In energy harvesting (EH) Internet of Bodies (IoB) networks, maintaining information freshness is critical for time-sensitive applications such as electrocardiogram (ECG) monitoring. Timely transmissions are essential for reliable ECG analysis, which requires complete and synchronous data from all sensors, as each ECG lead is derived from potential differences between paired sensors. To this end, we leverage model-free deep reinforcement learning (DRL) to learn adaptive scheduling policies that minimize the age of information (AoI) in dynamic and intermittent energy arrival conditions. Specifically, in a human body communication (HBC)-enabled EH-ECG network, wearable ECG sensors transmit data to a central wearable hub acting as the scheduler. The hub implements the DRL scheduling algorithm that selects sensor transmissions based on their battery and AoI levels. Furthermore, we propose an ECG synthesis framework for a 3-lead ECG that mitigates the impact of EH scarcity by reconstructing missing leads from data transmitted by active sensors, thereby ensuring complete signal availability at the hub. Simulation results show that the proposed solution reduces AoI by up to 57% compared to the greedy myopic policy and up to 96% relative to a baseline learning policy.

Reconfigurable intelligent surfaces (RIS) have emerged as a transformative technology for next-generation wireless networks, offering unprecedented control over radio propagation environments. However, their passive nature and ease of deployment introduces security vulnerabilities that remain largely unexplored. This paper investigates a spoofing attack where a malicious RIS strategically manipulates its reflection coefficients to impersonate a legitimate RIS, thereby deceiving the base station (BS) and gaining unauthorized network access. To counter this threat, we propose a novel authentication framework that formulates the detection problem as a data-driven binary classification task, leveraging conditional generative adversarial networks (cGAN). The framework employs a U-Net-based generator to synthesize realistic attack scenarios during training, while the discriminator serves as a lightweight authenticator enabling robust authentication without requiring apriori knowledge of attacker strategies. Through extensive simulations across diverse attack scenarios, including co-located and correlated configurations, we demonstrate that the trained discriminator achieves 96.4% detection accuracy against malicious RIS attackers positioned near the BS (co-located) and maintains 86.2% accuracy under correlated attack conditions.

Although massive multi-input multi-output (mMIMO) systems offer higher directivity gains to mitigate propagation losses at millimeter wave (mmWave) frequencies, they present challenges in channel state information (CSI) acquisition and beam alignment, particularly in highly mobile vehicle-to-vehicle (V2V) scenarios with short channel coherence times. To address these issues, we propose a target-in-the-loop beam tracking approach that leverages GPS data and Long Short-Term Memory (LSTM) networks to select beams from predefined beamforming codebooks. By transforming GPS data into relative coordinates and extracting features such as relative velocity and orientation, our model predicts future beam states up to 500 ms in advance, enabling proactive beam selection and blockage avoidance. Using the DeepSense V2V dataset, our method achieves up to 7.2 dB power loss reduction and a 32% improvement in top-5 accuracy compared to a linear interpolation baseline. This approach highlights the potential of integrating GPS data and machine learning to enhance beam tracking in dynamic V2V mmWave networks.

Accurate angle of arrival (AoA) and angle of departure (AoD) estimation underpins spatial precision, efficient beamforming, and the overall Quality of Service (QoS) and Quality of Experience (QoE) of integrated sensing and communication (ISAC). This paper introduces a generative AI (GenAI) framework that synthesizes the complete set of multipath 2D AoA/AoD parameters directly from transmitter (TX) and receiver (RX) locations, overcoming the limitations of traditional model-based and current data-driven learning methods. The proposed classifiers-guided conditional generative adversarial network (CG-CGAN) offers a system-agnostic approach that removes the need for pilot signals and operates effectively under challenging coherent multipath conditions. Its three-stage architecture integrates classification and conditional generative modeling to jointly infer line-of-sight (LoS) status, number of propagation paths, and angular parameters. Simulations on the DeepMIMO dataset demonstrate over 99.5% classification accuracy and 92% angle generation accuracy, significantly outperforming classical techniques while reducing computational complexity and enhancing the QoS/QoE of ISAC-enabled systems.

With the deployment of large antenna arrays at high-frequency bands, future wireless communication systems are likely to operate in the radiative near-field. Unlike far-field beam steering, near-field beams can be focused on a spatial region with a finite depth, enabling spatial multiplexing in the range dimension. Moreover, in the line-of-sight MIMO near-field, multiple spatial degrees of freedom (DoF) are accessible, akin to a scattering-rich environment. In this paper, we derive the beamdepth for a generalized uniform rectangular array (URA) and investigate how the array geometry influences near-field beamdepth and its limits. We define the effective beamfocusing Rayleigh distance (EBRD), to present a near-field boundary with respect to beamfocusing and spatial multiplexing gains for the generalized URA. Our results demonstrate that under a fixed element count constraint, the array geometry has a strong impact on beamdepth, whereas this effect diminishes under a fixed aperture length constraint. Moreover, compared to uniform square arrays, elongated configurations such as uniform linear arrays (ULAs) yield narrower beamdepth and extend the effective near-field region defined by the EBRD. Building on these insights, we design a polar codebook for compressed-sensing-based channel estimation that leverages our findings. Simulation results show that the proposed polar codebook achieves a 2 dB NMSE improvement over state-of-the-art methods. Additionally, we present an analytical expression to quantify the effective spatial DoF in the near-field, revealing that they are also constrained by the EBRD. Notably, the maximum spatial DoF is achieved with a ULA configuration, outperforming a square URA in this regard.

The evolution of next-generation wireless networks demands intelligent, adaptive, and explainable decision-making for robust communication in dynamic environments. This paper presentsEnwar 2.0, the first agentic large language model (LLM) framework integrating adaptive retrieval-augmented generation (RAG) and chain-of-thought (CoT) reasoning into situation-aware and explainable wireless network management.Enwar 2.0introduces two specialized agents: a transformer-fusion (TransFusion)-based beam prediction agent and an environment perception agent, both of which fuse multi-modal sensory inputs—including camera, LiDAR, radar, and GPS—from the DeepSense6G dataset. The beam prediction agent enables infrastructure-to-vehicle (I2V) target-in-the-loop beam tracking and real-time adaptation based on dynamic environmental conditions. In contrast, the environment perception agent provides situation-aware reasoning and justifications for beam decisions. Unlike its predecessor,Enwar 1.0, which relied on static knowledge bases (KBs) and text-only LLMs,Enwar 2.0is designed for CoT reasoning, leverages LLaMa3.2-3B/LLaMa3.1-8B/LLaMa3.3-70B for text-generation, the multi-modal capabilities of LLaMa 3.2, and employs LlamaIndex for fine-grained, dynamic context retrieval, eliminating retrieval ambiguities and enhancing response relevance. Numerical results show that the beam prediction agent achieves up to 90.0% Top-3 accuracy at$t+3$, effectively predicting optimal beam selections three time steps ahead. Overall,Enwar 2.0achieves state-of-the-art performance, with up to 89.7%/83.5% interpretation/perception correctness, 81.6%/80.9% faithfulness, and 89.9%/88.2% relevancy. In comparison, the baseline pretrained LLaMa3 models without adaptive RAG achieves up to 80.3%/77.3% correctness, and the baseline without RAG performs significantly worse at 67.1%/64.8%. Additionally,Enwar 2.0reduces processing time by over 100% relative to the baseline, while its adaptive RAG improves performance by up to 13.7% compared to static RAG.

Next generation wireless networks focus on improving spectral efficiency (SE) while reducing power consumption and hardware cost. Reconfigurable intelligent surfaces (RISs) offer a viable solution to meet these requirements. In order to enhance the SE, index modulation (IM) has been regarded as one of the enabling technologies via the transmission of additional information bits over the transmission media such as subcarriers, antennas and spatial paths. In this work, we explore the usage of spatial paths and introduce spatial path IM (SPIM) for RIS-aided massive multiple-input multiple-output (mMIMO) systems. Thus, the proposed framework improves the network efficiency and the coverage with the use of RIS while SPIM provides SE improvement. In order to perform SPIM, we exploit the spatial diversity of the millimeter wave channel and assign the index bits to the spatial patterns of the channel between the base station and the users through RIS. We introduce a low complexity approach for the design of hybrid beamformers, which are constructed by the steering vectors corresponding to the selected spatial path indices for SPIM-mMIMO. Furthermore, we conduct a theoretical analysis on the SE of the proposed SPIM approach, and derive the SE relationship between the SPIM-based hybrid beamforming and fully digital (FD) beamforming. Via numerical simulations, we validate our theoretical results and show that the proposed SPIM approach presents an improved SE performance, even higher than that of the use of FD beamformers while using a few RF chains.

In line with the AI-native 6G vision, explainability and robustness are crucial for building trust and ensuring reliable performance in millimeter-wave (mmWave) systems. Efficient beam alignment is essential for initial access, but deep learning (DL) solutions face challenges, including high data collection overhead, hardware constraints, lack of explainability, and susceptibility to adversarial attacks. This paper proposes a robust and explainable DL-based beam alignment engine (BAE) for mmWave multiple-input multiple-output (MIMO) systems. The BAE uses received signal strength indicator (RSSI) measurements from wide beams to predict the best narrow beam, reducing the overhead of exhaustive beam sweeping. To overcome the challenge of real-world data collection, this work leverages a site-specific digital twin (DT) to generate synthetic channel data closely resembling real-world environments. A model refinement via transfer learning is proposed to fine-tune the pre-trained model residing in the DT with minimal real-world data, effectively bridging mismatches between the digital replica and real-world environments. To reduce beam training overhead and enhance transparency, the framework uses deep Shapley additive explanations (SHAP) to rank input features by importance, prioritizing key spatial directions and minimizing beam sweeping. It also incorporates the Deep k-nearest neighbors (DkNN) algorithm, providing a credibility metric for detecting out-of-distribution inputs and ensuring robust, transparent decision-making. Experimental results show that the proposed framework reduces real-world data needs by 70%, beam training overhead by 62%, and improves outlier detection robustness by up to 8.5×, achieving near-optimal spectral efficiency and transparent decision making compared to traditional softmax based DL models.

The rapid evolution of mobile technologies presents a formidable security challenge, as traditional cryptographic methods struggle to keep pace. Integrating physical layer security (PLS) solutions with cutting-edge technologies such as in-band full-duplex (IBFD) and reconfigurable intelligent surfaces (RISs) holds promise for addressing these challenges effectively. This study introduces a novel RIS-driven protected zone (PZ) formation approach that employs artificial noise (AN) to safeguard legitimate users without requiringa prioriknowledge of eavesdropper locations, channels, or numbers. The proposed methodology partitions the RIS into two distinct segments: while the former segment enhances the achievable data rate for legitimate signal, the latter segment concurrently amplifies AN to jam illegitimate users within the PZ.We present formulations and solutions for maximizing secrecy capacity (SC) and minimizing power consumption through optimized transmit power allocation factors, RIS segmentation, and beams’ directions, all subject to stringent quality-of-service (QoS) constraints. Closed-form expressions are derived to facilitate efficient implementation and performance optimization. Simulation results validate closed-form solutions and demonstrate that the proposed scheme can significantly enhance SC compared to benchmarks where RIS and AN are used separately, with the proposed scheme achieving approximately 81% greater capacity than the “RIS-Only” approach and a substantial advantage over the “AN-Only” approach, which results in no secrecy. Additionally, this work includes an analysis of energy efficiency, emphasizing the critical importance of optimizing power consumption in practical applications. This dual focus on improving security while effectively managing energy resources underscores the scheme’s practical relevance and efficiency.

Ultra-massive multiple-input multiple-output (UM-MIMO) technology is a key enabler for 6G networks, offering exceptional high data rates in millimeter-wave (mmWave) and Terahertz (THz) frequency bands. The deployment of large antenna arrays at high frequencies transitions wireless communication into the radiative near-field, where precise beam alignment becomes essential for accurate channel estimation. Unlike far-field systems, which rely on angular domain only, near-field necessitates beam search across both angle and distance dimensions, leading to substantially higher training overhead. To address this challenge, we propose a discrete Fourier transform (DFT) based beam alignment to mitigate the training overhead. We highlight that the reduced path loss at shorter distances can compensate for the beamforming losses typically associated with using far-field codebooks in near-field scenarios. Additionally, far-field beamforming in the near-field exhibits angular spread, with its width determined by the user's range and angle. Leveraging this relationship, we develop a correlation interferometry (CI) algorithm, termed CI-DFT, to efficiently estimate user angle and range parameters. Simulation results demonstrate that the proposed scheme achieves performance close to exhaustive search in terms of achievable rate while significantly reducing the training overhead by 87.5%.

Integrated artificial intelligence (AI) and communication has been recognized as a key pillar of 6 G and beyond networks. In line with AI-native 6 G vision, explainability and robustness in AI-driven systems are critical for establishing trust and ensuring reliable performance in diverse and evolving environments. This paper addresses these challenges by developing a robust and explainable deep learning (DL)-based beam alignment engine (BAE) for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. The proposed convolutional neural network (CNN)-based BAE utilizes received signal strength indicator (RSSI) measurements over a set of wide beams to accurately predict the best narrow beam for each UE, significantly reducing the overhead associated with exhaustive codebook-based narrow beam sweeping for initial access (IA) and data transmission. To ensure transparency and resilience, the Deep k-Nearest Neighbors (DkNN) algorithm is employed to assess the internal representations of the network via nearest neighbor approach, providing human-interpretable explanations and confidence metrics for detecting out-of-distribution inputs. Experimental results demonstrate that the proposed DL-based BAE exhibits robustness to measurement noise, reduces beam training overhead by 75 % compared to the exhaustive search while maintaining near-optimal performance in terms of spectral efficiency. Moreover, the proposed framework improves outlier detection robustness by up to$5 \times$and offers clearer insights into beam prediction decisions compared to traditional softmax-based classifiers.

The rapid proliferation of mobile technologies introduces substantial security vulnerabilities, with conventional cryptographic approaches increasingly inadequate. Physical layer security (PLS) approaches, especially when paired with advanced methods like full-duplex (FD) communication and reconfigurable intelligent surfaces (RISs), offer substantial potential for enhanced security. This work proposes a novel RIS-aided inband FD (IBFD) framework that employs artificial noise (AN) to establish a protected zone (PZ) around the legitimate user, operating independently of any a priori knowledge regarding the number, positions, or channels of potential eavesdroppers. To achieve this, the RIS is functionally partitioned into two distinct segments: one enhances the legitimate user's data rate, while the other simultaneously intensifies AN to jam any unauthorized users within the PZ. We formulate a secrecy capacity (SC) maximization problem that optimizes both the legitimate user's transmission power and the RIS configuration, while meeting quality-of-service (QoS) requirements. Simulation results indicate the superior efficacy of the proposed technique in enhancing SC compared to benchmarks that employ RIS and AN independently. In particular, the proposed approach achieves an approximate SC improvement of 80% and 49% over the “RISonly” and “AN-only” approaches, respectively, underscoring its significant potential to enable robust PLS in next-generation communication networks.

The next generation wireless networks focus on improving the energy and spectral efficiency (SE/EE) of the communication systems in response to the demand for massive number of users and data rate. In this work, we aim to achieve the enhancement of SE and EE by employing index modulation (IM) techniques for reconfigurable intelligent surface (RIS)-aided communication systems. While RIS offers the network efficiency and improve the coverage, IM provides SE improvement by the transmission of additional index bits. In IM, we utilize the indices of the spatial paths between the base station and the user through the RIS. We introduce a low complexity approach for the design of hybrid beamformers, which are constructed by the steering vectors corresponding to the selected spatial path indices for IM. Via numerical experiments, we show that the proposed approach presents an improved SE performance, even higher than that of the use of fully-digital beamformers while using a few RF chains.

With the deployment of large antenna arrays at high-frequency bands, future wireless communication systems are likely to operate in the radiative near-field. Unlike far-field beam steering, near-field beams can be focused within a spatial region of finite depth, enabling spatial multiplexing in both the angular and range dimensions. This paper derives the beamdepth for a generalized uniform rectangular array (URA) and investigates how array geometry influences the near-field beamdepth and the limits where near-field beamfocusing is achievable. To characterize the near-field boundary in terms of beamfocusing and spatial multiplexing gains, we define the effective beamfocusing Rayleigh distance (EBRD) for a generalized URA. Our analysis reveals that while a square URA achieves the narrowest beamdepth, the EBRD is maximized for a wide or tall URA. However, despite its narrow beamdepth, a square URA may experience a reduction in multiuser sum rate due to its severely constrained EBRD. Simulation results confirm that a wide or tall URA achieves a sum rate of 3.5× more than that of a square URA, benefiting from the extended EBRD and improved spatial multiplexing capabilities.

The IMT-2030 vision emphasizes two key 6G directions: integrated sensing and communication (ISAC) alongside artificial intelligence (AI)-native frameworks, where multimodal sensory data inputs enhance situational awareness and adaptive decision-making of communication systems. Accordingly, this paper introduces a deep reinforcement learning (DRL)-based beam selection framework for downlink multi-user reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) massive MIMO (mMIMO) systems. Targeting maximized sum rates under quality of service (QoS) and fairness constraints, the framework employs two primary sensing modalities: a stereo camera mounted on the RIS for user equipment (UE) detection and inertial measurement units (IMUs) on UEs to obtain 3D Cartesian coordinates, thus eliminating the need for channel state information (CSI) acquisition. The DRL framework combines two algorithms—double deep Q-network (DDQN) and proximal policy optimization (PPO)—to jointly optimize RIS phase shifts and UE receive beamformers through predefined codebooks and adaptive beam selection. A testbed was developed to validate the system, leveraging real-world data to train the DRL algorithms. Experimental results demonstrate that both agents achieve nearoptimal sum rates across diverse base station (BS) transmit power levels and QoS thresholds while reducing computational complexity by 95%, illustrating the framework’s potential for efficient and scalable beam alignment for AI-native wireless systems.

The incorporation of reconfigurable intelligent surfaces (RISs) and unmanned aerial vehicles (UAVs) presents considerable potential for improving the functionality of ground-based terrestrial Internet of Things (IoT) networks. This paper introduces a novel UAV deployment and aerial RIS partitioning mechanism for the uplink non-orthogonal multiple access (NOMA)-based IoT networks under practical hardware constraints. The low-cost hardware of IoT devices results in imperfect successive interference cancellation (SIC) and distortion noise due to transceiver hardware impairments (T-HIs). Specifically, we have optimized the aerial RIS partitioning and its deployment to maximize the minimum rate under the impact of imperfect SIC and T-HIs. The numerical results show that the proposed scheme outperforms the benchmark. Through extensive simulations, we prove that the proposed UAV-based aerial RIS-aided uplink NOMA network significantly enhances the max-min fair rate.

Reconfigurable intelligent surfaces (RISs) are gaining significant attention as a key enabler of future wireless networks. However, its practical deployment is hindered by challenges in modeling and integration. Existing analytical approaches often depend on idealized assumptions, limiting their ability to reflect the complexity of real-world environments. In this work, we explore the integration of digital twin (DT) technology with ray tracing (RT), enabling a more accurate representation of practical scenarios and bridging the gap between theoretical models and the real-world implementation of RIS. RT allows accurate prediction of signal behavior, such as received signal strength indicator (RSSI) levels, without the need for expensive experimental measurements. We evaluate the performance of the DT with RT in three RIS-aided setups: a single RIS-aided, cascaded RISs-aided, and RIS partitioning. Our results show that the proposed DT model closely matches the experimental RSSI data with an error margin below 1 dB for single and partitioned RIS setups and under 2 dB for cascaded RISs system. These findings highlight the potential of DT with RT simulations as a practical tool for performance evaluation and optimization of RIS-assisted wireless systems.

The Internet of Bodies (IoB) represents a transformative technological innovation that merges the bio-physical and digital realms through networks of intelligent devices positioned in, on, and around the human body. Human Body Communication (HBC) offers a promising method for enabling IoB networks, using the human body as a communication channel for multiple wearable nodes. Despite the prevalence of HBC peer-to-peer communication methods in the literature, the challenge of implementing multiple access techniques for HBC at the physical layer remains largely unexplored. In this paper, we propose a new multiple access HBC (MA-HBC) system that leverages autoencoders to design and implement low-power and efficient transceivers sharing a common channel. The proposed MA-HBC system consists of multiple autoencoder-based transceivers trained jointly to optimize overall network performance. It supports various data rates ranging from 164 kbps to 5.25 Mbps, making it suitable for a wide range of IoB applications. To validate the design, a prototype implementation is presented. Additionally, to ensure suitability for wearable devices, a low-power hardware transceiver architecture is provided with an estimated energy efficiency of 105 pJ/b when implemented using TSMC 65nm LP technology. The results show that the proposed MA-HBC system outperforms traditional IEEE 802.15.6 based transceivers for two users with time Sharing, achieving a 3.9 dB improvement in Signal-to-Noise Ratio (SNR) at a block error rate of$10^{-2}$.

This paper proposes an innovative approach for enhancing network capacity and coverage by integrating cell-free massive multiple-input multiple-output (CF-mMIMO) networks with reconfigurable intelligent surfaces (RISs). A significant challenge in leveraging RIS-assisted CF-mMIMO lies in the cooperative beam training across multiple access points (APs) and RISs, complicated by the passive nature of reflective elements and the complexity channel state information (CSI) acquisition in millimeter wave mMIMO systems. To address these challenges, we develop a multi-agent deep reinforcement learning (MA-DRL) framework that jointly designs beamforming and reflection codebooks for distributed APs and RISs, eliminating the need for CSI and relying solely on received power measurements feedback. The joint beamforming and reflection codebook design problem is decomposed into two sub-problems: one for beam codebook design at APs and another for sequential reflection codebook design at RISs. We employ transfer learning to speed up learning convergence and reduce computational complexity for training multiple RISs. Additionally, we introduce an AP and RIS selection scheme that improves overall energy efficiency and reduces backhaul overhead. Extensive simulations demonstrate that our proposed MA-DRL approach curtails number of beams significantly, thereby outperforming the widely adopted discrete Fourier transform (DFT) codebooks by achieving an 84% reduction in beam training overhead. Our findings suggest that increasing the number of passive RISs allows putting more APs into idle mode, leading to substantial savings in hardware and energy costs.

Artificial intelligence (AI) is expected to significantly enhance radio resource management (RRM) in sixth-generation (6G) networks. However, the lack of explainability in complex deep learning (DL) models poses a challenge for practical implementation. This paper proposes a novel explainable AI (XAI)-based framework for feature selection and model complexity reduction in a model-agnostic manner. Applied to a multi-agent deep reinforcement learning (MADRL) setting, our approach addresses the joint sub-band assignment and power allocation problem in cellular vehicle-to-everything (V2X) communications. We propose a novel two-stage systematic explainability framework leveraging feature relevance-oriented XAI to simplify the DRL agents. While the former stage generates a state feature importance ranking of the trained models using Shapley additive explanations (SHAP)-based importance scores, the latter stage exploits these importance-based rankings to simplify the state space of the agents by removing the least important features from the model’s input. Simulation results demonstrate that the XAI-assisted methodology achieves ~97% of the original MADRL sum-rate performance while reducing optimal state features by ~28%, average training time by ~11%, and trainable weight parameters by ~46% in a network with eight vehicular pairs.

This paper presents an end-to-end (E2E) autoencoder learning framework that relies on unsupervised deep learning for the joint design of millimeter wave (mmWave) probing beams and hybrid precoding matrices in multi-user communication systems. Our model utilizes prior channel observations to achieve two main objectives: designing a compact set of probing beams and predicting off-grid radio frequency (RF) beamforming vectors. The E2E learning framework optimizes probing beams in an unsupervised manner, concentrating sensing power on promising spatial directions based on the environment. To this aim, we develop a neural network architecture respecting RF chain constraints and model received signal strength (RSS) using complex-valued convolutional layers. The autoencoder is trained to directly produce RF beamforming vectors for hybrid architectures based on projected RSS indicators (RSSIs). Once RF beamforming vectors for multi-users are predicted, baseband digital precoders are designed by accounting for multi-user interference. The autoencoder neural network is trained E2E in an unsupervised manner with a customized loss function aimed at maximizing RSS. In a system with 64 antennas, 4 RF chains, and 4 users, our approach requires only 8 probing beams to design RF beamforming vectors, compared to the conventional predefined codebooks with 64 or 128 beams.

Beam-squint is a challenging issue in ultra-wideband systems, e.g., terahertz (THz) integrated sensing and communications (ISAC). In order to compensate for the loss due to beam-squint, this paper leverages index modulation in spatial domain, which enables the transmission of additional information bits to improve the spectral efficiency (SE). Specifically, a joint antenna and spatial path index modulation (JASPIM) technique is proposed by exploiting the spatial diversity of both antenna and path indices. We present a hybrid beamforming technique with JASPIM for ISAC, wherein the analog beamformers are designed in accordance with the radar targets and the communications user. Numerical simulations demonstrate that our JASPIM-ISAC approach exhibits a significant SE improvement even higher than that of the use of fully digital beamformers in the presence of beam-squint.

This work investigates simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) in multiple-access (MA) bidirectional network architecture. The proposed optimization framework incorporates two practical operational protocols of STAR-RIS to optimize the channel gains for users and obviate the necessity for uplink power control while adhering to specified quality of service (QoS) constraints. The proposed approach undergoes thorough evaluation under key optimization problems: QoS feasible region, and energy efficiency. The closed-form solutions are validated through simulations, showcasing the notable advantages that STAR-RIS can provide to the considered multiple-access networks. Simulation findings revealed that in the context of the proposed system model, the mode switching approach can attain a superior QoS threshold rate compared to the energy splitting mode. The proposed framework has demonstrated its ability to meet bidirectional communication needs with a high degree of accuracy and precision.

The integration of reconfigurable intelligent surfaces (RIS) and artificial noise (AN) significantly enhances physical layer security (PLS) in wireless networks, provided that RIS’s phase shifts are precisely optimized to prevent security vulnerabilities. This paper introduces a reinforcement learning (RL)based algorithm designed to optimize the phase shifts in RIS-partitioning-aided PLS systems operating in the millimeter wave (mm-Wave), without requiring channel state information (CSI) for any users. The RL algorithm optimizes the phase shifts by efficiently selecting the best beam from a predefined codebook for different partitions, which simultaneously enhances the intended signal for legitimate users and increases the effectiveness of AN on eavesdroppers, thereby maximizing the system’s secrecy capacity (SC) and addressing the inherent non-convex challenges. Additionally, the paper details the development of an experimental testbed that provides essential data to refine the algorithm. The numerical results from the testbed highlight the significant impact of RIS partitioning in PLS, which can enhance the SC by an average of 55% over the full RIS scenario, and confirm the effectiveness of the RL-based algorithm in reducing computational complexity by approximately 80% compared to the exhaustive search algorithm.

In recent years, surface electromyography (sEMG) signals have emerged as a valuable tool for assisting individuals with physical disabilities. Traditional methods for transmitting sEMG signals often rely on radio frequency (RF) links, which are energy-intensive and lack robust security. This paper presents a new design for a Human Body Communication (HBC) transceiver tailored for sEMG sensors. The proposed HBC transceiver employs an end-to-end autoencoder approach, offering enhanced energy efficiency and security. The architecture supports the required data rates for sEMG applications and is optimized for low power consumption, making it suitable for wearable devices. The results show that the HBC transceiver design demonstrates a peak data rate of 62.5 Kbps and a block error rate of approximately$10^{-2}$at −5.17 dB of Ec/N0 when operating at a clock speed of 2 MHz. Moreover, the power consumption results indicate that the HBC transceiver consumes 287$\mu \mathrm{W}$resulting in an energy efficiency of 4.5 nJ/bit,

In this work, we examine the potential of reconfigurable intelligent surfaces (RISs) to facilitate and enhance uplink (UL) transmissions in grant-free non-orthogonal multiple access (GF-NOMA) networks. The proposed RIS-assisted GF-NOMA approach employs virtual partitioning of RIS, with each partition tailored to optimize channel conditions for individual NOMA user equipment (UE). The resulting channel gain disparity bolsters the NOMA gain and obviates the necessity for UL power control of the grant-based NOMA schemes. Our approach is evaluated under three practical operational regimes: 1) quality-of-service (QoS) sufficient regime, 2) efficient RIS usage regime, and 3) max-min fair regime, all subject to UL-QoS constraints. We derive closed-form solutions to elucidate how optimal RIS partitioning can fulfill UL-QoS requirements across all three operational regimes. Comprehensive simulations are conducted to validate the precision of our analytical findings, demonstrating that the proposed approach substantially improves wireless communication system performance while mitigating signaling overhead and computational complexity.

In this paper, we propose a robust interference management approach for the integrated sensing and communication (ISAC) system that employs non-orthogonal multiple access (NOMA) for multiplexing. Our proposed approach effectively addresses interference challenges by optimizing the pairing of communication users (CUs) and radar targets (RTs) while simultaneously designing receiving beamformers. These optimizations aim to maximize the combined utility of communication rates and the radar estimation information rate (REIR), inherently constituting a challenging non-convex combinatorial problem. To tackle this intricate problem, we employ the upper confidence bound (UCB) algorithm, a powerful online learning technique rooted in multi-armed bandit (MAB) theory. Along with UCB, we harness zeroforcing beamforming to optimize the receiving beamformer. The numerical results underscore the importance of CU-RT pairing, with a $65 \%$ average performance improvement over traditional NOMA-ISAC and OMA-ISAC, close to the exhaustive search performance by only $2 \%$. It also substantially reduces complexity, with about $90 \%$ less computational complexity than exhaustive search.

This paper investigates the maximization of har-vested data in a laser-powered uncrewed aerial vehicle (UAV) supporting Internet of Things (IoT) deployment. The system enables battery-free IoT devices to establish communication links with the UAV via bistatic backscattering with the aid of a power beacon source. Upon considering an unspecified flying time, we adopt path discretization and resort to the single-block successive convex approximation (SCA) to solve the data collection maximization problem. In addition to considering the UAV dynamics and power budget, two novel SCA-compatible bounds are introduced for the product of mixed convex/concave positive functions. Finally, the simulations conducted show that the proposed algorithm provides 90% increase in collected data under different operation conditions.

In terahertz (THz) wideband systems, beam-squint causes deviations in the generated beam directions at different subcarriers due to the use of subcarrier-independent analog beamformers. In order to combat the performance loss due to beam-squint effect, this work employs spatial path index modulation (SPIM) to improve the spectral efficiency (SE) performance of the overall system, thereby compensating the loss due to beam-squint. Specifically, SPIM allows the transmission of additional information bits to the receiver via modulating the indices of the spatial paths. The proposed approach is evaluated in a THz integrated sensing and communications (THz-ISAC) scenario, wherein the beamformer design allows generating multiple beams toward both radar targets and the communications user. Numerical simulations demonstrate that the proposed approach exhibits significant SE performance even higher than that of the use of fully digital beamformers without SPIM in the presence of beam-squint.

Reconfigurable intelligent surfaces (RISs) play a vital role in future wireless systems with the capability of enhancing propagation environments by intelligently reflecting the signals toward the target receivers. However, optimal tuning of the phase shifters at the RIS is challenging due to the passive nature of reflective elements and the high complexity of acquiring channel state information (CSI). Furthermore, the joint active beamforming and RIS reflection beam design is a tedious task due to the high computational complexity and the dynamic nature of the wireless environment. Today’s cellular networks establish data transmission by relying on pre-defined generic beamforming codebooks, which are neither site-specific nor adaptive to the changes in the wireless environment. Moreover, identifying the best beam is typically performed using an exhaustive search approach that prohibits the use of large codebook sizes due to the resulting high beam training overhead. Depending merely on the binary received signal strength, this work develops a multi-agent deep reinforcement learning (MA-DRL) framework that jointly designs the active and the passive reflection beam codebooks for the BS and the RIS, reflectively. To accelerate learning convergence and reduce the search space, the proposed model divides the RIS into multiple partitions and associates beam patterns to the surrounding environments with low computational complexity. Moreover, a hierarchical beam training solution is proposed to further reduce the beam training overhead of the single-beam training approach. Simulation results show that the proposed MA-DRL approach can provide a 97% beam training overhead reduction over the discrete Fourier transform (DFT) codebook.

Recently, the use of uncrewed aerial vehicles (UAVs) in joint sensing and communication applications has received a lot of attention. However, integrating UAVs in current cellular systems presents major challenges related to trajectory optimization and interference management among others. This paper considers a multi-cell network including a UAV, which senses and forwards the sensory data from different events to the central base station. Particularly, the current manuscript covers how to design the UAV’s (i) 3D trajectory, (ii) power allocation, and (iii) sensing scheduling such that (a) a set of events are sensed, (b) interference to neighboring cells is kept at bay, and (c) the amount of energy required by the UAV is minimized. The resulting nonconvex optimization problem is tackled through a combination of (i) low-complexity binary optimization, (ii) successive convex approximation, and (iii) the Lagrangian method. Simulation results over a range of various key parameters have shown the merits of our approach, which consumes 33%-200% less energy compared to different benchmarks.

As the demand for wireless connectivity continues to soar, the fifth generation and beyond wireless networks are exploring new ways to efficiently utilize the wireless spectrum and reduce hardware costs. One such approach is the integration of sensing and communications (ISAC) paradigms to jointly access the spectrum. Recent ISAC studies have focused on upper millimeter-wave and low terahertz bands to exploit ultrawide bandwidths. At these frequencies, hybrid beamformers that employ fewer radio-frequency chains are employed to offset expensive hardware but at the cost of lower multiplexing gains. Wideband hybrid beamforming also suffers from the beam-split effect arising from the subcarrier-independent (SI) analog beamformers. To overcome these limitations, we introduce a spatial path index modulation (SPIM) ISAC architecture, which transmits additional information bits via modulating the spatial paths between the base station and communications users. We design the SPIM-ISAC beamformers by estimating both radar and communications parameters through our proposed beam-split-aware algorithms. We then develop a family of hybrid beamforming techniques – hybrid, SI, subcarrier-dependent analog-only, and beam-split-aware beamformers – for SPIM-ISAC. Numerical experiments demonstrate that the proposed approach exhibits significantly improved spectral efficiency performance in the presence of beam-split when compared with even fully digital non-SPIM beamformers.

This study delves into the capabilities of reconfigurable intelligent surfaces (RISs) in enhancing bidirectional non-orthogonal multiple access (NOMA) networks. The proposed approach partitions RIS to optimize the channel conditions for NOMA users, improving NOMA gain and eliminating the requirement for uplink (UL) power control. The proposed approach is rigorously evaluated under four practical operational regimes; 1) Quality-of-Service (QoS) sufficient regime, 2) RIS and power efficient regime, 3) max-min fair regime, and 4) maximum throughput regime, each subject to both UL and downlink (DL) QoS constraints. By leveraging decoupled nature of RIS portions and base station (BS) transmit power, closed-form solutions are derived to show how optimal RIS partitioning can meet UL-QoS requirements while optimal BS power control can ensure DL-QoS compliance. Analytical findings are validated by simulations, highlighting the significant benefits that RISs can bring to the NOMA networks in the aforementioned operational scenarios.

The integration of reconfigurable intelligent surfaces (RISs) and grant-free non-orthogonal multiple access (GF-NOMA) has emerged as a promising solution for enhancing spectral efficiency and massive connectivity in future wireless networks. This paper proposes a GF-NOMA communication network enabled by simultaneously transmitting and reflecting RISs (STAR-RIS). In the proposed GF-NOMA, all user equipments (UEs) have instantaneous access to resource blocks (RBs) without the need for grant acquisition and power control as in the traditional grant-based NOMA schemes. Specifically, we have considered two regimes of interest: 1) the max-min fair (MMF) regime and 2) the max-sum throughput (MST) regime. To achieve the required power disparity, a two-level power control mechanism is proposed; initially, the UEs are clustered according to their channel gains. Additionally, we introduce a multi-level GF-NOMA (MGF-NOMA) scheme that adjusts the transmit power levels for each UE in the cluster. The second level of power disparity is achieved through the assignment of STAR-RISs to the clusters and optimal partitioning of STAR-RIS to support each of the cluster members. Specifically, we have also derived the closed-form equations for the optimal partitioning of STAR-RIS within the clusters for both regimes of interest. Simulation results demonstrate that the proposed STAR-RIS-aided MGF-NOMA yields a gain of 60% and 20% in the MST regime with active and passive RIS realization, respectively. Furthermore, the active and passive RIS-based MGF-NOMA achieve nearly the equivalent fairness that can be obtained through optimal power control in the MMF regime. The finding emphasizes the potential of integrating STAR-RIS with GF-NOMA as a robust and promising solution for future wireless communication systems.

Reconfigurable intelligent surfaces (RISs) are envisioned to play a pivotal role in future wireless systems with the capability of enhancing propagation environments by intelligently reflecting the signals toward the target receivers. However, the optimal tuning of the phase shifters at the RIS is a challenging task due to the passive nature of reflective elements and the high complexity of acquiring channel state information (CSI). Conventionally, wireless systems rely on pre-defined reflection beamforming codebooks for both initial access and data transmission. However, these existing pre-defined codebooks are commonly not adaptive to the environments. Moreover, identifying the best beam is typically performed using an exhaustive search that leads to high beam training overhead. To address these issues, this paper develops a multi-agent deep reinforcement learning framework that learns how to jointly optimize the active beamforming from the BS and the RIS-reflection beam codebook relying only on the received power measurements. To accelerate learning convergence and reduce the search space, the proposed model divides the RIS into multiple partitions and associates beam patterns to the surrounding environments with low computational complexity. Simulation results show that the proposed learning framework can learn optimized active BS beamforming and RIS reflection codebook. For instance, the proposed MA-DRL approach with only 6 beams outperforms a 256-beam discrete Fourier transform (DFT) codebook with a 97% beam training overhead reduction.

Cell-free massive MIMO (CF-mMIMO) is a transformative wireless network technology that surmounts conventional cellular network limitations concerning coverage, capacity, and interference management. Despite offering numerous benefits, CF-mMIMO also presents significant challenges, particularly in signal processing and power allocation. This paper introduces an unsupervised learning framework for downlink (DL) power allocation in CF-mMIMO networks, utilizing only large scaling fading coefficients instead of the hard-to-obtain exact user equipment (UE) locations or channel state information. We consider the sum spectral efficiency (sum-SE) optimization objective and investigate two distinct precoding schemes-maximum ratio (MR) and regularized zero-forcing (RZF)-for multi-antenna access points (APs). A custom loss function is formulated to maximize the sum-SE at each UE while accounting for pilot contamination and ensuring that power budget constraints are satisfied at each AP. The proposed unsupervised learning approach circumvents the arduous task of training data computations typically required in supervised learning methods, bypassing the use of conventional complex optimization methods and heuristic methodologies. The simulation results demonstrate that the proposed unsupervised learning approach outperforms existing methods in terms of SE, showcasing an improvement up to 20%. The proposed unsupervised neural network also approximates the optimal solutions generated by convex solvers while significantly reducing computational complexity.

The integration of reconfigurable intelligent surfaces (RISs) and grant-free non-orthogonal multiple access (GF-NOMA) has emerged as a promising solution for enhancing spec-tral efficiency (SE) and massive connectivity in future wireless networks. This paper proposes a novel virtual RIS partitioning mechanism for GF-NOMA, where all user equipments (UEs) within a specific NOMA cluster have instantaneous access to resource blocks (RBs) without the need for grant acquisition and power control as in the traditional grant-based NOMA schemes. To achieve the required power disparity, RIS portions are allocated to the UEs in a manner that increases the reception power disparity. We derive closed-form equations for optimal RIS portions in two regimes of interest: 1) max-min fair regime and 2) maximum throughput regime. Simulation results demonstrate that the proposed RIS-assisted GF-NOMA yields a gain of 28% and 15% in terms of max-sum rate and max-min rate, respectively, outperforming existing grant-based approaches. The study highlights the potential of combining RIS with GF-NOMA as a powerful solution for future wireless communication systems.

This paper introduces a reconfigurable intelligent surface (RIS)-assisted grant-free non-orthogonal multiple access (GF-NOMA) scheme. To ensure the power reception disparity required by the power domain NOMA (PD-NOMA), we propose a joint user clustering and RIS assignment/alignment approach that maximizes the network sum rate by judiciously pairing user equipments (UEs) with distinct channel gains, assigning RISs to proper clusters, and aligning RIS phase shifts to the cluster members yielding the highest cluster sum rate. Once UEs are acknowledged with the cluster index, they are allowed to access their resource blocks (RBs) at any time requiring neither further grant acquisitions from the base station (BS) nor power control as all UEs are requested to transmit at the same power. In this way, the proposed approach performs an implicit over-the-air power control with minimal control signaling between the BS and UEs, which has shown to deliver up to 20% higher network sum rate than benchmark GF-NOMA and grant-based optimal (OPT) PD-NOMA schemes depending on the network parameters. The given numerical results also investigate the impact of UE density, RIS deployment, and RIS hardware specifications on the overall performance of the proposed RIS-aided GF-NOMA scheme.

We demonstrate the design and functionality of a flexible, miniaturized, ultra-low power, and affordable health monitoring system enabling continuous monitoring of individuals' health metrics, a.k.a. a wireless body area network (WBAN). To date, the most commonly-used means of communication for WBAN modules has been based on Radio Frequency (RF) communications. Though they significantly facilitated human healthcare monitoring, they require complex, power-hungry, RF front ends. As an alternative, we design our system to communicate utilizing human body communication (HBC), which has inherent physical layer security and enhanced overall energy efficiency. The live demo presents a vital signal monitoring system based on point-to-point HBC communication. Fig. 1 illustrates the transmitter and receiver diagrams and PCB boards respectively. The transmitter comprises a microcontroller, an oscillator, an on-off-keying (OOK) modulator, and signal/ground electrodes. A 3.7 V lithium-ion battery with a 3.3 V output low dropout regulator (LDO) powers the whole system. The receiver first detects the envelope of the received signal and slices it to binary digits by comparing the input signal with its average level extracted by low-pass filtering. Interested readers can find full details at [1], where the system has shown an energy efficiency of 8.3 nJ/b at a data rate of up to 1.3 Mbps.

The Internet of Bodies (IoB) is a network formed by wearable, implantable, ingestible, and injectable smart devices to collect physiological, behavioral, and structural information from the human body. Thus, the IoB technology can revolutionize the quality of human life by using these context-rich data in myriad smart-health applications. Radio frequency (RF) transceivers have been typically preferred due to their availability and maturity. However, for most RF standards (e.g., Bluetooth low energy), the highly radiative omnidirectional RF propagation (even at the lowest settings) reaches tens of meters of coverage, thereby reducing energy efficiency, causing interference and co-existence issues, and raising privacy and security concerns. On the other hand, body channel communication (BCC) confines low-power and low-frequency (10 kHz–100 MHz) signals to the human body, leading to more secure and efficient communications. Since energy efficiency is one of the critical design parameters of IoB networks, this article focuses on energy-efficient orthogonal body channel access (OBA) and non-OBA (NOBA) schemes with and without cooperation. To this aim, three main BCC topologies are presented: 1) point-to-point channel; 2) medium access channel; and 3) broadcast channel. These topologies are then used as building blocks to create IoB networks relying on OBA and NOBA schemes for downlink (DL) and uplink (UL) traffic. For all schemes and traffic directions, optimal transmit power and phase time allocations are derived in closed-form, which is essential to reduce energy consumption by eliminating computational power. The closed-form expressions are further leveraged to obtain maximum network size as a function of data rate requirement, bandwidth, and hardware parameters.

Reconfigurable intelligent surface (RIS) assisted wireless systems require accurate channel state information (CSI) to control wireless channels and improve both the bandwidth and energy efficiency. However, CSI acquisition is non-trivial for two reasons: 1) the passive nature of RIS does not allow transceiving and processing pilot signals, and 2) the dimensions of the cascaded channel between transceivers increases with the large number of RIS elements, which yields high training overhead and computational complexity. While prior art has mainly focused on frequency-flat channel estimation, this paper proposes novel data-driven and compressive sensing based approaches for estimating both frequency-flat and frequency-selective cascaded channels of RIS-assisted multi-user millimeter-wave large multiple input multiple output (MIMO) systems with limited training overhead. The proposed methods exploit the common sparsity property among the different subcarriers and the double-structured sparsity property of the angular cascaded channel matrices as different angular cascaded channels observed by different users share completely common non-zero rows and user-specific column supports. The proposed data-driven cascaded channel estimation approaches use denoising neural networks to accurately detect channel supports. Alternatively, when data-training capabilities are not available, the compressive sensing based orthogonal matching pursuit (OMP) approach relies on sparsity properties and applies simultaneous OMP to detect the channel supports. Simulation results show that the pilot overhead required by the proposed scheme is lower than existing schemes. When compared to other OMP approaches that achieve an NMSE gap of 5 to 6 dB with respect to the Oracle least square lower bound, the proposed algorithms reduce the lower bound gap to only 1 dB, while reducing complexity by more than two orders of magnitude.

Wireless body area network (WBAN) provides a means for seamless individual health monitoring without imposing restrictive limitations on normal daily routines. To date, Radio Frequency (RF) transceivers have been the technology of choice, however, drawbacks such as vulnerability to body shadowing effects, higher power consumption due to omnidirectional radiation and security concerns, have prompted the adoption of transceivers that use the human body channel for communication. In this paper, a vital signal monitoring transceiver prototype based on the human body channel communication (HBC), using commercially available chipsets is presented. RF and HBC communications are briefly reviewed and compared, and different schemes of HBC are introduced. A circuit model that represents the human body channel is then discussed and simulations are presented to illustrate the influence of the return path capacitance and receiver terminations on the path loss. The architecture of the transceiver prototype is then introduced where it is designed at a 21 MHz IEEE 802.15.6 standard-compliant carrier frequency. Finally, the performance of the transceiver, including the bit error rate (BER) and power efficiency, are characterized. Path loss is measured for two different scenarios, where variations of up to 5 dB were observed due to environmental effects. Energy efficiency measured at a maximum data-rate of 1.3 Mbps was found to be 8.3 nJ/b.

Reconfigurable intelligent surface (RIS) assisted wireless systems require accurate channel state information (CSI) to control wireless channels and improve overall network performance. However, CSI acquisition is non-trivial due to the passive nature of RIS, and the dimensions of the cascaded channel between transceivers increase with the large number of RIS elements, which requires high training overhead. Prior art has considered frequency-selective channel estimation without considering the beam squint effect in wideband systems, severely degrading channel estimation performance. This paper proposes a novel data-driven approach for estimating wideband cascaded channels of RIS-assisted multi-user millimeter-wave massive multiple-input multiple-output (MIMO) systems with limited training overhead, explicitly considering the effect of beam squint. To circumvent the beam squint effect, the proposed method exploits the common sparsity property among the different subcarriers as well as the double-structured sparsity property of the users’ angular cascaded channel matrices. The proposed data-driven cascaded channel estimation approach exploits denoising neural networks to detect channel supports accurately. Compared to beam squint effect agnostic traditional orthogonal matching pursuit (OMP) approaches, the proposed data-driven approach achieves 5-6dB less normalized mean square error (NMSE) and reduces the lower bound gap to only 1dB for the oracle least-square benchmark.

Intelligent surfaces have recently been presented as a revolutionary technique and recognized as one of the candidates for beyond fifth-generation wireless networks. This paper investigates the physical layer security of a large intelligent surface (LIS) aided wireless system over Nakagami-m channels. We propose a phase-based adaptive modulation scheme, where LIS’s phase-shift optimization process is effectively utilized to enhance the system’s security. Moreover, the effect of the Nakagami-m fading parameter (m), correlation parameter ($\rho$), and a number of passive LIS elements (M) on the system performance are examined. The significant improvement in confidentiality is shown while evaluating the bit error rate performance of the proposed scheme.

Reconfigurable intelligent surfaces (RISs) have sparked a renewed interest in the research community envisioning future wireless communication networks. In this study, we analyzed the performance of RIS-enabled non-orthogonal multiple access (NOMA) based device-to-device (D2D) wireless communication system, where the RIS is partitioned to serve a pair of D2D users. Specifically, closed-form expressions are derived for the upper and lower limits of spectral efficiency (SE) and energy efficiency (EE). In addition, the performance of the proposed NOMA-based system is also compared with its orthogonal counter-part. Extensive simulation is done to corroborate the analytical findings. The results demonstrate that RIS highly enhances the performance of a NOMA-based D2D network.

The Internet of Bodies (IoB) is an imminent extension of the vast Internet of Things (IoT) domain, where wearable, ingestible, injectable, and implantable smart objects form a network in, on, and around the human body. The highly radiative nature of radio-frequency (RF) IoB devices unnecessarily extends the coverage range beyond the human body, which reduces energy efficiency, causes co-existence and interference issues, and exposes sensitive personal data to security threats. Alternatively, capacitive body channel communication (BCC) confine signal transmission to the human body to reduce signal leakage, experience less propagation loss, and reach pJ/b energy efficiency levels. Therefore, capacitive BCC is a key enabler to reach the ultimate design goals of ultra low power, high throughput, and small form-factor IoB devices. Albeit these attractive features, the communication and networking aspects of the capacitive BCC are not thoroughly explored yet. Therefore, this article proposes orthogonal and nonorthogonal capacitive body channel access schemes with or without cooperation among the IoB nodes. In order to address the Quality of Service (QoS) demand scenarios of different IoB applications, we present and formulate the max–min rate, max-sum rate, and QoS sufficient operational regimes, and then provide closed-form and numerical solution optimal power and phase time allocations. Extensive numerical results are analyzed to compare the performance of orthogonal and nonorthogonal schemes with and without cooperation for various design parameters under prescribed QoS regimes. The obtained results show that capacitive body channel access schemes can provide several Mb/s rates even at low transmission powers ranging between −60 and −90 dBm. Moreover, the cooperative schemes are shown to be effective to avoid performance degradation caused by increasing network size, low transmission power, and poor channel quality.

The Internet of Bodies (IoBs) is an imminent extension to the vast Internet of Things domain, where interconnected devices (e.g., worn, implanted, embedded, swallowed, etc.) are located in-on-and-around the human body form a network. Thus, the IoB can enable a myriad of services and applications for a wide range of sectors, including medicine, safety, security, wellness, entertainment, to name but a few. Especially, considering the recent health and economic crisis caused by the novel coronavirus pandemic, also known as COVID-19, the IoB can revolutionize today’s public health and safety infrastructure. Nonetheless, reaping the full benefit of IoB is still subject to addressing related risks, concerns, and challenges. Hence, this survey first outlines the IoB requirements and related communication and networking standards. Considering the lossy and heterogeneous dielectric properties of the human body, one of the major technical challenges is characterizing the behavior of the communication links in-on-and-around the human body. Therefore, this article presents a systematic survey of channel modeling issues for various link types of human body communication (HBC) channels below 100 MHz, the narrowband (NB) channels between 400 and 2.5 GHz, and ultrawideband (UWB) channels from 3 to 10 GHz. After explaining bio-electromagnetics attributes of the human body, physical, and numerical body phantoms are presented along with electromagnetic propagation tool models. Then, the first-order and the second-order channel statistics for NB and UWB channels are covered with a special emphasis on body posture, mobility, and antenna effects. For capacitively, galvanically, and magnetically coupled HBC channels, four different channel modeling methods (i.e., analytical, numerical, circuit, and empirical) are investigated, and electrode effects are discussed. Finally, interested readers are provided with open research challenges and potential future research directions.

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.

In this paper, we propose and study an energy-efficient trajectory optimization scheme for unmanned aerial vehicle (UAV) assisted Internet of Things (IoT) networks. In such networks, a single UAV is powered by both solar energy and charging stations (CSs), resulting in sustainable communication services, while avoiding energy outage. In particular, we optimize the trajectory design of UAV by jointly considering the average data rate, the total energy consumption, and the fairness of coverage for the IoT terminals. A dynamic spatial-temporal configuration scheme is operated for terminals working in the discontinuous reception (DRX) mode. The module-free, action-confined on-policy and off-policy reinforcement learning (RL) approaches are proposed and jointly applied to solve the formulated optimization problem in this paper. We evaluate the effectiveness of the proposed strategy by comparing it with other dynamic benchmark algorithms. The extensive simulation results provided in this paper reveal that the proposed scheme outperforms the benchmarks in terms of data transmission, energy efficiency and adaptivity of avoiding battery depletion. By deploying the proposed trajectory scheme, the UAV is able to adapt itself according to the temporal and dynamic conditions of communication networks.

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems typically employ hybrid mixed signal processing to avoid expensive hardware and high training overheads. However, the lack of fully digital beamforming at mmWave bands imposes additional challenges in channel estimation. Prior art on hybrid architectures has mainly focused on greedy optimization algorithms to estimate frequency-flat narrowband mmWave channels, despite the fact that in practice, the large bandwidth associated with mmWave channels results in frequency-selective channels. In this paper, we consider a frequency-selective wideband mmWave system and propose two deep learning (DL) compressive sensing (CS) based algorithms for channel estimation. The proposed algorithms learn critical apriori information from training data to provide highly accurate channel estimates with low training overhead. In the first approach, a DL-CS based algorithm simultaneously estimates the channel supports in the frequency domain, which are then used for channel reconstruction. The second approach exploits the estimated supports to apply a low-complexity multi-resolution fine-tuning method to further enhance the estimation performance. Simulation results demonstrate that the proposed DL-based schemes significantly outperform conventional orthogonal matching pursuit (OMP) techniques in terms of the normalized mean-squared error (NMSE), computational complexity, and spectral efficiency, particularly in the low signal-to-noise ratio regime. When compared to OMP approaches that achieve an NMSE gap of$\mathrm {\{4-10\}\,\,dB}$with respect to the Cramer Rao Lower Bound (CRLB), the proposed algorithms reduce the CRLB gap to only$\mathrm {\{1-1.5\}\,\,dB}$, while reducing complexity by two orders of magnitude.

The Internet of bodies is a network of wearable, ingestible, injectable, and implantable smart objects located in, on, and around the body. Although radio frequency (RF) systems are considered the default choice for implementing on-body communications, which need to be localized in the vicinity of the human body (typically < 5 cm), highly radiative RF propagations unnecessarily extend several meters beyond the human body. This intuitively degrades energy efficiency, leads to interference and co-existence issues, and exposes sensitive personal data to security threats. As an alternative, the capacitive body channel communication (BCC) couples the signal (between 10 kHz-100 MHz) to the human body, which is more conductive than air. Hence, BCC provides a lower propagation loss, better physical layer security, and nJ/bit to pJ/bit energy efficiency. Accordingly, this paper investigates orthogonal and non-orthogonal capacitive body channel access schemes for ultra-low-power IoB nodes. We present the optimal uplink and downlink power allocations in closed-form, which deliver better fairness and network lifetime than benchmark numerical solvers. For a given bandwidth and data rate requirement, we also derive the maximum affordable number of IoB nodes for both directions of orthogonal and non-orthogonal schemes.

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.

This paper considers hybridization of non-orthogonal multiple access (NOMA) and Orthogonal multiple access (OMA) schemes for next-generation cellular networks. The proposed hybrid multiple access (HMA) scheme considers a multi-cell environment that combines NOMA/OMA mode selection as well as channel and power allocation to improve the resource utilization and bandwidth efficiency. The NOMA/OMA modes are categorized into intra-cell and inter-cell OMA and NOMA modes based on an interference map. The HMA focuses on determining the best mode of operation between user pairs to improve the overall sum rate and quality of service (QoS). Results show that the proposed NOMA/OMA mode selection provides superior performance to the conventional OMA schemes without compromising the QoS demands.

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.

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.

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.

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.

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.

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.

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 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 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.

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.

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.

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.

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.

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.

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.

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.

In this paper, we investigate energy efficient and energy harvesting (EH) in heterogeneous networks (HetNets) where all base stations (BSs) are equipped to harvest energy from renewable energy sources, e.g., solar. We consider a hybrid power supply of green (renewable) and traditional micro-grid, such that traditional micro-grid is not exploited as long as the BSs can meet their power demands from harvested and stored green energy. Therefore, our goal is to minimize the network-wide energy consumption subject to users' certain quality of service and BSs' power consumption constraints. As a result of binary BS sleeping status and user-cell association variables, proposed is formulated as a binary linear programming (BLP) problem. Two cases based on the knowledge level about future renewable energy (RE) statistics are investigated: (i) an online knowledge case where future RE statistics are unknown, (ii) an offline knowledge case where future network's statistics are a priori perfectly estimated. A green communication algorithm based on binary particle swarm optimization is implemented to solve the problem with low complexity time.

Due to the increasing bandwidth demand of mobile users and their devices with energy hungry wireless networking modules, attention of research efforts has been recently shifting to find answers to the paradox of achieving more spectrum for less energy consumption. In this paper, cognitive radios have been employed to obtain more spectrum by utilizing unused licensed spectrum in an opportunistic manner. Defining the opportunity cost as the consumed energy per achieved unit of free spectrum, we propose a cooperative sensing scheduling framework to optimize the cost with the consideration of the sensing, reporting and channel switching costs in terms of energy expenditure subject to a licensed user protection threshold. In the proposed scheme, all primary channels are scheduled to be cooperatively sensed within a cycle which consists of rounds. In every round, secondary/unlicensed users (SUs) are first assigned to cooperatively sense the scheduled primary/licensed user (PU) channels. Consequently, SUs report their local sensing results to a fusion center for a global decision. Finally, SUs assigned to sense other PU channels perform channel switching for the next round. This scheme not only provides a feasible network set up in case there does not exist a sufficient number of SUs to satisfy the PU protection in a single round, but also offers an apparent reduction in the opportunity cost.

Cooperative spectrum sensing (CSS) has been extensively studied in the literature to mitigate the weakness of spectrum sensing against hostile propagation phenomenon. Especially for large networks, clustered CSS is preferred to alleviate the energy efficiency, delay and overhead problems. In this study, reporting and sensing channels are first modeled with the consideration of path loss and fading. Then, CSS is divided into three phases: 1) In sensing phase, optimal sensing time is obtained for each local user subject to local detection and false alarm probability thresholds, 2) In reporting phase, adopting Dijkstra's algorithm, multi-hop paths with the maximum success rate and cluster head (CH) selection which gives the mimimum total error rate within each cluster is computed, and 3) In decision phase, collecting independent but unidentically distributed (i.u.d.) member decisions, the CH decides on channel occupancy based on an optimal voting rule for i.u.d. reports. Next, following the phases above, a multi-objective clustering optimization (MOCO) is formulated to select SUs into cluster seeking energy and throughput efficiency goals subject to global detection and false alarm probability constraints. Finally, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is employed to solve MOCO. Results based on our approach are presented and the merits of this approach are demonstrated.