This paper presents a deep neural network (DNN)-based resource allocation framework aimed at maximizing energy efficiency (EE) in beam-hopping (BH) low-Earth orbit (LEO) satellite communication systems. Specifically, in BH-LEO satellite systems, it is challenging to solve the joint optimization of time slot scheduling and transmit power control, due to the intrinsic complexity and non-convex nature of the original mixed-integer nonlinear programming formulation. To mitigate this issue, we propose a strategy to decompose the problem into two tractable subproblems: an integer programming model for time slot allocation and a nonlinear programming model for transmit power allocation. For the time slot allocation subproblem, we employ a dueling double deep Q-network (D3QN), combining the strengths of both dueling and double Q-learning techniques to enable stable and efficient decision-making. Then, for the power allocation subproblem, we design a novel unsupervised DNN (UDNN)-based model that estimates spectral efficiency to indirectly determine transmit power, thereby avoiding the difficulties of solving a non-convex optimization problem for EE maximization. Extensive simulation results show that the proposed D3QN and UDNN-based schemes outperform existing iterative and DNN-based approaches in terms of both EE and outage performance, while achieving a significant reduction in computational overhead.

The proliferation of large-scale Internet-of-things (IoT) deployments and the emergence of 6G wireless technologies have created a pressing need for intelligent, energy-aware, and low-latency communication frameworks. In this work, we propose a novel two-phase reinforcement learning (RL)-based architecture designed to minimize the age of information (AoI) in 6G-enabled IoT networks. Our approach integrates (i) a deep deterministic policy gradient (DDPG)-driven backscatter-assisted cognitive radio non-orthogonal multiple access (CR-NOMA) scheme in the uplink, and (ii) a lightweight Q-learning-based power-domain NOMA (PD-NOMA) strategy for the downlink. In the uplink, energy harvesting (EH) sensors employ deep RL to jointly optimize backscatter reflection coefficients and transmission scheduling over shared spectrum using CR-NOMA. This enables energy-efficient communication and reduced AoI under dynamic energy and channel conditions. In the downlink, the edge node serves multiple IoT users simultaneously using PD-NOMA, where a Q-learning agent intelligently decides whether to transmit fresh or cached data to each user based on battery levels, channel quality, and information freshness. Both phases are modeled as Markov decision processes (MDPs), allowing agents to learn independently and converge toward optimal policies that balance information freshness, spectral efficiency (SE), and energy constraints. Extensive simulations demonstrate that the proposed framework effectively reduces AoI across both phases, with consistent convergence even under varying sensor densities and EH conditions. Moreover, by relying on explainable and verifiable learning mechanisms, our model addresses emerging concerns around reliability and trustworthiness in artificial intelligence (AI)-driven 6G-IoT systems. This framework represents a step toward scalable, adaptive, and responsible AI integration for future mission-critical IoT applications.

Simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has received significant attention as a potential technology for the sixth generation (6G) of wireless network due to its ability to boost signal coverage and enhance system efficiency. In this paper, we investigate the potential of a near-optimal hybrid quantum-classical optimization approach to jointly optimize beamforming and the discrete phase shifts of the STAR-RIS assisted wireless network. In particular, we formulate a discrete optimization problem to maximize the total power transmitted to the ground users. This is achieved by optimizing the beamforming at the base station (BS) and the phase shift of the STAR-RIS under minimal power allocation for each user and the maximum power budget at the BS. Since the addressed problem is NP-hard, we propose a quantum approximate optimization algorithm with alternating optimization (QAOA-AO) method that iteratively addresses beamforming components and discrete phase shifts to search for the near-optimal solutions for the problem. Numerical results validate the effectiveness and robustness of the proposed QAOA-AO compared to the classical benchmarks in terms of runtime and system power, and highlight its potential for practical deployment when solving medium-to-large-scale networks.

In this paper, we explore a maritime 6G-enhanced satellite-air-ground integrated network (SAGIN) that incorporates a UAV-carried reconfigurable intelligent surface (UCR) relay, and low Earth orbit (LEO) satellites equipped with mobile edge computing (MEC) facilities. The system captures dynamic maritime conditions, including ultra-reliable low-latency communication (URLLC) user mobility and UCR movements across harbor environments. The primary objective is to minimize the total system cost by jointly optimizing task offloading decisions, bandwidth allocation, local computational resource distribution, transmission power control, and caching management, while satisfying strict latency and resource constraints. To address this, we formulate a mixed-integer nonlinear programming (MINLP) problem that captures the complexity of resource optimization in the maritime 6G-enhanced SAGIN. Two quantum-enhanced deep reinforcement learning algorithms, namely quantum-enhanced deep deterministic policy gradient (QEDDPG) and quantum-enhanced proximal policy optimization (QEPPO), are proposed to solve the formulated MINLP problem. Moreover, higher-order quantum feature encoding and quantum neural networks are utilized to accelerate learning and enhance decision-making. Simulation results demonstrate that QEDDPG and QEPPO significantly outperform conventional deep reinforcement learning methods by achieving lower system costs and more efficient resource allocation. These findings shows that the potential of quantum-driven reinforcement learning for enabling scalable, efficient, and intelligent resource management in future 6G-enhanced SAGINs.

The emergence of intent-based networking (IBN) has created new opportunities for Internet of Things (IoT) ecosystems to evolve from rigid, device-centric management toward artificial intelligence (AI)-native architectures capable of translating high-level intents into autonomous actions. In such systems, the dual requirements of information freshness and communication secrecy are critical, yet existing designs largely treat them in isolation. This paper introduces an IBN-inspired hierarchical deep reinforcement learning (HDRL) framework for reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mm-wave) IoT networks threatened by unmanned aerial vehicle (UAV) eavesdroppers. The framework integrates a high-level RIS intent manager with a low-level power controller for transmit and jamming power adaptation, both trained via proximal policy optimization (PPO). A secrecy-gated transmission protocol further ensures that packets are withheld when confidentiality cannot be guaranteed. By embedding the tradeoff between age of information (AoI) and age of leaked information (AoLI) into the reward structure, the framework translates the high-level intent offresh yet securecommunication into context-aware, real-time network policies. Simulation results demonstrate significant reductions in AoI, improvements in AoLI, and enhanced secrecy efficiency under realistic fading and interference conditions. These findings underscore the potential of IBN-driven HDRL frameworks as foundational enablers for AI-powered, intent-aware, and resilient IoT communication in beyond-5G and 6G networks.

The proliferation of low-powered devices in the Internet of Things (IoT) necessitates energy-efficient communication paradigms. Backscatter communication (BackCom) combined with cognitive radio-inspired non-orthogonal multiple access (CRNOMA) offers a promising solution. However, optimizing such systems is complex, especially when considering realistic energy harvesting (EH) models. This paper investigates the sum rate optimization of an EH-enabled passive backscatter node (BN) coexisting with primary devices (PDs) in a CR-NOMA network, employing a practical non-linear EH model. We leverage deep reinforcement learning (DRL) to dynamically optimize the BN’s reflection coefficient. Crucially, we conduct a comparative study of several DRL algorithms. These include deep deterministic policy gradient (DDPG), its prioritized replay variants (PERDDPG and CER-DDPG), and the stability-enhanced twin delayed DDPG (TD3). We also evaluate on-policy methods such as proximal policy optimization (PPO), as well as entropy-regularized algorithms like soft actor-critic (SAC) and its recurrent extension (RSAC). Additionally, we include the asynchronous advantage actor-critic (A3C) for comparison. We evaluate their performance in terms of sum rate, reflection coefficient adaptation, and harvested energy, contrasting results under non-linear versus linear EH models. Our findings provide insights into algorithm suitability for optimizing BackCom systems under realistic EH constraints, highlighting performance trade-offs and the impact of EH non-linearity.

With the increasing number of Internet-of-things (IoT) devices, the need for energy-efficient and spectrum-efficient networks that can support resource-constrained devices within existing wireless infrastructures becomes critical. This paper investigates the application of deep reinforcement learning (DRL) algorithms to optimize the energy efficiency (EE) of a secondary device (SD) equipped with radio frequency energy harvesting (RF-EH) antennas. The system models a wireless powered communication network (WPCN) where the SD employs a cognitive-radio non-orthogonal multiple access (CR-NOMA) scheme to transmit data during uplink communications of neighboring primary devices (PDs). Among the DRL approaches evaluated, proximal policy optimization (PPO) emerged as the most effective, achieving the highest EE values and demonstrating its suitability for this problem. Additionally, our results show that equal gain combining (EGC) consistently achieves superior EE compared to other diversity-combining techniques, making it a favorable choice for self-sustaining IoT networks. These findings provide valuable insights into the role of diversity-combining techniques and DRL algorithms in enhancing SD performance in dynamic EH environments.

With the advent of sixth-generation (6 G) networks, efficient management of bandwidth and transmit power has become an increasingly important concern. With increasing network complexity and uncertainty, traditional optimization methods face multiple challenges. Large language models (LLMs) offer a flexible alternative to these methods due to their adaptability across varied conditions. In this study, we formulate a resource allocation problem involving multiple device-to-device (D2D) pairs and develop an LLM-based approach to maximize either energy efficiency (EE) or spectral efficiency (SE). We evaluate three LLM adaptation methods, namely fine-tuning, retrieval-augmented generation (RAG), and few-shot prompting, and identify fine-tuning as the most effective for the study under consideration. Finetuning achieves 94.24 % of optimal SE and 86.51 % of optimal EE, outperforming RAG and few-shot prompting in terms of performance. To further test its robustness, we examine finetuning under varied path loss distributions and increased system complexity, and observe how the LLM's predictions respond to these conditions. Additionally, fine-tuned models like Phi-3 Mini achieve inference times of less than one second (0.66s) and have a considerable time complexity advantage over exhaustive search, RAG, and few-shot prompting.

Multi-beam low Earth orbit (LEO) satellite communication is recognized as a promising technology that delivers high data rates and wide area coverage. However, managing multiple beams and controlling transmit power pose significant challenges due to the impact of inter-beam interference on performance and the impact of power consumption on satellite battery life. Generally, LEO satellite systems focus on reducing the gap between capacity and demand for uneven traffic distributions of ground users. In contrast to conventional approaches, this paper presents a theoretical analysis of the optimal conditions for minimizing satellite transmit power while meeting user traffic demands in multi-beam LEO satellite systems. Based on this analysis, we propose algorithms for user-beam association, beam pattern selection, timeslot scheduling, and power allocation to minimize the total power while satisfying the traffic demand. Simulation results show that the proposed methods have superior performance over conventional schemes in terms of power consumption and capacity-demand gap.

This paper investigates the synergistic potential of reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) to enhance the energy efficiency and performance of next-generation wireless networks. We delve into the design of energy-efficient passive beamforming (PBF) strategies within RIS-assisted coordinated multi-point (CoMP)-NOMA networks. Two distinct RIS configurations, namely, enhancementonly PBF (EO) and enhancement & cancellation PBF (EC), are proposed and analyzed. Our findings demonstrate that RISassisted CoMP-NOMA networks offer significant efficiency gains compared to traditional CoMP-NOMA systems. Furthermore, we formulate a PBF design problem to optimize the RIS phase shifts for maximizing energy efficiency. Our results reveal that the optimal PBF design is contingent upon several factors, including the number of cooperating base stations (BSs), the number of RIS elements deployed, and the RIS configuration. This study underscores the potential of RIS-assisted CoMP-NOMA networks as a promising solution for achieving superior energy efficiency and overall performance in future wireless networks.

Towards addressing emerging energy challenges posed by unfair heterogeneous Semantic Communication (SC) codec updates within future wireless networks, this paper presents a novel Anti-discrimination Federated learning (AdFed) approach. Inspired by the economics of discrimination, unique fairness-associated energy concerns in SC systems are formulated as model discrimination challenges, with the SC-deployed wireless network conceptualized as an anti-discrimination labor market. A novel “affirmative action” strategy, based on training epochs, is proposed and adopted according to historical training unfairness results. To address the reverse discrimination issues in “affirmative action” caused by quota fairness impacting training energy cost, we formulate this problem as a coupled integer non-linear programming problem. Moreover, a new quota trade-off mechanism based on the Rubinstein bargaining game is also designed. Simulation results verify that AdFed outperforms SC training baselines, effectively addressing the unique model discrimination challenges of SC codec model heterogeneity updating. The efficacy of the game theoretical trade-off mechanism is demonstrated in achieving optimal outcomes.

In this paper, we examine the effectiveness of distributed null-steering-based physical layer security (PLS) in over-the-air computation (AirComp), considering the impact of imperfect channel estimation. AirComp has been extensively studied for its ability to efficiently aggregate data in Internet of Things (IoT) networks, but dealing with imperfect channel estimation is a major pragmatic challenge in these networks. This work focuses on how phase errors in imperfect channel estimation affect AirComp’s distributed null-steering-based PLS by evaluating their impact on the mean square error (MSE) performance. A closed-form expression is derived to quantify the degradation in PLS performance of AirComp systems caused by channel estimation errors. The analysis shows that imperfect channel state information (CSI) causes a non-negligible degradation in the MSE performance and also reduces the effectiveness of the PLS technique employed. The degradation of up to 10 times in the average MSE received at the desired location is observed at the loop SNR value of 5 dB. Furthermore, the average MSE gap between the legitimate receiver and the eavesdropper is reduced by more than 0.1 with imperfect channel estimation. The results provide insights into the potential degradation due to imperfect CSI, which helps to quantify the relationship between the CSI estimation error and its impact on the PLS performance of AirComp networks.

The rapid surge in network sizes, driven by the proliferation of wireless applications, has placed unprecedented demands on wireless systems leading to high interference, spectrum bottlenecks, and increased power utilization. Reconfigurable intelligent surfaces (RISs) have emerged as a promising candidate for these challenges owing to their passive beamforming action, but have limited gains due to independently functioning elements. Consequently, beyond-diagonal RISs (BDRISs) are proposed as the key-enabler of next-generation systems. In this paper, we propose a fairness-aware design for BDRIS-assisted fluid antenna systems. We jointly optimize the antenna position vector, beamforming vectors, and BDRIS configuration matrix to address service fairness while meeting the power budget in a multi-user downlink system. To tackle this non-convex problem, we utilize proximal policy optimization and obtain a fast-converging solution. Our results demonstrate a 2.8 bps/Hz mean rate improvement as compared to conventional RIS systems.

This paper investigates the integration of drone (aka UAV)-assisted networks with a reconfigurable intelligent surface (RIS) to enhance energy efficiency in near-and far-field communication scenarios. The coexistence of near-field and far-field communications introduces unique challenges in ensuring efficient resource allocation, managing interference, and meeting quality of service requirements for users. Primary users in the near-field areas have stronger signal links, while secondary users and primary far-field users face increased path loss and interference, necessitating sophisticated optimisation strategies to balance their performance. To address these challenges, we propose a joint optimisation framework for transmission power allocation and RIS phase-shift design. The framework aims to maximise energy efficiency while maintaining reliable communication for all user groups, leveraging the complementary characteristics of UAV and RIS technologies. The low-complexity optimisation approach is developed, leveraging advanced successive convex approximation techniques and iterative algorithms. The framework consists of the Dinkelbach algorithm for the outer loop and a combination of linear and convex optimisation algorithms for the inner loop. Linear programming is employed to handle the large number of variables, such as phase-reflecting coefficients, while convex programming is used to optimise power allocation in UAVs, with convergence guaranteed. Simulation results reveal significant energy efficiency gains compared to baseline methods, demonstrating the effectiveness of the proposed framework in managing the coexistence of near-and far-field communications. The findings underscore the importance of energy-efficient design in enabling scalable and sustainable UAV-assisted networks, offering valuable insights for the development of high-performance next-generation communication systems.

Multibeam low-Earth orbit (LEO) satellite communication is a promising solution for providing high-data rate and wide area coverage. Therefore, satellite communication is introduced into Internet of Things (IoT) networks to support large-scale connectivity. In the satellite communication system, multibeam management and power control are challenging issues because interbeam interference severely affects system performance and power consumption influences the battery life of the satellite. Thus, traditional LEO satellite systems mainly focus on minimizing a capacity-demand gap to develop an effective power reduction algorithm. In contrast to this approach, in this article, we present a theoretical analysis of the optimal conditions for minimizing the transmit power of the satellite while satisfying the traffic demands of users in multibeam LEO satellite-assisted IoT networks. Based on this analysis, we propose algorithms for user-beam association, beam pattern selection, timeslot scheduling, and power allocation to minimize the transmit power while satisfying the traffic demands. In addition, we provide low-complexity algorithms for power minimization to reduce the computational complexity. Simulation results demonstrate that the proposed methods outperform the conventional schemes in terms of power consumption, capacity-demand gap, and computational complexity.

Age of information (AoI) is a critical metric for ensuring data freshness in carbon-intelligent and energy-efficient industrial Internet-of-things (IIoT) networks. We consider a user-specific sensing framework operating in a time division duplexing (TDD)–based IIoT network, consisting of energy harvesting (EH) sensors, users, and a cache-enabled edge node. The proposed framework aims to minimize AoI during data transmission to users while addressing the sensors’ energy constraints. For the uplink transmission of data from sensors to edge node, a quality-of-service-aware cognitive-radio non-orthogonal multiple access (CR-NOMA) is employed to enhance spectrum efficiency and minimize cache AoI. During the downlink transmission, upon user’s request, the edge node dynamically decides whether to retrieve cached data or request a fresh update from the sensors. This decision-making process is driven by reinforcement learning (RL) using a Q-table-based approach, where the edge node infers sensor battery levels from received updates and prioritizes user requests accordingly. We formulate this problem as a Markov decision process (MDP) and define an optimal policy that strikes a balance between minimizing AoI and adhering to energy constraints. To achieve this, we develop RL-based solutions, including Q-learning and deep Q-networks (DQN). Extensive simulations demonstrate that our approach achieves up to 90% AoI reduction while significantly improving energy efficiency, making it well-suited for real-time, delay-sensitive applications in modern IIoT networks.

The learning of fundamental quantum properties—namely coherence, discord, and entanglement—benchmarks the security, computational, and metrological capability of noisy intermediate-scale quantum (NISQ) communication, computing, and sensing networks. The current learning techniques vary widely for these fundamental quantum properties, including standard tomographic procedures that involve exhaustive optimization. Fortunately, the fundamentally distinct quantum properties feature an intricate connection. In this paper, we put forth the concept of universal quantum witness machines (UQWMs) to develop a unified framework for quantum property learning (QPL) of a quantum system. We first formulate the certification and quantification of quantum properties based on quantum witnesses. The witness-based certification method is experimentally accessible and resource-efficient but lacks reliability and generality. To universalize the scope and circumvent the unreliability, we transform the certification task into a classification task by employing UQWMs with classical machine learning to construct quantum property classifiers. This formalism offers a unifying perspective on the certification, quantification, and classification of these enigmatically linked fundamental quantum properties. To demonstrate our UQWM approach, we provide a comparative numerical analysis of quantum property quantification with quantum witnesses and classification performance analysis of quantum property classification with convolutional neural networks, specifically for$4 \times 4$quantum systems.

Physical-layer security (PLS) is a new paradigm for secure communication without requiring secret key exchange and management. Moreover, PLS with frequency diverse subarray (FDSA) can better control information leakage in the angle-range domain, which mitigates the security weakness of the phased array caused by its lack of range resolution. In this paper, we propose a three-dimensional (3D) directional modulation (DM) using randomized radiation with FDSA for enhanced PLS, employing a planar array. In addition, nonuniform frequency offsets (FOs) are considered as FO configurations (FOCs) for FDSA to concentrate on the mainlobe and suppress the undesired sidelobes in 3D space, where logarithmically increasing FOC (L-FOC), Hamming window-based FOC (H-FOC), and piecewise trigonometric FOC (P-FOC) are introduced. Characterizing the process of selecting the random subsets for randomized radiation, we provide the exact analysis of the secrecy rate of the proposed scheme. Moreover, FOs applied to FDSA and the number of random subsets are optimized with a genetic algorithm (GA)-based optimization strategy. We evaluate the proposed schemes in terms of secrecy rate and vulnerable volume, where the simulation results verify our analysis and show that nonuniform FOCs are a more favorable choice for FDSA compared to uniform FOC (U-FOC).

This paper explores the potential of aerial reconfigurable intelligent surfaces (ARIS) to enhance coordinated multipoint non-orthogonal multiple access (CoMP-NOMA) networks. We consider a system model where a UAV-mounted RIS assists in serving multiple users through NOMA while coordinating with multiple base stations. The optimization of UAV trajectory, RIS phase shifts, and NOMA power control constitutes a complex problem due to the hybrid nature of the parameters, involving both continuous and discrete values. To tackle this challenge, we propose a novel framework utilizing the multi-output proximal policy optimization (MO-PPO) algorithm. MO-PPO effectively handles the diverse nature of these optimization parameters, and through extensive simulations, we demonstrate its effectiveness in achieving near-optimal performance and adapting to dynamic environments. Our findings highlight the benefits of integrating ARIS in CoMP-NOMA networks for improved spectral efficiency and coverage in future wireless networks.

Ubiquitous coverage, high data rate connectivity, and mobile edge computing (MEC) are regarded as essential components, exemplifying the advancements anticipated in sixth-generation (6G) technology. Nevertheless, the successful implementation of these services heavily relies on the availability of robust network access and communication infrastructure often lacking in remote areas. In this context, there has been a considerable interest in non-terrestrial networks (NTNs) as a mean to compliment terrestrial communication. Targeting the 6G horizon, in this paper, a non-orthogonal multiple access (NOMA)-MEC enabled aerial-terrestrial network operating at millimeter wave (mmWave) is proposed where the terrestrial users are provided access and edge computing services by high altitude platforms (HAPs). We analyze the overall performance of the proposed model and aim to reduce the difference in execution time among the users in a NOMA cluster by optimizing the transmission power of users and the computational resource allocation at MEC servers via successive convex approximation method. Equalization of the execution time of paired users minimizes the inefficiencies in both the frequency and computational resource usage along with improving average uplink system throughput.

Amidst the ongoing debate about limited spectral availability, there remains a persistent demand for the development of spectrally efficient self-sustainable network (SSN) models. This paper addresses this challenge by optimizing spectral efficiency (SE) in uplink transmissions for an energy harvesting (EH)-enabled secondary user (SU) that operates opportunistically among multiple primary users (PUs) in an Internet-of-things (IoT) network. The PUs are assumed to employ a rotational time division multiple access (TDMA) scheme for transmissions, where the signals are divided into time slots for each PU to transmit data in a cyclic manner, while the SU uses an opportunistic non-orthogonal multiple access (NOMA) technique to transmit data without interfering with the PU transmissions, such that, at any given time slot, a PU and a SU share the same frequency band simultaneously. The SE of the system is maximized jointly by employing convex optimization and a deep reinforcement learning (DRL) model, specifically the deep deterministic policy gradient (DDPG) algorithm. Simulations demonstrate that the proposed approach significantly improves the SE of the considered IoT network, highlighting its potential for efficient spectrum management in IoT networks. We present a comprehensive SE analysis of the system, which further underscores the robustness and adaptability of our approach in optimizing SE under diverse operational conditions.

Intelligent reflecting surfaces (IRSs) have been considered to revolutionize beyond 5G and 6G systems as they help increase signal strength through their ability to control radio environments effectively. Introducing IRS assistance in a single-input single-output (SISO) network has been proven to improve the system's performance. This paper compares the performance of a single IRS-assisted SISO system against a double IRS-assisted system under various wireless network setups. Our work relies on a shared allocation scheme of IRS elements, where a practical phase-dependent amplitude phase shift model is utilized along with discrete phase shifts to develop a reliable and energy-efficient system. We observe energy efficiency while altering system parameters to identify the limits where each system works better. The simulation results show that two IRSs perform better at large deployments, whereas a single IRS performs better in compact environments.

Intelligent reflecting surface (IRS) is considered as a key technology for the sixth generation (6G) networks for creating a controlled environment for users to achieve higher data rates, throughput, and energy efficiency. IRS-assisted networks provide indirect line-of-sight (LoS) to non line-of-sight (NLoS) users, enabling them to achieve higher data rates. The purpose of this paper is to investigate the performance of IRS-assisted ultra-high frequency (UHF) networks in actual rural, sub-urban, and urban environment, in terms of rate coverage probability, spectral and energy efficiency. The actual building locations of all three locations with their heights are modeled as blockages. The analysis is carried out for different densities of BSs and IRS surfaces for different number of mobile users. Our analysis underlines the difference between the coverage probability of 2D versus 3D building areas. It also shows that the deployment of IRS surfaces significantly increases the rate per unit area of the conventional BS networks in practical environments. Our simulation results provide the optimal number of IRS elements and mobile users to achieve a certain rate coverage probability with maximum energy efficiency. Our results also highlight the optimal number of BSs and IRS surfaces for each location that can be deployed to achieve higher energy efficiency.

In a multi-cell network, the combination of coordinated multipoint (CoMP) transmissions and non-orthogonal multiple access (NOMA) is known to be an effective solution to mitigate inter-cell interference and enhance the data rates of the cell edge users. While the existing studies heavily focus on maximizing the data rate of the cell edge users or the sum rate of the network, however, the energy efficiency has not been thoroughly investigated. In particular, when it comes to amalgamating CoMP with cooperative NOMA (C-NOMA), various system configurations should be considered, which impact both data rate and energy efficiency. Therefore, in this paper, we provide an optimization framework of energy efficiency specifically for the edge user using a Markov decision process (MDP) where the underlying system leverages the benefits of both CoMP and C-NOMA. The simulation results demonstrate the effectiveness of the proposed approach for the joint optimization of data rate and energy efficiency.

Mobile edge computing (MEC) has emerged as a promising paradigm to enhance the computational capabilities of resource-constrained secondary devices (RCSDs) in proximity to prescheduled primary devices (PDs). In this context, we introduce a novel framework where an energy harvesting (EH)-enabled RCSD efficiently offloads computational tasks to an MEC server, while employing a cognitive radio-inspired non-orthogonal multiple access (CR-NOMA) scheme for efficient data transmission. The RCSD also harvests energy from the ambient radio frequency (RF) signals of the surrounding PDs. We propose a deep reinforcement learning (DRL)-based optimization strategy, specifically the deep deterministic policy gradient (DDPG) algorithm to minimize the service delay of the RCSD and optimize the time-sharing coefficient for harvesting energy when offloading computational tasks to the MEC server. This dynamic resource allocation strategy intelligently determines the duration for which RCSDs transmit data and allocate time for energy harvesting, thereby ensuring an optimal balance between computation offloading and energy sustainability. Simulations demonstrate the effectiveness of the proposed scheme in max-imizing the utility of the RCSDs while minimizing the overall service delay of the RCSD.

An exponential increase in the volume of connected user proximity wireless devices (UPWDs) is spearheading a hyper-connected ecosystem, which may enable smart cities, industries, and connected healthcare. However, this increase in the number of connected UPWDs results in significant amplification in electromagnetic field (EMF) exposure among users and consequently may result in potential physiological effects. Integrated sensing and communication (ISAC)-enabled ambient backscatter communication (ABC) is a promising technology that can power low-energy sensors and facilitate communication between data sources and sinks by reusing the available resources. The power-domain non-orthogonal multiple access (PD-NOMA) has the potential to provide channel resources to an increasing number of users simultaneously while adhering to the quality of service (QoS) requirements. However, empowering PD-NOMA with machine learning (ML) can mitigate challenges in massive channel access. This work uses a k-medoid and Silhouette analysis for sub-carrier allocation and optimizes power allocation to the users with optimization techniques using PD-NOMA in an ABC-enabled cellular network. The proposed system demonstrates a significant capability to reduce the aggregated uplink EMF exposure using robust and low-complexity ML techniques. The simulations show a superior performance compared to the state-of-the-art methods.

In this work, we propose a novel algorithm for subchannel and power allocation for Internet of Things (IoT) networks using downlink multicarrier nonorthogonal multiple access (MC-NOMA). Unlike single-carrier NOMA, MC-NOMA can utilize multiple subchannels, which is more suitable for supporting the massive connectivity of IoT users. However, in MC-NOMA, the joint subchannel and power allocation problem leads to a mixed-integer nonlinear programming problem, which is challenging to find an optimal solution. Therefore, in this article, we reformulate the joint subchannel and power allocation problem into a binary decision problem for subchannel allocation with a mathematical analysis of power allocation. Then, using the transformed problem, we propose a subchannel allocation scheme for MC-NOMA to improve the sum rate and outage performances compared with the conventional approaches. Even though many prior studies on the power allocation for MC-NOMA focused on deep learning-based methods to achieve an optimal solution with low complexity, it is difficult to jointly optimize the maximum power of each subchannel and the power for each NOMA user. Thus, we propose a deep learning-based training algorithm to optimize the maximum per-subchannel power with a mathematical analysis of power allocation for NOMA users. In addition, we introduce the user selection algorithm to avoid performance loss due to an outage user, where the presented algorithm can select users satisfying the data rate requirement. Through simulations, we show that the proposed subchannel and power allocation schemes have outstanding sum rate and outage performances compared with the existing schemes.

In the rapidly evolving landscape of advanced wireless networks, self-sustainable Internet of Things (IoT) networks become pivotal, necessitating to seamlessly accommodate additional resource-limited devices into the existing wireless infrastructures. To this end, this article considers an IoT scenario with a wireless-powered communication network (WPCN) where a resource-constrained secondary node (SN) with energy harvesting (EH) capabilities harvests energy from the ambient radio-frequency (RF) signals to meet its energy requirements. Notably, we introduce RF-EH diversity-combining techniques, such as equal gain combining (EGC), maximum ratio combining (MRC), and selection combining (SC), tailored for linear EH models. To address the spectrum scarcity, the SN employs a Quality of Service (QoS)–aware nonorthogonal multiple access (NOMA) scheme to opportunistically transmit data within the uplink transmissions of the primary devices (PDs) operating around. Aiming to maximize the sum rate of the SN, we jointly optimize the EH time and transmit power of the SN using deep reinforcement learning (DRL). Specifically, we implement a set of DRL and non-DRL algorithms to investigate their robustness in diverse RF-EH diversity-combining environment settings. Simulation results demonstrate the influence of diversity combining techniques on the sum rate performance of the SN, providing valuable insights into their role in optimizing SN performance under dynamic EH environments.

While targeting the energy-efficient connectivity of the Internet-of-things (IoT) devices in the sixth-generation (6G) networks, in this paper, we explore the integration of non-orthogonal multiple access-based backscatter communication (BAC-NOMA) and simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs). To this end, first, for the performance evaluation of the STAR-RIS-assisted BAC-NOMA system, we derive the statistical distribution of the channels under Nakagami-m fading. Second, by leveraging the derived statistical channel distributions, we present the effective capacity analysis under the delay quality-of-service (QoS) constraint. In particular, we derive the closed-form expressions for the effective capacity of the reflecting and transmitting backscatter nodes (BSNs) under the energy-splitting protocol of STAR-RIS. To obtain more insight into the performance of the considered system, we provide the asymptotic analysis, and derive the upper bound on the effective capacity, which represents the ergodic capacity. Our simulation results validate the analytical analysis, and reveal the effectiveness of the STAR-RIS-assisted BAC-NOMA system over the conventional RIS (C-RIS)- and orthogonal multiple access (OMA)-based counterparts. Finally, to highlight the trade-off between the effective capacity and energy consumption, we analyze the link-layer energy efficiency. Overall, this paper provides useful guidelines for the performance analysis and design of the STAR-RIS-assisted BAC-NOMA systems.

In this paper, we investigate the reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access-based backscatter communication (BAC-NOMA) system under Nakagami-m fading channels and element-splitting protocol. To evaluate the system performance, we first approximate the composite channel gain, i.e., the product of the forward and backscatter channel gains, as a Gamma random variable via the central limit theorem (CLT) and method of moments (MoM). Then, by leveraging the obtained results, we derive the closed-form expressions for the ergodic rates of the strong and weak backscatter nodes (BNs). To provide further insights, we conduct the asymptotic analysis in the high signal-to-noise ratio (SNR) regime. Our numerical results show an excellent correlation with the simulation results, validating our analysis, and demonstrate that the desired system performance can be achieved by adjusting the power reflection and element-splitting coefficients. Moreover, the results reveal the significant performance gain of the RIS-assisted BAC-NOMA system over the conventional BAC-NOMA system.

The applications of networking have undergone drastic evolutions since the commercialization of the first generation (1 G) wireless systems. These evolutions have gone from human-to-human (H2H) voice and text communications to the seamless network of people, devices, and objects, which exchange information to assist, accelerate, monitor, and actuate different social, personal, and interpersonal processes. The next-generation systems envision a unified networking architecture which meets the diverse technical specifications of industry 5.0 systems. Currently deployed systems do not provide an eco-friendly architecture for the mass-scale integration of wireless networking. Additionally, the high capital costs of deployment hinder the realization of seamless connectivity for the next industrial revolution. Intelligent reflecting surfaces (IRS) have emerged as a potentially revolutionary and environmentally sustainable technology to enable the concentrated real-world networks required to support advanced cyber-physical systems in the Internet-of-everything (IoE). In this work, we propose a machine learning-enabled resource allocation architecture for IRS-aided unmanned aerial vehicle (UAV) networks. Simulations show that the proposed scheme outperforms traditional resource allocation systems, and is feasible for real-time wireless network optimizations.

This study presents an analysis of the bit error rate (BER) and system capacity in a multi-user multiple-input multiple-output (MU-MIMO) wireless system that deploys or-thogonal frequency-division multiplexing (OFDM) for wideband communication in industrial Internet-of- Things (IIoT) networks. The focus is on analyzing and evaluating the impact of imperfect channel state information (CSI) on MU-MIMO-OFDM system performance in comparison to perfect CSI within industrial settings. For this, we consider an IIoT network consisting of a base station (BS) and multiple IIoT devices, each equipped with multiple antennas. The CSI is computed using the least squares (LS) estimation technique. Furthermore, we investigate the tradeoff between system capacity and BER performance, considering various MIMO configurations to determine an optimal setup. The simulation results demonstrate that both imperfect CSI and MIMO configurations significantly influence BER performance. The findings of this research could provide valuable insights for the design and optimization of MU-MIMO-OFDM-based IIoT networks.

Frequency diverse subarrays (FDSA) with linear frequency offsets (Lin-FO) can generate mainlobe to be confined around the desired receiver on an angle-range plane, which mitigates vulnerabilities in conventional randomized radiation techniques implemented with a phased array. However, the angle-range-coupled beampattern of subarrays imposes limitations on improving the secrecy rate. In this paper, we propose randomized radiation using FDSA with nonlinear FO designs for enhanced physical-layer security (PLS) in mmWave communications. Non-linear FO designs, including logarithmically increasing FO (Log-FO), Hamming window-based FO (HW-FO), and piecewise trigonometric FO (PT-FO), enable the formation of angle-range-decoupled beampatterns focused on the intended receiver. The exact secrecy rate of the proposed scheme is derived and validated through simulations. The evaluation of various FO designs reveals reduced vulnerable areas and highlights the impact of transmit power on their performance, offering valuable insights for FO design selection.

In today's world of burgeoning IoT and 6G communications, supporting low-powered devices is crucial to fully capitalizing on the Next Generation Internet of Things (NG-IoT) revolution. The proliferation of these devices will unlock unprecedented opportunities for energy efficiency, sustainability, and ubiquitous connectivity. This paper investigates the sum rate optimization of a quality-of-service (QoS)-aware EH-enabled passive IoT device in a cognitive radio inspired non-orthogonal multiple access (CR-NOMA)-assisted backscatter communication network. Our goal is to optimize the sum rate of a secondary passive IoT device while guaranteeing the QoS requirements of the scheduled primary device. The deep deterministic policy gradient (DDPG) algorithm is employed to dynamically adjust the reflection coefficient of the backscatter node, yielding optimal performance. Our results demonstrate significant improvements in the sum rate, highlighting the importance of incorporating advanced machine learning (ML) techniques into IoT and wireless communication domains to address critical challenges and enhance the overall performance of NG-IoT networks.

Targeting the delay-constrained Internet-of-Things (IoT) applications in sixth-generation (6G) networks, in this paper, we study the integration of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) and non-orthogonal multiple access-based backscatter communication (BAC-NOMA) under statistical delay quality-of-service (QoS) requirements. In particular, we derive the closed-form expressions for the effective capacity of the STAR-RIS assisted BAC-NOMA system under Nakagami-m fading channels and energy-splitting protocol of STAR-RIS. Our simulation results demonstrate the effectiveness of STAR-RIS over the conventional RIS (C-RIS) and show an excellent correlation with analytical results, validating our analysis. The results reveal that the stringent QoS constraint degrades the effective capacity; however, the system performance can be improved by increasing the STAR-RIS elements and adjusting the energy-splitting coefficients. Finally, we determine the optimal pair of power reflection coefficients subject to the per-BSN effective capacity requirements.

While conventional communication systems sufficiently meet the demands of human-to-human (H2H) information exchange, they cannot support the seamless mass-scale inclusion of non-human communication entities for next generation technological applications. In this context, intelligent reflecting surfaces (IRS) appear as a promising eco-friendly disruptive technology for the extremely dense practical realizations of wireless infrastructures required for futuristic cyber-physical systems. To better exploit IRS to enable ultra-massive connectivity, this work employs element-sharing between multiple users in a practical reflection model enabled IRS-aided wireless system. The element-sharing paradigm of allotment provides spectral efficiency gains while keeping the scale of IRS panels in feasible physical deployment and cost constraints. We investigate the dedicated and shared element-sharing schemes for IRSs under different operating conditions. Simulation results show that element-sharing outperforms dedicated element-allotment in systems with a higher number of served users and a limited number of reflecting elements while also being more robust to channel estimation and phase optimization errors.

Network sustainability relies on many important parameters where the timely dissemination of information has a prime role to improve network operations and henceforth the network sustainability. Age of Information is a critical metric in many applications of future networks including smart transportation systems as these networks require fresh updates from the various network entities for the successful delivery of their services. This paper considers smart vehicles in a vehicle-to-infrastructure network where each vehicle has a stream of data for transmission to the roadside unit (RSU). The information from vehicles is collected when they enter the communication range of an RSU and stay within the coverage area of that RSU for a particular time. During this time, the RSU attempts to receive information from each vehicle as timely as possible. This paper proposes a hybrid access mechanism consisting of both orthogonal and non-orthogonal multiple access that schedules the transmission of packets from vehicles to the RSU where each vehicle has a finite length queue. The transmission of the packets is modeled using a Markov decision process, where a specific cost function is optimized to collect maximum information from the vehicles in a minimum amount of time.

To achieve a global coverage and enhance throughput, low Earth orbit (LEO) satellites have been adopted in B5G and 6G communications, because they can provide lower latency and higher service density compared to those with higher orbital altitude. However, in the LEO satellite networks, ground users may observe significant Doppler, which should be estimated and compensated for reliable communications. Thus, in this paper, we provide a comprehensive review of the existing studies on Doppler characterization and compensation.

In this paper, we consider a non-orthogonal multiple access (NOMA)-enabled satellite-aerial-terrestrial network, where a batch of unmanned-aerial-vehicles (UAVS) act as decode-and-forward (DF) relays to simultaneously serve ground user equipments (UEs). The UAVs employ joint-transmission coordinated multi-point (JT-CoMP) to cooperatively provide coverage to a cluster of UEs utilizing the same resource block (RB), in a low signal-to-interference-plus-noise-ratio (SINR) setting. Our main objective is to increase the quality-of-service (QoS) of the UEs by maximizing the system sum-rate, which is accomplished by presenting a relay selection and a power allocation scheme, under limited available transmission power at the satellite and UAVs, QoS of UEs, and decoding order of UEs constraints. We first present an optimal UAV relays selection scheme which selects a group of UAVs satisfying the rate and decoding order constraints, and then sequentially solve the power allocation optimization problem for the two stages of transmission using Lagrange multipliers method and Karush-Kuhn-Tucker (KKT) conditions. Simulation results prove the effectiveness of our proposed system in successfully increasing the sum-rate of the network compared to baseline schemes, hence amplifying spectral efficiency.

Beyond 5G networks require low latency, high throughput and high data rates while maintaining appreciable coverage. To achieve this, a wider bandwidth is required. Terahertz (THz) band can provide such large bandwidth, however, it is not as reliable as the sub 6 GHz band due to absorption effects. Hence, we need infrastructure level approaches such as heterogeneous networks (HetNet) that provide backwards compatibility to increase coverage and reliability. In this paper, we consider a HetNet comprised of small base stations at THz and mmWave frequencies, and macro base station at sub-6 GHz frequency at different building densities based on multiple cities around the world. For quality of service (QoS) performance metrics, we take data rate coverage and power efficiency. Different system parameters are varied for six different locations to analyze the effectiveness of the proposed HetNet. Our results show that B5G networks are considerably more effective in environments with low building densities.

Federated learning has attracted considerable research interest to better shape the next-generation communication systems. Along this line, in this paper, different combinations of edge devices and convolutional layers of neural network are tested for global model convergence. We investigate the number of communication rounds (CRs) required to make a global model converge, when the number of convolutional layer channels and edge devices taking part in global model convergence varies. We observe the effects of additive white Gaussian noise (AWGN) on gradient vectors (GVs) that are shared with the parameter server (PS) through a wireless channel. Further, we add channel impairments and observe the CRs required to make the model converge. With higher values of noise power and channel impairments, even after exhausting the maximum number of CRs, the global model do not converges for lower number of edge devices and convolutional layer channels. However, if either or both the number of edge devices and convolutional layer channels are increased, the global model converges with substantially higher accuracy even for stronger noise and channel effects.

Wireless use cases in the industrial Internet of Things networks often require guaranteed data rates ranging from a few kilobits per second to a few gigabits per second. Supporting such a requirement in a single radio access technique is difficult, especially when bandwidth is limited. Although nonorthogonal multiple access (NOMA) can improve the system capacity by simultaneously serving multiple devices, its performance suffers from strong device interference. In this article, we propose a Q-learning-based algorithm for handling many-to-many matching problems, such as bandwidth partitioning, device assignment to sub-bands, interference-aware access mode selection [orthogonal multiple access or NOMA], and power allocation to each device. The learning technique maximizes system throughput and spectral efficiency (SE) while maintaining quality-of-service (QoS) for a maximum number of devices. The simulation results show that the proposed technique can significantly increase overall system throughput and SE while meeting heterogeneous QoS criteria.

The use of an unlicensed band has significantly boosted the capacity of cellular technology via LTE in unlicensed band, license assisted access, and new radio in unlicensed band. Likewise, cellular vehicle to everything in the shared band is also gaining momentum for intelligent transportation systems. Nevertheless, the cellular operator has to wisely decide the proper allocation of this unlicensed band as well as its licensed band, to its users. As the cellular operator cannot guarantee the quality of service in the unlicensed band, motivating the users to offload into the unlicensed band is one of the challenging tasks for the operator. In this paper, we propose an economical approach to encourage users to offload in the unlicensed band while maximizing the utility function for the users and revenue for the operator. Under the proposed scheme, fairness with legacy WiFi users operating in common channel is considered. We investigate the interaction between the operator and the user using a Stackelberg game. We derive the best response function for both operator and user to maximize its utility under complete information, such as service contract and usage pattern. However, it is not always practical to know the comprehensive knowledge of the user in a highly dynamic environment. Thus, a various multi-armed bandit algorithms are used and compared to drive convergence towards an optimal solution. Simulation results have been presented to compare and verify the performance of our proposed scheme.

Mobile edge computing(MEC) has emerged as an attractive solution by executing computation-intensive services at a powerful edge server instead of mobiles. Two types of data are necessary to this end. One is user-specific data acquired from mobiles, calledcomputation offloading(CO). The other is service-specific data downloaded from a central cloud, calledservice caching(SC). It is noteworthy that CO and SC decisions are coupled when each user’sservice preference(SP) is personalized. Specifically, noting that the optimal SC is to cache services likely to be requested more frequently, the resultant SC tends to be biased to the SP of the user whose offloading rate is high. On the other hand, such an SC decision causes longer computing latency of users with a relatively low offloading rate, which ultimately limits a CO decision for agile MEC services. This work tackles this issue from a sum-utility maximization perspective under radio-resource and computation-latency constraints. The average computation latency is first derived in closed-form by modeling a computation as a stochastic process following a hyper-exponential distribution. Based on it, we first consider the case for homogeneous SP where CO and SC decisions are decoupled. Thus, SC can be deterministically controlled using the homogeneous SP, while CO decision is independently determined, lying between water-filling and channel-inversion allocations. Next, we design a joint CO-and-SC policy for heterogeneous SP. CO and SC decisions are iteratively optimized with the other fixed by leveraging the homogeneous SP’s result. The optimal stopping rules are derived, guaranteeing the sum-utility enhancement. The proposed algorithm’s effectiveness is verified by simulations that the proposed CO-and-SC design for heterogenous SP always outperforms that for homogeneous SP.

In the above article, Hong et al. proposed a frequency region beamforming scheme exploiting frequency diverse subarray. We found that there is a mathematical flaw in precoding vector normalization in their sidelobe randomization scheme called the inverted subarray subset technique (ISST). We show that it is not only a matter of how to define and interpret the array factor, but it leads to the wrong performance optimization and misoperation of their own proposed scheme, which may cause detrimental security risks. Furthermore, to avoid any false conclusions in the future study caused by the irrational normalization, we also present the related techniques and their correct normalization.

The staggering growth of the number of vehicles worldwide has become a critical challenge resulting in tragic incidents, environment pollution, congestion, etc. Therefore, one of the promising approaches is to design a smart vehicular system as it is beneficial to drive safely. Present vehicular system lacks data reliability, security, and easy deployment. Motivated by these issues, this paper addresses a drone-enabled intelligent vehicular system, which is secure, easy to deploy and reliable in quality. Nevertheless, an increase in the number of operating drones in the communication networks makes them more vulnerable towards the cyber-attacks, which can completely sabotage the communication infrastructure. To tackle these problems, we propose a blockchain-based registration and authentication system for the entities such as drones, smart vehicles (SVs) and roadside units (RSUs). This paper is mainly focused on the blockchain-based secure system design and the optimal placement of drones to improve the spectral efficiency of the overall network. In particular, we investigate the association of RSUs with the drones by considering multiple communication-related factors such as available bandwidth, maximum number of links a drone can support, and backhaul limitations. We show that the proposed model can easily be overlaid on the current vehicular network reaping benefits of secure and reliable communications.

In this article, we investigate the downlink performance of a three-tier heterogeneous network (HetNet). The objective is to enhance the edge capacity of a macro cell by deploying unmanned aerial vehicles (UAVs) as flying base stations and small cells (SCs) for improving the capacity of indoor users in scenarios such as temporary hotspot regions or during disaster situations where the terrestrial network is either insufficient or out of service. UAVs are energy-constrained devices with a limited flight time, therefore, we formulate a two layer optimization scheme, where we first optimize the power consumption of each tier for enhancing the system energy efficiency (EE) under a minimum quality-of-service (QoS) requirement, which is followed by optimizing the average hover time of UAVs. We obtain the solution to these nonlinear constrained optimization problems by first utilizing the Lagrange multipliers method and then implementing a sub-gradient approach for obtaining convergence. The results show that through optimal power allocation, the system EE improves significantly in comparison to when maximum power is allocated to users (ground cellular users or connected vehicles). The hover time optimization results in increased flight time of UAVs thus providing service for longer durations.

The demand for massive connectivity and low latency in future wireless networks has outlined the non-orthogonal multiple access (NOMA) scheme a key technology due to its effectiveness to meet the massive capacity requirement of the beyond fifth-generation (B5G) and the sixth-generation (6G) systems. However, with the increase in the total number of users in NOMA, the number of successive interference cancellations (SICs) at each user is also increased, which ultimately incurs high computational complexity and hardware cost of the overall system. Space-time block code (STBC)-based cooperative NOMA (STBC-NOMA) requires a smaller number of SICs as compared to that of conventional cooperative NOMA. However, prior work on STBC-NOMA assumes perfect SIC, which is challenging in practice. Motivated by this fact, in this paper, we study the impact of imperfect SICs on the performance of STBC-NOMA. Further, we derive the closed-form expression for the ergodic capacity of STBC-NOMA for imperfect SICs and use that to draw a comparative analysis between STBC-NOMA, conventional cooperative NOMA, and non-cooperative NOMA. We find the maximum number of users accommodated by STBCNOMA for different levels of imperfect SICs.

Backscatter communication (BackCom) has been emerging as a prospective candidate in tackling lifetime management problems for massively deployed Internet-of-Things (IoT) devices. This passive sensing approach allows a backscatter node (BN) to transmit information by reflecting the incident signal from a reader without initiating its transmission. Power-domain non-orthogonal multiple access (PD-NOMA), i.e., multiplexing the BNs with different backscatter power levels, being a prime candidate for multiple access in 5G systems is fully exploited in this work to multiplex multiple BNs. Recently, a great deal of attention has been devoted to the study of NOMA-aided BackCom networks in the context of outage probabilities and system throughput. However, the exact closed-form expressions of bit error rate (BER) for such a system has not been studied in the literature. In this paper, we derive the analytical BER expressions for a two BN BackCom system employing NOMA with imperfect successive interference cancellation (SIC) over an additive white Gaussian noise (AWGN) channel. The obtained BER expressions are utilized to evaluate the optimum reflection coefficients of BNs needed for the most optimal system performance in terms of BER and range of communication.

In recent years, unmanned aerial vehicles (UAVs) have been deployed in a range of new applications such as remote surveillance, package delivery and relief operations. The existing scenario of next-generation communications systems envisions the use of UAVs as low altitude platforms (LAPs) as one of the enabling technologies of next-gen networks. Telecom operators have been exploring low-altitude UAV-based communications solutions for on-demand deployment. The emerging possibilities of UAVs in air-to-ground (AG) communication necessitate accurate channel models in order to facilitate the design and implementation of such AG links. However, the propagation channels of Pakistan and in general the South Asian region have not been as of yet widely investigated. In this paper, a comprehensive study is presented on the air-to-ground channel parameters along with details of measurement campaigns as well as the limitations of this work and future research directions.

Unmanned aerial vehicles (UAVs) provide us with the ability for rapid, on demand, and infrastructure-less deployment. This capability can be exploited to meet the rising demands of future fifth generation (5G) and Internet of things (IoT) networks. In this study, we consider a downlink scenario in a multi-tier heterogeneous network (HetNet), with sub-6GHz and millimeter wave (mmWave) UAVs coexisting as aerial base stations (ABSs), to meet the network quality-of- service (QoS) requirements. The considered QoS metrics are network coverage and data rates. The network area has no pre-existing communication infrastructure. We study the impact of various network configurations, by varying ratio of mmWave UAVs to total UAVs in the HetNet, number of users, and bias factor for mmWave tier. Our results validate that sub-6GHz and mmWave UAVs can be deployed together to complement each other in a multi-tier HetNet where the sub-6GHz tier will enhance the coverage and the mmWave tier will improve the data rates. Through extensive simulations, we propose and implement a methodology to configure the HetNet for an efficient coverage-rate trade off, while meeting the desired QoS metrics at the same time.

A wireless sensor network (WSN) refers to a network of sensor nodes that collaboratively work to sense, monitor, and control their surrounding environments. As WSNs are integrated into the Internet of Things, it is crucial to protect the network against malicious security attacks considering its wide applicability, such as military monitoring, healthcare, and civilian applications. Thus, in this paper, we consider a physical-layer security technique exploiting analog cooperative beamforming (ACB), where multiple sensor nodes create a virtual antenna array (VAA) and locally adapt their phases. As WSNs inherently have clustered topology, we model the VAA elements' locations by Gaussian distributions. The secrecy capacity of the ACB with the Gaussian-distributed elements is derived in a closed-form expression. The theoretical and numerical results indicate that the ACB-based schemes provide a better secrecy rate compared to the conventional co-located antenna array-based schemes. In addition, the ACB with Gaussian-distributed elements can better suppress side-lobe, which causes undesired information leakage to certain directions, compared to the ACB with uniformly distributed elements. Furthermore, we investigate the impacts of fading channel and phase estimation error in the ACB.

Internet of things (IoT) is a smart technology that connects anything anywhere at any time. Intelligent device-to-device (D2D) communication, in which devices will communicate with each other autonomously without any centralized control, is an integral part of the Internet of Things (IoT) ecosystem. Thus, for D2D applications such as local file sharing or swarm sensing, we study communications between devices in proximity in ultra-dense urban environments, where devices are stacked vertically and dispersed in the horizontal plane. To reflect the spatiotemporal correlation inherently embedded in the D2D communications, we model and analyze clustered D2D networks in three-dimensional (3D) space based on Thomas cluster process (TCP), where the locations of clusters follow Poisson point process, and cluster members (devices) are normally distributed around their cluster centers. We assume that multiple device pairs in the network can share the same frequency band simultaneously. Thus, in the presence of cochannel interference from both the same cluster and the other clusters, we investigate the coverage probability and the area spectral efficiency of the clustered D2D networks in 3D space.

For multi-hop wireless sensor networks (WSNs), time synchronization (TS) across distributed nodes is important for certain applications (i.e., surveillance, gunshot location system, etc.). In this paper, a method combining two forms of cooperative transmission (CT), digital-based Concurrent Cooperative Transmission (CCT) and analog-based Semi-Cooperative Spectrum Fusion (SCSF), is proposed and demonstrated to achieve fast and accurate time synchronization over large multihop WSNs. CCT, considered in many physical layer system studies and realistically demonstrated, enables range extension. Range extension implies that the source timestamp can be diffused to the network faster (i.e., in fewer hops) than non-CT-based TS protocols. SCSF is an analog CT method where different cooperators transmit correlated information simultaneously. The two methods combine to create a new distributed method of network TS, called the Cooperative Analog and Digital (CANDI) TS protocol, which promises significantly shorter protocol time and smaller time errors in multi-hop networks. This paper presents the algorithm description, and physical layer simulations as well as network experimental results over a line network that show the performance advantage of CANDI over TPSN.

The Opportunistic Large Array (OLA) is one type of cooperative transmission, which provides fast and reliable broadcasts and unicasts in multi-hop networks. While the existing literature on OLAs assumes one-shot transmission with a single packet, we analyze multi-packet OLA transmission within a single flow along strip-shaped networks or OLA-based cooperative routes. When multiple packets are transmitted from the same source using the same channel before the previous packet has cleared the network, which is called spatial pipelining, intra-flow interference is produced by OLAs transmitting co-channel packets. Using the continuum assumption (approximated by high density networks), the authors optimize the throughput. This paper theoretically shows spatial pipelining over strip networks is always feasible for path loss exponent α≥2, which is not true for disk-shaped networks. Moreover, the impacts of system parameters are analyzed, and the upper bound of the optimal packet spacing (the lower bound of the optimal throughput) is derived. Also, spatial pipelining in finite networks is studied with numerical results, which confirm the theoretical analysis.

Cooperative transmission (CT), in which spatially separated wireless nodes collaborate to form a virtual antenna array or virtual multiple-input-multiple-output (VMISO) link, is an effective technique to mitigate multi-path fading by spatial diversity. In this paper, we study the disparities in path losses between the randomly placed relay nodes in a transmit cluster and the destination nodes. Many authors assume that the elements in a virtual antenna array are co-located, even though they are spread out. In this paper, we show that a signal-to-noise-ratio (SNR) penalty of up to 3dB should be included when making this assumption. By the high-SNR approximation of the outage rates, we show that the performance degradation caused by the path-loss disparity can be characterized equivalently by log-normal shadowing. Moreover, we derive the upper and lower bounds of the real outage probabilities in closed forms based on the log-normal shadowing model, by which we can estimate the SNR penalty of the co-located assumption.

The Opportunistic Large Array (OLA) is a simple form of cooperative transmission, in which a group of single-antenna nodes decode the same packet, then a short time later relay the packet simultaneously in orthogonal or space-time coded channels. The authors have previously shown that OLA transmissions can be adequately synchronized so they appear to a receiver as having come from a conventional array with co-located antennas doing transmit diversity. OLAs have been considered as a basis for rapid single-packet broadcasting in multi-hop networks, however, there are few studies that consider OLA broadcasting of multiple co-channel packets. Using the continuum assumption (approximated by high density networks), we focus on broadcast throughput optimization, as determined by the packet insertion rate at the source. We consider spatial pipelining, which means broadcasting a packet before the previous co-channel packet has cleared the network. We show theoretically that for infinitely large networks the feasibility of spatial pipelining depends on the path loss exponent. For finite networks, we study the propagation behavior of spatially pipelined packets using numerical analysis.

The Opportunistic Large Array (OLA), a simple form of concurrent cooperative transmission that extends range, is known to provide fast and reliable broadcasting of a single packet on both disk- and strip-shaped networks. This paper studies multi-packet OLA transmission within a single flow along a strip-shaped network, which also models an OLA-based cooperative route in a large multi-hop network. One packet makes interference on many other packets in the same flow, which generally retards OLA sizes for all packets, and can cause some packets to die off. However, by using the continuum and deterministic channel assumptions, which correspond to high node density, the authors show that multi-packet transmission can be made reliable and throughput can be optimized, by selecting the correct inter-packet spacing.