed to approach the capacity of the spatial modulation (SM) in visible light communication (VLC) channels with intensity modulation and direct detection (IM/DD). In the proposed scheme, spatial and constellation symbols are probabilistically shaped depending on the user’s location inside the room and the optical signal-to-noise ratio (OSNR). Moreover, we optimize the channel coding rate to maximize further the achievable rate of the proposed scheme for a given OSNR. Finally, we propose an algorithm to compute the capacity-achieving distribution of the proposed scheme with unipolar$M$-ary pulse amplitude modulation (PAM) signaling. The proposed scheme outperforms uniform and an orthogonal frequency-division multiplexing (OFDM) based scheme in terms of spectral efficiency (SE) and/or frame error rate (FER). For example, for 8-PAM signaling with$N=8$transmit antennas, the proposed scheme operates within 0.2 dB from the unipolar$M$-PAM SM VLC channel signaling capacity and outperforms the uniform and OFDM based schemes in terms of FER by at least 1.1 dB and 1.3 dB at a normalized data rate of 1.33 bits per channel use per sub-carrier (b/cu/sc), respectively.

Integrating terrestrial networks with burgeoning high-altitude platform stations (HAPSs) will be a disruptive challenge for beyond-5G systems provisioning large-scale three-dimensional connectivity. Here, we study the problem of forwarding packets between terrestrial terminals and backhaul through multi-HAPS relaying. Considering the limited wireless backhaul, which is the practical constraint of HAPS relaying, dual-hop mixed radiofrequency/free-space optical (RF/FSO) networks are investigated, where backhaul-to-relay and relay-to-user communications employ FSO and RF links, respectively. To maximize the end-to-end network throughput, including downlink and uplink rate, we formulate the optimization problem for variables; the association between aerial and terrestrial terminals, transmit power, and deployment of multiple HAPSs, respectively. We tackle this problem using an iterative algorithm with proposed surrogate functions to efficiently obtain the locally optimal solution. Simulation results corroborate that our proposed optimal method achieves up to 11.3% spectral efficiency compared to the conventional heuristic method. Furthermore, we answer three questions; 1) what the wireless backhaul requirements are, 2) how the number of HAPSs and terrestrial terminals impact the network, and 3) what if a certain user terminal has a particular demand on data rate.

In this paper, the performance of a mixed Terahertz/free-space optical (THz/FSO) wireless transmission system is studied, where the joint effects of channel fading and pointing errors are considered for both THz and FSO links. Employing the semi-blind amplify-and-forward protocol, we derive the cumulative distribution function and probability density function of the end-to-end signal-to-noise ratio. By applying the derived statistics, exact expressions for the outage probability (OP), average bit error rate (BER), and average channel capacity are obtained. In order to attain useful physical insights, asymptotic OP and average BER expressions are also presented. Based on them, the diversity gain is determined, which is shown to depend on channel fading and pointing errors of both links. In addition, by taking into account the hardware impairments, the OP of the non-ideal hardware system is derived. Moreover, the analysis is extended to multi-antenna scenarios and the asymptotic OP is obtained. Finally, illustrative numerical results are plotted and it can be observed that the path loss, channel fading, pointing errors and hardware impairments lead to a considerable degradation in system performance.

Satellite communication (SatCom) is an essential component of next-generation wireless communication to achieve a goal of ubiquitous connectivity on globe. The outage event of SatCom link connecting to a network is more critical in an infrastructure-deficient remote area. In this paper, we analyze outage probability (OP) and symbol error rate (SER) over SatCom downlink channels when the users are randomly located in single beam and multibeam area. The downlink beams will suffer from propagation loss and the shadowed-Rician fading depending on the user location which is assumed to follow a Poisson point process. For mathematically tractable, informative, and insightful interpretation, we obtain the asymptotic OP and SER expressions of user link under several channel conditions in the high power regime. Finally, numerical results are presented to verify the analysis and show the accuracy of the asymptotic results.

The massive machine-type communications (mMTC) paradigm based on media modulation in conjunction with massive multi-input multi-output base stations (BSs) is emerging as a viable solution to support the massive connectivity for the future Internet-of-Things, in which the inherent massive access at the BSs poses significant challenges for device activity and data detection (DADD). This paper considers the DADD problem for both uncoded and coded media modulation based mMTC with a slotted access frame structure, where the device activity remains unchanged within one frame. Specifically, due to the slotted access frame structure and the adopted media modulated symbols, the access signals exhibit adoubly structured sparsityin both the time domain and the modulation domain. Inspired by this, a doubly structured approximate message passing (DS-AMP) algorithm is proposed for reliable DADD in the uncoded case. Also, we derive the state evolution of the DS-AMP algorithm to theoretically characterize its performance. As for the coded case, we develop a bit-interleaved coded media modulation scheme and propose an iterative DS-AMP (IDS-AMP) algorithm based on successive inference cancellation (SIC), where the signal components associated with the detected active devices are successively subtracted to improve the data decoding performance. In addition, the channel estimation problem for media modulation based mMTC is discussed and an efficient data-aided channel state information (CSI) update strategy is developed to reduce the training overhead in block fading channels. Finally, simulation results and computational complexity analysis verify the superiority of the proposed DS-AMP algorithm over state-of-the-art algorithms in the uncoded case. Also, our results confirm that the proposed SIC-based IDS-AMP algorithm can enhance the data decoding performance in the coded case and verify the validity of the proposed data-aided CSI update strategy.

This paper considers a dual-hop cooperative satellite-unmanned aerial vehicle (UAV) communication system including a satellite (S) and a group of cluster headers (CHs) which are respectively with a group of uniformly distributed UAVs. Specifically, the CHs serve as aerial decode-and-forward relays to forward the information transmitted by the S to the UAVs. Moreover, free-space optical (FSO) and radio frequency (RF) technologies are respectively adopted over S-CH and CH-UAV links to exploit the high directivity of FSO over long-distance transmission and the omnidirectional coverage ability of RF. The positions of the CHs in the three-dimensional space follow the Matérn hard-core point processes type-II, in which each CH cannot be closer to another by less than a predefined distance. Three different cases over CH-UAV links are considered during the performance modeling: interference-free, interference-dominated, and interference-and-noise. Then, the coverage performance of the S-CH link and the CH-UAV link in the aforementioned three cases is studied and the closed-form analytical expressions of the coverage probability (CP) over both two links are derived. Additionally, the asymptotic expressions for the CP over S-CH link and CH-UAV link in the interference-free case are derived. Finally, numerical results are provided to validate our proposed analytical models and thus some meaningful conclusions are achieved.

Reconfigurable intelligent surfaces (RISs) appear as one of the most promising paradigms for future wireless communications, because of their high adjustability for diverse communication demands and the additional information-carrying capability by reflecting patterns. This paper investigates the capacity of RIS-assisted multiple-input multiple-output (MIMO) symbiotic communications utilizing multiple reflecting patterns, where each reflecting pattern is non-uniformly activated to carry additional information. To enhance transmission performance, the reflecting patterns, reflecting activation probability, and the transmit covariance matrix are jointly designed. Since the exact expression of the system capacity is intractable, the lower and upper bounds on the capacity are derived and used for optimization in this paper. Based on the lower bound on the capacity, a gradient ascent algorithm is developed to find the optimal reflecting patterns, reflecting activation probability, and the transmit covariance matrix. By taking advantage of the concise-form upper bound on the capacity, closed-form solutions of the reflecting activation probability and transmit covariance matrix can be derived after optimizing the reflecting patterns. The superiority of the proposed design is investigated and verified by computer simulations. Some selected numerical results demonstrate that the proposed design can achieve a higher capacity than the benchmark adopting only one reflecting pattern.

Reconfigurable intelligent surface (RIS) has emerged as a promising mean to enhance wireless transmission. The effective reflected paths provided by RIS are able to alleviate the susceptibility to blockage effects, especially in high-frequency band communications, where signals experience severe path loss and high directivity. This paper is concerned with an RIS-assisted system over the millimeter wave (mmWave) channel characterized by sparse propagation paths. A base station tries to connect with the desired user through an RIS, while the undesired user can also receive the signal transmitted from BS unavoidably, which is treated as the interference signal. All terminals are assumed to be equipped with a single antenna for the sake of simplicity. The paper aims to propose an appropriate design of the phase shifts of each element at the RIS so as to maximize the received signal power transmitted from the base station (BS) at the desired user, while nulling the received interference signal power at the undesired user. The proposed reflecting design relies on the decomposition of the reflecting beamforming vectors and all channel path vectors into Kronecker product of factors being uni-modulus vectors. By exploiting characteristics of Kronecker mixed products, different factors of the reflecting are designed for either nulling the interference signal at the undesired user, or coherently combining data paths at the desired user. Furthermore, a channel estimation strategy is proposed to enable the proposed reflecting beamforming design. The magnitude, azimuth, and elevation arrival and departure angles of desired and undesired paths are estimated by an efficient 2-dimension (2-D) line spectrum optimization technique based on the atomic norm minimization (ANM) framework. The performance of the reflecting designs and channel estimation scheme is analyzed and demonstrated by simulation results.

In this paper, we study the hard and soft support vector regression techniques applied to a set of $n$ linear measurements of the form $y_i=\boldsymbol{\beta}_\star^{T}{\bf x}_i +n_i$ where $\boldsymbol{\beta}_\star$ is an unknown vector, $\left\{{\bf x}_i\right\}_{i=1}^n$ are the feature vectors and $\left\{{n}_i\right\}_{i=1}^n$ model the noise. Particularly, under some plausible assumptions on the statistical distribution of the data, we characterize the feasibility condition for the hard support vector regression in the regime of high dimensions and, when feasible, derive an asymptotic approximation for its risk. Similarly, we study the test risk for the soft support vector regression as a function of its parameters. Our results are then used to optimally tune the parameters intervening in the design of hard and soft support vector regression algorithms. Based on our analysis, we illustrate that adding more samples may be harmful to the test performance of support vector regression, while it is always beneficial when the parameters are optimally selected. Such a result reminds a similar phenomenon observed in modern learning architectures according to which optimally tuned architectures present a decreasing test performance curve with respect to the number of samples.

Wireless traffic prediction is essential for cellular networks to realize intelligent network operations, such as load-aware resource management and predictive control. Existing prediction approaches usually adopt centralized training architectures and require the transferring of huge amounts of traffic data, which may raise delay and privacy concerns for certain scenarios. In this work, we propose a novel wireless traffic prediction framework named Dual Attention-Based Federated Learning (FedDA), by which a high-quality prediction model is trained collaboratively by multiple edge clients. To simultaneously capture the various wireless traffic patterns and keep raw data locally, FedDA first groups the clients into different clusters by using a small augmentation dataset. Then, a quasi-global model is trained and shared among clients as prior knowledge, aiming to solve the statistical heterogeneity challenge confronted with federated learning. To construct the global model, a dual attention scheme is further proposed by aggregating the intra-and inter-cluster models, instead of simply averaging the weights of local models. We conduct extensive experiments on two real-world wireless traffic datasets and results show that FedDA outperforms state-of-the-art methods. The average mean squared error performance gains on the two datasets are up to 10% and 30%, respectively.

This work considers the deployment of unmanned aerial vehicles (UAVs) over a predefined area to serve a number of ground users. Due to the heterogeneous nature of the network, the UAVs may cause severe interference to the transmissions of each other. Hence, a judicious design of the user-UAV association and UAV locations is desired. A potential game is defined where the players are the UAVs. The potential function is the total sum rate of the users. The agents' utility in the potential game is their marginal contribution to the global welfare or their so-called wonderful life utility. A game-theoretic learning algorithm, binary log-linear learning (BLLL), is then applied to the problem. Given the potential game structure, a consequence of our utility design, the stochastically stable states using BLLL are guaranteed to be the potential maximizers. Hence, we optimally solve the joint user-UAV association and 3-D-location problem. Next, we exploit the submodular features of the sum rate function for a given configuration of UAVs to design an efficient greedy algorithm. Despite the simplicity of the greedy algorithm, it comes with a performance guarantee of 1-1/e of the optimal solution. To further reduce the number of iterations, we propose another heuristic greedy algorithm that provides very good results. Our simulations show that, in practice, the proposed greedy approaches achieve significant performance in a few iterations.

Recent advances in the Internet of Things (IoT) are giving rise to a proliferation of interconnected devices, allowing the use of various smart applications. The enormous number of IoT devices generates a large volume of data that requires further intelligent data analysis and processing methods such as deep learning (DL). Notably, DL algorithms, when applied to the Industrial IoT (IIoT), can provide various new applications, such as smart assembling, smart manufacturing, efficient networking, and accident detection and prevention. Motivated by these numerous applications, in this article, we present the key potentials of DL in IIoT. First, we review various DL techniques, including convolutional neural networks, autoencoders, and recurrent neural networks, as well as their use in different industries. We then outline a variety of DL use cases for IIoT systems, including smart manufacturing, smart metering, and smart agriculture. We delineate several research challenges with the effective design and appropriate implementation of DL-IIoT. Finally, we present several future research directions to inspire and motivate further research in this area.

In this article, we investigate the nonlinear energy harvesting (EH)-based unmanned aerial vehicle (UAV)-assisted full-duplex (FD) Internet of Things (IoT) network with infinite and finite blocklength (FBL) codes. The reliability performance of the considered network, having two half-duplex UAVs and an FD IoT device, is analyzed in terms of the block error rate (BLER) with given ultrareliable and low-latency communication constraints. With the assumption of the combined effect of fading and shadowing, the closed-form expressions for BLER and network goodput are obtained over the Rician shadowed fading channels considering various shadowing scenarios, EH receiver architecture, IoT device mobility, inter-UAV interference, and self-interference (SI) cancelation capabilities at FD IoT device. The obtained results over the Rician shadowed fading for the nonlinear EH receiver architecture are also compared with the linear EH and over the Rician fading channels. The numerical results reveal important observations related to the impact of time-selective fading channels with imperfect channel state information, shadowing severity in the suburban areas, SI cancelation capabilities, blocklength, and the number of channel uses on the reliability performance of the UAV-assisted FD IoT network. Furthermore, the tightness of the approximation presented is verified through the Monte-Carlo simulations.

The upcoming Internet of Things (IoT) is foreseen to encompass massive numbers of connected devices, smart objects, and cyber-physical systems. Due to the large scale and massive deployment of devices, it is deemed infeasible to safeguard 100% of the devices with state-of-the-art security countermeasures. Hence, large-scale IoT has inevitable loopholes for network intrusion and malware infiltration. Even worse, exploiting the high density of devices and direct wireless connectivity, malware infection can stealthily propagate through susceptible (i.e., unsecured) devices and form an epidemic outbreak without being noticed to security administration. A malware outbreak enables adversaries to compromise a large population of devices, which can be exploited to launch versatile cyber and physical malicious attacks. In this context, we utilize spatial firewalls, to safeguard the IoT from malware outbreak. In particular, spatial firewalls are computationally capable devices equipped with state-of-the-art security and anti-malware programs that are spatially deployed across the network to filter the wireless traffic in order to detect and thwart malware propagation. Using tools from percolation theory, we prove that there exists a critical density of spatial firewalls beyond which malware outbreak is impossible. This, in turn, safeguards the IoT from malware epidemics regardless of the infection/treatment rates. To this end, a tractable upper bound for the critical density of spatial firewalls is obtained. Furthermore, we characterize the relative communications ranges of the spatial firewalls and IoT devices to ensure secure network connectivity. The percentage of devices secured by the firewalls is also characterized.

One of the promising technologies for the next generation wireless networks is the reconfigurable intelligent surfaces (RISs). This technology provides planar surfaces the capability to manipulate the reflected waves of impinging signals, which leads to a more controllable wireless environment. One potential use case of such technology is providing indirect line-of-sight (LoS) links between mobile users and base stations (BSs) which do not have direct LoS channels. Objects that act as blockages for the communication links, such as buildings or trees, can be equipped with RISs to enhance the coverage probability of the cellular network through providing extra indirect LoS-links. In this article, we use tools from stochastic geometry to study the effect of large-scale deployment of RISs on the performance of cellular networks. In particular, we model the blockages using the line Boolean model. For this setup, we study how equipping a subset of the blockages with RISs will enhance the performance of the cellular network. We first derive the ratio of the blind-spots to the total area. Next, we derive the probability that a typical mobile user associates with a BS using an RIS. Finally, we derive the probability distribution of the path-loss between the typical user and its associated BS. We draw multiple useful system-level insights from the proposed analysis. For instance, we show that deployment of RISs highly improves the coverage regions of the BSs. Furthermore, we show that to ensure that the ratio of blind-spots to the total area is below 10-5, the required density of RISs increases from just 6 RISs/km2when the density of the blockages is 300 blockage/km2to 490 RISs/km2when the density of the blockages is 700 blockage/km2.

The emerging space-air-ground integrated network has attracted intensive research and necessitates reliable and efficient aeronautical communications. This paper investigates terahertz Ultra-Massive (UM)-MIMO-based aeronautical communications and proposes an effective channel estimation and tracking scheme, which can solve the performance degradation problem caused by the uniquetriple delay-beam-Doppler squint effectsof aeronautical terahertz UM-MIMO channels. Specifically, based on the rough angle estimates acquired from navigation information, an initial aeronautical link is established, where the delay-beam squint at transceiver can be significantly mitigated by employing a Grouping True-Time Delay Unit (GTTDU) module (e.g., the designedRotman lens-based GTTDU module). According to the proposed prior-aided iterative angle estimation algorithm, azimuth/elevation angles can be estimated, and these angles are adopted to achieve precise beam-alignment and refine GTTDU module for further eliminating delay-beam squint. Doppler shifts can be subsequently estimated using the proposed prior-aided iterative Doppler shift estimation algorithm. On this basis, path delays and channel gains can be estimated accurately, where the Doppler squint can be effectively attenuated via compensation process. For data transmission, a data-aided decision-directed based channel tracking algorithm is developed to track the beam-aligned effective channels. When the data-aided channel tracking is invalid, angles will be re-estimated at the pilot-aided channel tracking stage with an equivalent sparse digital array, where angle ambiguity can be resolved based on the previously estimated angles. The simulation results and the derived Cramér-Rao lower bounds verify the effectiveness of our solution.

Terahertz (THz)-band communications are a key enabler for future-generation wireless communication systems that promise to integrate a wide range of data-demanding applications. Recent advances in photonic, electronic, and plasmonic technologies are closing the gap in THz transceiver design. Consequently, prospect THz signal generation, modulation, and radiation methods are converging, and corresponding channel model, noise, and hardware-impairment notions are emerging. Such progress establishes a foundation for well-grounded research into THz-specific signal processing techniques for wireless communications. This tutorial overviews these techniques, emphasizing ultramassive multiple-input–multiple-output (UM-MIMO) systems and reconfigurable intelligent surfaces, vital for overcoming the distance problem at very high frequencies. We focus on the classical problems of waveform design and modulation, beamforming and precoding, index modulation, channel estimation, channel coding, and data detection. We also motivate signal processing techniques for THz sensing and localization.

This work presents the symbol error rate (SER) and outage probability analysis of multi-layer unmanned aerial vehicles (UAVs) wireless communications assisted by intelligent reflecting surfaces (IRS). In such systems, the UAVs may experience high jitter, making the estimation and compensation of the end-to-end phase for each propagation path prone to errors. Consequently, the imperfect phase knowledge at the IRS should be considered. The phase error is modeled using the von Mises distribution and the analysis is performed using the Sinusoidal Addition Theorem (SAT) to provide accurate results when the number of reflectors$L\leq 3$, and the Central Limit Theorem (CLT) when$L\geq 4$. The achieved results show that accurate phase estimation is critical for IRS based systems, particularly for a small number of reflecting elements. For example, the SER at 10−3degrades by about 5 dB when the von Mises concentration parameter$\kappa =2$and$L=30$, but the degradation for the same$\kappa $surges to 25 dB when$L=2$. The air-to-air (A2A) channel for each propagation path is modeled as a single dominant line-of-sight (LoS) component, and the results are compared to the Rician channel. The obtained results reveal that the considered A2A model can be used to accurately represent the A2A channel with Rician fading.

Acquisition and tracking systems form an important component of free-space optical communications due to directional nature of the optical signal. Acquisition subsystems are needed in order to search and locate the receiver terminal in an uncertainty/search region with very narrow laser beams. In this paper, we have proposed and analyzed two adaptive search schemes for acquisition systems that perform better-for the low probability of detection-than the spiral scanning approach. The first of these schemes, the adaptive spiral search, provides a better acquisition time performance by dividing the search region into a number of smaller subregions, and prioritizing search in regions of higher probability mass. The second technique-the shotgun approach-searches the region in a random manner by sampling the search region according to a Gaussian distribution. The adaptive spiral scheme outperforms the shotgun approach in terms of acquisition time, especially if the number of search subregions is large enough. However, a higher pointing accuracy is required by the adaptive spiral search in order to search the region precisely. On the other hand, the shotgun scanning approach does not require such stringent pointing accuracy.

Free-space optical (FSO) communications will play an important role in the backhaul of future generation of wireless networks in order to support high data rates. Because of narrow beamwidth inherent to an optical signal, acquisition and tracking form an important component of any FSO communication system. In this study, we have analyzed the optimization of received power allocation between tracking and data channels in an FSO receiver. In this regard, we have carried out a detailed analysis of the error statistics of the centroid estimator that is used for tracking the beam with the help of an array. The objective function that is optimized (minimized) are the probability of error and the probability of outage, and the optimization of power allocation is carried out as a function of parameters such as noise power, pointing error variance, pointing error correlation coefficient, and the threshold of outage. We have analyzed the optimization concerning the lognormal and exponentiated Weibull fading scenarios as well. We learn that the optimal power allocation is a function of the received signal-to-noise ratio: a lower signal-to-noise ratio dictates that a higher fraction of received power should be diverted to the tracking channel and vice versa.

Hardware impairment is inevitable in many wireless systems. It is particularly severe in low-cost applications due to the imperfect components used. In this paper, the channel estimation and non-coherent detection problems of hardware impaired signals are studied for a single-carrier, single-antenna and single-hop system. Specifically, three different cases are investigated: signals with additive distortion only, signals with in-phase and quadrature imbalance only, and signals with both impairments. The maximum likelihood and Gaussian approximation methods are used to derive the new non-coherent detectors for amplitude modulated signals, while the maximum likelihood and moment-based methods are employed to design the new channel estimators for all signals. Numerical results show that the new non-coherent detectors outperform the existing non-coherent detectors in the presence of hardware impairment. The performance gain can be as high as 8 dB. They also show that the new channel estimators have much higher accuracy than the existing estimator. In some conditions, the accuracy of the new estimator is about 100 times that of the existing estimator.

This paper investigates the massive access for a satellite-aerial-terrestrial network (SATN), where a high-altitude platform (HAP) is deployed as a relay to assist the uplink transmission from terrestrial user equipment (UE) to satellite. Unlike previous works, we adopt radio frequency (RF) and free space optical for the aerial-terrestrial and satellite-aerial links, respectively. Specifically, by assuming that imperfect angular information (IAI) of each UE is known at the HAP, we develop a space division multiple access (SDMA) scheme to maximize the ergodic sum rate (ESR). To this end, we first exploit the IAI to calculate the analytical expression of channel correlation matrix. Then, by considering the limitation of array freedom, we propose a subspace-based UE grouping and scheduling scheme to cluster all UEs into groups. Next, we present a computationally effective beamforming (BF) scheme for each UE at HAP to efficiently implement SDMA in the RF link. Furthermore, a closed-form expression for the ESR of the SATN is derived to validate the proposed BF and SDMA schemes. Finally, simulation results corroborate the derived theoretical formulas and reveal the impacts of array size, angular estimation error, the number of UEs and scheduling threshold on the system performance.

Hardware distortions (HWDs) render drastic effects on the performance of communication systems. They are recently proven to bear asymmetric signatures; and hence can be efficiently mitigated using improper Gaussian signaling (IGS), thanks to its additional design degrees of freedom. Discrete asymmetric signaling (AS) can practically realize the IGS by shaping the signals' geometry or probability. In this paper, we adopt the probabilistic shaping (PS) instead of uniform symbols to mitigate the impact of HWDs and derive the optimal maximum a posterior detector. Then, we design the symbols' probabilities to minimize the error rate performance while accommodating the improper nature of HWD. Although the design problem is a non-convex optimization problem, we simplified it using successive convex programming and propose an iterative algorithm. We further present a hybrid shaping (HS) design to gain the combined benefits of both PS and geometric shaping (GS). Finally, extensive numerical results and Monte Carlo (MC) simulations highlight the superiority of the proposed PS over conventional uniform constellation and GS. Both PS and HS achieve substantial improvements over the traditional uniform constellation and GS with up to one order magnitude in error probability and throughput.

We propose a holographic version of a reconfigurable intelligent surface (RIS) and investigate its application to terahertz (THz) massive multiple-input multiple-output systems. Capitalizing on the miniaturization of THz electronic components, RISs can be implemented by densely packing sub-wavelength unit cells, so as to realize continuous or quasi-continuous apertures and to enable holographic communications. In this paper, in particular, we derive the beam pattern of a holographic RIS. Our analysis reveals that the beam pattern of an ideal holographic RIS can be well approximated by that of an ultra-dense RIS, which has a more practical hardware architecture. In addition, we propose a closed-loop channel estimation (CE) scheme to effectively estimate the broadband channels that characterize THz massive MIMO systems aided by holographic RISs. The proposed CE scheme includes a downlink coarse CE stage and an uplink finer-grained CE stage. The uplink pilot signals are judiciously designed for obtaining good CE performance. Moreover, to reduce the pilot overhead, we introduce a compressive sensing-based CE algorithm, which exploits the dual sparsity of THz MIMO channels in both the angular domain and delay domain. Simulation results demonstrate the superiority of holographic RISs over the non-holographic ones, and the effectiveness of the proposed CE scheme.

A free-space optical communication system using non-mode-selective photonic lantern (PL) based coherent receiver is studied. Based on the simulation of photon distribution, the power distribution at the single-mode fiber end of the PL is quantitatively described as a truncated Gaussian distribution over a simplex. The signal-to-noise ratios (SNRs), bit-error rate (BER), and outage probability of PL based receiver using selection combining (SC), equal-gain combining (EGC), and maximal-ratio combining (MRC) over Gamma-Gamma turbulence channels are analyzed and compared with the single-mode fiber (SMF) receiver and multimode fiber (MMF) receiver. We demonstrate that EGC is the most suitable combining for PL based receiver because the PL power distribution has limited influence on the BER and outage probability when EGC is used and can greatly affect the BER and outage probability when SC is used. Numerical results show that PL based receiver with EGC requires 6 dB less SNR than the SMF receiver and 5.5 dB less SNR than the MMF receiver at BER of 10−6in moderate turbulence.

In this paper, we investigate the transmission probabilities in three cases (depending only on the legitimate receiver, depending only the eavesdropper, and depending on both legitimate receiver and eavesdropper) in quantum key distribution (QKD) systems over free-space optical links. To be more realistic, we consider a generalized pointing error scenario, where the azimuth and elevation pointing error angles caused by stochastic jitters and vibrations in the legitimate receiver platform are independently distributed according to a non-identical normal distribution. Taking these assumptions into account, we derive approximate expressions of transmission probabilities by using the Gaussian quadrature method. To simplify the expressions and get some physical insights, some asymptotic analysis on the transmission probabilities is presented based on asymptotic expression for the generalized Marcum Q-function when the telescope gain at the legitimate receiver approaches to infinity. Moreover, from the asymptotic expression for the generalized Marcum Q-function, the asymptotic outage probability over Beckmann fading channels (a general channel model including Rayleigh, Rice, and Hoyt fading channels) can be also easily derived when the average signal-to-noise ratio is sufficiently large, which shows the diversity order and array gain.

With the prevalence of orthogonal frequency-division multiplexing (OFDM) in many standards, e.g., IEEE 802.11, IEEE 802.16, DVB-T, and DVB-T2, a number of variant modulation schemes based on OFDM have been proposed, which resort to signal sparsity to further enhance spectral efficiency and mitigate the high peak-to-average ratio (PAPR) problem. Among these variants, OFDM with subcarrier number modulation (OFDM-SNM) has been proven to be efficient for simple communication systems with low constellation modulation orders and limited decoding capability. To rigorously verify the performance advantages of OFDM-SNM, we present the study of OFDM-SNM in this paper from the information-theoretic perspective. In particular, we determine an upper bound on the mutual information of OFDM-SNM in closed form by using the log sum inequality. Also, we analyze the optimal pattern utilization probabilities (PUPs) for OFDM-SNM by channel-dependent coding and propose an easy-to-implement iterative algorithm to approach the optimal PUPs. Moreover, considering the practical achievability, we propose a Huffman coding based achievable PUP vector construction scheme to obtain the achievable PUPs and the corresponding achievable rate. We carry out numerical simulations to verify the effectiveness of this study and illustrate the efficiency of the obtained PUPs in comparison with several benchmarks.

Small cells (SCs) mounted on top of Unmanned Aerial Vehicles (UAVs) can be used to boost the radio capacity in hotspot zones. However, UAV-SCs are subject to tight battery constraints, resulting in frequent recharges operated at the ground sites. To meet the UAV-SCs energy demanded to the ground sites, the operator leverages a set of Solar Panels (SPs) and grid connection. In this work, we demonstrate that both i) the level of throughput provided to a set of areas and ii) the amount of energy that is exchanged with the grid by the ground sites play a critical role in such UAV-aided cellular network. We then formulate the J-MATE model to jointly optimize the energy and throughput through revenue and cost components. In addition, we design the BBSR algorithm, which is able to retrieve a solution even for large problem instances. We evaluate J-MATE and BBSR over a realistic scenario composed of dozens of areas and multiple ground sites, showing that: i) both J-MATE and BBSR outperform previous approaches targeting either the throughput maximization or the energy minimization, and ii) the computation time and the memory occupation of BBSR are reduced up to five orders of magnitude compared to J-MATE.

This paper considers the deployment of intelligent reflecting surfaces (IRSs) technology for wireless multi-hop backhauling of multiple basestations (BSs) connected in a mesh topology. The performance of the proposed architecture is evaluated in terms of outage and symbol error probability in Rician fading channels, where closed-form expressions are derived and demonstrated to be accurate for several cases of interest. The analytical results corroborated by simulation, show that the IRS-mesh backhauling architecture has several desired features that can be exploited to overcome some of the backhauling challenges, particularly the severe attenuation at high frequencies. For example, using IRS with four elements, N=4, provides a symbol error rate of about 10-5at a signal-to-noise ratio of about 0 dB, even for a large number of hops. Moreover, the obtained analytical results corroborated by Monte Carlo simulation show that the gain obtained by increasing N decreases significantly for N > 5. For example, increasing N from 1 to 2 provides about 8dB of gain, while the increase from 3 to 4 provides about 4dB. Moreover, the degradation caused by the relaying process becomes negligible when the number of IRS elements N= 3.

Unmanned aerial vehicles (UAVs) have recently received a significant interest to assist terrestrial wireless networks thanks to their strong line-of-sight links and flexible/instant deployment. However, UAVs' assistance is limited by their battery lifetime and wireless backhaul link capacity. At the expense of limited mobility, tethered UAVs (T-UAVs) can be a viable alternative to provide seamless service over a cable that simultaneously supplies power and data from a ground station (GS). Accordingly, this paper presents a comparative performance analysis of T-UAV and regular/untethered UAV (U-UAV)-assisted cellular traffic offloading from a geographical area that undergoes heavy traffic conditions. By using stochastic geometry tools, we first derive joint distance distributions between the hot-spot users, the terrestrial base station (TBS), and the UAV. To maximize the end-to-end signal-to-noise ratio, a user association policy is developed, and corresponding association regions are analytically identified. Then, the overall coverage probability of the U-UAV/T-UAV-assisted system is derived for given locations of the TBS and the U-UAV/T-UAV. Moreover, we analytically prove that optimal UAV location falls within a partial surface of the spherical cone centered at the GS. Numerical results show that T-UAV outperforms U-UAV given that sufficient GS locations accessibility and tether length are provided.

In this paper, a robust iterative block decision feedback equalization (DFE) algorithm is developed for uplink single-carrier frequency division multiple access (SC-FDMA) systems. Three important problems that can adversely affect the performance of the DFE in SC-FDMA systems are to be addressed here, i.e., the feedback symbols reliability, the feedback correlation metric, and the phase noise due to inaccuracies in the fabrication process of the crystal oscillator. Instead of using all the detected symbols in the feedback loop of the DFE, only the highly reliable symbols are selected to be fed back. This results in the improvement of the error propagation, one of the common problems in a DFE. Also, the feedback correlation is an important metric in the design of the DFE, and hence an elegant method is proposed in this design and found to perform better than the available existing designs in the literature. Finally, the transmitter and receiver phase noise is iteratively compensated using its corresponding time- and frequency-domain properties. Simulation results demonstrate the robustness of our designed iterative block DFE.

This paper investigates the performance of dual-hop amplify-and-forward (AF) mixed radio frequency (RF)/free-space optical (FSO) transmissions with partial relay selection (PRS) based on outdated channel state information (CSI) estimates. Turbulence-induced fading, path loss, and pointing errors, are all considered in the FSO channel modeling. Both the fading and the path loss are described through general models which encompass the commonly used models. Novel expressions for the cumulative distribution function, probability density function, moment generating function, and moments of the end-to-end signal-to-noise ratio (SNR) are obtained in closed-form. Thereafter, novel closed-form expressions for key performance metrics, namely, outage probability, average bit error probability and spectral efficiency, are derived. The analysis is unified, applying to both types of detection techniques, intensity modulation/direct direction and heterodyne detection. Asymptotic analysis is further conducted, which open the door to additional important results. Monte Carlo simulation results confirm the effectiveness of the proposed analysis, and attest that in AF RF/FSO systems with PRS based on outdated CSI estimates, where implementing higher number of relays is highly difficult and costly, the low SNR regime may be prohibitive because it hinders the diversity advantages promised by the use of more relays in PRS systems.

In this paper, we investigate a mobile relaying system assisted by an unmanned aerial vehicle (UAV) with a finite size of the buffer. Under the buffer size limit and delay constraints at the UAV relay, we consider a dual-hop mixed free-space optical/radio frequency (FSO/RF) relaying system (i.e., the source-to-relay and relay-to-destination links employ FSO and RF links, respectively). Taking an imbalance in the transmission rate between RF and FSO links into consideration, we address the trajectory design of the UAV relay node to obtain the maximum data throughput at the ground user terminal. Specifically, we classify two relaying transmission schemes according to the delay requirements, i.e., i) delay-limited transmission and ii) delay-tolerant transmission. Accordingly, we propose an iterative algorithm to effectively obtain the locally optimal solution to our throughput optimization problems and further present the complexity analysis of this algorithm. Through this algorithm, we present the resulting trajectories over the atmospheric condition, the buffer size, and the delay requirement. In addition, we show the optimum buffer size and the throughput-delay tradeoff for a given system. The numerical results validate that the proposed buffer-aided and delay-considered mobile relaying scheme obtains 223.33% throughput gain compared to the conventional static relaying scheme.

Despite the promising high-data rate features of visible light communications (VLC), they still suffer from unbalanced services due to blockages and channel fluctuation among users. This paper introduces and evaluates a new transmission scheme which adopts cooperative non-orthogonal multiple access (Co-NOMA) in hybrid VLC/radio-frequency (RF) systems, so as to improve both system sum-rate and fairness. Consider a network consisting of one VLC access point (AP) and multiple strong and weak users, where each weak user is paired with a strong user. Each weak user can be served either directly by the VLC AP, or via the strong user which converts light information received through the VLC link, and forwards the information to the weak user via the RF link. The paper then maximizes a network-wide weighted sum-rate, so as to jointly determine the strong-weak user-pairs, the serving link of each weak user (i.e., either direct VLC or hybrid VLC/RF), and the power of each user message, subject to user connectivity and transmit power constraints. The paper tackles such a mixed-integer non-convex optimization problem using an iterative approach. Simulations show that the proposed scheme significantly improves the VLC network performance (i.e., sum-rate and fairness) as compared to the conventional NOMA scheme.

In this paper, we propose a non-stationary satellite communication system model considering the impacts of Earth rotation by adopting the Earth-centered inertial (ECI), and the Earth-centered Earth-fixed (ECEF) coordinates. The position variations of a satellite (S) and a ground user (U) via coordinate transformations are demonstrated. Considering the variations of the distance between S and U, the instantaneous outage probability (OP) and channel capacity are calculated, as well as the system throughput within finite communication time. A simplified case is considered and analyzed while ignoring the Earth's rotation. Furthermore, the asymptotic expressions for the OP, capacity, and throughput are developed in the high signal-to-noise ratio (SNR) regime to obtain some insights. We also provide new definitions for throughput and OP within a short communication duration. The application and future research directions based on the derived results, including resource allocation, satellite handover, communication scenarios with multiple satellites and mobile users, are also discussed. Finally, some selected numerical results are provided to validate our proposed analysis models.

This paper investigates the uplink space division multiple access in a satellite-aerial-terrestrial network, where multiple users access to satellite with the aid of a unmanned aerial vehicle, and the two hops adopt radio frequency (RF) and free space optical (FSO), respectively. We first formulate an optimization problem to maximize the ergodic sum rate (ESR) of the considered system. Then a statistical channel state information (CSI)-based beamforming (BF) scheme is proposed to solve the non-convex ESR maximization problem. The main advantage of the proposed method is that only statistical CSI is used to obtain BF vectors so that a low implementation complexity can be achieved. Then, by assuming that the RF and FSO links undergo Nakagami-m and Gamma-Gamma fading, respectively, closed-form expression for ESR of the considered system is derived. Simulation results validate the derived expression and confirm the effectiveness of the proposed BF scheme.

This paper carries out a large dimensional analysis of a variation of kernel ridge regression that we call centered kernel ridge regression (CKRR), also known in the literature as kernel ridge regression with offset. This modified technique is obtained by accounting for the bias in the regression problem resulting in the old kernel ridge regression but with centered kernels. The analysis is carried out under the assumption that the data is drawn from a Gaussian distribution and heavily relies on tools from random matrix theory (RMT). Under the regime in which the data dimension and the training size grow infinitely large with fixed ratio and under some mild assumptions controlling the data statistics, we show that both the empirical and the prediction risks converge to a deterministic quantities that describe in closed form fashion the performance of CKRR in terms of the data statistics and dimensions. A key insight of the proposed analysis is the fact that asymptotically a large class of kernels achieve the same minimum prediction risk. This insight is validated with synthetic data.

By exploiting fractional-Fourier-transform (FrFT), a novel technique for the QRS complex detection is proposed. The application of the FrFT rotates the Electrocardiograph (ECG) signal in the time-frequency plane. We claim this rotation can give simple and effective QRS complex detection even in the presence of versatile artifacts, such as left-bundle-branch-block, right-bundle-branch-block, and negative polarization. In this work, in the first step, the noise and baseline drifts are removed by applying a wavelet transform on the given ECG signal. While, in the next step, the clean ECG signal is passed through the proposed algorithm, which rotates the ECG signal in the time-frequency plane and detects the QRS complex very easily. The proposed algorithm validated over the 48 signals of the MIT-BIH arrhythmia database, and it yielded 26 false-positive and only five false-negatives compared to the 80 and 42, the best result reported so far.

In contrast to phased-array radar, frequency-diverse-array (FDA) radar transmits signals of linearly increasing frequencies across the array. As a consequence, the beampattern of an FDA radar becomes range, angle, and time dependent, which is different from only angle dependent beampattern of phased-array radar. The main limitation of FDA is its shorter dwell time. In this work, a novel algorithm with low complexity is proposed to focus the transmitted power of an FDA radar in the desired region-of-interest for longer dwell time. The proposed algorithm exploits the discrete-Fourier-transform and provides closed-form solution to find the weights of individual antenna elements of the array. The proposed algorithm is validated through simulation on both continuous-wave (CW) and pulsed FDA radars. Moreover, in contrast to the "S" shaped beampattern of the conventional CW-FDA radar, which is more difficult to deal with at the receiver, the beampattern of our proposed CW-FDA radar changes linearly with respect to the range.

In this paper, we consider the problem of recovering a binary phase shift keying (BPSK) modulated signal in a massive multiple-input-multiple-output (MIMU) system. The recovery process is done using the box-relaxation method, in which the discrete set {±I}nis relaxed to the convex set [-I,+I]nand solved by a convex optimization program followed by hard thresholding. We assume that the system has a Gaussian channel matrix with one sided left correlation. The entries of the noise vector are assumed to be independent and identically distributed (iid) zero-mean Gaussian. In this work, we precisely characterize the mean squared error (MSE) and the bit error rate (BER) of the box-relaxation decoder in the asymptotic regime where both dimensions grow simultaneously large at a fixed ratio. Numerical simulations validate the theoretical expressions derived in this paper.

To best provision the wireless data deluge, service providers are increasingly considering the use of Unmanned aerial vehicles (UAVs) for enhancing wireless connectivity. UAVs are especially important in case of disasters and accidents which may cripple terrestrial networks. In order to maintain the communication of UAVs with the core network, it becomes particularly important to connect UAVs to terrestrial base stations (BSs) via wireless backhaul links. In this work, we use stochastic geometry to study the impact of millimeter-wave (mmWave) backhauling of UAVs in a hybrid aerial-terrestrial cellular network, where the UAVs are added to assist terrestrial BSs in delivering service to users (UEs). In the proposed model, the UE can associate with either a terrestrial BS or a UAV connected to a BS to get backhaul support. The performance of the model is evaluated in terms of coverage probability and validated against intensive simulations. The obtained results unveil that the quality of the UAVs backhaul link has a significant role in improving the UEs experience. The results further illustrate the impact of the different UAVs heights regimes on the coverage probability.

This paper investigates the broadband channel estimation (CE) for intelligent reflecting surface (IRS)-aided millimeter-wave (mmWave) massive MIMO systems. The CE for such systems is a challenging task due to the large dimension of both the active massive MIMO at the base station (BS) and passive IRS. To address this problem, this paper proposes a compressive sensing (CS)-based CE solution for IRS-aided mmWave massive MIMO systems, whereby the angular channel sparsity of large-scale array at mmWave is exploited for improved CE with reduced pilot overhead. Specifically, we first propose a downlink pilot transmission framework. By designing the pilot signals based on the prior knowledge that the line-of-sight dominated BS-to-IRS channel is known, the high-dimensional channels for BS-to-user and IRS-to-user can be jointly estimated based on CS theory. Moreover, to efficiently estimate broadband channels, a distributed orthogonal matching pursuit algorithm is exploited, where the common sparsity shared by the channels at different subcarriers is utilized. Additionally, the redundant dictionary to combat the power leakage is also designed for the enhanced CE performance. Simulation results demonstrate the effectiveness of the proposed scheme.

In this paper, we consider multi-aperture multiuser dual-hop amplify-and-forward (AF) free-space optical/radio frequency (FSO/RF) communication systems. The FSO-RF channels are assumed to follow Málaga- M/shadowed κ-μ fading models with pointing errors on the FSO links. Under the assumption of transmit aperture selection at the source and opportunistic user scheduling at the destination, we derive exact closed-form expressions for the outage probability and average symbol error probability (SEP). Additionally, the system performance is studied at the high signal-to-noise ratio (SNR) regime where an approximate expression for the system outage probability is derived, in addition to deriving the system diversity order and coding gain.

The controversy and argument on the usefulness of the physical layer (PHY) academic research for wireless communications are long-standing since the cellular communication paradigm gets to its maturity. In particular, researchers suspect that the performance improvement in cellular communications is primarily attributable to the increases in telecommunication infrastructure and radio spectrum instead of the PHY academic research, whereas concrete evidence is lacking. To respond to this controversy from an objective perspective, we employ econometric approaches to quantify the contributions of the PHY academic research and other performance determinants. Through empirical analysis and the quantitative evidence obtained, albeit preliminary, we shed light on the following issues: 1) what determines the cross-national differences in cellular network performance; 2) to what extent the PHY academic research and other factors affect cellular network performance; 3) what suggestions we can obtain from the data analysis for the stakeholders of the PHY research.

In this work, we address the optimization of vertical backhaul framework where multiple aerial base station (ABS) are deployed to conFigure the intermediate backhaul links for terrestrial base stations (TBS) in wireless networks. Here, we focus on maximizing the downlink network throughput in mixed RF/FSO backhaul networks by optimizing resource allocation and placement. Specifically, the ABS-TBS association, transmit power, and positions of ABSs are alternately and iteratively optimized. To solve the corresponding mixed-integer non-convex optimization problem, we propose an efficient iterative algorithm by applying the block coordinate descent method and successive convex optimization techniques. Although we obtain suboptimal solutions due to the non-convexity of the problems, simulation results indicate that the proposed scheme demonstrates the significant network throughput gain compared with the conventional scheme.

In this paper, we investigate the soft-reconfiguration of optical wireless data centers (WDCs). In the considered physical topology, edge top-of-rack (ToR) switches in the leaf layer are inter-connected with core switches in the spine layer via wavelength division multiplexing (WDM) based free-space optical (FSO) links. We propose an agile load balancing (LB) solution, namely SoftFG, to cope with the dynamically changing link load variations and the low-utilization time intervals within the wireless data centers (DCs). SoftFG executes flow grooming (FG) and soft reconfigurations on the virtual topology depending upon the fine-grain network statistics. Unlike the long-term LBs, SoftFG offloads large flows of congested paths onto underutilized links without making any hardware reconfiguration on path capacity and routes. Flows can be offloaded to other wavelengths within the same FSO link (i.e., intra-link), to other FSO links (i.e., inter-link), or within/across topologies (i.e., intra/inter topology). To do so, SoftFG ensures clear visibility on network paths, early congestion detection, and fast-accurate reaction to reroute offloaded flows onto underutilized wavelengths or links. Therefore, SoftFG is designed as a kernel module installed on the virtual switches/hypervisor. The module collects flow statistics based on a source-destination collaborative scheme and records them in flow and path information tables. SoftFG accordingly makes quick decisions on offloading and reroutes flows with high accuracy. Emulation results show that SoftFG delivers about 12 and 17 faster flow completion time (FCT) than LetFlow and CONGA LBs, respectively.

Unmanned aerial vehicles (UAVs) will be extensively utilized in various Internet of Things (IoT) scenarios due to their flexible mobility and rapid on-demand deployment. It becomes necessary and urgent to investigate the secrecy performance of the UAV IoT communication systems because of the open characteristics of wireless channels. In this article, the physical layer security of UAV IoT communication systems is studied, in which a ground IoT device transmits some confidential messages to a UAV hovering in the air, while a random number of eavesdroppers are randomly positioned around the ground source. The ground-to-air channel is assumed to experience Rician fading for the case of line-of-sight propagation and Rayleigh fading for the case of nonline-of-sight propagation, respectively. In addition, in order to improve the security of the system, we also investigate the secrecy performance of the UAV IoT system with a friendly UAV that generates jamming signals to distract the eavesdroppers. Utilizing the stochastic geometry theory, the cumulative distribution functions for the signal-to-interference-plus-noise ratio of the main and eavesdropping links are derived, and then the analytical expressions of the secrecy outage probability and the average secrecy rate are obtained. Finally, the accuracy of the analytical results is verified by Monte Carlo simulation.

Localization is a fundamental task for the optical Internet of Underwater Things (O-IoUT) to enable various applications, such as data tagging, routing, navigation, and maintaining link connectivity. The accuracy of the localization techniques for O-IoUT greatly relies on the location of the anchors. Therefore, recently, the localization techniques for O-IoUT which optimize the anchor's location have been proposed. However, the optimization of the anchors' location for all the smart objects in the network is not a useful solution. Indeed, in a network of densely populated smart objects, the data collected by some sensors are more valuable than the data collected from other sensors. Therefore, in this article, we propose a 3-D accurate localization technique by optimizing the anchor's location for a set of smart objects. Spectral graph partitioning is used to select the set of valuable sensors. The numerical results show that the proposed technique of optimizing anchor's location for a set of selected sensors provides a better location accuracy.

Underwater optical wireless communication is an emerging field that can provide reliable connectivity for future generation Internet of Underwater Things devices. In this article, we propose a communication system based on single and superposition of Laguerre-Gaussian modes to transfer information and rely on a convolutional neural network for the mode identification in an underwater environment. A 100% recovery fidelity is reported at clear and turbid water. Beyond 90% of identification, accuracy is achieved under different laboratory-emulated underwater turbulence conditions. The practical implementation of the proposed spatial-mode-based communication scheme is further discussed.

This article studies the problem of localization and tracking of a mobile target ship with an autonomous underwater vehicle (AUV). A hybrid acoustic-optical underwater communication solution is proposed, in which the acoustic link is used for the Non-Line-of-Sight (NLoS) localization, and the optical link is for the Line-of-Sight (LoS) transmission. By coordinating these two complementary technologies, it is possible to overcome their respective weaknesses and achieve accurate localization, tracking, and high-rate underwater data transmission. The main challenge for reliable operation is to maintain the AUV over an optical link range while the target dynamics is unknown at all times. Hence, we design an error-based adaptive model predictive control (MPC) and a proportional-derivative (PD) controller incorporating a real-time acoustic localization system to guide the AUV toward the sensor node mounted on the surface ship. We define a connectivity threshold cone with its apex coinciding with the sensor node such that when the underwater vehicle stays inside of this cone, a minimum bit rate is guaranteed. The localization, tracking control, and optical communication scheme is validated through online simulations that integrate a realistic AUV model where the effectiveness of the proposed adaptive MPC and PD controllers is demonstrated.

Linear discriminant analysis (LDA) is a popular classifier that is built on the assumption of common population covariance matrix across classes. The performance of LDA depends heavily on the quality of estimating the mean vectors and the population covariance matrix. This issue becomes more challenging in high-dimensional settings where the number of features is of the same order as the number of training samples. Several techniques for estimating the covariance matrix can be found in the literature. One of the most popular approaches are estimators based on using a regularized sample covariance matrix, giving the name regularized LDA (R-LDA) to the corresponding classifier. These estimators are known to be more resilient to the sample noise than the traditional sample covariance matrix estimator. However, the main challenge of the regularization approach is the choice of the optimal regularization parameter, as an arbitrary choice could lead to severe degradation of the classifier performance. In this work, we propose an improved LDA classifier based on the assumption that the covariance matrix follows a spiked covariance model. The main principle of our proposed technique is the design of a parametrized inverse covariance matrix estimator, the parameters of which are shown to be easily optimized. Numerical simulations, using both real and synthetic data, show that the proposed classifier yields better classification performance than the classical R-LDA while requiring lower computational complexity.

Future wireless networks will be as pervasive as the air we breathe, not only connecting us but embracing us through a web of systems that support personal and societal well-being. That is, the ubiquity, speed and low latency of such networks will allow currently disparate devices and services to become a distributed intelligent communications, sensing, and computing platform.

Reconfigurable intelligent surfaces (RISs) are an emerging transmission technology for application to wireless communications. RISs can be realized in different ways, which include (i) large arrays of inexpensive antennas that are usually spaced half of the wavelength apart; and (ii) metamaterial-based planar or conformal large surfaces whose scattering elements have sizes and inter-distances much smaller than the wavelength. Compared with other transmission technologies, e.g., phased arrays, multi-antenna transmitters, and relays, RISs require the largest number of scattering elements, but each of them needs to be backed by the fewest and least costly components. Also, no power amplifiers are usually needed. For these reasons, RISs constitute a promising software-defined architecture that can be realized at reduced cost, size, weight, and power (C-SWaP design), and are regarded as an enabling technology for realizing the emerging concept of smart radio environments (SREs). In this paper, we (i) introduce the emerging research field of RIS-empowered SREs; (ii) overview the most suitable applications of RISs in wireless networks; (iii) present an electromagnetic-based communication-theoretic framework for analyzing and optimizing metamaterial-based RISs; (iv) provide a comprehensive overview of the current state of research; and (v) discuss the most important research issues to tackle. Owing to the interdisciplinary essence of RIS-empowered SREs, finally, we put forth the need of reconciling and reuniting C. E. Shannon's mathematical theory of communication with G. Green's and J. C. Maxwell's mathematical theories of electromagnetism for appropriately modeling, analyzing, optimizing, and deploying future wireless networks empowered by RISs.

Since its first use by Euler on the problem of the seven bridges of Königsberg, graph theory has shown excellent abilities in solving and unveiling the properties of multiple discrete optimization problems. The study of the structure of some integer programs reveals equivalence with graph theory problems making a large body of the literature readily available for solving and characterizing the complexity of these problems. This tutorial presents a framework for utilizing a particular graph theory problem, known as the clique problem, for solving communications and signal processing problems. In particular, this article aims to illustrate the structural properties of integer programs that can be formulated as clique problems through multiple examples in communications and signal processing. To that end, the first part of the tutorial provides various optimal and heuristic solutions for the maximum clique, maximum weight clique, and ${k}$ -clique problems. The tutorial, further, illustrates the use of the clique formulation through numerous contemporary examples in communications and signal processing, mainly in maximum access for nonorthogonal multiple access networks, throughput maximization using index and instantly decodable network coding, collision-free radio-frequency identification networks, and resource allocation in cloud-radio access networks. Finally, the tutorial sheds light on the recent advances of such applications, and provides technical insights on ways of dealing with mixed discrete-continuous optimization problems.

This month’s issue offers insight into efficient compression and execution of DNNs, the challenge of connecting rural areas, and the clique problem in wireless communication. which

Providing connectivity to around half of the world population living in rural or underprivileged areas is a tremendous challenge, but, at the same time, a unique opportunity. Access to the Internet would provide the population living in these areas a possibility to progress on the educational, health, environment, and business levels. In this article, a survey of technologies for providing connectivity to rural areas, which can help address this challenge, is provided. Although access/fronthaul and backhaul techniques are discussed in this article, it is noted that the major limitation for providing connectivity to rural and underprivileged areas is the cost of backhaul deployment. In addition, energy requirements and cost-efficiency of the studied technologies are analyzed. In fact, the challenges faced for deploying an electricity network, as a prerequisite for deploying communication networks, are huge in these areas, and they are granted an important share of the discussions in this article. Furthermore, typical application scenarios in rural areas are discussed, and several country-specific use cases are surveyed. The main initiatives by key international players aiming to provide rural connectivity are also described. Moreover, directions for the future evolution of rural connectivity are outlined in this article. Although there is no single solution that can solve all rural connectivity problems, building gradually on the current achievements in order to reach ubiquitous connectivity, while taking into account the particularities of each region and tailoring the solution accordingly, seems to be the most suitable path to follow.

In this paper, we propose a practical adaptive coding modulation scheme to approach the capacity of free-space optical (FSO) channels with intensity modulation/direct detection based on probabilistic shaping. The encoder efficiently adapts the transmission rate to the signal-to-noise ratio, accounting for the fading induced by the atmospheric turbulence. The transponder can support an arbitrarily large number of transmission modes using a low complexity channel encoder with a small set of supported rates. Hence, it can provide a solution for FSO backhauling in terrestrial and satellite communication systems to achieve higher spectral efficiency. We propose two algorithms to determine the capacity and capacity-achieving distribution of the scheme with unipolar M-ary pulse amplitude modulation (M-PAM) signaling. Then, the signal constellation is probabilistically shaped according to the optimal distribution, and the shaped signal is channel encoded by an efficient binary forward error correction scheme. Extensive numerical results and simulations are provided to evaluate the performance. The proposed scheme yields a rate close to the tightest lower bound on the capacity of FSO channels. For instance, the coded modulator operates within 0.2 dB from the M-PAM capacity, and it outperforms uniform signaling with more than 1.7 dB, at a transmission rate of 3 bits per channel use.

This paper presents an asymptotic capacity analysis for multiple-input multiple-output (MIMO) communications with insufficient transmit radio frequency (RF) chains and sufficient receive RF chains, which is named as iMIMO communications. We characterize the iMIMO channel capacity by the maximum mutual information given any vector inputs subject to not only an average power constraint but also a sparsity constraint. It is proven that an optimized Gaussian mixture input distribution is capacity-achieving in the high signal-to-noise-ratio (SNR) regime. The optimal mixture coefficients and the covariance matrices of the Gaussian mixtures are derived and also the corresponding asymptotic capacity. Furthermore, we discuss the impact of insufficient receive RF chains on the achievable spectral efficiency. We investigate the superiority of the capacity-achieving technique, which is an optimized non-uniform subspace modulation (NUSM), by comparing it with the best subspace selection (BSS) and the uniform subspace modulation (USM). The comparison results reveal that the optimized NUSM is optimal in the high SNR regime. Numerical results are presented to validate our analysis.

In this paper, we investigate both the bit error rate (BER) and outage performance of free-space optical (FSO) links over strong turbulence combined with various pointing error conditions. Considering atmospheric turbulence and pointing errors as main factors that deteriorate the quality of an optical link, we obtain a unified finite series approximation of the composite probability density function, which embraces generalized pointing error models. This approximation leads to new unified formulas for the BER and outage capacity of an FSO link, which account for the two possible detection mechanisms of intensity modulation/direct detection and heterodyne detection. Selected simulation results confirm that the newly derived approximations can give precise predictions of both the average BER and the outage capacity of FSO communication that are generally applicable to all environments.

In this paper, we propose studying the disease spread mechanism in the atmosphere as an engineering problem. Aerosol transmission is the most significant mode among the viral transmission mechanisms that do not include physical contact, where airflows carry virus-laden droplets over long distances. Throughout this work, we study the transport of these droplets as a molecular communication problem, where one has no control over the transmission source, but a robust receiver can be designed using bio-sensors. To this end, we present a complete system model and derive an end-to-end mathematical model for the transmission channel under certain constraints and boundary conditions. We derive the system response for both continuous sources such as breathing and jet or impulsive sources such as coughing and sneezing. In addition to transmitter and channel, we assumed a receiver architecture composed of air sampler and Silicon Nanowire field-effect transistor. Then, we formulate a detection problem to maximize the likelihood decision rule and minimize the corresponding missed detection probability. Finally, we present several numerical results to observe the impact of parameters that affect the performance and justify the feasibility of the proposed setup in related applications.

One of the main challenges slowing the deployment of airborne base stations (BSs) using unmanned aerial vehicles (UAVs) is the limited on-board energy and flight time. One potential solution to such problem, is to provide the UAV with power supply through a tether that connects the UAV to the ground. In this paper, we study the optimal placement of tethered UAVs (TUAVs) to minimize the average path-loss between the TUAV and a receiver located on the ground. Given that the tether has a maximum length, and the launching point of the TUAV (the starting point of the tether) is placed on a rooftop, the TUAV is only allowed to hover within a specific hovering region. Beside the maximum tether length, this hovering region also depends on the heights of the buildings surrounding the rooftop, which requires the inclination angle of the tether not to be below a given minimum value, in order to avoid tangling and ensure safety. We first formulate the optimization problem for such setup and provide some useful insights on its solution. Next, we derive upper and lower bounds for the optimal values of the tether length and inclination angle. We also propose a suboptimal closed-form solution for the tether length and its inclination angle that is based on maximizing the line-of-sight probability. Finally, we derive the probability distribution of the minimum inclination angle of the tether length. We show that its mean value varies depending on the environment from 10° in suburban environments to 31° in high rise urban environments. Our numerical results show that the derived upper and lower bounds on the optimal values of the tether length and inclination angle lead to tight suboptimal values of the average path-loss that are only 0 - 3 dBs above the minimum value.

In this work, we investigate the relationship between the reliability and security of a typical two-user downlink non-orthogonal multiple access (NOMA) communication system. The level of successive interference cancellation on NOMA user is considered. Firstly, the impact of various key parameters on transmit signal-to-noise ratio (SNR) of the NOMA users with the reliability outage probability (ROP) constraint is discussed. Taking the minimum of transmit SNR for ROP into account, the secrecy outage performance of the downlink NOMA systems is studied and the analytical expressions of the secrecy outage probability of the NOMA system are derived under two cases of eavesdropping capability. Furthermore, the sum effective secrecy throughput of the NOMA system under two scenarios with considering ROP constraint is derived. Monte Carlo simulations are provided to verify the accuracy of our analysis.

In this work, we analyze the secrecy outage performance of a dual-hop relay system composed of multipleinput- multiple-output radio-frequency (RF) links and a freespace optical (FSO) link while a multiple-antenna eavesdropper wiretaps the confidential information by decoding the received signals from the source node. The channel state information (CSI) of the RF and FSO links is considered to be outdated and imprecise, respectively. We propose four transmit antenna selection (TAS) schemes to enhance the secrecy performance of the considered systems. The secrecy outage performance with different TAS schemes is analyzed and the effects of misalignment and detection technology on the secrecy outage performance of mixed systems are studied. We derive the closed-form expressions for probability density function (PDF) and cumulative distribution function (CDF) over M´alaga channel with imperfect CSI. Then the closed-form expressions for the CDF and PDF of the equivalent signal-to-noise ratio (SNR) at the legitimate receiver over Nakagami-m and M´alaga channels are derived. Furthermore, the bound of the effective secrecy throughput (EST) with different TAS schemes are derived. Besides, the asymptotic results for EST are investigated by exploiting the unfolding of Meijer’s G-function when the electrical SNR of FSO link approaches infinity. Finally, Monte-Carlo simulation results are presented to testify the correctness of the proposed analysis. The results illustrate that outdated CSI shows a strong effect on the secrecy performance of the mixed RF-FSO systems. In addition, increasing the number of antennas at the source cannot significantly enhance the secrecy performance of the considered systems

This paper analyzes the integration of Non-Orthogonal Multiple Access (NOMA) in a Fog Radio Access Network (FRAN) architecture with limited fronthaul capacity. More precisely, it proposes methods for optimizing the resource allocation for the downlink of a NOMA-based FRAN with multiple resource blocks (RB). The resource allocation problem is formulated as a mixed-integer optimization problem, which determines the user-to-RB assignment, the power allocated to each RB, and the power split levels of the NOMA users served by each RB. The optimization problem maximizes a network-wide rate-based utility function subject to fronthaul-capacity constraints. The paper proposes a feasible decoupled solution for such a non-convex optimization problem using a three-step hybrid centralized/distributed approach, which in part relies on the edge-devices computation capabilities. The paper proposes and compares two distinct methods for solving the assignment problem, namely a Hungarian-based method, and a Multiple Choice Knapsack-based method. The power allocation to RBs and the NOMA power split optimization are solved using the alternating direction method of multipliers (ADMM). Simulations results illustrate the advantages of the proposed methods compared to different baseline schemes, including the conventional Orthogonal Multiple Access (OMA), for different utility functions and different network environments.

Full-duplex communication allows a terminal to transmit and receive signals simultaneously, and hence, it is helpful in general to adapt transmissions to received signals. However, this often requires unaffordable complexity. This work focuses on simple non-adaptive transmission, and provides two classes of channels for which Shannon's information capacity regions are achieved without adaptation. The first is the injective semi-deterministic two-way channel that includes additive channels with various types of noises modeling wireless, coaxial cable, and other settings. The other is the Poisson two-way channel, for which we show that non-adaptive transmission is asymptotically optimal in the high dark current regime.

In this work, we present a unified framework for the performance analysis of dual-hop underwater wireless optical communication (UWOC) systems with amplify-and-forward fixed gain relays in the presence of air bubbles and temperature gradients. Operating under either heterodyne detection or intensity modulation with direct detection, the UWOC is modeled by the unified mixture Exponential-Generalized Gamma distribution that we have proposed based on an experiment conducted in an indoor laboratory setup and has been shown to provide an excellent fit with the measured data under the considered lab channel scenarios. More specifically, we derive the cumulative distribution function (CDF) and the probability density function of the end-to-end signal-to-noise ratio (SNR) in exact closed-form in terms of the bivariate Fox's H function. Based on this CDF expression, we present novel results for the fundamental performance metrics such as the outage probability, the average bit-error rate (BER) for various modulation schemes, and the ergodic capacity. Additionally, very tight asymptotic results for the outage probability and the average BER at high SNR are obtained in terms of simple functions. Furthermore, we demonstrate that the dual-hop UWOC system can effectively mitigate the short range and both temperature gradients and air bubbles induced turbulences, as compared to the single UWOC link. All the results are verified via computer-based Monte-Carlo simulations.

In this work, we investigate the problem of secure broadcasting over block-fading wiretap channels with limited channel knowledge at the transmitter. More particularly, we analyze the effect of having a finite rate feedback on the throughput of multi-user broadcast wiretap channels. We consider that the transmitter is only provided by a b -bits feedback of the main channel state information (CSI) sent by each legitimate receiver, at the beginning of each fading block, over error-free public links with limited capacity. Also, we assume that the transmitter is aware of the statistics of the eavesdropper's CSI but not of its channel's realizations. Under these assumptions of CSI uncertainty, we characterize the ergodic secrecy capacity of the system when a common message is broadcasted to all legitimate receivers, the ergodic secrecy sum-capacity when multiple independent messages are transmitted, and the ergodic secrecy capacity region for the broadcast channel with confidential messages (BCCM). In all three scenarios, we show that as long as the transmitter has some knowledge of the main CSI, obtained even through a 1-bit CSI feedback, a non-zero secrecy rate can still be achieved. The impact of having the feedback sent over a binary erasure channel (BEC) is also investigated for the BCCM case. Here again, and even with the possibility of having the feedback bits erased, a positive secrecy rate can still be achieved as long as the erasure event is not a probability-one event. An asymptotic analysis of the obtained results is provided for the high SNR regime, and the scaling law of the system, when the number of legitimate receivers is large, is also presented.

LightFDG is an integrated approach to flow detection (FD) and flow grooming (FG) in optical wireless data center networks (DCNs), which is interconnected via wavelength division multiplexing (WDM) based free-space optical (FSO) links. Since forwarding bandwidth-hungry elephant flows (EFs) and delay-sensitive mice flows (MFs) on the same path can cause severe performance degradation, the LightFDG optically grooms flows of each class into rack-to-rack (R2R) flows. Then, R2R-MF and R2R-EF flows are separately forwarded over lightpaths of separate MF and EF virtual topologies, respectively. Lightpaths are provisioned by jointly determining the capacity and route based on flows' arrival rate, size, and completion time request. To prevent EFs from congesting the MF lightpaths, high speed and accurate flow-detection mechanisms are also necessary for classifying EFs as soon as possible. Therefore, a fast-lightweight-and-accurate flow detection framework is developed by leveraging the transmission control protocol (TCP) behaviors. The proposed FD scheme has the flexibility of being implemented as in-network or centralized to classify flows of modifiable and unmodifiable hosts, respectively. Since the centralized scheme incurs considerable overhead, the processing and communication overhead is also mitigated by proposed techniques. Numerical results show that LightFDG outperforms traditional load balancers by about 3× for EFs and 10× for MFs. Along with the developed overhead mitigation methods, the centralized scheme is shown to provide up to 62× lower overhead with 100% accuracy and with about 224× higher detection speeds than the existing centralized solutions.

In this paper, we study the performance of simultaneous lightwave information and power transfer (SLIPT) systems in a multi-cell indoor scenario. We investigate the energy harvesting and data rate performance of multiple users while meeting the lighting constraints. To this end, we develop optimization frameworks that tune the parameters of transmitters or receivers to improve the SLIPT system performance. Firstly, we model the relationship between tunable lens-based optical receivers concentrator gain and their fields of view. Next, we develop algorithms to maximize the spectral efficiency (SE) considering per-user minimum harvested energy requirements and lightning constraints by controlling the average light emitting diode (LED) excitation current or tuning the optical receivers' fields of view. Then, we study the joint system performance limits of SE and total harvested energy. Towards this aim, we formulate multi-objective optimization problems and propose algorithms to find the best SE - energy harvesting tradeoff for both designing strategies. Finally, we present some extensive simulations to explore the benefits of the proposed algorithms comparing with basic benchmarks. Besides, we monitor the effect of changing several system parameters on the two objectives and the behavior of the inherent trade-off between them under both transmitter and receiver sides designing strategies.

Amplitude-coherent (AC) detection is an efficient technique that can simplify the receiver design while providing reliable symbol error rate (SER). Therefore, this work considers AC detector design and SER analysis using M-ary amplitude shift keying (MASK) modulation with receiver diversity over Rician fading channels. More specifically, we derive the optimum, near-optimum and a suboptimum AC detectors and compare their SER with the coherent, phase-coherent, noncoherent and the heuristic AC detectors. Moreover, the analytical and asymptotic SER at high signal-to-noise ratios (SNRs) are derived for the heuristic detector using single and multiple receiving antennas. The obtained analytical and simulation results show that the SER of the AC and coherent MASK detectors are comparable, particularly for high values of the Rician K-factor, and small number of receiving antennas. In most of the considered scenarios, the heuristic AC detector outperforms the optimum noncoherent detector significantly, except for the binary ASK case at low SNRs. Moreover, the obtained results show that the heuristic AC detector is immune to phase noise, and thus, it outperforms the coherent detector in scenarios where the system is subject to considerable phase noise.

In this paper, we propose a novel wireless scheme that integrates satellite, airborne, and terrestrial networks aiming to support ground users. More specifically, we study the enhancement of the achievable users' throughput assisted with terrestrial base stations, high-altitude platforms (HAPs), and satellite stations. The goal is to optimize the resource allocations and the HAPs' locations in order to maximize the users' throughput. In this context, we formulate and solve an optimization problem in two stages: a short-term stage and a long-term stage. In the short-term stage, we start by proposing an approximated solution and a low complexity solution to solve the associations and power allocations. In the approximated solution, we formulate and solve a binary linear optimization problem to find the best associations and then we use the Taylor expansion approximation to optimally determine the power allocations. In the latter solution, we propose a low complexity approach based on a frequency partitioning technique to solve the associations and power allocations. On the other hand, in the long-term stage, we optimize the locations of the HAPs by proposing an efficient algorithm based on a recursive shrink-and-realign process. Finally, selected numerical results underline the advantages provided by our proposed optimization scheme.

This paper proposes a unified statistical channel model to characterize the atmospheric turbulence induced distortions faced by orbital angular momentum (OAM) in free space optical (FSO) communication systems. In this channel model, the self-channel irradiance of OAM modes as well as crosstalk irradiances between different OAM modes are characterized by a Generalized Gamma distribution (GGD). The latter distribution is shown to provide an excellent match with simulated data for all regimes of atmospheric turbulence. Therefore, it can be used to overcome the computationally complex numerical simulations to model the propagation of OAM modes through atmospheric turbulent FSO channels. The GGD allows obtaining very simple tractable closed-form expressions for a variety of performance metrics. Indeed, the average capacity, the bit-error rate, and the outage probability are derived for FSO systems using single OAM mode transmission with direct detection. Furthermore, we extend our study to FSO systems using OAM mode diversity to improve the performance. By using a maximum ratio combining (MRC) at the receiver, the GGD is also shown to fit the simulated combined received optical powers. Finally, space-time (ST) coding is proposed to provide diversity and multiplexing gains, and the error probability is theoretically derived under the newly proposed generic model.

Pointing and acquisition are an important aspect of free-space optical communications because of the narrow beamwidth associated with the optical signal. In this paper, we have analyzed the pointing and acquisition problem in free-space optical communications for photon-counting detector arrays and Gaussian beams. In this regard, we have considered the maximum likelihood detection for detecting the location of the array, and analyzed the one-shot probabilities of missed detection and false alarm using the scaled Poisson approximation. Moreover, the upper/lower bounds on the probabilities of missed detection and false alarm for one complete scan are also derived, and these probabilities are compared with Monte Carlo approximations for a few cases. Additionally, the upper bounds on the acquisition time and the mean acquisition time are also derived. The upper bound on mean acquisition time is minimized numerically with respect to the beam radius for a constant signal-to-noise ratio scenario. Finally, the complementary distribution function of an upper bound on acquisition time is also calculated in a closed form. Our study concludes that an array of smaller detectors gives a better acquisition performance (in terms of acquisition time) as compared to one large detector of similar dimensions as the array.

This paper investigates the self-sustainability of an overlay Internet of Things (IoT) network that relies on harvesting energy from a downlink cellular network. Using stochastic geometry and queueing theory, we develop a spatiotemporal model to derive the steady state distribution of the number of packets in the buffers and energy levels in the batteries of IoT devices given that the IoT and cellular communications are allocated disjoint spectrum. Particularly, each IoT device is modelled via a two-dimensional discrete-time Markov Chain (DTMC) that jointly tracks the evolution of the data buffer and energy battery. In this context, stochastic geometry is used to derive the energy generation at the batteries and the packet transmission success probability from buffers taking into account the mutual interference from other active IoT devices. To this end, we show the Pareto-Frontiers of the sustainability region, which define the network parameters that ensure stable network operation and finite packet delay. Furthermore, the spatially averaged network performance, in terms of transmission success probability, average queueing delay, and average queue size are investigated. For self-sustainable networks, the results quantify the required buffer size and packet delay, which are crucial for the design of IoT devices and time critical IoT applications.

On the contrary of low speed and high delay acoustic systems, underwater optical wireless communication (UOWC) can deliver a high speed and low latency service at the expense of short communication ranges. Therefore, multihop communication is of utmost importance to extend the range, improve degree of connectivity, and overall performance of underwater optical wireless networks (UOWNs). In this regard, this paper investigates relaying and routing techniques and provides their end-to-end (E2E) performance analysis under the location uncertainty. To achieve robust and reliable links, we first consider adaptive beamwidths and derive the divergence angles under the absence and presence of a pointing-acquisitioning-and-tracking (PAT) mechanism. Thereafter, important E2E performance metrics (e.g., data rate, bit error rate, transmission power, amplifier gain, etc.) are obtained for two potential relaying techniques; decode & forward (DF) and optical amplify & forward (AF). We develop centralized routing schemes for both relaying techniques to optimize E2E rate, bit error rate, and power consumption. Alternatively, a distributed routing protocol, namely Light Path Routing (LiPaR), is proposed by leveraging the range-beamwidth tradeoff of UOWCs. LiPaR is especially shown to be favorable when there is no PAT mechanism and available network information. In order to show the benefits of multihop communications, extensive simulations are conducted to compare different routing and relaying schemes under different network parameters and underwater environments.

In this paper, we propose an enhanced Huffman coded orthogonal frequency-division multiplexing with index modulation (EHC-OFDM-IM) scheme. The proposed scheme is capable of utilizing all legitimate subcarrier activation patterns (SAPs) and adapting the bijective mapping relation between SAPs and leaves on a given Huffman tree according to channel state information (CSI). As a result, a dynamic codebook update mechanism is obtained, which can provide more reliable transmissions. We take the average block error rate (BLER) as the performance evaluation metric and approximate it in closed form when the transmit power allocated to each subcarrier is independent of channel states. Also, we propose two CSI-based power allocation schemes with different requirements for computational complexity to further improve the error performance. Subsequently, we carry out numerical simulations to corroborate the error performance analysis and the proposed dynamic power allocation schemes. By studying the numerical results, we find that the depth of the Huffman tree has a significant impact on the error performance when the SAP-to-leaf mapping relation is optimized based on CSI. Meanwhile, through numerical results, we also discuss the trade-off between error performance and data transmission rate and investigate the impacts of imperfect CSI on the error performance of EHC-OFDM-IM.

This paper investigates adaptive signal shaping methods for millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications based on the maximizing the minimum Euclidean distance (MMED) criterion. In this work, we utilize the indices of analog precoders to carry information and optimize the symbol vector sets used for each analog precoder activation state. Specifically, we firstly propose a joint optimization based signal shaping (JOSS) approach, in which the symbol vector sets used for all analog precoder activation states are jointly optimized by solving a series of quadratically constrained quadratic programming (QCQP) problems. JOSS exhibits good performance, however, with a high computational complexity. To reduce the computational complexity, we then propose a full precoding based signal shaping (FPSS) method and a diagonal precoding based signal shaping (DPSS) method, where the full or diagonal digital precoders for all analog precoder activation states are optimized by solving two small-scale QCQP problems. Simulation results show that the proposed signal shaping methods can provide considerable performance gain in reliability in comparison with existing mmWave transmission solutions.

In this work, we address the trajectory optimization of a fixed-wing unmanned aerial vehicle (UAV) using free space optical communication (FSOC). Here, we focus on maximizing the flight time of the UAV by considering practical constraints for wireless UAV communication, including limited propulsion energy and required data rates. We find optimized trajectories in various atmospheric environments (e.g., moderate-fog and heavy-fog conditions), while also considering the channel characteristics of FSOC. In addition to maximizing the flight time, we consider the energy efficiency maximization and operation-time minimization problem to find the suboptimal solutions required to meet those constraints. Furthermore, we introduce a low-complexity approach to the proposed framework. In order to address the optimization problem, we conduct a bisection method and sequential programming and introduce a new feasibility check algorithm. Although our design considers suboptimal solutions owing to the nonconvexity of the problems, our simulations indicate that the proposed scheme exhibits a gain of approximately 44.12% in terms of service time when compared to the conventional scheme.

The aggregated visible light communication (VLC) and radio frequency (RF) system, which can be viewed as a heterogeneous multi-input-multi-output system, can improve data rate compared to the conventional RF communication systems. In this paper, we first develop optimal power allocation schemes for the aggregated VLC-RF systems for the single and the multi-light-emitting diode scenarios under different dimming control setups. Moreover, we study the energy efficiency maximization problem of the considered system with the minimum rate requirement, transmitted power constraint, and the dimming control consideration which is non-convex. By using the Dinkelbach-type algorithm, we tackle this problem by solving a sequence of convex problems which converges to the global solution. Finally, the effect of critical parameters, such as total power threshold, dimming level, and bandwidths, are revealed by some selected numerical results.

This work focuses on the downlink of a single-cell multi-user system in which a base station (BS) equipped with M antennas communicates with K single-antenna users through a reconfigurable intelligent surface (RIS) installed in the line-of-sight (LoS) of the BS. RIS is envisioned to offer unprecedented spectral efficiency gains by utilizing N passive reflecting elements that induce phase shifts on the impinging electromagnetic waves to smartly reconfigure the signal propagation environment. We study the minimum signal-to-interference-plus-noise ratio (SINR) achieved by the optimal linear precoder (OLP), that maximizes the minimum SINR subject to a given power constraint for any given RIS phase matrix, for the cases where the LoS channel matrix between the BS and the RIS is of rank-one and of full-rank. In the former scenario, the minimum SINR achieved by the RIS-assisted link is bounded by a quantity that goes to zero with K. For the high-rank scenario, we develop accurate deterministic approximations for the parameters of the asymptotically OLP, which are then utilized to optimize the RIS phase matrix. Simulation results show that RISs can outperform half-duplex relays with a small number of passive reflecting elements while large RISs are needed to outperform full-duplex relays.

By observing the fact that moving in a straight line is a common flying behavior of unmanned aerial vehicles (UAVs) in normal applications, e.g., power line inspections, and air patrols along with highway/streets/borders, in this paper we investigate the secrecy outage performance of a UAV system with linear trajectory, where a UAV (S) flies in a straight line and transmits its information over the downlink to a legitimate receiver (D) on the ground while an eavesdropping UAV (E) trying to overhear the information delivery between S and D. Meanwhile, some information is delivered to S over the uplink from D, such as commanding messages to control S's detecting operations, which can also be eavesdropped by E. The locations of S, D, and E are randomly distributed. We first characterize the statistical characteristics (including cumulative distribution functions and probability density function) of the received signal-to-noise ratio over both downlink and uplink, and then the closed form analytical expressions for the lower boundary of the secrecy outage probability of both downlink and uplink have also been derived accordingly. Finally, Monte-Carlo simulations are given to testify our proposed analytical models.

As an efficient supplement for terrestrial communications, e.g., fifth-generation communication networks, satellite communication has been proved as a promising choice to realize seamless coverage, due to its inherent superiority over common wireless communication systems, like large-scale coverage area and flexibility of deployment. In this article, a satellite-terrestrial transmission (STT) system including a satellite transmitter (S) and a group of terrestrial receivers (D) is focused on. Considering the randomness of D and employing stochastic geometry, the outage performance and diversity gain of three hybrid automatic repeat request (HARQ) schemes, generalized slotted ALOHA (GSA), repetition time diversity (RTD) and general incremental redundancy (IR), are respectively studied. In detail, the exact closed-form analytical expression for the outage probability (OP) is derived for the GSA scheme, while the approximated expression for the OP of RTD scheme is presented and a useful expression for the OP of IR scheme is derived. Furthermore, the diversity gains of the three considered HARQ schemes are respectively investigated in STT scenarios. Finally, some numerical results are provided to validate the proposed analysis models.

Aerial relays have been regarded as an alternative and promising solution to extend and improve satellite-terrestrial communications, as the probability of line-of-sight transmissions increases compared with adopting terrestrial relays. In this paper, a cooperative satellite-aerial-terrestrial system including a satellite transmitter (S), a group of terrestrial receivers (D), and an aerial relay (R) is considered. Specifically, considering the randomness of S and D and employing stochastic geometry, the coverage probability of R-D links in non-interference and interference scenarios is studied, and the outage performance of S-R link is investigated by deriving an approximated expression for the outage probability. Moreover, an optimization problem in terms of the transmit power and the transmission time over S-R and R-D links is formulated and solved to obtain the optimal end-to-end energy efficiency for the considered system. Finally, some numerical results are provided to validate our proposed analysis models, as well as to study the optimal energy efficiency performance of the considered system.

In this paper, a cooperative hybrid visible light communication (VLC)-radio frequency (RF) system with spatially random terminals is considered. Especially, a source node ($S$) with a group of light-emitting diode (LED) lamps transmits information bits to a destination ($D$), which is located out of$S$’s coverage area, via a relay node ($R$).$R$is equipped with a photodetector and an antenna to set up a VLC link between$S$and$R$, and a radio frequency (RF) link between$R$and$D$, respectively. Meanwhile, an eavesdropper ($E$) equipped with a photodetector and an antenna tries to overhear the information delivery over VLC and RF links. Also, diversity receiving scheme is considered at$E$, while the same modulation scheme is considered over both VLC and RF links. Furthermore, decode-and-forward scheme is adopted at$R$to process and forward the received VLC signals. By employing stochastic geometry, we first characterize the probability density function and cumulative distribution function of the received signal-to-noise-ratio over both VLC and RF links, while considering the randomness of the locations of both$R$,$E$, and$D$. Then, the secrecy performance of the target system is studied by deriving the approximated expressions for the secrecy outage probability under various cases. Finally, the proposed analytical models are verified via Monte-Carlo simulations.

We propose a unified rare-event estimator for the performance evaluation of wireless communication systems. The estimator is derived from the well-known multilevel splitting algorithm. In its original form, the splitting algorithm cannot be applied to the simulation and estimation of time-independent problems, because splitting requires an underlying continuous-time Markov process whose trajectories can be split. We tackle this problem by embedding the static problem of interest within a continuous-time Markov process, so that the target time-independent distribution becomes the distribution of the Markov process at a given time instant. The main feature of the proposed multilevel splitting algorithm is its large scope of applicability. For illustration, we show how the same algorithm can be applied to the problem of estimating the cumulative distribution function (CDF) of sums of random variables (RVs), the CDF of partial sums of ordered RVs, the CDF of ratios of RVs, and the CDF of weighted sums of Poisson RVs. We investigate the computational efficiency of the proposed estimator via a number of simulation studies and find that it compares favorably with existing estimators.

This paper presents and analyzes a novel multiuser network based on hybrid free-space optical (FSO)/radiofrequency (RF) transmission system, where every user is serviced by a primary FSO link. When more than one FSO link fail, the central node services these corresponding users of non-equal priority by using a common backup RF link according to a p persistence servicing protocol. A novel discrete-time Markov chain model is developed for the proposed network, where different transmission rates over RF and FSO links are assumed. We investigate the throughput from central node to the user, the average size of the transmit buffer allocated for every user, the frame queuing delay in the transmit buffer, the efficiency of the queuing system, the frame loss probability, and the RF link utilization. Numerical examples show that transmitting a data frame with probability p when using the common backup RF link achieves considerable network performance improvement while ensuring high-priority users enjoy better performance. Meanwhile, the performance of low-priority users approaches the performance when using equal priority protocol to serve all the remote users.

By incorporating the merits of satellite, aerial, and terrestrial communications, the space-air-ground integrated network (SAGIN) emerges in recent years as a promising solution to support seamless, high-rate, and reliable transmission with an extremely larger coverage than a classic terrestrial network. In essence, SAGIN is a cooperative relay network, in which high-altitude platforms (HAPs) and terrestrial base stations (BSs) serve as intermediates relaying signals between end device and satellite. In this article, we thereby view the SAGIN from the perspective of cooperative communications and introduce relay networking technologies to model and construct the framework of SAGIN. Meanwhile, we take the realistic propagation environment, HAP mobility and mathematical tractability into account and reconstruct the cooperative channel models for SAGIN, including the space-air, space-ground and air-ground links. Based on the constructed framework of SAGIN, we analyze the outage performance and approximate the outage probability as well as asymptotic outage probability in closed form. Numerical results generated by computer simulations verify our analysis and provide insight into the applicability of SAGIN. Although the relaying scenarios considered in this work are simplistic, the good tractability and expandability of the constructed framework provide a solid foundation for further research of advanced systems with complex configurations.

This paper investigates the use of a reconfigurable intelligent surface (RIS) to aid point-to-point multi-data-stream multiple-input multiple-output (MIMO) wireless communications. With practical finite alphabet input, the reflecting elements at the RIS and the precoder at the transmitter are alternatively optimized to minimize the symbol error rate (MSER). In the reflecting optimization with a fixed precoder, two reflecting design methods are developed, referred as eMSER-Reflecting and vMSER-Reflecting. In the optimization of the precoding matrix with a fixed reflecting pattern, the matrix optimization is transformed to be a vector optimization problem and two methods are proposed to solve it, which are referred as MSER-Precoding and MMED-Precoding. The superiority of the proposed designs is investigated by simulations. Simulation results demonstrate that the proposed reflecting and precoding designs can offer a lower SER than existing designs with the assumption of complex Gaussian input. Moreover, we compare RIS with a full-duplex Amplify-and-Forward (AF) relay system in terms of SER to show the advantage of RIS.

This paper studies the ergodic rate performance of maximum ratio transmission (MRT) precoding in the downlink of a multi-user multiple-input single-output (MISO) system, where the channel between the base station (BS) and each user is modeled using the double scattering model. We utilize the minimum-mean-square-error (MMSE) channel estimate for this model, which is used in the design of the MRT precoding. Within this setting, we are interested in deriving tight approximations of the ergodic rate under the assumption that the number of BS antennas (N), the number of users (K) and that of scatterers (S) grow large with the same pace. These approximations are expressed in simplified closed-form expressions for the special case of multi-keyhole channels. They reveal that unlike the standard Rayleigh channel in which the SINR grows as O(N), K the SINR associated with a multi-keyhole channel scales as O(S). This particularly shows that the reaped gains of the K large-scale MIMO over double scattering channels do not linearly increase with the number of antennas and are limited by the number of scatterers. We further provide simulation results that confirm the close match provided by the asymptotic analysis for moderate system dimensions and provide some useful insights into the interplay between N, K and S.

Due to recent advances in laser satellite communications technology, free-space optical (FSO) links are presented as an ideal alternative to the conventional radio frequency (RF) feeder links of the geostationary satellite for next generation very high throughput satellite (VHTS) systems. In this paper, we investigate the performance of multibeam VHTS systems that account for nonlinear high power amplifiers at the transparent fixed gain satellite transponder. Specifically, we consider the forward link of such systems, where the RF user link is assumed to follow the shadowed Rician model and the FSO feeder link is modeled by the Gamma-Gamma distribution in the presence of beam wander and pointing errors where it operates under either the intensity modulation with direct detection or the heterodyne detection. Moreover, zero-forcing precoder is employed to mitigate the effect of inter-beam interference caused by the aggressive frequency reuse in the user link. The performance of the system under study is evaluated in terms of the outage probability, the average bit-error rate (BER), and the ergodic capacity that are derived in exact closed-forms in terms of the bivariate Meijer's G function. Simple asymptotic results for the outage probability and the average BER are also obtained at high signal-to-noise ratio.

Optical orthogonal frequency division multiplexing (O-OFDM) schemes are variations of OFDM schemes that produce non-negative signals. Asymmetrically-clipped O-OFDM (ACO-OFDM) is a single-layer O-OFDM scheme, whose spectral efficiency can be enhanced by adopting multiple ACO-OFDM layers or a combination of ACO-OFDM and other O-OFDM schemes. However, since symbol detection in such enhanced ACO-OFDM (eACO-OFDM) is done iteratively, erroneous detection leads to residual clipping noise (RCN) which can degrade performance in practice. Thus, it is necessary to develop an accurate model for RCN in designing RCN-aware eACO-OFDM schemes. To this end, this paper provides a mathematical analysis of RCN leading to an accurate model of RCN power. The obtained model is used to analyse the performance of various eACO-OFDM schemes. It is shown that the model provides an accurate evaluation of symbol error rate (SER), which would be underestimated if RCN is ignored. Moreover, the model is shown to be useful for designing an RCN-aware resource allocation that increases the robustness of the system in terms of meeting a target SER, compared to an RCN-unaware design.

Ambient radio frequency (RF) energy harvesting is widely promoted as an enabler for self-powered wireless networks. This paper jointly characterizes the harvested energy and the packet transmission success probability in grant-free uplink IoT networks energized via harvesting downlink energy. To do that, a joint queueing theory and stochastic geometry model is exploited and a spatiotemporal analytical model is developed accordingly. Particularly, the harvested energy and packet transmission success probability are characterized using tools from stochastic geometry. Moreover, each device is modeled using a two-dimensional discrete-time Markov chain (DTMC) to track the time evolution of the joint states of the scavenged energy and the data buffer. Consequently, the adopted queueing model represents the devices as spatially interacting queues. To that end, the network performance is assessed in light of the packet throughput, the average waiting time, and the average buffer size which offer valuable insights for network design.

We investigate random projections in the context of randomly projected linear discriminant analysis (LDA). We consider the case in which the data of dimension p is randomly projected onto a lower dimensional space before being fed to the classifier. Using fundamental results from random matrix theory and relying on some mild assumptions, we show that the asymptotic performance in terms of probability of misclassification approaches a deterministic quantity that only depends on the data statistics and the dimensions involved. Such results permits to reliably predict the performance of projected LDA as a function of the reduced dimension d <; p and thus helps to determine the minimum d to achieve a certain desired performance. Finally, we validate our results with finite-sample settings drawn from both synthetic data and the popular MNIST dataset.

The characterization of the latency is essential for operation of 5G and beyond 5G cellular networks. This paper develops a spatiotemporal model to characterize latency in the downlink of large-scale cellular networks with random scheduling. In particular, the framework integrates stochastic geometry and queueing theory, to capture the interwoven interactions between the microscopic behavior of each wireless link and the macroscopic mutual interference between all links in the network. The developed framework enables a traffic-aware characterization of the transmission success probability and of the latency across the network.

Spatial multiplexing (SMX) multiple-input multiple-output (MIMO) over the best beamspace was considered as the best solution for millimeter wave (mm ave) communications regarding spectral efficiency (SE), referred as the best beamspace selection (BBS) solution. The equivalent MIMO water-filling (F-MIMO) channel capacity was treated as an unsurpassed SE upper bound. Recently, researchers have proposed various schemes trying to approach the benchmark and the performance bound. In this paper, we challenge the benchmark and the corresponding bound by proposing a better transmission scheme that achieves higher SE, namely the Generalized Beamspace Modulation using Multiplexing (GBMM). Inspired by the concept of spatial modulation, we not only use the selected beamspace to transmit information but also use the selection operation to carry information. e prove that GBMM is superior to BBS in terms of SE and can break through the well known upper bound. That is, GBMM renews the upper bound of the system SE. e investigate SE-oriented precoder activation probability optimization, fully-digital precoder design and hybrid precoder design for GBMM. Comparisons with the benchmark (i.e., F-MIMO channel capacity) are made under different system configurations to show the superiority of GBMM.

In this paper, we investigate the outage performance of a multiuser threshold-based decode-and-forward integrated satellite-terrestrial relay network (ISTRN) with opportunistic user scheduling, where the satellite link and terrestrial links undergo Shadowed-Rician (SR) fading and correlated Rayleigh fading, respectively. First, we propose a new probability density function (PDF) to statistically characterize the square sum of independent and identically distributed SR random variables, which is more accurate and concise than existing expressions. Next, based on the new PDF, we derive an analytical expression of outage probability (OP) of the considered network. Furthermore, the asymptotic OP expression is developed at high signal-to-noise ratio to reveal the achievable diversity order of the considered ISTRN. Finally, computer simulation is conducted to validate the analytical results, and show the impact of various parameters on the outage performance.

In this paper, we consider an unmanned aerial vehicle (UAV) aided mobile relaying system under a buffer constraint at the relay node. We propose a new relaying protocol employing mixed free-space optical/radio frequency (FSO/RF) communication, i.e., the source-relay and relay-destination link utilize FSO communication and RF communication, respectively, under the buffer constraint which is required to consider practical relay system. We study the trajectory optimization problem of buffer-constrained UAV-relaying in order to maximize the end-to-end data throughput. Taking the conditions of the mixed FSO/RF systems (e.g., a full-duplex relaying network that works in decode-and-forward, atmospheric attenuation, transmit power, and bandwidth in FSO and RF links) into consideration, we characterize the channel and throughput models. Furthermore, corresponding to the buffer-aided relaying, we derive a limited buffer constraint regarding the state of the queue in the buffer of relay. We solve the optimal trajectory problem of the UAV to maximize the throughput of user terminal using quadratically constrained programming. As a result, we propose an iterative algorithm that efficiently finds a local optimum solution for the throughput maximization problems. Our numerical results show that proposed buffer-aided mobile relaying achieves 161.3% throughput gains compared to a static relaying scheme.

In this paper, we deal with the performance analysis of a cumulative distribution function (CDF)-based multiuser scheduling scheme for downlink non-orthogonal multiple access (NOMA). With this scheme, the multiple users having relatively better channel gain are selected. We first statistically analyze the probability of user selection in terms of the weight of each user determining the time fraction. We confirm that equal weight ensures fair resource allocation. Under this equal weight condition for fairness, we derive the distribution of signal-to-interference-plus-noise ratio (SINR) when the specific user is selected. Simulation results show that with this scheme, the time resources can be allocated more fairly compared to the proportional fair scheduling scheme, especially under NOMA conditions. Further, by adjusting the weight of desired user, the average rate and selection probability can be controlled.

With adaptive transmission, the transmission rate and/or power are adaptively adjusted according to the channel realization, which leads to a variable amount of energy consumption for the transmission of the same amount of data. We propose an analytical framework to statistically characterize the transmitter energy consumption of adaptive transmission over fading channels. For slow fading scenario, we derive the probability density function (PDF) and cumulative distribution function (CDF) of energy consumption assuming continuous-time Markov channel model. For the fast fading case, we apply the statistical mixture model to obtain the approximate PDF of energy consumption. Selected numerical results are presented to illustrate and to validate the mathematical formulations.

Non-orthogonal multiple access (NOMA) is a prospective technology for radio resource constrained future mobile networks. However, NOMA users far from base station (BS) tend to be more susceptible to eavesdropping because they are allocated more transmit power. In this paper, we aim to jointly optimize the precoding vectors at BS to ensure the legitimate security in a downlink multiple-input single-output (MISO) NOMA network. In the proposed scheme, we can maximize the sum secrecy rate by joint precoding optimization. Owing to its non-convexity, the problem is converted into a convex one, which is solved by a second-order cone programming based iterative algorithm. Simulation results are presented to demonstrate that the proposed schemes can improve the security performance for MISO NOMA systems effectively.

The superiority of optical communications in underwater mediums, in terms of higher data rate and reliability, makes underwater optical wireless communications (UOWC) more favorable to provide ultra-reliable low-latency underwater communications, as compared to other wireless technologies, e.g., acoustic and radio frequency (RF) communications. UOWC limited transmission range, however, remains a major hurdle against assessing its true deployment benefits, which motivates for the necessity of developing practical routing protocols for multi-hop underwater optical wireless sensor networks (UOWSNs). This paper sheds light on the existing state-of-art UOWC routing protocols, the majority of which requires centralized implementation with large end-to-end delay. The article further proposes routing algorithms which can be implemented in a distributed fashion across the multi-hop links, with a reasonable amount of information exchange. The merits of the proposed algorithms are particularly highlighted through illustrative simulations, which show how the proposed strategies outperform the classical protocols, both in terms of reliability and end-to-end latency. Finally, the paper shows how the proposed distributive routing protocols achieve ultra-reliable low-latency underwater communications.

Underwater optical wireless communications (UOWC) is an emerging technology to provide underwater applications with high speed and low latency connections. However, it suffers from limited range and requires effective multi-hop routing solutions for the proper operation of underwater optical wireless networks (UOWNs). In this regard, this paper proposes a distributed Sector-based Opportunistic Routing (SectOR) protocol. Unlike the traditional routing techniques which unicast packets to a unique relay, opportunistic routing (OR) targets a set of candidate relays by leveraging the broadcast nature of the UOWC channel. OR is especially suitable for UOWNs as the link connectivity can be disrupted easily due to the underwater channel impairments (e.g., pointing errors, misalignment, turbulence, etc.) and sea creatures passing through the transceivers' line-of-sight. In such cases, OR improves the packet delivery ratio as the likelihood of having at least one successful packet reception is much higher than that in conventional unicast routing. Contingent upon the performance characterization of a single-hop link, we obtain distance progress (DP) and expected (DP) metrics to evaluate the fitness of a candidate set (CS) and prioritize the members of a CS. Since rate↔error and range↔beamwidth tradeoffs yield different candidate set diversities, we develop a candidate selection and prioritization (CSPA) algorithm to find the optimal sector shaped coverage region by scanning the feasible search space. Moreover, a hybrid acoustic/optic coordination mechanism is considered to avoid duplicate transmission of the relays. Numerical results show that SectOR protocol can perform even better than an optimal unicast routing protocol in well-connected UOWNs.

In this paper, we study the joint 3D placement of unmanned aerial vehicles (UAVs) and users association under bandwidth limitation and quality of service constraints. In order to allow to UAVs to dynamically change their 3D locations in a distributed fashion while maximizing the network's sum-rate, we break the underlying optimization into 3 subproblems where we separately solve the 2D UAVs positioning, the altitude optimization, and the UAVs-users association. First, given fixed 3D positions of UAVs, we propose a fully distributed matching based association that alleviates the bottlenecks of the bandwidth and guarantees the required quality of service. Next, to address the 2D positions of UAVs, we adopt a modified version of K-means algorithm, with a distributed implementation, where UAVs dynamically change their 2D positions in order to reach the barycenter of the served users cluster. In order to optimize the UAVs altitudes, we study a naturally defined game-theoretic version of the problem and show that under fixed UAVs 2D coordinates, a predefined association scheme, and limited-interferences, the UAVs altitudes game is a non-cooperative potential game where the players (UAVs) can maximize the limited-interference sum-rate by only optimizing a local utility function. Our simulation results show that, using the proposed approach, the network's sum rate of the studied scenario is improved by 200% as compared with the trivial case where the classical version of K-means is adopted and users are assigned, at each iteration, to the closest UAV.

In this paper, we investigate the impact of the wavelength and the water turbidity on the performance of non-line-of-sight underwater communication links. Using the chlorophyll-based model proposed in [1], we demonstrate the variability of the scattering and absorption properties for a given wavelength with respect to the water turbidity level. To facilitate the use of this model in Monte Carlo simulations, we approximate the phase function by a two-term Henyey Greenstein function. The obtained phase function as well as the absorption and scattering coefficients of this model are then injected to Monte Carlo simulations to assess the path loss performance. Interestingly, we show by simulations that water turbidity, often regarded as a limiting performance factor, can lead to better received power in non-line of sight environments.

This study proposes adaptive downlink association schemes for controlled device‐to‐device (D2D) communication in cellular networks. The proposed schemes utilise active devices under specific conditions to improve the network performance. Specifically, active devices are classified into two disjoint groups according to their individual quality of service (QoS) requirements from their base station (BS). The BS adaptively allocates downlink physical channels to meet individual QoS of devices using a minimal number of these available channels. The unused channels at each served device (in the first class) can be then utilised by that device to serve other devices from the second class, which are not served by the BS, via controlled D2D associations. Herein, D2D pair discovery as well as the conditions for establishing successful D2D association between the classified devices are treated. Furthermore, two D2D association schemes that vary in terms of their performance and implementation complexity to meet certain objectives at the device of interest are presented. The developed analytical results address the scenarios of idealised perfect and practical imperfect D2D association. Numerical results are provided to further explain the performance variations between the proposed association schemes under perfect and imperfect operation scenarios.

A novel modulation scheme termed orthogonal frequency-division multiplexing with subcarrier number modulation (OFDM-SNM) has been proposed and regarded as one of the promising candidate modulation schemes for next generation networks. Although OFDM-SNM is capable of having a higher spectral efficiency (SE) than OFDM with index modulation (OFDM-IM) and plain OFDM under certain conditions, its reliability is relatively inferior to these existing schemes, because the number of active subcarriers varies. In this regard, we propose an enhanced OFDM-SNM scheme in this paper, which utilizes the flexibility of placing subcarriers to harvest a coding gain in the high signal-to-noise ratio (SNR) region. In particular, we stipulate a methodology that optimizes the subcarrier activation pattern (SAP) by subcarrier assignment using instantaneous channel state information (CSI) and therefore the subcarriers with higher channel power gains will be granted the priority to be activated, given the number of subcarriers is fixed. We also analyze the proposed enhanced OFDM-SNM system in terms of outage and error performance. The average outage probability and block error rate (BLER) are derived and approximated in closed-form expressions, which are further verified by numerical results generated by Monte Carlo simulations. The high-reliability nature of the enhanced OFDM-SNM makes it a promising candidate for implementing in the Internet of Things (IoT) with stationary machine-type devices (MTDs), which are subject to slow fading and supported by proper power supply.

This paper proposes a generalized beamspace modulation using multiplexing (GBMM) scheme for millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications with reduced radio frequency (RF) chains. Besides achieving a multiplexing gain over the selected beamspace set, GBMM additionally makes use of the index of the beamspace set to carry information. In the proposed GBMM, the beamspace sets are non-uniformly activated. We investigate the spectral efficiency (SE) of the proposed GBMM and the SE-oriented beamspace set activation probability optimization as well as the hybrid precoder design. In the hybrid precoder design procedure, we first design the fully-digital precoders and then adopt the optimized fully-digital precoders to design the hybrid precoders. A gradient ascent algorithm is developed to find the optimal fully-digital precoders and precoder activation probabilities. In the high signal-to-noise-ratio (SNR) regime, closed-form solutions of the fully-digital precoders and the precoder activation probabilities are derived. Moreover, we investigate the impact of the hybrid receiver structure on the performance of GBMM, propose a coding method to realize the optimized precoder activation, and discuss the extension to orthogonal frequency division multiplexing (OFDM)-based mmWave broadband communications. Both analytical and numerical results show that GBMM outperforms the spatial multiplexing over the best beamspace set in terms of SE, which has been well recognized as the best transmission solution in mmWave MIMO communications.

The prospect of ultra-massive multiple-input multiple-output (UM-MIMO) technology to combat the distance problem at the Terahertz (THz) band is considered. It is well-known that the very large available bandwidths at THz frequencies come at the cost of severe propagation losses and power limitations, which result in very short communication distances. Recently, graphene-based plasmonic nano-antenna arrays that can accommodate hundreds of antenna elements in a few millimeters have been proposed. While such arrays enable efficient beamforming that can increase the communication range, they fail to provide sufficient spatial degrees of freedom for spatial multiplexing. In this paper, we examine spatial modulation (SM) techniques that can leverage the properties of densely packed configurable arrays of subarrays of nano-antennas, to increase capacity and spectral efficiency, while maintaining acceptable beamforming performance. Depending on the communication distance and the frequency of operation, a specific SM configuration that ensures good channel conditions is recommended. We analyze the performance of the proposed schemes theoretically and numerically in terms of symbol and bit error rates, where significant gains are observed compared to conventional SM. We demonstrate that SM at very high frequencies is a feasible paradigm, and we motivate several extensions that can make THz-band SM a future research trend.

In this paper, we investigate the physical-layer security for a spatial modulation (SM)-based indoor visible light communication (VLC) system, which includes multiple transmitters, a legitimate receiver, and a passive eavesdropper (Eve). At the transmitters, the SM scheme is employed, i.e., only one transmitter is active at each time instant. To choose the active transmitter, a uniform selection (US) scheme is utilized. Two scenarios are considered: one is with non-negativity and average optical intensity constraints and the other is with non-negativity, average optical intensity, and peak optical intensity constraints. Then, lower and upper bounds on the secrecy rate are derived for these two scenarios. Besides, the asymptotic behaviors for the derived secrecy rate bounds at high signal-to-noise ratio (SNR) are analyzed. To further improve the secrecy performance, a channel adaptive selection (CAS) scheme and a greedy selection (GS) scheme are proposed to select the active transmitter. Numerical results show that the lower and upper bounds of the secrecy rate are tight. At high SNR, small asymptotic performance gaps exist between the derived lower and the upper bounds. Moreover, the proposed GS scheme has the best performance, followed by the CAS scheme and the US scheme.

In this paper, we analyze the secrecy performance of the decode-and-forward (DF) relay system in generalized-K fading channels. In a typical four-node communications model, a source (S) sends confidential information to a destination (D) via a relay (R) using DF strategy in two time slots, while an eavesdropper (E) wants to overhear the information from S to D over generalized-K fading channels. To be more realistic, we assume that E can receive the signals of two time slots, and there is no direct link between S and D because of heavy fading. Based on those assumptions, we derive closed-form expressions for the secrecy outage probability (SOP) and ergodic secrecy capacity (ESC) by using a tight approximate probability density function of the generalized-K model. Furthermore, asymptotic expressions for the SOP and ESC are also derived in the high signal-to-noise ratio region, not only because we can get some insights about SOP and ESC, but also because expressions for SOP and ESC can be simplified significantly. The single relay system is subsequently extended into a multi-relay system, where the asymptotic SOP analysis of three proposed relay selection strategies is investigated. Moreover, the security-reliability tradeoff analysis in the multi-relay system is also presented given that S adopts a constant code rate. Finally, the Monte-Carlo simulation is used to demonstrate the accuracy of the derived closed-form expressions.

The continuous improvement in optical energy harvesting devices motivates the development of visible light communication systems that utilize such available free energy. In this paper, an outdoor visible light communications (VLC) system is considered where a VLC base station sends data to multiple users that are capable of harvesting optical energy. The proposed VLC system serves multiple users using time division multiple access (TDMA) with unequal time and power allocation, which are allocated to achieve the system communications and illumination objectives. In an outdoor setup, the system lighting objective is to maximize the average illumination flux, while the communication design objective is to maximize the spectral efficiency (SE). A multiobjective optimization problem is formulated to obtain the Pareto front of the SE-illumination region. To this end, the marginal optimization problems are solved first using low complexity algorithms. Then, based on the proposed algorithms, a Karush-Kuhn-Tucker-based algorithm is developed to obtain an inner bound of the Pareto front for the SE-illumination tradeoff. The inner bound for the Pareto-front is shown to be close to the optimal Pareto-frontier via several simulation scenarios for different system parameters.

A network model is considered, where Poisson distributed base stations transmit to N power-domain nonorthogonal multiple access (NOMA) users (TIEs) each that employ successive interference cancellation (SIC) for decoding. We propose three models for the clustering of NOMA TIEs and consider two different ordering techniques for the NOMA TIEs: mean signal power-based and instantaneous signal-to-intercell-interference-and-noise-ratio-based. For each technique, we present a signal-to-interference-and-noise ratio analysis for the coverage of the typical TIE. We plot the rate region for the two-user case and show that neither ordering technique is consistently superior to the other. We propose two efficient algorithms for finding a feasible resource allocation that maximize the cell sum rate Rtot, for general N, constrained to: 1) a minimum throughput T for each TIE, 2) identical throughput for all TIEs. We show the existence of: 1) an optimum N that maximizes the constrained Rtotgiven a set of network parameters and 2) a critical SIC level necessary for NOMA to outperform orthogonal multiple access. The results highlight the importance in choosing the network parameters N, the constraints, and the ordering technique to balance the Rtotand fairness requirements. We also show that interference-aware TIE clustering can significantly improve performance.

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

We carry out a theoretical analysis of the uplink (UL) of a massive MIMO system with per-user channel correlation and Rician fading, using two processing approaches. First, we examine the linear-minimum-mean-square-error receiver under training-based imperfect channel estimates. Second, we propose a statistical combining technique that is more suitable in environments with strong line-of-sight (LoS) components. We derive closed-form asymptotic approximations of the UL spectral efficiency (SE) attained by each combining scheme in single and multi-cell settings, as a function of the system parameters. These expressions are insightful in how different factors such as LoS propagation conditions and pilot contamination impact the overall system performance. Furthermore, they are exploited to determine the optimal number of training symbols, which is shown to be of significant interest at low Rician factors. The study and numerical results substantiate that stronger LoS signals lead to better performances, and under such conditions, the statistical combining entails higher SE gains than the conventional receiver.

In a downlink non-orthogonal multiple access (NOMA) system, the reliable transmission of cell-edge users cannot be guaranteed due to severe channel fading. On the other hand, the presence of eavesdroppers can severely threaten the secure transmission due to the open nature of wireless channel. Thus, a two-user NOMA system assisted by a multi-antenna decode-and-forward relay is considered in this paper, and a two-stage jamming scheme, full-duplex-jamming (FDJam), is proposed to ensure the secure transmission of NOMA users. In the FDJam scheme, using full-duplex, the relay transmits the jamming signal to the eavesdropper while receiving confidential messages in the first stage, and the base station generates the jamming signal in the second stage. Furthermore, we eliminate the self-interference and the jamming signal at the relay and the legitimate node, respectively, through relay beamforming. To measure the secrecy performance, analytical expressions for secrecy outage probability (SOP) are derived for both the cell-center and cell-edge users, and the asymptotic SOP analysis at high transmit power is presented as well. Moreover, two benchmark schemes, half-duplex-jamming and full-duplex-no-jamming, are also considered. Simulation results are presented to show the accuracy of the analytical expressions and the effectiveness of the proposed scheme.

Traditional wired data center networks (DCNs) suffer from cabling complexity, lack flexibility, and are limited by the speed of digital switches. In this paper, we alternatively develop a top-down traffic grooming (TG) approach to the design and provisioning of mission-critical optical wireless DCNs. While switches are modeled as hybrid optoelectronic cross-connects, links are modeled as wavelength division multiplexing capable free-space optic channels. Using the standard TG terminology, we formulate the optimal mixed-integer TG problem considering the virtual topology, flow conversation, connection topology, non-bifurcation, and capacity constraints. Thereafter, we develop a fast yet efficient sub-optimal solution, which grooms mice flows (MFs), mission-critical flows (CFs), and forward on predetermined rack-to-rack (R2R) lightpaths. On the other hand, elephant flows (EFs) are forwarded over dedicated server-to-server express lightpaths whose routes and capacity are dynamically determined based on the availability of wavelength and capacity. To prioritize the CFs, we consider low and high-priority queues and analyze the delay characteristics such as waiting times, maximum hop counts, and blocking probability. As a result of grooming, the sub-wavelength traffic and adjusting the wavelength capacities, numerical results show that the proposed solutions can achieve significant performance enhancement by utilizing the bandwidth more efficiently, completing the flows faster than delay sensitivity requirements, and avoiding the traffic congestion by treating EFs and MFs separately.

In this paper, we consider a non-ideal successive interference cancellation receiver-based imperfect non-orthogonal multiple access (NOMA) schemes whose performance is limited by three factors: 1) power disparity & sensitivity constraints; 2) intra-cluster interference (ICRI); and 3) intercell-interference (ICI). By quantifying the residual interference with a fractional error factor (FEF), we show that NOMA cannot always perform better than orthogonal multiple access (OMA) especially under certain receiver sensitivity and FEF levels. Assuming the existence of an offline/online ICI management scheme, the proposed solution accounts for the ICI which is shown to deteriorate the NOMA performance particularly when it becomes significant compared to the ICRI. Then, a distributed cluster formation (CF) and power-bandwidth allocation (PBA) approach are proposed for downlink heterogeneous networks (HetNets) operating on the imperfect NOMA. We develop a hierarchically distributed solution methodology, where BSs independently form clusters and distributively determine the power-bandwidth allowance of each cluster. A generic CF scheme is obtained by creating a multi-partite graph via partitioning user equipment with respect to their channel gains since NOMA performance is primarily determined by the channel gain disparity of cluster members. A sequential weighted bi-partite matching method is proposed for solving the resulted weighted multi-partite matching problem. Thereafter, we present a hierarchically distributed PBA approach which consists of the primary master, secondary masters, and slave problems. For a given cluster power and bandwidth pair, optimal power allocations and Lagrange multipliers of slave problems are derived in closed-form. While power allowance of clusters is updated by the secondary masters based on dual variables of slave problems, bandwidth proportions of clusters are iteratively allocated by the primary master as per the utility achieved by the secondary masters at the previous iteration. Finally, the proposed CF and PBA approaches under the operation of imperfect NOMA are investigated and compared to the OMA scheme by extensive simulations results in DL-HetNets.

In this paper, we propose a distributed cluster formation (CF) and resource allocation (RA) framework for non-ideal non-orthogonal multiple access (NOMA) schemes in heterogeneous networks. The imperfection of the underlying NOMA scheme is due to the receiver sensitivity and interference residue from non-ideal successive interference cancellation (SIC), which is generally characterized by a fractional error factor (FEF). Our analytical findings first show that several factors have a significant impact on the achievable NOMA gain. Then, we investigate fundamental limits on NOMA cluster size as a function of FEF levels, cluster bandwidth, and quality of service (QoS) demands of user equipments (TIEs). Thereafter, a clustering algorithm is developed by taking feasible cluster size and channel gain disparity of TIEs into account. Finally, we develop a distributed α-fair RA framework where α governs the tradeoff between maximum throughput and proportional fairness objectives. Based on the derived closed-form optimal power levels, the proposed distributed solution iteratively updates bandwidths, clusters, and TIEs' transmission powers. Numerical results demonstrate that proposed solutions deliver a higher spectral and energy efficiency than traditionally adopted basic NOMA cluster size of two. We also show that an imperfect NOMA cannot always provide better performance than orthogonal multiple access under certain conditions. Finally, our numerical investigations reveal that NOMA gain is maximized under downlink/uplink decoupled (DTIDe) TIE association.

In one of the several manifestations, the future cellular networks are required to accommodate a massive number of devices, several orders of magnitude compared to today's networks. At the same time, the future cellular networks will have to fulfill stringent latency constraints. To that end, one problem that is posed as a potential showstopper is extreme congestion for requesting uplink scheduling over the physical random access channel (PRACH). Indeed, such congestion drags along scheduling delay problems. In this paper, the use of self-organized device-to-device (D2D) clustering is advocated for mitigating PRACH congestion. To this end, this paper proposes two D2D clustering schemes, namely, random-based clustering and channel-gain-based clustering. Accordingly, this paper sheds light on random access within the proposed D2D clustering schemes and presents a case study based on a stochastic geometry framework. For the sake of objective evaluation, the D2D clustering is benchmarked by the conventional scheduling request procedure. Accordingly, this paper offers insights into useful scenarios that minimize the scheduling delay for each clustering scheme. Finally, this paper discusses the implementation algorithm and some potential implementation issues and remedies.

This paper proposes an ordered sequence detection (OSD) for digital pulse interval modulation (DPIM) in optical wireless communications. Leveraging the sparsity of DPIM sequences, OSD shows a comparable performance to the optimal maximum likelihood sequence detection with much lower complexity. Compared with the widely adopted sample-by-sample optimal threshold detection (OTD), it considerably improves the bit-error-rate (BER) performance by mitigating error propagation. Moreover, this paper proposes a barrier signal-aided digital pulse interval modulation (BDPIM), where the last of every $K$ symbols is allocated with more power as an inserted barrier signal. BDPIM with OSD (BDPIM-OSD) can limit the error propagation between two adjacent barriers. To reduce the storing delay when using OSD to detect extremely large packets, we propose BDPIM with a combination of OTD and OSD (BDPIM-OTD-OSD), within which long sequences are cut into pieces and separately detected. Approximate upper bounds of the average BER performance of DPIM-OTD, DPIM-OSD, BDPIM-OSD, and BDPIM-OTD-OSD are analysed. Simulations are conducted to corroborate our analysis. Optimal parameter settings are also investigated in uncoded and coded systems by simulations. Simulation results show that the proposed OSD and BDPIM bring significant improvement in uncoded and coded systems over various channels.

In this paper, we investigate adaptive power allocation for generalized polar optical wireless communications (OWC), where a general complex bipolar signal is converted into magnitude and phase signals for intensity modulation. Mean square errors (MSE) between the input complex signals and the re-constructed complex signals are derived to characterize signal distortion. Optimal power scaling factors and power allocation are investigated to minimize the distortion. Under a sole average intensity constraint, closed-form optimal power scaling factors are derived and found to be input-dependent. Specifically, they are determined by the first and second moments of the magnitude signals, the first moment of the phase signals as well as the channel state. Under both average and peak intensity constraints, the expression of MSE regarding the power scaling factors is derived but it is too complicated to find the optimal power allocation. Thus, we propose to use a small-scale numerical search for practical power allocation. As an example, we adopt the proposed power allocation to polar optical orthogonal frequency division multiplexing (P-OFDM) systems and analyze its achievable bit error rate (BER). Numerical simulations are presented to validate the analysis. It is shown that the proposed power allocation greatly improves the performance in terms of MSE and BER and the proposed power allocation-enhanced P-OFDM (EP-OFDM) outperforms existing optical OFDM schemes over various channels in the high signal-to-noise ratio (SNR) regime, especially in the systems with a peak intensity constraint.

The level crossing rate (LCR) and the average outage duration (AOD) are two important second order statistics that allow a deeper understanding of the behavior of the channel. In this paper, we study these metrics in order to assess the performance of free space optical (FSO) communication links in the presence of weak atmospheric turbulence and rice-induced pointing errors. More specifically, we derive an integral and a Gauss-Laguerre quadrature representation for both the LCR and the AOD in the single hop case and for their respective bounds in the multihop case. Selected numerical simulations are presented to show the accuracy of the derived results and to study the effect of certain system parameters on these two performance metrics.

In this paper, we analyze the performance degradation of a multi-hop decode-and-forward full-duplex relaying system caused by the residual self-interference (RSI) and hardware distortions (HWD) imposed by the FDR operation and imperfect hardware, respectively. In addition, we study the benefits of employing improper Gaussian signaling (IGS) in the MH-FDR system. Different from the traditional symmetric signaling scheme, i.e., proper Gaussian signaling (PGS), IGS has non-zero pseudo-variance that can limit the impact of RSI and HWD in the MH-FDR system. To evaluate the system performance gain using IGS, first we express the end-to-end achievable rate of the MH system as the minimum rate supported by all participating links. Then, we optimize the pseudo-variance of all participating transmitters, including source and relays to compensate the interference impact and improve the end-to-end achievable rate. We propose two network optimization schemes based on the system characteristics, i.e., joint optimization framework and distributed optimization scenario. Interestingly, IGS-based scheme outperforms its counterpart PGS-based scheme, especially at higher interference-to-noise ratio. Our findings reveal that using IGS in single-user detection systems that suffer from both RSI and HWD can effectively mitigate the degradation in the achievable rate performance.

This paper considers the secrecy outage performance of a multiple-relay assisted non-orthogonal multiple access (NOMA) network over Nakagami-m fading channels. Two time slots are utilized to transmit signals from the base station to destination. At the first time slot, the base station broadcasts the superposition signal of the two users to all decode-and-forward relays by message mapping strategy. Subsequently, the selected relay transmits superposition signal to the two users via power-domain NOMA technology. Three relay selection schemes, i.e., optimal single relay selection (OSRS) scheme, two-step single relay selection (TSRS) scheme, and optimal dual relay selection (ODRS) scheme are proposed and the secrecy outage performance is analyzed. As a benchmark, we also examine the secrecy outage performance of the NOMA systems with traditional multiple relay forwarding (TMRF) scheme in which all the relay that successfully decode signals from the source forward signals to the NOMA users with equal power. Considering the correlation between the secrecy capacity of two users and different secrecy requirement for two NOMA users, the analytical expressions for the security outage probability (SOP) of the proposed OSRS, TSRS, and ODRS schemes along with the TMRF scheme are derived and validated via simulations. To get more insights, we also derive the analytical expressions for the asymptotic SOP for all the schemes with fixed and dynamic power allocations. Furthermore, the secrecy diversity order (SDO) and secrecy array gain of cooperative NOMA systems are obtained. The results demonstrate that our proposed schemes can significantly enhance the secrecy performance compared to the TMRF scheme and that all the schemes with fixed power allocation obtain zero SDO and the OSRS scheme with dynamic power allocation obtains the same SDO as TMRF.

This paper proposes a closed-loop sparse channel estimation (CE) scheme for wideband millimeter-wave hybrid full-dimensional multiple-input multiple-output and time division duplexing based systems, which exploits the channel sparsity in both angle and delay domains. At the downlink CE stage, random transmit precoding matrix is designed at base station (BS) for channel sounding, and receive combining matrices at user devices (UDs) are designed whereby the hybrid array is visualized as a low-dimensional digital array for facilitating the multi-dimensional unitary ESPRIT (MDU-ESPRIT) algorithm to estimate respective angle-of-arrivals (AoAs). At the uplink CE stage, the estimated downlink AoAs, namely, uplink angle-of-departures (AoDs), are exploited to design multi-beam transmit precoding matrices at UDs to enable BS to estimate the uplink AoAs, i.e., the downlink AoDs, and delays of different UDs, whereby the MDU-ESPRIT algorithm is used based on the designed receive combining matrix at BS. Furthermore, a maximum likelihood approach is proposed to pair the channel parameters acquired at the two stages, and the path gains are then obtained using least squares estimator. According to spectrum estimation theory, our solution can acquire the super-resolution estimations of the AoAs/AoDs and delays of sparse multipath components with low training overhead. Simulation results verify the better CE performance and lower computational complexity of our solution over state-of-the-art approaches.

Location is one of the basic information required for underwater optical wireless sensor networks (UOWSNs) for different purposes, such as relating the sensing measurements with precise sensor positions, enabling efficient geographic routing techniques, and sustaining link connectivity between the nodes. Even though various 2-D UOWSNs' localization methods have been proposed in the past, the directive nature of optical wireless communications and 3-D deployment of sensors require to develop 3-D underwater localization methods. Additionally, the localization accuracy of the network strongly depends on the placement of the anchors. Therefore, we propose a robust 3-D localization method for partially connected UOWSNs, which can accommodate the outliers and optimize the placement of the anchors to improve the localization accuracy. The proposed method formulates the problem of missing pairwise distances and outliers as an optimization problem, which is solved through half quadratic minimization. Furthermore, analysis is provided to optimally place the anchors in the network, which improves the localization accuracy. The problem of optimal anchor placement is formulated as a combination of Fisher information matrices for the sensor nodes where the condition of D-optimality is satisfied. The numerical results indicate that the proposed method outperforms the literature substantially in the presence of outliers.

Considering the widespread use of effective capacity in cross-layer design and the extensive existence of renewal service processes in communication networks, this paper thoroughly investigates the effective capacity for renewal processes. Exact expressions of the effective capacity at a given quality of service (QoS) exponent are derived for the renewal processes with either constant or variable reward. The simple expressions reveal meaningful insights, such as the monotonicity and bounds of the effective capacity. The analytical results are then applied to evaluate the cross-layer throughput for diverse hybrid automatic repeat request (HARQ) systems, including fixed-rate HARQ (FR-HARQ, e.g., Type I HARQ, HARQ with chase combining (HARQ-CC) and HARQ with incremental redundancy (HARQ-IR)), variable-rate HARQ (VR-HARQ), and cross-packet HARQ (XP-HARQ). Furthermore, aiming at maximizing the effective capacity via the optimal rate selection, it is disclosed that the VR-HARQ and XP-HARQ attain almost the same performance, and both of them perform better than FR-HARQ. In contrast, most of the prior outcomes approximate the effective capacity with the lack of insightful discussions that hampers the optimal design of HARQ systems. Finally, the numerical results corroborate the analytical ones.

A unified statistical model is proposed to characterize turbulence-induced fading in underwater wireless optical communication (UWOC) channels in the presence of air bubbles and temperature gradient for fresh and salty waters, based on experimental data. In this model, the channel irradiance fluctuations are characterized by the mixture exponential-generalized gamma (EGG) distribution. We use the expectation-maximization algorithm to obtain the maximum likelihood parameter estimation of the new model. Interestingly, the proposed model is shown to provide a perfect fit with the measured data under all channel conditions for both types of water. The major advantage of the new model is that it has a simple mathematical form making it attractive from a performance analysis point of view. Indeed, we show that the application of the EGG model leads to closed-form and analytically tractable expressions for key UWOC system performance metrics such as the outage probability, the average bit-error rate, and the ergodic capacity. To the best of our knowledge, this is the first-ever comprehensive channel model addressing the statistics of optical beam irradiance fluctuations in underwater wireless optical channels due to both air bubbles and temperature gradient.

The explosive data traffic and connections in 5G networks require the use of non-orthogonal multiple access (NOMA) to accommodate more users. Unmanned aerial vehicle (UAV) can be exploited with NOMA to improve the situation further. In this paper, we propose a UAV-assisted NOMA network, in which the UAV and base station (BS) cooperate with each other to serve ground users simultaneously. The sum rate is maximized by jointly optimizing the UAV trajectory and the NOMA precoding. To solve the optimization, we decompose it into two steps. First, the sum rate of the UAV-served users is maximized via alternate user scheduling and UAV trajectory with its interference to the BS-served users below a threshold. Then, the optimal NOMA precoding vectors are obtained using two schemes with different constraints. The first scheme intends to cancel the interference from the BS to the UAV-served user, while the second one restricts the interference to a given threshold. In both schemes, the non-convex optimization problems are converted into tractable ones. An iterative algorithm is designed. Numerical results are provided to evaluate the effectiveness of the proposed algorithms for the hybrid NOMA and UAV network.

In this paper, we focus on the downlink ergodic sum rate of a single-cell large-scale multiuser MIMO system in which the base station employs N antennas to communicate with K single-antenna user equipments (TIEs). A regularized zero-forcing (RZF) scheme is used for precoding under the assumption that each TIE uses a specific power and each link forms a spatially correlated MIMO Rician fading channel. The analysis is conducted assuming that N and K grow large with a given ratio and perfect channel state information is available at the base station. New results from random matrix theory and large system analysis are used to compute an asymptotic expression of the signal-to-interference-plus-noise ratio as a function of system parameters, spatial correlation matrix, and Rician factor. Numerical results are used to validate the accuracy of asymptotic approximations in the finite system regime and to evaluate the performance under different operating conditions. It turns out that the asymptotic expressions provide accurate approximations even for relatively small values of N and K.

The characterization of fundamental performance bounds of many-to-many communication systems in which participating nodes are active in an intermittent way is one of the major challenges in communication theory. In order to address this issue, we introduce the multiple-input multiple-output (MIMO) three-way channel (3WC) with an intermittent node and study its degrees-of-freedom (DoF) region and sum-DoF. We devise a non-adaptive encoding scheme based on zero-forcing, interference alignment, and erasure coding, and show its DoF region (and thus sum-DoF) optimality for non-intermittent 3WCs and its sum-DoF optimality for (node-) intermittent 3WCs. However, we show by example that in general some DoF tuples in the intermittent 3WC can only be achieved by adaptive schemes such as decode-forward relaying. This shows that non-adaptive encoding is sufficient for the non-intermittent 3WC and for the sum-DoF of intermittent 3WCs but adaptive encoding is necessary for the DoF region of intermittent 3WCs. This paper contributes to a better understanding of the fundamental limits of multi-way communication systems with intermittency and the impact of adaptation therein.

Underwater optical wireless links have limited range and intermittent connectivity due to the hostile aquatic channel impairments and misalignment between the optical transceivers. Therefore, multi-hop communication can expand the communication range, enhance network connectivity, and provide a more precise network localization scheme. In this regard, this paper investigates the connectivity of underwater optical wireless sensor networks (UOWSNs) and its impacts on the network localization performance. First, we model UOWSNs as randomly scaled sector graphs where the connection between sensors is established by point-to-point directed links. Thereafter, the probability of network connectivity is analytically derived as a function of network density, communication range, and optical transmitters' divergence angle. Second, the network localization problem is formulated as an unconstrained optimization problem and solved using the conjugate gradient technique. Numerical results show that different network parameters such as the number of nodes, divergence angle, and transmission range significantly influence the probability of a connected network. Furthermore, the performance of the proposed localization technique is compared to well-known network localization schemes and the results show that the localization accuracy of the proposed technique outperforms the literature in terms of network connectivity, ranging error, and number of anchors.

The massive wireless networks (MWNs) enable surging applications for the Internet of Things and cyber physical systems. In these applications, nodes typically exhibit stringent power constraints, limited computing capabilities, and sporadic traffic patterns. This paper develops a spatiotemporal model to characterize and design uncoordinated multiple access (UMA) strategies for MWNs. By combining stochastic geometry and queueing theory, the paper quantifies the scalability of UMA via the maximum spatiotemporal traffic density that can be accommodated in the network, while satisfying the target operational constraints (e.g., stability) for a given percentile of the nodes. The developed framework is then used to design UMA strategies that stabilize the node data buffers and achieve desirable latency, buffer size, and data rate.

Visible light communications (VLCs), in general, and resource allocation for VLC networks in particular, have gained lots of attention recently. In this paper, we consider the resource allocation problem of a VLC downlink transmission system employing dynamic time division multiple access, where time and power variables are tuned to maximize the downlink spectral efficiency (SE). As for the operational conditions, we impose constraints on the average optical intensity, the energy budget, and the quality-of-service. To solve this non-convex problem, we transform the objective function into a difference of concave functions by solving a second-order differential inequality. Then, we propose a low-complexity algorithm to solve the resource allocation problem. Finally, we show by simulations the SE performance gains achieved by optimizing time and power allocation over the initial total power minimization solution for the considered system.

The next era of information revolution will rely on aggregating big data from massive numbers of devices that are widely scattered in our environment. Most of these devices are expected to be of low-complexity, low-cost, and limited power supply, which imposes stringent constraints on the network operation. In this regard, this paper investigates aerial data aggregation and field estimation from a finite spatial field via an unmanned aerial vehicle (UAV). Instead of fusing, relaying, and routing the data across the wireless nodes to fixed locations access points, a UAV flies over the field and collects the required data for two prominent missions: data aggregation and field estimation. To accomplish these tasks, the field of interest is divided into several subregions, over which the UAV hovers to collect samples from the underlying nodes. To this end, we formulate and solve an optimization problem to minimize the total hovering and traveling time of each mission. While the former requires the collection of a prescribed average number of samples from the field, the latter ensures, for a given field spatial correlation model, that the average mean-squared estimation error of the field value is no more than a predetermined threshold at any point. These goals are fulfilled by optimizing the number of subregions, the area of each subregion, the hovering locations, the hovering time at each location, and the trajectory traversed between hovering locations. The proposed formulation is shown to be NP-hard mixed integer problem, and hence, a decoupled heuristic solution is proposed. The results show that there exists an optimal number of subregions that balance the tradeoff between hovering and traveling times, such that the total time for collecting the required samples is minimized.

This paper investigates the generic signal shaping methods for the multiple-data-stream generalized spatial modulation (GenSM) and the generalized quadrature spatial modulation (GenQSM). Three cases with different channel state information at the transmitter (CSIT) are considered, including no CSIT, statistical CSIT, and perfect CSIT. A unified optimization problem is formulated to find the optimal transmit vector set under size, power, and sparsity constraints. We propose an optimization-based signal shaping (OBSS) approach by solving the formulated problem directly and a codebook-based signal shaping (CBSS) approach by finding the sub-optimal solutions in discrete space. In the OBSS approach, we reformulate the original problem to optimize the signal constellations used for each transmit antenna combination (TAC). Both the size and the entry of all signal constellations are optimized. Specifically, we suggest the use of a recursive design for the size optimization. The entry optimization is formulated as a non-convex large-scale quadratically constrained quadratic programming (QCQP) problem and can be solved by the existing optimization techniques with rather high complexity. To reduce the complexity, we propose the CBSS approach using a codebook generated by the quadrature amplitude modulation (QAM) symbols and a low-complexity selection algorithm to choose the optimal transmit vector set. The simulation results show that the OBSS approach exhibits the optimal performance in comparison with existing benchmarks. However, the OBSS approach is impractical for large-size signal shaping and adaptive signal shaping with instantaneous CSIT due to the demand of high computational complexity. As a low-complexity approach, the CBSS shows comparable performance and can be easily implemented in large-size systems.

In this paper, we propose a distributed algorithm that allows unmanned aerial vehicles (UAVs) to dynamically learn their optimal 3D locations and associate with ground users while maximizing the network's sum-rate. Our approach is referred to as 'Learn-As-You-Fly' (LAYF) algorithm. LAYF is based on a decomposition process that iteratively breaks the underlying optimization into three subproblems. First, given fixed 3D positions of UAVs, LAYF proposes a distributed matching-based association that alleviates the bottlenecks of bandwidth allocation and guarantees the required quality of service. Next, to address the 2D positions of UAVs, a modified version of K-means algorithm, with a distributed implementation, is adopted. Finally, in order to optimize the UAVs altitudes, we study a naturally defined game-theoretic version of the problem and show that under fixed UAVs 2D coordinates, a predefined association scheme, and limited interference, the UAVs altitudes game is a potential game where UAVs can maximize the limited interference sum-rate by only optimizing a local utility function. Our simulation results show that the network's sum-rate is improved as compared to both a centralized suboptimal solution and a distributed approach that is based on closest UAVs association.

Error probability study of hardware impaired (HWI) systems highly depends on the adopted model. Considering the distinct improper Gaussian features of HWI systems, captured by recent models, HWI-aware receivers are designed. An optimal maximum likelihood (ML) receiver serves as a performance benchmark, and a sub-optimal linear minimum mean square error introduces a reduced-complexity implementation. Whereas, the conventional HWI-unaware minimum Euclidean distance receiver, based on the proper noise assumption, exhibits substandard performance. Next, the average error probability of the proposed optimal ML-receiver is analyzed, where several tight bounds and approximations are derived for various HWI systems. Motivated by the benefit of improper Gaussian signaling in mitigating HWI, which is proven in recent studies, asymmetric modulation is adopted and optimized for transmission. The numerical results demonstrate a bit error rate (BER) reduction up to 70% of the proposed HWI-aware receivers over HWI-unaware receivers. Moreover, the asymmetric modulation is shown to reduce the BER by 93%. These results signify the importance of incorporating accurate HWI models, designing appropriate receivers and optimizing signal transmission for the BER performance compensation.

This paper studies the sum throughput of the multi-user multiple-input-single-output networks in the cases with a large-but-finite number of transmit antennas and users. Considering continuous and bursty communication scenarios with different users' data request probabilities, we derive quasi-closed-form expressions for the maximum achievable throughput of the networks using optimal schedulers. The results are obtained in various cases with different levels of interference cancellation. Also, we develop an efficient scheduling scheme using genetic algorithms (GAs) and evaluate the effect of different parameters, such as channel/precoding models, number of antennas/users, scheduling costs, and power amplifiers' efficiency, on the system performance. Finally, we use the recent results on the achievable rates of finite block-length codes to analyze the system performance in the cases with short packets. As demonstrated, the proposed GA-based scheduler reaches (almost) the same throughput as in the exhaustive search-based optimal scheduler, with substantially less implementation complexity. Moreover, the power amplifiers' inefficiency and the scheduling delay affect the performance of the scheduling-based systems significantly.

In this paper, we comprehensively investigate the achievable rates of selected band-limited intensity modulation schemes, which are important for optical wireless communication applications, while accounting for the specific nature of their signal construction (non-negative, real, and baseband), and imposing identical bandwidth and average optical power constraints. Furthermore, we identify/devise methods to effectively trade between these parameters. Three variants of orthogonal frequency division multiplexing (OFDM), namely, asymmetrically clipped optical OFDM (ACO-OFDM), spectrally and energy efficient OFDM (SEE-OFDM), and dc-biased optical OFDM (DCO-OFDM), and single-carrier pulse amplitude modulation are studied. The clipping noise in ACO-OFDM and SEE-OFDM is found to consume a large excess bandwidth. The detrimental effects of this excess bandwidth on the achievable rate are evaluated. For SEE-OFDM, the problem of optimal power allocation among its components is formulated and solved using the Karush-Kuhn-Tucker method. For DCO-OFDM, the clipping noise is modeled and incorporated in the analysis. Among the existing schemes, DCO-OFDM yields the best overall performance, due to its compact spectrum. In order to improve the achievable rate, we propose and analyze two improved distortionless variants, filtered ACO-OFDM and filtered SEE-OFDM (FSEE-OFDM), which yield better spectral efficiency than ACO-OFDM and SEE-OFDM, respectively. FSEE-OFDM, being the most spectrally efficient, outperforms all schemes.

This paper studies the ergodic rate performance of regularized zero-forcing (RZF) precoding in the downlink of a multi-user multiple-input single-output (MISO) system, where the channel between the base station (BS) and each user is modeled by the double scattering model. This non-Gaussian channel model is a function of both the antenna correlation and the structure of scattering in the propagation environment. This paper makes the preliminary contribution of deriving the minimum-mean-square-error (MMSE) channel estimate for this model. Then under the assumption that the users are divided into groups of common correlation matrices, this paper derives deterministic approximations of the signal-to-interference-plus-noise ratio (SINR) and the ergodic rate, which are almost surely tight in the limit that the number of BS antennas, the number of users, and the number of scatterers in each group grow infinitely large. The derived results are expressed in a closed-form for the special case of multi-keyhole channels. The simulation results confirm the close match provided by the asymptotic analysis for moderate system dimensions. We show that the maximum number of users that can be supported simultaneously, while realizing large-scale MIMO gains, is equal to the number of scatterers.

Visible light communications (VLC) have emerged as a strong candidate for meeting the escalating demand for high data rates. In this paper, we consider a VLC network, where multiple access points (APs) serve both energy-harvesting users (EHUs), i.e., users who harvest energy from light emitted by diodes and information users (IUs), i.e., users who gather data information. In order to jointly balance the achievable sum rate at the IUs and the energy harvested by the EHUs, the paper considers maximizing a network-wide utility, which consists of a weighted sum of the IUs sum rate and the EHUs harvested energy, subject to individual IU rate constraint, individual EHU harvested-energy constraint, and AP power constraints, so as to jointly determine the direct current (DC) bias value at each AP, and the power of the alternating-current (AC) signals of the users. A difficult non-convex optimization problem is solved using an iterative approach which relies on inner convex approximations, and compensates for the used approximations using proper outer-loop updates. The paper further considers solving the special cases of the problem, i.e., maximizing the sum rate, and maximizing the total harvested-energy, both subject to the same constraints. Numerical results highlight the significant performance improvement of the proposed algorithms, and illustrate the impacts of the network parameters on the performance trade-off between the sum rate and harvested-energy.

Simultaneous co-channel transmission and reception, denoted as in-band full-duplex (FD) communications, has been promoted as a solution to improve the spectral efficiency in wireless networks. For cellular networks, in addition to the existing aggregate interference in half-duplex transmission, the residual self-interference and cross-mode interference [i.e., between uplink (UL) and downlink (DL)] impose major obstacles for FD communications' deployment. Although the FD communication's promising impact on the overall network data rate has been established in the literature, the rate gains are achieved in the DL transmissions at the expense of marginal gain, or even degradation, for the UL transmissions. This paper, therefore, focuses on the analysis of UL ergodic rate in FD cellular networks where a minimum distance between BSs using the same time-frequency resource block is imposed. Hence, the mutually interfering BSs' locations are modeled by Matérn hard core point process. The distribution of the aggregate interference and the channel-to-interference-plus-noise ratio at the UL of a typical user are characterized using a stochastic geometry analysis. Several UL power control techniques are presented and their resulting ergodic rates are derived and compared. The simulation results suggest that the UL performance highly depends on the network parameters and the UL power control techniques.

This paper proposes a received signal strength (RSS)-based localization framework for energy harvesting underwater optical wireless sensor networks (EH-UOWSNs), where the optical noise sources and channel impairments of seawater pose significant challenges on range estimation. In UOWSNs, energy limitation is another major problem due to the limited battery power and difficulty to replace or recharge the battery of an underwater sensor node. In the proposed framework, sensor nodes with insufficient battery harvest ambient energy and start communicating once they have sufficient storage of energy. Network localization is carried out by measuring the RSSs of active nodes, which are modeled based on the underwater optical communication channel characteristics. Thereafter, block kernel matrices are computed for the RSS-based range measurements. Unlike the traditional shortest-path approach, the proposed technique reduces the estimation error of the shortest path for each block kernel matrix. Once the complete block kernel matrices are available, a closed form localization technique is developed to find the location of every optical sensor node in the network. An analytical expression for the Cramer-Rao lower bound is also derived as a benchmark to evaluate the localization performance of the developed technique. The extensive simulations show that the proposed framework outperforms the well-known network localization techniques.

Small-cell network is a promising solution to high video traffic. However, with the increasing number of mobile devices, it cannot meet the requirements from all users. Thus, we propose a caching device-to-device (D2D) scheme for small-cell networks, in which caching placement and D2D establishment are combined. In this scheme, a limited cache is equipped at each user, and the popular files can be prefetched at the local cache during off-peak period. Thus, dense D2D connections can be established during peak time aided by these cached users. To do this, first, an optimal caching scheme is formulated according to the popularity to maximize the total offloading probability of the D2D system. Then, the sum rate of D2D links is analyzed in different signal-to-noise ratio (SNR) regions. Furthermore, three D2D-link scheduling schemes are proposed with the help of bipartite graph theory and Kuhn-Munkres algorithm for low, high and medium SNRs, respectively. Simulation results are presented to show the effectiveness of the proposed scheme.

In this paper, we study the performance of simultaneous lightwave information and power transfer (SLIPT) systems of multi-cell indoor scenario. We aim to investigate the energy harvesting and data rate performance of multiple users while meeting the lightning constraints. To this end, we develop optimization frameworks and tune the light emitting diodes average currents to improve the performance of the SLIPT system. Firstly, we propose an algorithm to maximize the spectral efficiency (SE) subject to lighting and minimum harvested energy per user requirements. The proposed algorithm can be implemented in a distributed fashion with a reduced computational burden at each node. Then, we consider the energy harvesting maximization problem to investigate the maximum possible energy gain and its corresponding SE performance. Finally, we present some extensive simulations to explore the benefit of the optimization frameworks with respect to standard equal allocation setting. In addition, we monitor the effect of changing several system parameters on the two objectives and highlight the underlying trade-off between them.

Wireless data centers (DCs) are enablers of reconfigurable data center network (DCN) topologies by augmenting the cabling complexity and inflexibility of traditional wired DCs. In this paper, we propose an optical traffic grooming (TG) for mice flows (MFs) and elephant flows (EFs) in a wireless DCN which is interconnected with free-space optical (FSO) links operating on wavelength division multiplexing (WDM). Since handling the bandwidth-hungry EFs along with delay-sensitive MFs over the same network resources have undesirable consequences, proposed TG policy treat MFs and EFs separately. MFs/EFs destined to the same rack are groomed into larger rack-to-rack MF/EF flows over dedicated lightpaths whose routes and capacities are jointly determined taking the load balancing into account. Performance evaluations of proposed TG policy show a significant throughput improvement thanks to bandwidth efficient utilization of the wireless links. Therefore, proposed TG requires expeditious flow detection mechanisms which can immediately classify EFs with very high accuracy. Since these demands cannot be met by existing sampling and port-mirroring based solutions, we propose a lightweight and fast in-network flow detection (LightFD) mechanism. LightFD is designed as a module on the Virtual-Switch/Hypervisor, which detects EFs based on acknowledgment sequence number of flow packets. Emulation results show that LightFD can provide up to 110 times faster detection speeds than sampling-based methods with %100 detection accuracy. We also demonstrate that the EF detection speed has a considerable impact on achievable EF throughput.

In this paper, we aim to maximize the end-to-end achievable rate of multiple-input multiple-output (MIMO) decode-and-forward (DF) where the relay is an energy harvesting (EH) node using the time switching (TS) scheme. The relay first harvests the energy from the source, then uses its harvested energy to forward the information carrying signal from the source to the destination. The EH model at the relay is a nonlinear model. Also, we assume that the channel knowledge is imperfect at the relay and destination. We propose the structure of the optimal covariance matrices at the source (during EH and information decoding periods), the optimal covariance matrix at the relay and the optimal EH time ratio. Through the simulation results, we compare between different linear/nonlinear EH models and we show the gain/loss performance of the linear model compared to other nonlinear EH models.

This paper investigates the self-sustainability of an overlay Internet of Things (IoT) network that relies on harvesting energy from a downlink cellular network. Using stochastic geometry and queueing theory, we develop a spatiotemporal model to derive the steady state distribution of the number of packets in the buffers and energy levels in the batteries of IoT devices given that the IoT and cellular communications are allocated disjoint spectrum. Particularly, each IoT device is modeled via a two- dimensional discrete-time Markov Chain (DTMC) that jointly tracks the evolution of data buffer and energy battery. In this context, stochastic geometry is used to derive the energy generation at the batteries and the packet transmission probability from buffers taking into account the mutual interference from other active IoT devices. To this end, we show the Pareto-Frontiers of the sustainability region, which defines the network parameters that ensure stable network operation and finite packet delay. The results provide several insights to design self-sustainable IoT networks.

This work considers the uplink (UL) of a multicell massive MIMO system with L cells, having each K monoantenna users communicating with an N-antennas base station (BS). The channel model involves Rician fading with distinct per-user Rician factors and channel correlation matrices and takes into account pilot contamination and imperfect CSI. The objective is to evaluate the performances of such systems with different single-cell and multi-cell detection methods. In the former, we investigate MRC and single-cell MMSE (S-MMSE); as to the latter, we are interested in multi-cell MMSE (MMMSE) that was recently shown to provide unbounded rates in Rayleigh fading. The analysis is led assuming the infinite N limit and yields informative closed-form approximations that are substantiated by a selection of numerical results for finite system dimensions. Accordingly, these expressions are accurate and provide relevant insights into the effects of the different system parameters on the overall performances.

The next era of information revolution will rely on aggregating big data from massive numbers of devices that are widely scattered in our environment. The majority of these devices are expected to be of low-complexity, low-cost, and limited power supply, which impose stringent constraints on the network operation. In this regards, this paper proposes aerial data aggregation from a finite spatial field via an unmanned aerial vehicle (UAV). Instead of fusing, relaying, and routing the data across the wireless nodes to fixed locations access points, an UAV flies over the field and collects the required data. Particularly, the field is divided into several subregions over which the UAV hovers to collect samples from the underlying nodes. To this end, an optimization problem is formulated and solved to find the optimal number of subregions, the area of each subregion, the hovering locations, the hovering time at each location, and the trajectory traversed between hovering locations such that an average number of samples are collected from the field in minimal time. The proposed formulation is shown to be np-hard mixed integer problem, and hence, a decoupled heuristic solution is proposed. The results show that there exists an optimal number of subregions that balance the tradeoff between hovering and traveling times such that the total time for collecting the required samples is minimized.

Cognitive radio (CR) systems can improve radio spectrum utilization by allowing secondary access of underutilized spectrum resources. With interweave cognitive implementation, secondary users have to wait for spectrum opportunities before their transmission. Therefore, the total delivery time of secondary transmission consists of both waiting slots and transmission slots. In this work, we study the resulting extended delivery time (EDT) of secondary transmission for a fixed amount of data over multiple primary channels. In particular, a birth-death process is introduced to model the number of available primary channels, based on which, we derive the distribution function of the EDT for both continuous and periodic sensing cases. We also present selected numerical results to illustrate the mathematical formulation.

This paper studies the sum-rate performance of regularized zero-forcing (RZF) precoding in a multi-user multiple-input single-output (MISO) system, where the channel between the base station (BS) and each user is modeled by the double scattering channel model. This non-Gaussian channel accounts for both the spatial correlation in the antenna arrays and the structure of scattering in the propagation environment. The user population is divided into G groups, where the users in the same group experience similar propagation conditions and are characterized by common correlation matrices. Under this setting, we derive deterministic approximations of the signal-to-interference-plus-noise ratio (SINR) and the sum-rate with RZF precoding, which are almost surely tight in the large system limit. Simulation results confirm the close match provided by the asymptotic analysis for moderate system dimensions.

In order to meet the new escalating demand for high data rate services and applications, visible light communication (VLC) has emerged as a promising solution for the fifth-generation (5G) wireless networks and beyond. Consider a VLC network, where multiple access points (APs) serve both energy-harvesting users (EHUs), i.e., users which harvest energy from light intensity, and information-users (IUs), i.e., users which gather data information. The performance of the system becomes a function of the direct current (DC) bias values allocated to each AP. After adopting a zero-forcing (ZF) precoding approach to cancel the inter-cell interference, the paper formulates the problem of maximizing the network harvested energy subject to individual harvested energy and data rate constraints at the EHUs and IUs, respectively, so as to determine the DC bias of every AP. The paper then proposes solving such a difficult non-convex optimization problem using an iterative approach. The proposed algorithm uses well-chosen approximations of the objective and constraints functions, and compensates for the approximations using proper outer-loop updates. The paper further proposes a sub optimal heuristic which provides a feasible, yet simple, solution to the problem. Numerical results illustrate the convergence of our proposed algorithms, and highlight the significant performance improvement of the proposed algorithm as compared to the proposed baseline approach.

We consider the evaluation of the outage probability (OP) for L-branch equal gain combining diversity receivers operating over fading channels, i.e. equivalently the cumulative distribution function (CDF) of the sum of the L channel envelopes. Generally, closed-form expressions of the OP values are out of reach. Moreover, the use of naive Monte Carlo (MC) simulations is not a good alternative since it is expensive in terms of number of samples when small values of OP are considered. In this paper, we use the concept of importance sampling (IS), being known to yield accurate estimates using few number of simulations runs. The proposed IS scheme is essentially based on sample rejection where the IS probability density function (PDF) is the truncation of the underlying PDF over the L dimensional sphere. It assumes the knowledge of the CDF of the sum of the L channel gains in a closed-form expression. Such an assumption is not restrictive since it holds for various challenging fading models. As an illustration, we apply the proposed estimator to the cases of independent Rayleigh, correlated Rayleigh, and independent and identically distributed Rice fading channels and prove that it achieves the well-desired bounded relative error property. Finally, we validate these theoretical results through some selected experiments.

Non-Orthogonal Multiple Access (NOMA) is a promising technology for 5G that enables each resource unit to simultaneously serve multiple users. This work evaluates the potential benefit of joint scheduling and power adaptation in NOMA- based downlink in Fog-Radio Access Networks (FRAN). We consider the downlink of a FRAN, where the Fog Access Points (FAPs) are connected to central cloud baseband units (BBUs) through capacity-constrained fronthaul links. The FAPs adopt a two-user NOMA scheme, within each resource block (RB), to serve a common set of users. The paper formulates an optimization problem which maximizes a network-wide {rate-based utility} function subject to fronthaul- capacity constraints, so as to determine both the user- to-FAP assignment and the power levels of the users served by each FAP. The main contribution of the paper is solving this mixed-integer non-convex optimization problem using a two- step centralized-distributed approach, which is aligned with FRAN operation that {aims to} %relies on partially shifting the network control to the FAPs so as to overcome delays due to fronthaul rate constraints. The assignment step is first solved at the centralized BBU pool by reformulating the problem such that the Hungarian algorithm is applicable. The power {adaptation} is then solved at every FAP using a barrier method. Simulation results show that the proposed NOMA-based algorithm outperforms conventional Orthogonal Multiple Access (OMA) algorithms, even with stringent fronthaul limitations. The proposed algorithm further shows an appreciable {performance trade-off} between the rate and fairness metrics.

We consider the problem of evaluating outage probability (OP) values of generalized selection combining diversity receivers over fading channels. This is equivalent to computing the cumulative distribution function (CDF) of the sum of order statistics. Generally, closed-form expressions of the CDF of order statistics are unavailable for many practical distributions. Moreover, the naive Monte Carlo method requires a substantial computational effort when the probability of interest is sufficiently small. In the region of small OP values, we propose instead an efficient, yet universal, importance sampling (IS) estimator that yields a reliable estimate of the CDF with small computing cost. The main feature of the proposed IS estimator is that it has bounded relative error under a certain assumption that is shown to hold for most of the challenging distributions. Moreover, an improvement of this estimator is proposed for the Pareto and the Weibull cases. Finally, the efficiency of the proposed estimators are investigated through various numerical experiments.

In this paper, a received signal strength (RSS) based localization technique is investigated for underwater optical wireless sensor networks (UOWSNs) where optical noise sources (e.g., sunlight, background, thermal, and dark current) and channel impairments of seawater (e.g., absorption, scattering, and turbulence) pose significant challenges. Hence, we propose a localization technique that works on the noisy ranging measurements embedded in a higher dimensional space and localize the sensor network in a low dimensional space. Once the neighborhood information is measured, a weighted network graph is constructed, which contains the one-hop neighbor distance estimations. A novel approach is developed to complete the missing distances in the kernel matrix. The output of the proposed technique is fused with Helmert transformation to refine the final location estimation with the help of anchors. The simulation results show that the root means square positioning error (RMSPE) of the proposed technique is more robust and accurate compared to baseline and manifold regularization.

In this paper, we propose a new regularized robust estimation approach based on the robust τ -estimator applied to linear ill-posed problems in the presence of noise outliers. Additionally, we introduce a new approach to obtain the optimal regularization parameter for the proposed robust estimator by using tools from random matrix theory. Simulation results demonstrate that the proposed approach with its automated regularization parameter selection outperforms a set of benchmark methods.

-Non-orthogonal multiple access (NOMA) is a spectrum reutilization technique that superposes messages in the power domain allowing multiple users to be served in the same time-frequency resource block. Successive interference cancellation (SIC) techniques are used for decoding NOMA. A network model is considered where Poisson distributed base stations transmit toNNOMA users each. We present a signal-to-interference-and-noise-ratio analysis for the coverage of the typical user. Due to SIC, coverage implies the ability to decode the messages of all weaker users in the SIC chain. An efficient algorithm for finding a feasible resource allocation that maximizes the cell sum rate ℛtotsubject to a minimum rate constraintTon the individual users is provided for generalN. We show the existence of an optimumNthat maximizes ℛtotgiven a set of network parameters. We also show that NOMA outperforms orthogonal multiple access if the residual intracell interference is below a certain level. The results highlight the importance in choosing network parametersNandTto balance ℛtotand fairness.

In this work, we focus on the uplink (UL) of a single-cell large-scale multi-user MIMO system where mono-antenna users communicate with a BS equipped with antennas. This latter estimates the Ricean correlated channels based on training symbols and uses maximum-ratio-combining or linear-minimum-mean-square-error receivers for signal processing. Assuming a fixed number of users with an asymptotic antenna regime, we derive closed-form approximations of the achievable UL rates as a function of the system's parameters, the Ricean factors and the training sequence's length. Accordingly, these expressions are instructive in how the aforementioned parameters impact the performances. Plus, a different processing approach using only the statistics of the channels is investigated and found to be a judicious choice when the Ricean factor is high enough. Although our analytical results are based on a large antenna-limit, we show by simulations that they provide very accurate approximations even for finite system dimensions.

A generalized cooperative spectrum sharing (GCSS) scheme for machine-to-machine (M2M) communications is pro- posed in internet-of-things (IoT) systems. The proposed scheme makes use of the existence of massive connected machines to overcome the challenges of spectrum scarcity while avoiding interference and meeting the green requirements of IoT systems. The cooperative proposed scheme extends the coverage of M2M wireless network as well as increasing the throughput while reducing the energy consumption of the connected low power devices. The performance of the GCSS scheme is evaluated analytically by the outage performance by deriving the outage probability. Furthermore, a numerical simulations are presented to support the theoretical findings.

Aerosol Transmission is one of the major spread mechanism for diseases and is responsible for transmission of virus over long distances. The advancement in nanotechnology has resulted in sensors and systems that allow us to deal with nanosized biological entities such as virus and bacteria. In this work, the idea of viewing virus transmission through aerosols and their transport as a molecular communication problem is introduced. In such problems one has little or no control over transmission, however, a robust receiver can be designed using nano-biosensors for information extraction. Thus, the objective of this work is to treat viral aerosol spread as a blind communication problem and present a mathematical model for it. Specifically, we study the virus transmission from an engineering perspective and derive an end-to-end mathematical model for virus transmission in the atmosphere. The receiver architecture composed of air sampler and Silicon Nanowire field effect transistor is also discussed. Furthermore, a detection problem is formulated and simulation results are reported that justify the feasibility of such setups in bio-monitoring applications.

This paper focuses on performance analysis of millimeter wave (mmWave) communications. We investigate how the interference behaves in the outdoor mesh network operating at 60-GHz when blockage and shadowing are present, using probability of collision as a metric, under both protocol model and physical model. In contrast with reported results in mmWave mesh network at 60-GHz advocating that the interference has only a marginal effect, our results show that for a short-range link of 100 m, the collision probability gets considerably large (beyond 0.1) at signal-to-interference-plus-noise ratio (SINR) of interest. Compensation or compromise should be made in order to maintain a low probability of collision, either by reducing transmitter node density which is at the cost of network connectivity, or by switching to a compact linear antenna array with more flat-top elements, which places a more stringent requirement in device integration techniques.

In this paper, we propose a joint cell formation and power allocation algorithms for energy efficiency (EE) maximization in visible light communication (VLC) networks. Unlike the previous works, we show that the cell formation and power allocation are interlinked problems and they should be solved jointly. We start by proposing a new algorithm for users clustering and then associating all the access points (APs) to the clustered users based on a proposed metric. Under the assumption that the vectored transmission is applied at each formed cell, we solve an optimization problem that aims to maximize the EE by allocating the powers for the users with quality of service (QoS) constraints. Then, we propose an algorithm that jointly allocates the power and decides which APs must participate in communication and which ones must be switched off. The numerical results demonstrate that the proposed algorithms significantly improve the EE compared to the traditional methods and algorithms.

The capacity of the intensity-modulation direct-detection (IM-DD) multiple-input multiple-output channel is studied under average and peak intensity constraints. We focus on the low signal-to-noise ratio (SNR) regime where the constraints proportionally vanish, or alternatively, where the noise power is large. A general upper bound on the capacity of this channel is derived. Then, this bound is shown to be tight at low SNR, where it coincides with the achievable rate of (i) on-off keying (OOK), spatial repetition coding, and maximum-ratio combining under individual average constraint and (ii) OOK with maximally-correlated inputs under a sum average constraint. This leads to a low-SNR capacity characterization of the channel.

This paper focuses on studying the performance of general regularized discriminant analysis (RDA) classifiers based on the Gaussian mixture model with different means and covariances. RDA offers a rich class of regularization options, covering as special cases the regularized linear discriminant analysis (RLDA) and the regularized quadratic discriminant analysis (RQDA) classifiers. Based on fundamental results from random matrix theory, we analyze RDA under the double asymptotic regime where the data dimension and the training size both increase in a proportional way. Under the double asymptotic regime and some mild assumptions, we show that the asymptotic classification error converges to a deterministic quantity that only depends on the data statistical parameters and dimensions. This result can be leveraged to select the optimal parameters that minimize the classification error, thus yielding the optimal classifier. Numerical results are provided to validate our theoretical findings on synthetic data showing high accuracy of our derivations.

We analyze the asymptotic average and effective throughputs of a multiuser diversity scheme for a secondary multiuser network consisting of multiple secondary users (transmitters) and one secondary receiver. Considering a transmit power adaptation strategy at the secondary users to satisfy the instantaneous interference constraint at the primary receiver, the secondary receiver selects the k-th best secondary user for transmission, namely, the one with the k-th highest signal-to-noise ratio (SNR). We use extreme value theory to show that the k-th highest SNR converges uniformly in distribution to an inverse gamma random variable for a fixed k and large number of secondary users. We use this result to derive closed-form asymptotic expressions for the average and effective throughputs of the k-th best secondary user.

Cloud radio access network (CRAN) emerges as a promising architecture for large-scale interference management. This paper addresses the benefit of one particular type of coordinated resource allocation in CRANs through the combined effect of joint scheduling and beamforming. Consider the downlink of a CRAN where the cloud is connected to several remote radio heads (RRHs), each equipped with multiple antennas. The transmit frame of every RRH is formed by several radio resource blocks (RRBs), each capable of serving multiple single-antenna users via spatial multiplexing using beamforming. The paper focuses on the problem of maximizing the network-wide weighted sum-rate by jointly determining the set of scheduled users at each RRB, and their corresponding beamforming vectors. The main contribution of the paper is to solve such a mixed discrete-continuous optimization problem using a graph-theoretical based approach. The paper introduces the joint scheduling and beamforming graph, wherein each independent set accounts for a feasible schedule and feasible beamforming vectors. Afterward, the joint scheduling and beamforming problem is shown to be equivalent to a maximum independent set problem in the proposed graph. Simulation results suggest that the proposed joint solution provides appreciable performance improvements as compared to the classical iterative approach.

In this paper, we consider the average secrecy capacity of a single-input multiple-output (SIMO) system with channel estimation error over generalized fading channels in the presence of co-channel interference and an eavesdropper. More specifically, we assume that selection combining is applied at both the receiver and eavesdropper. We first present statistical analysis for the signal-to-interference plus noise ratio (SINR). Then, closed-form expressions for the average secrecy capacity are derived for two special cases: Rayleigh fading and no interference. For the general case, we focus on the asymptotic secrecy capacity analysis. Finally, numerical results are provided for average secrecy capacity with with different values of NDand NE. Results show that the scaling law of the secrecy capacity is 1/L log2(ND/NE).

This paper studies the performance of two-way multiuser mixed radio frequency/free space optical (RF/FSO) relay networks with opportunistic user scheduling1and asymmetric fading channels. The considered system consists of multiple users communicating with a destination node through a decode-and forward (DF) relay in a two-way fashion. The links between the users and relay are assumed to be Rayleigh distributed RF channels, while the link between the relay and destination is Gamma-Gamma fading FSO channel. First, exact closed-form and asymptotic (high signal-to-noise ratio (SNR)) expressions are derived for the outage probability. Then, the asymptotic results are used to conduct a power optimization algorithm where exact expressions for the optimal transmission powers are provided. Additionally, performance comparisons between the considered two-way relaying (TWR) and one-way relaying (OWR) schemes under different network parameters are provided and discussed. The results show that the opportunistic user scheduling in TWR networks does not affect the network diversity order, but it enhances the system coding gain. Additionally, it is found that severe pointing error may result in a total service blockage. Additionally, the proposed power allocation scheme is found to enhance the network outage performance significantly compared to the scheme of equal power allocation.

In this paper, we propose a modified time-switching relaying (TSR) protocol for dual-hop relay networks with simultaneous wireless information and power transfer (SWIPT) technique. We study the outage performance of the proposed TSR protocol and compare it with the conventional TSR and power-splitting relaying (PSR) protocols. A unified analytical expression is derived for the outage probability, in addition to studying the performance at high signal-to-noise ratio (SNR) values where a unified approximate expression for the outage probability is provided and analyzed in terms of diversity order and coding gain. The results show that the proposed TSR protocol outperforms the conventional TSR protocol existing in literature. Also, findings illustrate that applying the SWIPT technique in multi-destination relay networks results in a unity diversity order.

Traditional wired data center networks (DCNs) suffer from cabling complexity, lack flexibility, and are limited by the speed of digital switches. In this paper, we alternatively develop a top-down traffic grooming (TG) approach for design and provisioning of optical wireless DCNs. While switches are modeled as hybrid opto-electronic cross-connects, links are modeled as wavelength division multiplexing (WDM) capable free-space optic (FSO) channels. Using the standard TG terminology, we formulate the optimal mixed integer linear problem considering the virtual topology, flow conversation, connection topology, non-bifurcation, and capacity constraints. Thereafter, we develop a fast sub-optimal solution where mice flows (MFs) are groomed and forwarded on predetermined rack-to-rack (R2R) lightpaths. On the other hand, elephant flows (EFs) are forwarded over dedicated server-to-server (S2S) express lightpaths whose routes and capacity are dynamically determined based on wavelength and capacity availability. Emulation results show that proposed models and algorithms provide a significant throughput improvement upon traditional DCNs for both MFs and EFs.

Underwater optical wireless networks (UOWNs) have recently gained attention as an emerging solution to the growing demand for broadband connectivity. Even though it is an alternative to low-bandwidth and high-latency acoustic systems, underwater optical wireless communications (UOWC) suffers from limited range and requires effective multi-hop solutions. Therefore, this paper analyzes and compares the performance of multihop underwater optical wireless networks under two relaying schemes: Decode & Forward (DF) and Amplify & Forward (AF). Noting that nodes close to the surface sink (SS) are required to relay more information, these nodes are enabled for retro-reflective communication, where SS illuminates these nodes with a continuous-wave beam which is then modulated and reflected back to the SS receivers. Accordingly, we analytically evaluate important performance metrics including end-to-end bit error rate, achievable multihop data rates, and communication ranges between node pairs. Thereafter, we develop routing algorithms for DF and AF schemes in order to maximize the end-to-end performance metrics. Numerical results demonstrate that multi-hop transmission can significantly enhance the network performance and expand the communication range.

In this paper, we are interested in determining the cumulative distribution function of the sum of α - μ random variables in the setting of rare event simulations. To this end, we present an efficient importance sampling estimator. The main result of this work is the bounded relative error property of the proposed estimator. This result is used to accurately estimate the outage probability of multibranch maximum ratio combining and equal gain diversity receivers over α-μ fading channels. Selected numerical simulations are discussed to show the robustness of our estimator compared to naive Monte Carlo.

We study the sum throughput of multiple-input-multiple-output (MIMO) networks in the cases with large but finite number of transmit and receive data terminals. We develop an efficient scheduling scheme using genetic algorithms (GAs), and evaluate the effect of various parameters, such as channel/precoding models, number of antennas/users, scheduling costs and power amplifiers efficiency, on the system performance. Also, considering continuous and bursty communication scenarios with different users' data request probabilities, we derive closed-form expressions for the maximum achievable throughput of the MIMO networks using optimal schedulers. As we show, our proposed GA-based scheduler reaches (almost) the same throughput as in the exhaustive search-based optimal scheduler, with substantially less implementation complexity. Also, the power amplifiers inefficiency affect the network throughput significantly. For instance, consider a MIMO setup with a 40-antenna base station, 60 users, total consumed power of 26 dB, continuous communications and the typical parameter settings of the power amplifiers. Then, the network throughput decreases by 50% when the power amplifiers efficiency reduces from 75% to 25%.

The 3GPP Release-13 has recently introduced full-dimension multiple-input multiple-output (FD-MIMO) technology as a practical way to deploy massive MIMO arrays within feasible base station (BS) form factors through the use of active antenna systems with two-dimensional (2D) planar array structures. The 2D arrangement of antenna elements, where the elements in each antenna port are fed with downtilt weights, allows for adaptive electronic beamforming in the elevation as well as the conventional azimuth dimensions. This work focuses on the previously unaddressed problem of determining the optimal downtilt weight vectors for the antenna ports in each cell of a multi-cell multi-user system. The optimization criterion is to maximize the minimum signal to intra-cell interference ratio within a cell while constraining the inter-cell interference leakage. The quasi-optimal weight vectors are obtained through the application of semi-definite relaxation and Dinkelbach's method. The proposed algorithm performs better than the existing approximate schemes even under the effects of pilot contamination.

In this paper, we propose the new design of pyramidal angle diversity receiver with a help of mirror in indoor multiple-input multiple output (MIMO) visible light communication (VLC) systems. Embedding the mirror around the photodetector (PD) planes can help compensate for the high correlation of MIMO-VLC channel matrix. The channel gain of newly proposed receiver structure is derived based on the geometry of transmitter/receiver and azimuth/elevation angles of PD and mirror. From the selected numerical results, we see that the proposed receiver structure can further improve the channel capacity of MIMO-VLC systems when the receiver is located in the region where the channel correlation can dominantly degrade the channel capacity.

The feasibility of resource allocation schemes is a major challenge in the practical implementation of wireless systems. Decentralized resource allocation is one such feasibility requirement, as it yields optimized schemes when no centralized processing is possible. This paper evaluates one particular type of decentralized interference management schemes in a full-duplex (FD) cellular network. Consider an FD cellular network that allows flexible partial overlap between the uplink (UL) and downlink (DL) frequency channel. The channel overlap generates self-interference, cross-mode interference and intra-mode interference. The performance of the system becomes, therefore, a function of the powers allocated at each base station (BS)-user pair, and the fraction of spectrum overlap between the DL and UL of each communicating pair. The paper considers the problem of maximizing a network-wide utility function subject to power constraints, so as to appropriately fine-tune the spectrum overlap and the transmit powers in a distributed manner across the network. The paper proposes solving the problem using the externalities approach, which can be implemented in a distributed fashion with a reasonable amount of information exchange between the network entities. The paper further examines three types of utility functions: the sum of log-rate, the network spectral efficiency, and the energy efficiency, so as to clearly describe the trade-off between the achieved rate, the consumed power, and UL/DL fairness in the FD setup. Simulation results highlight the convergence of the proposed distributed algorithm, and illustrate its performance under different utilities as compared to centralized solutions for various networks scenarios.

Visible light communication (VLC) is recognized as a promising technology to complement existing wireless communication systems due to its main advantages such as ease of deployment, low cost and large unlicensed bandwidth. This paper considers the precoding design for a multi-user multiple-input-single-output VLC system. Two major concerns need to be considered while solving such a problem. The first one is related to the inter-user interference, basically inherent to our consideration of a multi-user system, while the second results from the users' mobility, causing imperfect channel estimates. To address both concerns, we propose robust precoding designs that solve both max-min SINR and minimal illumination level problems. The first problem allows users' rates maximization while ensuring fairness and the second problem decides the feasibility of a certain set of target SINRs. The proposed robust designs are studied under different conditions, and are shown to achieve a high gain over their nonrobust counterparts.

The low-SNR asymptotic capacity of the multiple-input multiple-output (MIMO) optical intensity channel is studied under both average and peak intensity constraints. We focus on low SNR, which can be modeled as the scenario where both constraints proportionally vanish, or where the peak constraint is held constant while the average constraint vanishes. A capacity upper bound is derived and is shown to be tight at low SNR under both scenarios. The capacity achieving input distribution at low SNR is shown to be a maximally correlated vector-binary input distribution. Consequently, the low-SNR capacity of the channel is characterized. As a byproduct, it is shown that for a channel with peak intensity constraints only, or with peak intensity constraints and individual (per aperture) average intensity constraints, a simple scheme composed of coded ON-OFF keying, spatial repetition, and maximum-ratio combining is optimal at low SNR.

An optical wireless intensity-modulation direct-detection multiple-input multiple-output communication system is considered. The performance of $M$ -PAM rate-1 direct current offset space-time block codes is studied in terms of average worst-case pairwise error probability (WC-PEP) in quasi-static channels. It is shown that within this code class, the average WC-PEP is minimized by repetition coding (RC) under both electrical and optical individual power constraints, irrespective of channel statistics. This agrees with previously published results related to ON-OFF keying RC. This is further extended to sum power constraints, where it is shown that spatial beamforming minimizes the average WC-PEP within this code class, which simplifies to RC if the channel matrix has independent and indentically distributed columns and a sum electrical power constraint. Under a sum optical power constraint, this also holds true at high signal-to-noise ratio (SNR), but not at low SNR. Generally, the time dimension of this code class is redundant from an average WC-PEP perspective. Numerical results are provided to support the theoretical findings and to show that the average WC-PEP leads to a good approximation of the actual error probability at high SNR.

Recent studies on cloud-radio access networks assume either signal-level or scheduling-level coordination. This paper considers a hybrid coordinated scheme as a means to benefit from both policies. Consider the downlink of a multi-cloud radio access network, where each cloud is connected to several base-stations (BSs) via high capacity links and, therefore, allows for joint signal processing within the cloud transmission. Across the multiple clouds, however, only scheduling-level coordination is permitted, as low levels of backhaul communication are feasible. The frame structure of every BS is composed of various time/frequency blocks, called power-zones (PZs), which are maintained at a fixed power level. This paper addresses the problem of maximizing a network-wide utility by associating users to clouds and scheduling them to the PZs, under the practical constraints that each user is scheduled to a single cloud at most, but possibly to many BSs within the cloud, and can be served by one or more distinct PZs within the BSs' frame. This paper solves the problem using graph theory techniques by constructing the conflict graph. The considered scheduling problem is, then, shown to be equivalent to a maximum-weight independent set problem in the constructed graph, which can be solved using efficient techniques. This paper then proposes solving the problem using both optimal and heuristic algorithms that can be implemented in a distributed fashion across the network. The proposed distributed algorithms rely on the well-chosen structure of the constructed conflict graph utilized to solve the maximum-weight independent set problem. Simulation results suggest that the proposed optimal and heuristic hybrid scheduling strategies provide appreciable gain as compared with the scheduling-level coordinated networks, with a negligible degradation to signal-level coordination.

We study the potential employment of improper Gaussian signaling (IGS) in full-duplex relaying (FDR) with non-negligible residual self-interference (RSI) under Nakagami-m fading. IGS is recently shown to outperform traditional proper Gaussian signaling (PGS) in several interference-limited settings. In this paper, IGS is employed as an attempt to alleviate RSI. We use two performance metrics, namely, the outage probability and the ergodic rate. First, we provide upper and lower bounds for the system performance in terms of the relay transmit power and circularity coefficient, a measure of the signal impropriety. Then, we numerically optimize the relay signal parameters based only on the channel statistics to improve the system performance. Based on the analysis, IGS allows FDR to operate even with high RSI. The results show that IGS can leverage higher power budgets to enhance the performance, meanwhile it relieves RSI impact via tuning the signal impropriety. Interestingly, 1-D optimization of the circularity coefficient, with maximum relay power, offers a similar performance as the joint optimization, which reduces the optimization complexity. From a throughput standpoint, it is shown that IGS-FDR can outperform not only PGS-FDR, but also half-duplex relaying with/without maximum ratio combining over certain regions of the target source rate.

We analyze the outage and throughput performance of full-duplex relay selection (FDRS) in underlay cognitive networks. Contrary to half-duplex relaying, full-duplex relaying (FDR) enables simultaneous listening/forwarding at the secondary relay(s), thereby allowing for higher spectral efficiency. However, due to simultaneous source/relay transmissions in FDR, the superimposed signal at the primary receiver should now satisfy the existing interference constraint, which can considerably limit the secondary network throughput. In this regard, FDRS can offer an adequate solution to boost the secondary throughput while satisfying the imposed interference limit. We first analyze the performance of opportunistic FDRS with residual self-interference (RSI) by deriving the exact cumulative distribution function of its end-to-end signal-to-interference-plus-noise ratio under Nakagami-m fading. We also evaluate the offered diversity gain of relay selection for different full-duplex cooperation schemes in the presence/absence of a direct source-destination link under Rayleigh fading. When the RSI link gain model is sublinear in the relay power, which agrees with recent research findings, we show that remarkable diversity can be recovered even in the presence of an interfering direct link. Second, we evaluate the end-to-end performance of FDRS with interference constraints due to the presence of a primary receiver. Finally, the presented theoretical findings are verified by numerical simulations.

We analyze a secure dual-hop mixed radio frequency-free space optical (RF-FSO) downlink simultaneous wireless information and power transfer system. The FSO link and all RF links experience Gamma-Gamma, independent, and identical Nakagami-m fading, respectively. We analyze the effects of atmospheric turbulence, pointing error, detection technology, path loss, and energy harvesting on secrecy performance. Signalto-noise ratios at both the legitimate and illegitimate receivers are not independent since they are both simultaneously influenced by the FSO link. We derive the closed-form expression of the secrecy outage probability (SOP) as well as the asymptotic result for SOP when signal-to-noise ratios at relay and legitimate destinations tend to infinity. Monte-Carlo simulations are performed to verify the accuracy of our analysis. The results show that the secrecy diversity order (SDO) depends on the fading parameter of the relay-destination link and the number of the destination's antennas. In addition, the SDO also depends on the fading parameters, the pointing error parameter, and the detection type of the FSO link.

This paper discusses full-dimension multiple-input-multiple-output (FD-MIMO) technology, which is currently an active area of research and standardization in wireless communications for evolution toward Fifth Generation (5G) cellular systems. FD-MIMO utilizes an active antenna system (AAS) with a 2-D planar array structure that not only allows a large number of antenna elements to be packed within feasible base station form factors, but also provides the ability of adaptive electronic beamforming in the 3-D space. However, the compact structure of large-scale planar arrays drastically increases the spatial correlation in FD-MIMO systems. In order to account for its effects, the generalized spatial correlation functions for channels constituted by individual elements and overall antenna ports in the AAS are derived. Exploiting the quasi-static channel covariance matrices of users, the problem of determining the optimal downtilt weight vector for antenna ports, which maximizes the minimum signal-to-interference ratio of a multi-user multiple-input-single-output system, is formulated as a fractional optimization problem. A quasi-optimal solution is obtained through the application of semi-definite relaxation and Dinkelbach's method. Finally, the user-group specific elevation beamforming scenario is devised, which offers significant performance gains as confirmed through simulations. These results have direct application in the analysis of 5G FD-MIMO systems.

This paper develops an interference aware design for cooperative hybrid automatic repeat request (HARQ)-assisted non-orthogonal multiple access (NOMA) scheme for large-scale device-to-device (D2D) networks. Specifically, interference aware rate selection and power allocation are considered to maximize long term average throughput (LTAT) and area spectral efficiency. The design framework is based on stochastic geometry that jointly accounts for the spatial interference correlation at the NOMA receivers as well as the temporal interference correlation across HARQ transmissions. It is found that ignoring the effect of the aggregate interference, or overlooking the spatial and temporal correlation in interference, highly overestimates the NOMA performance and produces misleading design insights. An interference oblivious selection for the power and/or transmission rates leads to violating the network outage constraints. To this end, the results demonstrate the effectiveness of NOMA transmission and manifest the importance of the cooperative HARQ to combat the negative effect of the network aggregate interference. For instance, comparing to the non-cooperative HARQ-assisted NOMA, the proposed scheme can yield an outage probability reduction by 21%. Furthermore, an interference aware optimal design that maximizes the LTAT given outage constraints leads to 17% throughput improvement over HARQ-assisted orthogonal multiple access scheme.

The capacity of the intensity modulation direct detection multiple-input-multiple-output channel is studied. Therein, the nonnegativity constraint of the transmit signal limits the applicability of classical schemes, including precoding. Thus, new ways are required for deriving capacity bounds for this channel. To this end, capacity lower bounds are developed in this paper by deriving the achievable rates of two precoding-free schemes: channel inversion and orthogonal-upper triangular matrix product decomposition. The achievable rate of a dc-offset singular-value decomposition-based scheme is also derived as a benchmark. Then, capacity upper bounds are derived and compared against the lower bounds. As a result, the capacity at high signal-to-noise ratio (SNR) is characterized for the case where the number of transmit apertures is not larger than the number of receive apertures, and is shown to be achievable by the QR decomposition scheme. This is shown for a channel with average intensity or peak intensity constraints. Under both constraints, the high-SNR capacity is approximated within a small gap. Extensions to a channel with more transmit apertures than receive apertures are discussed, and capacity bounds for this case are derived.

Unmanned aerial vehicles (UAVs) have found many important applications in communications. They can serve as either aerial base stations or mobile relays to improve the quality of services. In this paper, we study the use of multiple UAVs in relaying. Considering two typical uses of multiple UAVs as relays that form either a single multi-hop link or multiple dual-hop links, we first optimize the placement of the UAVs by maximizing the end-to-end signal-to-noise ratio for three useful channel models and two common relaying protocols. Based on the optimum placement, the two relaying setups are then compared in terms of outage and bit error rate. Numerical results show that the dual-hop multi-link option is better than the multi-hop single link option when the air-to-ground path loss parameters depend on the UAV positions. Otherwise, the dual-hop option is only better when the source-to-destination distance is small. Also, decode-and-forward UAVs provide better performances than the amplify-and-forward UAVs. The investigation also reveals the effects of important system parameters on the optimum UAV positions and relaying performances to provide useful guidelines.

This paper considers the problem of reducing the broadcast decoding delay of wireless networks using instantly decodable network coding- based device-to-device communications. In contrast with the previous works that assume a fully connected network, this paper investigates a partially connected configuration in which multiple devices are allowed to transmit simultaneously. To that end, different events occurring at each device are identified so as to derive an expression for the probability distribution of the decoding delay. Afterward, the joint optimization problem over the set of transmitting devices and packet combination of each is formulated. The optimal solution of the joint optimization problem is derived using a graph-theoretic approach by introducing the cooperation graph in which each vertex represents a transmitting device with a weight translating its contribution to the network. This paper solves the problem by reformulating it as a maximum weight clique problem which can efficiently be solved. Numerical results suggest that the proposed solution outperforms state-of-the-art schemes and provides significant gain, especially for poorly connected networks.

This paper considers the problem of selecting a set of k measurements from n available sensor observations. The selected measurements should minimize a certain error function assessing the error in estimating a certain m dimensional parameter vector. The exhaustive search inspecting each of the (n) possible choices would require very high computational k complexity and as such is not practical for large n and k. Alternative methods with low complexity have recently been investigated but their main drawbacks are that they require perfect knowledge of the measurement matrix and they need to be applied at the pace of change of the measurement matrix. To overcome these issues, we consider the asymptotic regime in which k, n, and m grow large at the same pace. Tools from random matrix theory are then used to approximate in closed-form the most important error measures that are commonly used. The asymptotic approximations are then leveraged to properly select k measurements exhibiting low values for the asymptotic error measures. Two heuristic algorithms are proposed. The first one merely consists in applying the convex optimization artifice to the asymptotic error measure. The second algorithm is a low-complexity greedy algorithm that attempts to look for a sufficiently good solution for the original minimization problem. The greedy algorithm can be applied to both the exact and the asymptotic error measures and can be thus implemented in blind and channel-aware fashions. We present two potential applications where the proposed algorithms can be used, namely, antenna selection for uplink transmissions in large scale multiuser systems and sensor selection for wireless sensor networks. Numerical results are also presented and sustain the efficiency of the proposed blind methods in reaching the performances of channel-aware algorithms.

Cache-enabled small base station (SBS) densification is foreseen as a key component of 5G cellular networks. This architecture enables storing popular files at the network edge (i.e., SBS caches), which empowers local communication and alleviates traffic congestion at the core/backhaul network. This paper develops a mathematical framework, based on stochastic geometry, to characterize the hit probability in multi-channel cache-enabled 5G networks with both unicast/multicast capabilities and opportunistic spectrum access. To this end, we first derive the hit probability by characterizing the opportunistic spectrum access success probabilities, service distance distributions, and coverage probabilities. An optimization framework for file caching is then developed to maximize the hit probability. To this end, a simple concave approximation for the hit probability is proposed, which highly reduces the optimization complexity and leads to a closed-form solution. The sub-optimal solution is benchmarked against two widely employed caching distribution schemes, namely, uniform and Zipf caching, through numerical results and extensive simulations. It is shown that the caching strategy should be adapted to the network parameters and capabilities. For instance, diversifying file caching according to the Zipf distribution is better in multicast systems with large number of channels. However, when the number of channels is low and/or the network is restricted to unicast transmissions, it is better to confine caching to the most popular files only.

The Internet-of-Things (IoT) is the paradigm where anything will be connected. There are two main approaches to handle the surge in uplink (UL) traffic that the IoT is expected to generate, namely, scheduled UL (SC-UL) and random access uplink (RA-UL) transmissions. SC-UL is perceived as a viable tool to control quality-of-service levels while entailing some overhead in the scheduling request prior to any UL transmission. On the other hand, RA-UL is a simple single-phase transmission strategy. While this obviously eliminates scheduling overheads, very little is known about the scalability of RA-UL. At this critical junction, there is a dire need to analyze the scalability of these two paradigms. To that end, this paper develops a spatiotemporal mathematical framework to analyze and assess the performance of SC-UL and RA-UL. The developed paradigm jointly utilizes stochastic geometry and queuing theory. Based on such a framework, we show that the answer to the scheduling versus random access paradox actually depends on the operational scenario. Particularly, the RA-UL scheme offers low access delays but suffers from limited scalability, i.e., cannot support a large number of IoT devices. On the other hand, SC-UL transmission is better suited for higher device intensities and traffic rates.

In this paper, we are interested in determining the cumulative distribution function of the sum of α - μ, κ - μ, and η - μ random variables in the setting of rare event simulations. To this end, we present a simple and efficient importance sampling approach. The main result of this work is the bounded relative error property of the proposed estimators. Capitalizing on this result, we accurately estimate the outage probability of multibranch maximum ratio combining and equal gain diversity receivers over α - μ, κ - μ, and η - μ fading channels. Selected numerical simulations are discussed to show the robustness of our estimators compared with naive Monte Carlo estimators.

We consider the problem of evaluating the cumulative distribution function (CDF) of the sum of order statistics, which serves to compute outage probability (OP) values at the output of generalized selection combining receivers. Generally, closed-form expressions of the CDF of the sum of order statistics are unavailable for many practical distributions. Moreover, the naive Monte Carlo (MC) method requires a substantial computational effort when the probability of interest is sufficiently small. In the region of small OP values, we instead propose two effective variance reduction techniques that yield a reliable estimate of the CDF with small computing cost. The first estimator, which can be viewed as an importance sampling estimator, has bounded relative error under a certain assumption that is shown to hold for most of the challenging distributions. A possible improvement of this estimator is then proposed for the Pareto and the Weibull cases. The second is a conditional MC estimator that achieves the bounded relative error property for the generalized Gamma case and the logarithmic efficiency for the Log-normal case. Finally, the efficiency of these estimators is compared via various numerical simulations.

In this paper, we investigate the transmission time of a large amount of data over fading wireless channel with adaptive modulation and coding (AMC). Unlike traditional transmission systems, where the transmission time of a fixed amount of data is typically regarded as a constant, the transmission time with AMC becomes a random variable, as the transmission rate varies with the fading channel condition. To facilitate the design and optimization of wireless transmission schemes for big data applications, we present an analytical framework to determine statistical characterizations for the transmission time of big data with AMC. In particular, we derive the exact statistics of transmission time over block fading channels. The probability mass function and the cumulative distribution function of transmission time are obtained for both slow and fast fading scenarios. We further extend our analysis to the Markov channel, where transmission time becomes the sum of a sequence of exponentially distributed time slots. Analytical expression for the probability density function of transmission time is derived for both fast fading and slow fading scenarios. These analytical results are essential to the optimal design and the performance analysis of future wireless transmission systems for big data applications.

Non-orthogonal multiple access (NOMA) has recently drawn attentions on its ability to fairly serve multiple users on the same radio resource with a desirable performance. However, achievable NOMA gain is primarily limited by channel gain disparity and successive interference cancellation (SIC) receiver characteristics. Accordingly, we introduce an imperfect SIC receiver model considering the power disparity and sensitivity constraints, delay tolerance, and residual interference due to detection and estimation errors. Then, a generic cluster formation (CF) and Power-Bandwidth Allocation (PBA) is formulated as a mixed-integer non-linear programming (MINLP) problem for downlink (DL) heterogeneous networks (HetNets). After dividing the MINLP problem into mixed-integer and non-linear sub- problems, we first transform CF into a multi-partite matching problem, which is solved sequentially using bi-partite matching techniques. For sumrate maximization, max-min fairness, and energy & spectrum efficiency objectives, we secondly put highly non-convex joint PBA into a convex form using geometric programming (GP). Extensive simulations unleash the potential of NOMA to handle large number of users, traffic offloading, and user fairness.

Cache-enabled small base station (SBS) densification is foreseen as a key component of 5G cellular networks. This architecture enables storing popular files at the network edge (i.e., SBS caches), which empowers local communication and alleviates traffic congestions at the core/backhaul network. This paper develops a mathematical framework, based on stochastic geometry, to characterize the hit probability of a cache-enabled multicast 5G network with SBS multi-channel capabilities and opportunistic spectrum access. To this end, we first derive the hit probability by characterizing opportunistic spectrum access success probabilities, service distance distributions, and coverage probabilities. The optimal caching distribution to maximize the hit probability is then computed. The performance and trade-offs of the derived optimal caching distributions are then assessed and compared with two widely employed caching distribution schemes, namely uniform and Zipf caching, through numerical results and extensive simulations. It is shown that the Zipf caching almost optimal only in scenarios with large number of available channels and large cache sizes.

In one of the several manifestations, 5G networks are required to accommodate a massive number of devices; an order of magnitude compared to today's networks. At the same time, 5G networks will have to observe stringent latency constraints. To that end, one problem that is posed as a potential showstopper is extreme congestion over random access resources in the cellular uplink. Indeed, congestion drags along delay problems. In this paper, the use of network-orchestrated device-to-device (D2D) relaying is advocated for the mitigation of random access congestion. In particular, it is shown that D2D relaying reduces access delay only at high device densities but is rather an overkill for lower densities. For the sake of an objective evaluation, the overhead of device clustering protocols must be accounted for. As such, this paper provides protocol designers with benchmarks on how much protocol overhead can be tolerated. The feasibility of D2D relaying is demonstrated via extensive system-level simulations run on a super computer and based on a foundation of real-life networks parameters.

Improper Gaussian signaling (IGS) has proven its ability in improving the performance of underlay and overlay cognitive radio paradigms. In this paper, the interweave cognitive radio paradigm is studied when the cognitive user employs IGS. The instantaneous achievable rate performance of both the primary and secondary users are analyzed for specific secondary user sensing and detection capabilities. Next, the IGS scheme is optimized to maximize the achievable rate secondary user while satisfying a target minimum rate requirement for the primary user. Proper Gaussian signaling (PGS) scheme design is also derived to be used as benchmark of the IGS scheme design. Finally, different numerical results are introduced to show the gain reaped from adopting IGS over PGS under different system parameters. The main advantage of employing IGS is observed at low sensing and detection capabilities of the SU, lower PU direct link and higher SU interference on the PU side.

In this work, we investigate the ergodic secrecy rate region of a block-fading spectrum-sharing system, where a D2D communication is underlying a cellular channel. We consider that both the primary and the secondary transmissions require their respective transmitted messages to be kept secret from a common eavesdropper under a joint secrecy constraint. The presented results are for three different scenarios, each corresponding to a particular requirement of the cellular system. First, we consider the case of a fair cellular system, and we show that the impact of jointly securing the transmissions can be balanced between the primary and the secondary systems. The second scenario examines the case when the primary network is demanding and requires the secondary transmission to be at a rate that is decodable by the primary receiver, while the last scenario assumes a joint transmission of artificial noise by the primary and the secondary transmitters. For each scenario, we present an achievable ergodic secrecy rate region that can be used as an indicator for the cellular and the D2D systems to agree under which terms the spectrum will be shared.

In this paper, we accurately model the transceiver hardware impairments (HWIs) of multiple-input multiple-output (MIMO) systems considering different HWI stages at transmitter and receiver. The proposed novel statistical model shows that transceiver HWIs transform the transmitted symmetric signal to asymmetric one. Moreover, it shows that the aggregate self-interference has asymmetric characteristics. Therefore, we propose improper Gaussian signaling (IGS) for transmission in order to improve the achievable rate performance. IGS is considered as a general signaling scheme which includes the proper Gaussian signaling (PGS) as a special case. Thus, IGS has additional design parameters which enable it to mitigate the HWI self-interference. As a case study, we analyze the achievable rate performance of single-input multiple-output systems with linear and selection combiner. Furthermore, we optimize the IGS statistical characteristics for interference alignment. This improves the achievable rate performance as compared to the PGS, which is validated through numerical results.

The conventional minimum Euclidean distance (MED) receiver design is based on the assumption of ideal hardware transceivers and proper Gaussian noise in communication systems. Throughout this study, an accurate statistical model of various hardware impairments (HWIs) is presented. Then, an optimal maximum likelihood (ML) receiver is derived considering the distinct characteristics of the HWIs comprised of additive improper Gaussian noise and signal distortion. Next, the average error probability performance of the proposed optimal ML receiver is analyzed and tight bounds are derived. Finally, different numerical and simulation results are presented to support the superiority of the proposed ML receiver over MED receiver and the tightness of the derived bounds.

In this paper, we propose and study a new joint load balancing (LB) and power allocation (PA) scheme for a hybrid visible light communication (VLC) and radio frequency (RF) system consisting of one RF\ access point (AP) and multiple VLC\ APs. An iterative algorithm is proposed to distribute the users on the APs and distribute the powers of these APs on their users. In PA subproblem, an optimization problem is formulated to allocate the power of each AP to the connected users for the total achievable data rates maximization. It is proved that the PA optimization problem is concave but not easy to tackle. Therefore, we provide a new algorithm to obtain the optimal dual variables after formulating them in terms of each other. Then, the users that are connected to the overloaded APs and receive less data rates start seeking for other APs that offer higher data rates. Users with lower data rates continue re-connecting from AP to other to balance the load only if this travel increases the summation of the achievable data rates and enhances the system fairness. The numerical results demonstrate that the proposed algorithms improve the system capacity and system fairness with fast convergence.

This paper proposes new approaches for underlay device- to-device (D2D) communication in spectrum-shared het- erogeneous cellular networks. It considers devices that share downlink resources and have an enabled D2D feature to improve coverage. The mode of operation classifies devices according to their experienced base station (BS) coverage, potential to be served by BS, ability of BS to meet their quality of service (QoS), and their downlink resources occupancy. The initiation of D2D cooperation is conditioned on proposed provisional access by an active device, wherein its serving BS attempts to meet its QoS using as low number of spectrum channels as possible, while treating remaining channels for feasible D2D cooperation. Detailed formulations for the mode of operation and a proposed D2D path allocation scheme are presented under perfect and imperfect operation scenarios. The developed results are generally applicable for any performance metric and network model.

A novel statistical model is proposed to characterize turbulence-induced fading in underwater wireless optical channels in the presence of air bubbles for fresh and salty waters, based on experimental data. In this model, the channel irradiance fluctuations are characterized by the mixture Exponential-Gamma distribution. We use the expectation maximization (EM) algorithm to obtain the maximum likelihood parameter estimation of the new model. Interestingly, the proposed model is shown to provide a perfect fit with the measured data under all the channel conditions for both types of water. The major advantage of the new model is that it has a simple mathematical form making it attractive from a performance analysis point of view. Indeed, the application of the Exponential-Gamma model leads to closed-form and analytically tractable expressions for key system performance metrics such as the outage probability and the average bit-error rate.

Visible light communication (VLC) is an effective alternative technology to overcome the limitations related to the radio frequency (RF) spectrum. In the modern day of communication systems, the energy harvesting (EH) technique is considered as a promising technology to design more energy efficient communication systems. Integrating VLC with the EH technology in wireless networks guaranties the reliability of these networks. In this paper, we consider a dual-hop VLC/RF wireless communication, composed of two Light Emitting Diodes (LEDs) and two receivers, assisted by a decode-and-forward (DF) relaying system operating with EH in order to boost the coverage of VLC systems. Using successive interference cancellation, we derive achievable rates of both users. Afterwards, we determine the achievable rate-region for this communication system where we show that this region can take four shapes depending on the communication scenario. Then, we formulate the achievable rate-region maximization problem, and we develop solution to find the optimal design for the EH time switching protocol. Further, we show that EH enhances the performance of the communication system in a certain regime of its initial power. We finally present selected numerical result to verify the analytic results.

This paper investigates the problem of recovering an n-dimensional BPSK signal x0∈ {−1, 1}nfrom m-dimensional measurement vector y = Ax+z, where A and z are assumed to be Gaussian with iid entries. We consider two variants of decoders based on the regularized least squares followed by hard-thresholding: the case where the convex relaxation is from {−1, 1}nto ℝnand the box constrained case where the relaxation is to [−1, 1]n. For both cases, we derive an exact expression of the bit error probability when n and m grow simultaneously large at a fixed ratio. For the box constrained case, we show that there exists a critical value of the SNR, above which the optimal regularizer is zero. On the other side, the regularization can further improve the performance of the box relaxation at low to moderate SNR regimes. We also prove that the optimal regularizer in the bit error rate sense for the unboxed case is nothing but the MMSE detector.

In this paper, we study the benefits of employing improper Gaussian signaling (IGS) in full duplex relaying (FDR) suffering from in-phase and quadrature imbalance (IQI). Different from the traditional symmetric signaling scheme, proper Gaussian signaling (PGS), that is parametrized by its variance, IGS needs additional statistical-quantity called the pseudo-variance to be fully described. The cooperative system under consideration suffers from two types of interferences, the residual self-interference (RSI) and IQI. To evaluate the system performance gain using IGS, first we express the end-to-end achievable rate for different IQI. Then, we optimize the pseudo-variance to compensate the interferences impact and improve the end-to-end achievable rate. Interestingly, IGS-based scheme outperforms its counterpart PGS-based scheme, especially at higher interference-to-noise ratio. Our findings reveal that using single-user detection with asymmetric signaling can compensate both RSI and IQI and improve the system performance.

Simultaneous wireless power and information transfer (SWIPT) in a cognitive radio (CR) network is considered where a multiple antenna energy harvesting (EH) secondary receiver (SR) harvests the energy using the antenna switching (AS) technique. In fact, the AS technique selects a subset of the SR antennas to decode the information (namely the information decoding (ID) antennas) and the rest to harvest the energy (namely the EH antennas). In this context, we propose a thresholding-based antenna selection strategy, termed as the prioritizing data selection (PDS) scheme, which selects the ID antennas such that the received power from the secondary transmitter (ST) at these antennas is above a certain threshold. For this scheme, we derive the analytic expressions of the probability mass function (PMF) of the selected ID antennas, the average harvested energy, and the outage probability. In the simulation results, we illustrate the performance of the PDS scheme and we compare it to the prioritizing energy selection (PES) scheme which selects the EH antennas such that the received power from ST at these antennas is above a certain threshold. For both schemes, we show that there is a tradeoff between the outage probability and the average harvested energy.

In this paper, resource allocation and co-tier/cross-tier interference management are investigated for D2D-enabled heterogeneous networks (HetNets) where tiers 1, 2, and 3 consist of macrocells, smallcells, and D2D pairs, respectively. We first propose a D2D-enabled fractional frequency reuse scheme for uplink (UL) HetNets where macrocell subregions are preassigned to different subbands (SBs) in order to mitigate the tier-1↔tier-1 interference. Nevertheless, cell-edge macrocell user equipments (MUEs) with high transmission powers still form dead-zones for nearby smallcell UEs (SUEs) and D2D UEs (DUEs). One of the simple but yet novel means of the dead-zone alleviation is associating the cell-edge MUEs with nearby smallcells, which is also known as downlink (DL)/UL decoupling (DUDe). Subject to quality of service (QoS) requirements and power constraints, we formulate a joint SB assignment and resource block (RB) allocation optimization as a mixed integer non-linear programming (MINLP) problem to maximize the D2D sum rate and minimize the co-tier/cross-tier interference. Based on tolerable interference limit, we propose a fast yet high-performance suboptimal solution to jointly assign available SBs and RBs to smallcells. A D2D mode selection and resource allocation framework is then developed for DUEs. As traditional DL/UL Coupled (DUCo) scheme generates significant interference proportional to cellular user density and user association bias factor, results obtained from the combination of proposed methods and developed algorithms reveal the potential of DUDe for co-tier/cross-tier interference mitigation which opens more room for spectrum reuse of DUEs.

The capacity of MIMO intensity modulation channels is studied. The non-negativity of the transmit signal (intensity) poses a challenge on the precoding of the transmit signal, which limits the applicability of classical schemes in this type of channels. To resolve this issue, capacity lower bounds are developed by using precoding-free schemes. This is achieved by channel inversion or QR decomposition to convert the MIMO channel to a set of parallel channels. The achievable rate of a DC-offset SVD based scheme is also derived as a benchmark. Then, a capacity upper bound is derived and is shown to coincide with the achievable rate of the QR decomposition based scheme at high SNR, consequently characterizing the high-SNR capacity of the channel. The high-SNR gap between capacity and the achievable rates of the channel inversion and the DC-offset SVD based schemes is also characterized. Finally, the ergodic capacity of the channel is also briefly discussed.

With the proliferation of the Internet-of-things (IoT), there is an undeniable consensus that cellular LTE networks will have to support a dramatically larger number of uplink connections. This is true since most of the devices to be added incur machine-type communications which is dominantly upstream. Can current LTE network withstand this challenge? To answer this question, the joint performance of random access process and the uplink data transmission should be investigated. These two problems have been classically treated in the literature in a disjoint fashion. In this paper, they are jointly analyzed as an inseparable couple. To do that, a tandem queuing model is adopted whereby devices are represented as spatially interacting queues. The interaction between queues is governed by the mutual inter-cell and intra-cell interference. To that end, a joint stochastic geometry and queueing theory model is exploited to study this problem and a spatiotemporal analytical model is developed accordingly. Network stability and scalability are two prime performance criteria for performance assessment. In light of these two criteria, the developed model is poised to offer valuable insights into efficient access and resource allocation strategies.

In this paper, we accurately model the hardware impairments (HWI) as improper Gaussian signaling (IGS) which can characterize the asymmetric characteristics of different HWI sources. The proposed model encourages us to adopt IGS scheme for transmitted signal that represents a general study compared with the conventional scheme, proper Gaussian signaling (PGS). First, we express the achievable rate of HWI systems when both PGS and IGS schemes are used when the aggregate effect of HWI is modeled as IGS. Moreover, we tune the IGS statistical characteristics to maximize the achievable rate. Then, we analyze the outage probability for both schemes and derive closed form expressions. Finally, we validate the analytic expressions through numerical and simulation results. In addition, we quantify through the numerical results the performance degradation in the absence of ideal transceivers and the gain reaped from adopting IGS scheme compared with PGS scheme.

Information-theoretic and signal processing aspects of some modulation schemes designed for intensity modulation/direct detection (IM/DD) optical wireless communication (OWC) systems are studied. Due to the constraints of IM/DD signals (non-negative real and baseband signals), the construction of these signals along with their time and frequency characteristics differ from their RF counterparts. This necessitates a careful study of such schemes under practical constraints. Three schemes are studied in this paper, namely, single carrier pulse amplitude modulation (SC-PAM), asymmetrically clipped optical OFDM (ACO-OFDM), and DC biased optical OFDM (DCO-OFDM). Our aim is to carry out a comparative study of these schemes in the presence of identical constraints on bandwidth and average optical power. The study reveals that the clipping operation required in ACO-OFDM significantly reduces its information rate, and as a result, it is outperformed by SC-PAM. Such a limitation does not apply to DCO-OFDM which has a higher information rate, even though part of the available optical power is expended in the non-information-bearing DC bias.

This paper presents and analyzes a point-to-multi-point (P2MP) network that uses a number of free-space optical (FSO) links for data transmission from the central node to the different remote nodes. A common backup radio-frequency (RF) link is used by the central node for data transmission to any remote node in case of the failure of any one of FSO links. We develop a cross-layer Markov chain model to study the throughput from central node to a tagged remote node. Numerical examples are presented to compare the performance of the proposed P2MP hybrid FSO/RF network with that of a P2MP FSO-only network and show that the P2MP Hybrid FSO/RF network achieves considerable performance improvement over the P2MP FSO-only network.

In this paper, we develop selective retransmission schemes for multiple-channel systems. The proposed schemes are selective automatic repeat request with fixed bandwidth (SARQ-FB), selective chase combining with fixed bandwidth (SCC-FB) and selective automatic repeat request with variable bandwidth (SARQ-VB). The main objective of the proposed schemes is to use the available power and bandwidth budget effectively along with the selective retransmission to deliver the required data successfully within a limited number of transmissions. To investigate the performance of each scheme, we first analyze the average spectral and energy efficiency and derive closed form expressions for each scheme. Then, we compare the EE and SE of each scheme through numerical results.

Visible light communication (VLC) is an emerging technique that uses light-emitting diodes (LED) to combine communication and illumination. It is considered as a promising scheme for indoor wireless communication that can be deployed at reduced costs while offering high data rate performance. In this paper, we focus on the design of the downlink of a multi-user VLC system. Inherent to multi-user systems is the interference caused by the broadcast nature of the medium. Linear precoding based schemes are among the most popular solutions that have recently been proposed to mitigate inter-user interference. This paper focuses on the design of the optimal linear precoding scheme that solves the max-min signal-to-interference-plus-noise ratio (SINR) problem. The performance of the proposed precoding scheme is studied under different working conditions and compared with the classical zero-forcing precoding. Simulations have been provided to illustrate the high gain of the proposed scheme.

This paper develops a mathematical paradigm to study downlink error rates and throughput for half-duplex (HD) terminals served by a full-duplex (FD) base station (BS). Particularly, we study the dominant intra-cell interferer problem that appears between HD users scheduled on the same FD-channel. The distribution of the dominant interference is first characterized via its distribution function, which is derived in closed-form. Assuming Nakagami-m fading, the probability of error for different modulation schemes is studied and a unified closed-form expression for the average symbol error rate is derived. To this end, we show the effective downlink throughput gain, harvested by employing FD communication at a BS that serves HD users, as a function of the signal-to-interference-ratio when compared to an idealized HD interference and noise free BS operation.

The BOX-LASSO is a variant of the popular LASSO that includes an additional box-constraint. We propose its use as a decoder in modern Multiple Input Multiple Output (MIMO) communication systems with modulation methods such as the Generalized Space Shift Keying (GSSK) modulation, which produces constellation vectors that are inherently sparse and with bounded elements. In that direction, we prove novel explicit asymptotic characterizations of the squared-error and of the per-element error rate of the BOX-LASSO, under iid Gaussian measurements. In particular, the theoretical predictions can be used to quantify the improved performance of the BOX-LASSO, when compared to the previously used standard LASSO. We include simulation results that validate both these premises and our theoretical predictions.

The degrees-of-freedom (DoF) of the multi-antenna three-way channel (3WC) with an intermittent node is studied. Special attention is given to the impact of adaptation when the intermittent node has the largest number of antennas. A non-adaptive transmission scheme based on interference alignment, zero-forcing, and erasure-channel treatment is proposed, and its corresponding DoF region is derived. Then, it is shown that this scheme achieves the sum-DoF of the intermittent channel, in addition to the DoF region of the nonintermittent one. Thus, adaptation is not necessary from those perspectives. To the contrary, it is shown that adaptation is necessary for achieving the DoF region of the intermittent case. This is shown by deriving an outer bound for the intermittent channel with nonadaptive encoding, and proposing an adaptive scheme which achieves DoF tuples outside this bound. This highlights the importance of cooperation in this intermittent network.

The capacity region of the class of injective semi-deterministic two-way channels (TWCs) is investigated in this paper. To characterize this capacity, two conditions under which Shannon's bounds on the capacity region of TWCs are tight are first given. Using those conditions, it is shown that the capacity of this class of TWCs is characterized by the rectangle formed by the one-way capacities. This proves that adaptation is not needed for this class. This class encompasses, among others, all memoryless additive channels with input-independent noise, and hence, adaptation is useless for all such channels. This also shows that there exist continuous additive TWCs not of the exponential family type for which adaptation is not necessary. An example of a Cauchy TWC is given, and its capacity is characterized in closed form under a logarithmic constraint. Finally, the impact of the dependence of the noise on the inputs is discussed, and it is shown that adaptation may still be useless in such cases.

Order statistics find applications in various areas including communications and signal processing. In this paper, we introduce new results of the joint statistics of partial products of ordered random variables (RVs) based on a Mellin-transform-based unified analytical framework. With the proposed approach, we can systematically derive the joint statistics of any partial products of ordered statistics, in terms of the Mellin transform and the probability density function (PDF). Our Mellin-transform-based approach can apply when all the K-ordered RVs are involved even for more complicated cases, when only the Ks (Ks <; K) best RVs are also considered. In addition, the closed-form expressions for the exponential RV special case are presented. As an application example, these results can apply to the performance analysis of various wireless communication systems over fading channels.

We consider secret-key agreement with public discussion over a multiple-input single output (MISO) Gaussian channel with an amplitude constraint. We prove that the capacity is achieved by a discrete input, i.e., an input whose support is sparse. The proof follows from the concavity of the conditional mutual information in terms of the input distribution and hence the Karush-Kuhn-Tucker (KKT) condition provides a necessary and sufficient condition for optimality. Then, a contradiction argument that rules out the non-sparsity of any optimal input's support is utilized. The latter approach is essential to apply the identity theorem in a multidimensional setting as Rnis not an open subset of Cn.

In this paper, an optical wireless multiple-input single-output communication system employing intensity-modulation direct-detection is considered. Subject to a per transmit-aperture power constraint, the performance of direct current (DC) offset space-time block codes (STBC) is studied in terms of pairwise error probability (PEP). It is shown that among the class of DC-STBCs, the worst case PEP, i.e., the one corresponding to the minimum distance between two codewords, is minimized by repetition coding (RC) for any channel state. Therefore, it follows that among all DC-STBCs, RC is optimal in terms of worst case PEP under any turbulence statistics. This result agrees with previously published numerical results showing the superiority of RC in such systems. It also agrees with previously published analytical results on this topic under lognormal turbulence and further extends it to arbitrary turbulence statistics. Numerical results provided to verify this indicate that RC is not only optimal in terms of worst case PEP, but also in terms of average error probability.

This paper derives and analyzes the outage probability of mixed radio frequency (RF)/unified free space optical (FSO) dual-hop decode-and-forward (DF) relaying scheme, where heterodyne detection (HD) and intensity modulation-direct detection (IM-DD) are considered for FSO detection. In doing that, we correctly utilize, for the first time to the best of our knowledge, a precise channel capacity result for the IM-DD channel. Moreover, this is the first time that not only the (IM-DD input-independent) but also the (IM-DD cost-dependent) AWGN channel is considered in such system analysis. This work assumes that the first hop (RF link) follows Naka-gami-m fading, while the second hop (FSO link) follows Málaga (M) turbulence with pointing error. These fading and turbulence models include other ones (such as Rayleigh fading and Gamma-Gamma turbulence) as special cases, so our analysis can be considered as a generalized one from both RF and FSO fading models point of view. Additionally, the system outage probability is investigated asymptotically in high signal-to-noise ratio (SNR) regime, where a new non-reported diversity order and coding gain analysis are shown. Interestingly, we find that in the FSO hop, based on SNR, the HD or IM-DD cost-dependent results in a same diversity order which is twice the one of IM-DD input-independent. However, based on transmitted power all these FSO detectors result in a same diversity order. Furthermore, we offer simulation results which confirm the derived exact and asymptotic expressions.

In this paper, the performance of two-way relaying (TWR) multiuser mixed radio frequency/free space optical (RF/FSO) relay networks with opportunistic user scheduling and asymmetric channel fading is studied. First, closed-form expressions for the exact outage probability, asymptotic (high signal-to-noise ration (SNR)) outage probability, and average ergodic channel capacity are derived assuming heterodyne detection (HD) scheme. Additionally, impacts of several system parameters including number of users, pointing errors, and atmospheric turbulence conditions on the overall network performance are investigated. All the theoretical results are validated by Monte-Carlo simulations. The results show that the TWR scheme almost doubles the network ergodic capacity compared to that of one-way relaying (OWR) scheme with the same outage performance. Additionally, the overall diversity order of the network is shown to be affected not only by the number of users, but it is also a function of the pointing error and atmospheric turbulence conditions.