The Unmanned Aerial Vehicle (UAV)-assisted networks play a vital role in disaster circumstances for quick rescue operations by enabling emergency communication services. Unlike conventional networks, emergency networks have unique challenges, such as encountering Critical Nodes (CNs) that contain vital information. The effectiveness of rescue operations mainly depends on the coverage of these CNs to retrieve essential data for coordinating rescue efforts. In this context, Age-of-Information (AoI) is used to evaluate the timely collection of data from CNs. Voronoi diagram-based partitioning is employed as an adaptive mechanism linked with UAV swarm size and K-means clustering to enable nodes distribution-aware spatial partitioning, ensuring collision-free path planning in disaster scenarios. The distance-optimized CNA trajectory is proposed to optimize UAV swarm paths for coverage maximization and AoI minimization by adapting the scalarization approach. The performance of the proposed algorithm is analyzed based on coverage, AoI, trajectory length, and total flight time. Simulation results show that the proposed distance-optimized CNA trajectory outperforms the conventional distance-based and the CNA trajectory by 25.27% and 41.20%, respectively. It improves the CNs coverage and AoI of distance-based trajectory by 30% and 10%, priority-based Traveling Salesman Problem (TSP) by 47% and 7%, and CNA trajectory by 13% and 17%, respectively. When the percentage of CNs increases from 10% to 40%, the number of covered CNs increases linearly for a given number of hovering points.

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.

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.

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.

EV charging infrastructures traditionally rely on untrusted centralized infrastructures that pose several privacy and security threats to EVs’ personal information. Targeted advertisements, privacy leaks and selling data to third parties are among the threats to privacy and security. By utilizing blockchain-based solutions, recent work address the security and privacy problems associated with EV charging protocols. Most of them are geared toward maintaining EV anonymity rather than preserving end-to-end privacy. As EV owners’ charging histories and payment information are associated with their wallet addresses on the blockchain, any threat of linkability of these blockchain addresses to physical identities can pose a serious risk to their privacy. In this paper, we propose a ring signature based privacy-preserving end-to-end charging station (CS) reservation and payment protocol, which provides EV owners with the ability to reserve and pay for a charging slot privately without sharing private information or exposing their identity or addresses at CS locations. Additionally, we provide EV owners with a decentralized charging slot information verification protocol with the help of secure multiparty computation (SMC), which allows them to verify available slots. A dispute resolution mechanism is also proposed that handles disputes between EVs and CSs and penalizes them accordingly by utilizing trusted execution environment (TEE). Results show that the proposed protocol ensures end-to-end EV owners’ privacy with low blockchain transaction and computation overhead.

With the rapid growth of Internet-of-Things (IoT) devices and unmanned agents (UAs), there is a rising need for energy- and spectrum-efficient wireless networks that can support large-scale, resource-constrained deployments. To meet this demand, integration of deep reinforcement learning (DRL), non-orthogonal multiple access (NOMA), and energy harvesting (EH) offers a promising approach to enhance energy efficiency (EE) and spectrum utilization in future sixth-generation (6G) networks, particularly for sustainable Internet of UA (IUA) communications. In this paper, we investigate an IUA network where multiple low-power secondary users (SUs), equipped with radio frequency energy harvesting (RF-EH) antennas, use a cognitive radio NOMA (CR-NOMA) scheme to share uplink channels with nearby primary users (PUs). We formulate a joint transmit power control and EH scheduling problem to maximize the long-term EE of the SUs and spectrum utilization of the network, subject to quality-of-service (QoS) constraints. To address the decentralized nature of the problem, we model the environment as a multi-agent system where each SU independently optimizes its transmission and EH strategies. A range of DRL and non-DRL algorithms is then applied to solve this optimization problem. We also explore different RF-EH diversity combining techniques to further boost system performance. Simulation results highlight the impact of these techniques on EE of SU, offering insights for optimizing performance under dynamic EH conditions.

Deploying large-scale foundation models (FMs) in resource-constrained devices presents critical challenges due to their substantial computational and memory requirements. This is particularly relevant for multi-task wireless sensing FMs running on sensors. To overcome these limitations, we propose a tiny federated wireless foundation model (WFM) framework that combines spectrogram-guided structured block-wise pruning with federated learning (FL) for efficient on-device deployment. Our approach prunes non-essential encoder blocks in vision transformers (ViTs) by leveraging the masked spectrogram modeling (MSM) pretraining loss as an importance indicator, ensuring only the most structurally significant components are retained. This enables federated adaptation with frozen backbones and lightweight, task-specific heads, minimizing both computational burden and communication overhead. The pruning strategy preserves the integrity of spectrogram reconstruction, while federated fine-tuning supports decentralized learning across clients with heterogeneous data distributions. Experimental results on human activity sensing and radio signal identification tasks confirm the efficacy of our approach. Specifically, the pruned ViT-based WFMs achieve up to 93% multiply-accumulate operations (MACs) reduction, 85% lower CPU inference time, and 49% reduction in communication overhead, all while maintaining high task accuracy. Our method demonstrates strong generalization and robustness across varying pruning ratios and data heterogeneity levels, while substantially reducing communication overhead, making it highly suitable for real-world industrial IoT deployments.

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.

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.

Due to their potential to accomplish complicated missions more effectively, UAV swarms have attracted a lot of attention lately. UAV swarm offers enhanced intelligence, improved coordination, increased flexibility, survivability, and reconfigurability. It is a multi-disciplinary system that necessitates a tight integration of several sub-systems, including optimal trajectory planning, localization, task coordination, etc. This review covers the important aspects of UAV swarms including swarm formation control, communication, swarm path planning, autonomy, coordination, and security. It additionally explores recent technical advancements in UAV swarm algorithms that have made the development of complex UAV swarm systems possible. This paper also provides insight into ethical aspects and the use cases of UAV swarms in various military, civilian, and entertainment applications. This paper is concluded by highlighting the potential future directions and challenges of UAV swarm technology and the need for more research and development to exploit their potential fully. Overall, this paper presents a comprehensive review of UAV swarm technology, addressing its potential for revolutionizing many fields and supporting advancements in the future.

Over-the-air federated learning (OTA-FL) is a distributed machine learning technique where multiple devices collaboratively train a shared model without sharing their raw data with a central server. The devices exchange model updates concurrently over-the-air and they are aggregated without the need for dedicated wireless resources for each device. A major challenge in OTA-FL is that edge devices are limited in their computation, energy, and communication resources. To address this, we investigate how deep neural network compression techniques can be applied in an OTA-FL system. We propose a compression pipeline comprised of pruning and quantization-aware training that significantly reduces both the computation and communication requirements while maintaining an on-par accuracy to the uncompressed models. We thoroughly investigate the reduction in model size, accuracy, and convergence when pruning and quantization-aware training are applied individually and collectively at multiple pruning and quantization levels. Detailed experiments are conducted on two-different deep learning models and two-different datasets, and under varying signal-to-noise ratios (SNRs) and numbers of clients. We then thoroughly present and discuss the resulting trade-offs and findings. Our results demonstrate that deep compression is very effective in an OTA-FL system and negligible losses in accuracy are possible while maintaining up to 80 percent reductions in model size.

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.

Abstract Due to the increase in the growth of data in this era of the digital world and limited resources, there is a need for more efficient data compression techniques for storing and transmitting data. Data compression can significantly reduce the amount of storage space and transmission time to store and transmit given data. More specifically, text compression has got more attention for effectively managing and processing data due to the increased use of the internet, digital devices, data transfer, etc. Over the years, various algorithms have been used for text compression such as Huffman coding, Lempel-Ziv-Welch (LZW) coding, arithmetic coding, etc. However, these methods have a limited compression ratio specifically for data storage applications where a considerable amount of data must be compressed to use storage resources efficiently. They consider individual characters to compress data. It can be more advantageous to consider words or sequences of words rather than individual characters to get a better compression ratio. Compressing individual characters results in a sizeable compressed representation due to their less repetition and structure in the data. In this paper, we proposed the ArthNgram model, in which the N-gram language model coupled with arithmetic coding is used to compress data more efficiently for data storage applications. The performance of the proposed model is evaluated based on compression ratio and compression speed. Results show that the proposed model performs better than traditional techniques.

Industry 5.0 vision, a step toward the next industrial revolution and enhancement to Industry 4.0, conceives the new goals of resilient, sustainable, and human-centric approaches in diverse emerging applications such as factories-of-the-future and digital society. The vision seeks to leverage human intelligence and creativity in nexus with intelligent, efficient, and reliable cognitive collaborating robots (cobots) to achieve zero waste, zero-defect, and mass customization-based manufacturing solutions. However, it requires merging distinctive cyber–physical worlds through intelligent orchestration of various technological enablers, e.g., cognitive cobots, human-centric artificial intelligence (AI), cyber–physical systems, digital twins, hyperconverged data storage and computing, communication infrastructure, and others. In this regard, the convergence of the emerging computational intelligence (CI) paradigm and softwarized next-generation wireless networks (NGWNs) can fulfill the stringent communication and computation requirements of the technological enablers of the Industry 5.0, which is the aim of this survey. In this article, we address this issue by reviewing and analyzing current emerging concepts and technologies, e.g., CI tools and frameworks, network-in-box architecture, open radio access networks, softwarized service architectures, potential enabling services, and others, elemental and holistic for designing the objectives of CI-NGWNs to fulfill the Industry 5.0 vision requirements. Furthermore, we outline and discuss ongoing initiatives, demos, and frameworks linked to Industry 5.0. Finally, we provide a list of lessons learned from our detailed review, research challenges, and open issues that should be addressed in CI-NGWNs to realize Industry 5.0.

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.

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.

In this work, we consider the design of a radio resource management (RRM) solution for traffic steering (TS) use-case in the open radio access network (O-RAN). The O-RAN TS deals with the quality-of-service (QoS)-aware steering of the traffic by connectivity management (e.g., device-to-cell association, radio spectrum, and power allocation) for emerging heterogeneous networks (HetNets) in 5G-and-beyond systems. However, TS in HetNets is a complex problem in terms of efficiently assigning/utilizing the radio resources while satisfying the diverse QoS requirements of especially the cell-edge users due to their poor signal-to-interference-plus-noise ratio (SINR). In this respect, we propose an intelligent non-orthogonal multiple access (NOMA)-based RRM technique for a small cell base station (SBS) within a macro gNB. A Q-learning-assisted algorithm is designed to allocate the transmit power and frequency sub-bands at the O-RAN control layer such that interference from macro gNB to SBS devices is minimized while ensuring the QoS of the maximum number of devices. The numerical results show that the proposed method enhances the overall spectral efficiency of the NOMA-based TS use case without adding to the system's complexity or cost compared to traditional HetNet topologies such as co-channel deployments and dedicated channel deployments.

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.

By amalgamating recent communication and control technologies, computing and data analytics techniques, and modular manufacturing, Industry 4.0 promotes integrating cyber–physical worlds through cyber–physical systems (CPS) and digital twin (DT) for monitoring, optimization, and prognostics of industrial processes. A DT enables interaction with the digital image of the industrial physical objects/processes to simulate, analyze, and control their real-time operation. DT is rapidly diffusing in numerous industries with the interdisciplinary advances in the industrial Internet of things (IIoT), edge and cloud computing, machine learning, artificial intelligence, and advanced data analytics. However, the existing literature lacks in identifying and discussing the role and requirements of these technologies in DT-enabled industries from the communication and computing perspective. In this article, we first present the functional aspects, appeal, and innovative use of DT in smart industries. Then, we elaborate on this perspective by systematically reviewing and reflecting on recent research trends in next-generation (NextG) wireless technologies (e.g., 5G-and-Beyond networks) and design tools, and current computational intelligence paradigms (e.g., edge and cloud computing-enabled data analytics, federated learning). Moreover, we discuss the DT deployment strategies at different communication layers to meet the monitoring and control requirements of industrial applications. We also outline several key reflections and future research challenges and directions to facilitate industrial DT’s adoption.

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.

Cellular networks are envisioned to be a cornerstone in future industrial Internet of Things (IIoT) wireless connectivity in terms of fulfilling the industrial-grade coverage, capacity, robustness, and timeliness requirements. This vision has led to the design of vertical-centric service-based architecture of 5G radio access and core networks. The design incorporates the capabilities to include 5G-AI-Edge ecosystem for computing, intelligence, and flexible deployment and integration options (e.g., centralized and distributed, physical, and virtual) while eliminating the privacy/security concerns of mission-critical systems. In this article, driven by the industrial interest in enabling large-scale wireless IIoT deployments for operational agility, flexible, and cost-efficient production, we present the state-of-the-art 5G architecture, transformative technologies, and recent design trends, which we also selectively supplemented with new results. We also identify several research challenges in these promising design trends that beyond-5G systems must overcome to support rapidly unfolding transition in creating value-centric industrial wireless networks.

More data and information is being captured from systems, machines, and devices and made available to industrial information technology (IT) systems. The information is processed on-the-fly, enabling IT-based management systems to generate updated information for real-time control of the manufacturing processes. This data capturing and collection for IT systems is often referred to as the Internet of Things (IoT). When adopted to the industrial requirements, such as robustness, reliability, timeliness, and security, it is often termed as the industrial IoT (IIoT) [A1]. IIoT has attracted the attention of both the industry and academia since it is expected to enhance day-to-day activities, create new business models, products, and services, and as a broad source of research topics and ideas. Meanwhile, it is envisioned that the fifth-generation (5G) networks will be a cornerstone in future wireless industrial connectivity, and currently, there are multitude of ongoing research efforts in their design and optimization. Future industries willembrace use cases with numerous wireless-connected sensors and devices, and judging by the demand, massive machine-type communication and ultra-reliable low-latency communication (URLLC) in the literature and standardization activities, have been identified as two of the three main communication scenarios for 5G. These scenarios demand intelligent, scalable, and robust radio access techniques, network architectures, and deployment options to meet industrial demands [A2]. Therefore, more in-depth research is needed for IIoT and sensor networks in 5G-and-beyond wireless communication systems to address various challenges, including the following. 1)Transmit power control policy should be judiciously designed to improve both the spectrum efficiency and energy efficiency effectively; higher transmit powers can improve reliability but increase the interference and battery consumption. 2)Low-latency communication and computing is one of the significant challenges in 5G-and-beyond IIoT; uploading the device data to the cloud computing centers has high latency and resources waste issues in sensor networks. 3)Addressing privacy and security problems [A3] in the 5G-IIoT is fundamental to the further development and spread of 5G-IIoT. 4)Reliability and latency requirements of URLLC services, requiring less than 1-ms user plane latency and higher than 99.999% reliability, are demanding to meet, especially in time-varying industrial wireless channels. 5)Radio resource allocation, sharing, and isolation with performance guarantees under dynamic traffic conditions are critical issues for emerging IIoT applications requiring real-time support of massive connected devices. 6)5G-and-beyond IIoT networks must satisfy industrial-grade coverage, capacity, time-sensitive networking, and over-the-air time synchronization requirements [A4].

5G-and-beyond (B5G) networks are moving toward the higher end of the millimeter-wave (mmWave) spectrum (i.e., from 25 to 100 GHz) to support integrated communications and ranging (ICAR) services in next-generation factory deployments. The ICAR services in factory deployments require extreme bandwidth/capacity and large ranging coverage, which a mmWave-B5G system can fulfill using massive multi-input and multioutput (mMIMO), beamforming, and advanced ranging techniques. However, as mmWave signal propagation is sensitive to harsh channel conditions experienced in typical indoor factory environments, there is a growing interest in the realistic mmWave indoor channel modeling to evaluate the practical scope of the mmWave-B5G systems. In this article, we study and implement a 3-D stochastic channel model using the baseline third-generation partnership project model. Our channel model employs the time-cluster spatial-lobe (TCSL) technique and utilizes the temporal and spatial statistics to create the channel impulse response (CIR), reflecting realistic indoor factory conditions. Using the generated CIR, we present the performance analysis of an mmWave-B5G system in terms of power delay profile, path loss, communication and ranging coverage, and mMIMO channel capacity.

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.

Heterogeneous networks (HetNets) are becoming a promising solution for future wireless systems to satisfy the high data rate requirements. This paper introduces a stochastic geometry framework for the analysis of the downlink coverage probability in a multi-tier HetNet consisting of a macro-base station (MBS) operating at sub-6 GHz, millimeter wave (mmWave)-enabled unmanned aerial vehicles (UAVs) operating at 28 GHz, and small BSs operating both at mmWave and THz frequencies. The analytical expressions for the coverage probability for each tier have been derived in the paper. Monte Carlo simulations are then performed to validate the analytical expressions. The effectiveness of the HetNet is analyzed on various performance metrics including association and coverage probabilities for different network parameters. We show that the mmWave and THz-enabled cells provide significant improvement in the achievable data rates because of their high available bandwidths, however, they have a degrading effect on the coverage probability due to their high propagation losses.

As important services of the future sixth-generation (6G) wireless networks, vehicular communication and mobile edge computing (MEC) have received considerable interest in recent years for their significant potential applications in intelligent transportation systems. However, MEC-enabled vehicular networks depend heavily on network access and communication infrastructure, often unavailable in remote areas, making computation offloading susceptible to breaking down. To address this issue, we propose an MEC-enabled vehicular network assisted through aerial-terrestrial connectivity to provide network access and high data-rate entertainment services to a vehicular network. We present a time-varying, dynamic system model where high altitude platforms (HAPs) equipped with MEC servers, connected to a backhaul system of low-earth orbit (LEO) satellites, are used to provide computation offloading capability to the vehicles, as well as to provide network access for vehicle-to-vehicle (V2V) communications. Our main objective is to minimize the total computation and communication overhead of the joint computation offloading and resource allocation strategies for the system of vehicles. Since our formulated optimization problem is a mixed-integer non-linear programming (MINLP) problem, which is NP-hard, we propose a decentralized value-iteration-based reinforcement learning (RL) approach as a solution. In our Q-learning-assisted analysis, each vehicle acts as an intelligent agent to form optimal strategies for offloading and resource allocation. We further extend our solution to deep Q-learning (DQL) and double deep Q-learning to overcome the issues of dimensionality and the over-estimation of the value functions, as in Q-learning. Simulation results prove the effectiveness of our solution in successfully reducing system costs compared to baseline schemes.

The exponential growth of the Internet of Things (IoT) devices has resulted in huge power consumption issues in wireless devices. Cooperative communication with simultaneous wireless information and power transfer (SWIPT) is a promising technology to improve the coverage, capacity, power consumption, and reliability of the IoT networks. Relay selection plays a pivotal role in cooperative communications for improving the quality of service (QoS) of the network. In this paper, we propose a SWIPT based hybrid protocol to improve the QoS in terms of average end-to-end outage probability through proposed relay selection. Furthermore, we investigate the impact of time switching factor α and power splitting factor λ on the average outage probability and the amount of energy harvested. The simulation results demonstrate that by selecting the best relay through proposed relay selection, we observe maximum improvement of 94 % in terms of average end-to-end outage over the worst relay at transmit power of 38 dBm. Similarly, we observe an increase in the amount of energy harvested by approximately 90% over the worse relay at transmit power of 45 dBm.

With the increase of connected Internet-of-things (IoT) devices, the need for low-power wide-area networks (LP-WANs) is imminent, and LoRaWAN is one such technology that offers an elegant solution to the problem of long-range communication and battery consumption. A parameter of special interest in LoRaWANis the spreading factor (SF), and it is often assumed that communication between different SFs is independent of each other. However, this claim has been practically debunked by many works, proving that SFs have imperfect orthogonality. To maximize connectivity and throughput, several techniques have been introduced, such as non-orthogonal-multiple-access (NOMA) and dynamic resource allocation. NOMA is getting a lot of attention recently, especially for IoT networks, because it embraces interference and tries to obtain desired information packets from corrupted ones. Furthermore, NOMA can be easily implemented on the base-station side by using the principle of successive interference cancellation (SIC). In this paper, we investigate how SIC, under the assumption of imperfect orthogonality of SF channels, can be used to increase the performance of the system. We find the expressions for success and coverage probability considering various SF allocation schemes and found the most efficient scheme for different scenarios.

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.

Age of information (AoI) refers to the freshness of data generated by a status-update system. It is a crucial metric in networks such as Internet of things (IoT), specially when the underlying application demands fresh update. In environmental monitoring and smart agriculture, apart from the importance of AoI, energy efficiency (EE) becomes inevitable owing to network longevity. This paper studies an IoT network where the end devices transfer their information to a central gateway residing on a moving platform such as a tractor, which collects information from a large number of sensors in an agri-field. An optimal trajectory of the mobile reader is proposed using a modified nearest neighbor algorithm to gather the information from randomly distributed sensors. A clustering algorithm is also used to cluster the data in such a way that the overall EE of the network is maximized keeping a desired AoI and outage probability.

Unmanned aerial vehicles (UAVs) are expected to be extensively used as an integral part in the future generations of communication networks, to provide ubiquitous connectivity. The mobile nature of UAVs make them a tempting candidate to provide seamless connectivity in environments where the installation of conventional terrestrial base stations (BS) is not feasible. Nonetheless, there are major deployment issues related to optimal placement of UAV-mounted base stations (UAV-BSs) due to limited number of UAV-BSs, limited energy availability and trade-off between coverage area and its altitude. In this paper, we address UAV-BSs placement issues by proposing a novel Machine learning (ML) based intelligent deployment mechanism. More specifically, for intelligent deployment of UAV-BSs based on energy, computational power, nature of available data and criticality of the scenario, we use two different approaches: Support Vector Machine (SVM) and Deep Learning (DL), which is composed of sequential time series learning process. Moreover, to address the security and privacy challenges emanating from the wireless connectivity and untrusted broadcast nature of UAV-BSs, we propose a Blockchain-based novel information-sharing scheme. To evaluate the performance of our combined secure and intelligent proposed approach, we have improved energy consumption by almost twice in contrast with the normal deployment of UAV-BSs.

The auxiliary beam pair (ABP) method processes the outputs of a pair of beams to estimate the angular location of the desired receiver in a line-of-sight (LOS) millimeter wave wireless link. We show that wrong beam selection occurs about 50% of the time, even at high signal-to-noise ratio (SNR), when the desired receiver is at or near boresight of one of the beams, when certain constraints on beam separation and isotropic antennas are used. We further show that, depending on the SNR, the angle errors induced by wrong beam selection can range from 5 to 45 degrees. While the constraints enable analytical inversion of the discriminant function, they cause coincident nulls of the beams. These coincident nulls contribute to wrong beam selection. We show that slight reductions in the beam separations in the ABP method eliminate coincident nulls and reduces the root mean squared error in estimated angle by as much as 54.88%, at the cost of a small reduction in the field of view of the scanned beams and the requirement of a look-up table.

In this paper, we propose the optimization of beam separation in auxiliary beam pair (ABP) schemes to optimize the device discovery process in a millimeter wave (mmWave) device-to-device (D2D) network. Specifically, two beams are generated both at the transmitter (TX) and receiver (RX) for user detection through directional beamforming (BF). BF is employed by either splitting the uniform linear array into two halves (Auxiliary-Half (AH) scheme) or utilizing the whole antenna array (Auxiliary-Full (AF) scheme). It is shown that the separation between two beams plays a vital role in device discovery. Optimum values of probability of miss detection (PMD) and discovery delay (DD) are obtained for a suitable combination of beam pair separation at both TX and RX. Moreover, it is shown that the narrowest possible beams at TX and RX do not improve the detection probability in either AH or AF schemes.

Space time block codes (STBCs) have been studied to exploit the spatial and temporal diversities in wireless systems. Orthogonal space time polarization block codes (OSTPBCs) designed using the quaternion algebra promise gains in terms of higher data rates, diversity and spectral efficiency. In this context, quaternion modulation has been proposed using the dual-polarized antennas to generate efficient selection of the polarization and optimal decoding at the receiver end. In this paper, the quaternion modulation technique has been evaluated considering the quaternionic channel using the dual-polarized antennas. The results show promising diversity gains with benefits in terms of spectral efficiency and data rates. An extension of this scheme for higher number of symbols and higher dual-polarized antenna dimensions has also been presented. The proposal includes linear decoupled decoding of the quaternion orthogonal codes (QODs) at the receiver end where the complexity stays independent of the number of transmitted symbols. The design of the quaternion modulation using the quaternionic channel fully exploits the polarization diversity in addition to unfolding its applicability for future massive multiple-input multiple-output (MIMO) wireless systems.

Many variants of spatial modulation (SM), aiming to enhance data rate and bit error rate (BER) performance, have been proposed, however, their performance has not been compared for large-scale multiple-input multiple-output (MIMO) systems. This paper looks at various such schemes, namely spatial modulation (SM), generalized spatial modulation (GSM), quadrature spatial modulation (QSM) and enhanced spatial modulation (ESM) to study their performance for large-scale MIMO systems under generalized fading conditions. Our results indicate that for the same spectral efficiency and transmit power, QSM and ESM perform better than GSM and SM schemes. For lower order modulation, QSM outperforms all other schemes under various fading conditions, whereas ESM takes over for higher order modulation. The BERs for QSM and ESM under various transmit configurations have been evaluated via extensive Monte Carlo simulations. The results reveal that in QSM, a higher number of total transmit antennas, Nt, with a lower order modulation scheme provides a much better BER than a lower number of Ntwith a higher order modulation scheme, for the same data rate. This performance difference, however, becomes smaller in ESM, which can provide comparable performance using a higher order modulation and half the transmit antennas.

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.

Low power wide area network (LPWAN) technology has been widely adopted in different Internet-of-things (IoT) services. Long range wide area network (LoRaWAN) is an evolution of wireless sensor network (WSN) directed to IoT concept, mostly used in private outdoor applications. The existing LoRaWAN operates following the simple ALOHA standards. Therefore, it suffers from high packet loss and supports a very limited number of nodes. For the application of LoRaWAN in dense networks, an efficient channel access mechanism is required in order to improve the efficiency and robustness. In this paper, we investigate a modified listen-before-talk (LBT) mechanism. Specifically, we propose LoRa-BED, LoRa-BEB and LoRa-BEH, channel access protocols to reduce collisions in high density environment. Our results demonstrate that the proposed protocols significantly improve the channel utilization and efficiency with a slight increase in energy per device while sensing the channel.

Localization has gained great attention in recent years, where different technologies have been utilized to achieve high positioning accuracy. Fingerprinting is a common technique for indoor positioning using short-range radio frequency (RF) technologies such as Bluetooth Low Energy (BLE). In this paper, we investigate the suitability of LoRa (Long Range) technology to implement a positioning system using received signal strength indicator (RSSI) fingerprinting. We test in real line-of-sight (LOS) and non-LOS (NLOS) environments to determine appropriate LoRa packet specifications for an accurate RSSI-to-distance mapping function. To further improve the positioning accuracy, we consider the environmental context. Extensive experiments are conducted to examine the performance of LoRa at different spreading factors. We analyze the path loss exponent and the standard deviation of shadowing in each environment.

Small cell networks (SCNs) offer a cost-effective coverage solution to wireless applications demanding high data rates. However in SCNs, a challenging problem is the proper management of backhaul links to small cell base stations (SCBSs). To make a good backhaul link, perfect line-of-sight (LoS) communication between the SCBSs and the core network plays a vital role. In this study, we use the idea of employing unmanned aerial vehicles (UAVs) to provide connectivity between SCBSs and the core network. We focus on the association of SCBSs with UAVs by considering multiple communication-related factors including data rate limit and available bandwidth resources of the backhaul. In particular, we address the optimum placement of UAVs to serve a maximum number of SCBSs while considering available resources using unsupervised \textit{k}- means algorithm. Numerical results show that the proposed approach outperforms the conventional approach in terms of associated SCBSs, bandwidth consumption, available link utilization, and sum- rate maximization.

Following the tremendous success of 2G and 3G mobile networks and the fast growth of 4G, the next generation mobile networks (5G) was proposed aiming to provide infinite networking capability to mobile users. Differentiated from 4G, benefits offered by 5G is much more than the increased maximum throughput. It aims to involve and benefit from many current technical advances, including Internet of Things (IoT). As the IoT integrates many heterogeneous networks, such as wireless sensor networks, wireless local area networks, mobile communication networks (3G/4G/LTE/5G), wireless mesh networks, and wearable health care systems, it is critical to design self-organizing and smart protocols for heterogeneous ad hoc networks in various IoT applications, such as cyber-physical systems, cloud computing for heterogeneous ad hoc networks, large-scale sensor networks, data acquisition from distributed smart devices, green communication and applications, environmental monitoring and control, etc. Moreover, based on the survey conducted by the World Health Organization, the world will lack 12.9 million health care workers by 2035. Hence, it is important to develop wearable health care systems to perform self-health monitoring. In general, wearable health care systems demands low power consumption and high measurement accuracy. Smart technologies including green electronics, green radios, fuzzy neural approaches, and intelligent signal processing techniques play important roles for the developments of the wearable health care systems. This Special Issue aims at providing a forum to discuss the recent advances on 5G and beyond mobile technologies and applications for IoT.

This letter considers the estimation of modulation index for binary continuous phase modulated (CPM) partial response signals. The actual value of modulation index in low-cost implementations turns out to be different than the nominal value used at the transmitter, leading to severe performance degradation. One possible solution is the deployment of an estimation algorithm at the receiver. The proposed data-aided best linear unbiased estimator (BLUE) is based upon a linear model approximation of the differential phase of pre-averaged CPM signal. Theoretical analysis of the variance of the estimator is presented to establish its improved performance. Simulation results also corroborate significant improvement of the proposed estimator at lower signal-to-noise ratio (SNR) values as compared to existing estimators, while retaining the linear bound performance at high SNRs. We show that the bit error rate (BER) performance at low SNRs is also improved when the modified BLUE estimator is deployed at the receiver.

Low-power wide-area network (LPWAN) technologies are gaining momentum for Internet-of-things applications since they promise wide coverage to a massive number of battery operated devices using grant-free medium access. LoRaWAN, with its physical (PHY) layer design and regulatory efforts, has emerged as the widely adopted LPWAN solution. By using chirp spread spectrum modulation with qausi-orthogonal spreading factors (SFs), LoRa PHY offers coverage to wide-area applications while supporting high-density of devices. However, thus far its scalability performance has been inadequately modeled and the effect of interference resulting from the imperfect orthogonality of the SFs has not been considered. In this paper, we present an analytical model of a single-cell LoRa system that accounts for the impact of interference among transmissions over the same SF (co-SF) as well as different SFs (inter-SF). By modeling the interference field as Poisson point process under duty cycled ALOHA, we derive the signal-to-interference ratio distributions for several interference conditions. Results show that, for a duty cycle as low as 0.33%, the network performance under co-SF interference alone is considerably optimistic as the inclusion of inter-SF interference unveils a further drop in the success probability and the coverage probability of approximately 10% and 15%, respectively, for 1500 devices in a LoRa channel. Finally, we illustrate how our analysis can characterize the critical device density with respect to cell size for a given reliability target.

Device-to-device (D2D) communication is a novel paradigm in cellular networks and is being considered as one of the primary technologies for the upcoming fifth generation (5G) standard for cellular communication. On a similar note, millimeter wave (mmWave) communication is also one of the very enablers for 5G. For D2D communication, neighbor or device discovery is a fundamental problem in mmWave networks because of the use of highly directional antennas. In this paper, we consider this fundamental neighbor discovery problem: How can nodes discover their neighbors quickly and efficiently for communication in decentralized networks, without any prior coordination and with heterogeneous antenna configurations. We propose a novel D2D neighbor discovery algorithm that uses the idea of necklaces to reduce the worst case discovery delay as compared to the previous approaches. The results from numerical simulations confirm that the proposed algorithm leads to faster and energy efficient neighbor discover as compared to the existing algorithms.

The outage probability of a dual relay simultaneous wireless information and power transfer (SWIPT) system is investigated in the presence of Rayleigh fading. The message forwarding at the relays is categorized in three schemes. In the first two cases, both the relays are considered to be decode-and-forward (DF) and amplify-and-forward (AF), respectively, whereas in the third case, one of the relays is considered DF and the other as AF. The relaying model considers the source-relay-destination links whereas the direct link between source-destination does not exist. The power splitters at the relaying devices provide energy to the relays by splitting the received signal power into energy harvesting and information transfer parts. The outage probability is investigated using Monte-Carlo simulations and the results for all the three cases are compared. The all-AF forwarding scheme provides the least outage probability among all the three cases. The minimum outage probability is obtained for different values of PS factors for the said case at different transmit powers.

In this paper, link adaptation with decoupled code rates has been discussed in context of two-hop decode and forward (DAF) relay network. The source and the relay use variable rate low density parity check (LDPC) codes to encode the data, whereas the relay (in receiving mode) and the destination use the log domain iterative decoding algorithm to decode the data. Nakagami channels of different fading depths have been used. An algorithm has been designed to achieve the optimal performance by ensuring a target bit error rate (BER). Extensive Monte Carlo simulation results show that if decoupled code rates are used on both hops then a trade-off has to be maintained between system complexity, transmission delay and BER. More specifically, the system performs well in terms of lower BER, however, the complexity of the system also increases.

This paper proposes a method of recognizing and classifying the basic activities such as forward and backward motions by applying a deep learning framework on passive radio frequency (RF) signals. The echoes from the moving body possess unique pattern which can be used to recognize and classify the activity. A passive RF sensing test- bed is set up with two channels where the first one is the reference channel providing the un- altered echoes of the transmitter signals and the other one is the surveillance channel providing the echoes of the transmitter signals reflecting from the moving body in the area of interest. The echoes of the transmitter signals are eliminated from the surveillance signals by performing adaptive filtering. The resultant time series signal is classified into different motions as predicted by proposed novel method of convolutional neural network (CNN). Extensive amount of training data has been collected to train the model, which serves as a reference benchmark for the later studies in this field.

This paper studies the performance of a cooperative opportunistic large array (OLA) network, in which the nodes are deployed randomly in a strip-shaped fashion. Specifically, the source and the destination nodes are separated by a large distance and a number of randomly deployed relay nodes help the source message to be delivered to the destination using multi-hop cooperation strategy. The performance of network is carried out in terms of the success rate and the average number of hops to reach the destination. The reliability of the OLA network is increased, when the same message is transmitted by a group of nodes in multiple hops by exploiting spatial diversity gains. Towards the end, energy efficiency in the networks can be achieved by limiting few nodes in each hop without compromising on the quality of service (QOS). The paper summarizes and compares the performance of various network topologies in terms of energy efficiency and outage probability.

This paper considers the polling of a sensor network using a binary integration scheme. Binary integration is the combination of binary decisions from multiple sensors into a single decision at the base station. The proposed approach accomplishes binary integration in the physical layer in just two packet intervals regardless of the number of sensors, as long as the sensors are within the decoding range of the collector. We assume independent Rayleigh or Ricean links from the sensors to the collector. In the proposed scheme, the sensors simultaneously transmit their signals in each channel of a set of orthogonal channels to create diversity. The statistics of the squared envelopes of the received signals, in both line-of-sight and non line-of-sight channels, are used to perform hypothesis testing using the Neyman-Pearson criteria. It has been shown through the receiver operating characteristic (ROC) curves that the detection probability strongly depends upon the number of diversity channels available for transmission.

Solid waste management is a significant worldwide problem, mainly for municipalities located within large urban areas. Efficient waste management is an essential requisite for a clean and safe environment, and keep cities away from the harm caused by the mismanagement of solid waste produced in urban centers. There are many technologies for managing waste collection as well as recycling, but among the studied related works, none addresses the citizen's perspective, focusing only on the collection performed by the landowners. This paper proposes an integrated system combining identification through ultrasonic sensors and load cell sensors, location by Global Positioning System (GPS), and communication through Global System of Mobile Communications (GSM) / General Packet Radio Service (GPRS). In other words, everything to provide citizens with a better disposal methodology for waste generated in their homes, besides being easily integrated with the municipal collection service to assist in efficient collection scheduling by promoting optimized routes. The solution is demonstrated, validated, and is ready for use.

Network coding (NC) holds great significance in the upcoming 5G paradigm because of the potential improvements it offers in terms of network throughput, energy efficiency and data security. However, the evidence for these potential advantages comes mainly from within a theoretical framework or is based on simulations; there is a paucity of empirical evidence. Secondly, few works have evaluated NC in terms of its impacts on both throughput and bit error rate (BER) simultaneously. The work outlined in this paper addresses both of the aforementioned issues. We implement both the NC and non-NC schemes for a communication system in the context of n-source multiple-input single- output (MISO) topologies on software-defined radios (SDRs). The performances of the two schemes have been compared in terms of BER, throughput and goodput or useful throughput. Our results show that the goodput improvement in NC when compared to non-NC is more pronounced at higher transmitter (TX) gains and that below a certain TX gain threshold, non-NC should be preferred over NC.

The increasing adoption of the Internet of Things has led to the need for systems with higher spectral and energy efficiency (EE) in order to enable communication. Larger data rate demands had led researchers to look at millimeter wave (mmWave) bands to boost network rates. This paper investigates the downlink performance of a three-tier heterogeneous network that consists of sub-6 GHz macrocells overlaid with small cells operating on both the mmWave and sub-6 GHz bands. A model is developed using tools from stochastic geometry to analyze the coverage, rate, area spectral efficiency, and EE of such a network. Various deployment strategies and their impacts on the considered metrics are studied. Simulation results are used to verify the validity of the proposed model.

Over the past few decades, wireless communication aims to support high data rates and reliability and thus several techniques exploiting space, time, and polarization diversities have been used to achieve large diversity gains. Orthogonal space time block codes (OSTBC) in combination with polarization diversity promise optimal diversity gains. For dual-polarized antennas, we propose a new system model based on the quaternionic structure of the channel, which offers a way to exploit polarization diversity quite independently of other forms of diversities. Moreover, such OSTPBC achieve better throughput and provide a linear decoupled decoding solution at the receiver, which significantly reduces computational complexity.

Device-to-device (D2D)-enabled Internet-of-things promises higher spectral efficiency and system capacity by sharing the cellular spectrum and offloading the cellular traffic. However, it poses new challenges in terms of resource allocation because of interference from D2D to base station and vice versa in the downlink. Moreover, energy efficient operation of the network becomes a major challenge because of unattended operation of devices. In this study, we investigate resource allocation for the energy-harvesting-aided D2D communication underlaying cellular network. We formulate a joint subcarrier assignment and power allocation problem for multiple cellular and energy harvesting direct D2D links to maximize the overall sum rate subject to quality of service, subcarrier reuse, power, and energy harvesting constraints. The problem is formulated as a mixed integer nonlinear programming and is difficult to be solved in polynomial time. Therefore, a low complexity algorithm named energy harvesting and gain-based resource allocation (EHGRA) is proposed, which determines reuse partners considering interference among D2D and cellular links, and then, allocates power optimally to them such that the sum rate is maximized. Our performance evaluation demonstrates that energy harvesting can boost up the system performance and at the same time can achieve higher sum rate over short frame duration. Comparison shows that EHGRA performs better than the existing algorithms in terms of overall sum rate.

We propose a spectrally efficient and low complexity spatial modulation (SM) transmission scheme for a multiple-input multiple-output (MIMO) system. As compared to conventional generalized spatial modulation (GSM), which can achieve fixed data rates, the proposed adaptive generalized spatial modulation (AGSM) uses adaptive modulation in combination with the GSM. The AGSM MIMO scheme improves the spectral efficiency (SE) of GSM by increasing the modulation order of transmission in better channel conditions. GSM maps information into a spatial symbol and a constellation symbol of constant modulation order but in proposed AGSM, the constellation size increases as the channel conditions improves keeping the bit-error-rate (BER) below a certain threshold value. The proposed technique is compared with the adaptive spatial modulation (ASM). It is shown that for the same SE, the proposed AGSM requires less number of antennas than ASM. The performance of AGSM MIMO is validated through Monte-Carlo simulations. The results show that AGSM reduces the number of transmit antennas compared to ASM without any BER degradation.

To cope with the data surge problem and to enhance the coverage of existing cellular systems, heterogeneous networks (HetNets) are deployed in hierarchical manner, comprising of macrocells and overlaid femtocells. A novel interference mitigation technique of Reverse Frequency Allocation (RFA) scheme is introduced, which provides intercell orthogonality by dividing the cell into spatial regions and optimally allocating the frequency resources. RFA enhances the data rates of downlink femto users by eliminating the cross-tier interference from macro base station (MBS). In this paper, we extend the multiple region RFA scheme in multi-cellular network to further mitigate the impact of interference in the adjacent cells. In addition, we also develop a hybrid RFA scheme that merges the benefits of different RFA schemes in terms of large bandwidth and limited interference to achieve higher data rates. Simulation results show that the modified RFA (M-RFA) schemes exhibit superior performance as compared to the conventional RFA schemes in terms of user-fairness and improved sum capacity. For the evaluation of system performance, several metrics such as outage probability, sum rates and outage capacity have been analyzed for satisfying the constraint of minimum capacity requirement of cell edge users.

Non-orthogonal multiple access (NOMA) is considered as a promising candidate for fifth generation (5G) wireless networks. Although, NOMA promises large data rates, however, it also offers significant interference especially when the number of users is large. Therefore, in this paper, we propose a low complexity orthogonal frequency division multiple access (OFDMA)-based NOMA system, which uses the concept of successive bandwidth division (SBD) that not only reduces the complexity of the receiver, but also enhances the overall signal-to-interference plus noise ratio (SINR) of the uplink NOMA by supporting 2N users with just N base station (BS) antennas. Power allocation is being performed in SBD-NOMA to maximize the sum rate using a divide-and-allocate approach such that all users are allocated with an optimal transmission power. Simulations results are provided to access and compare the performance of the proposed scheme with other contemporary approaches.

This paper presents an end-to-end deep learning framework using passive WiFi sensing to classify and estimate human respiration activity. A passive radar test-bed is used with two channels where the first channel provides the reference WiFi signal, whereas the other channel provides a surveillance signal that contains reflections from the human target. Adaptive filtering is performed to make the surveillance signal source-data invariant by eliminating the echoes of the direct transmitted signal. We propose a novel convolutional neural network to classify the complex time series data and determine if it corresponds to a breathing activity, followed by a random forest estimator to determine breathing rate. We collect an extensive dataset to train the learning models and develop reference benchmarks for the later studies in the field. Based on the results, we conclude that deep learning techniques coupled with passive radars offer great potential for end-to-end human activity recognition.

Quaternion orthogonal designs (QODs) are considered the foundation of orthogonal space time polarization block codes (OSTPBCs). OSTPBCs benefit from orthogonal polarizations and orthogonal space and time block coding simultaneously to enhance the capacity of wireless communication systems. To exploit these advantages of OSTPBCs, this paper explores two generalized construction techniques of QODs, where the first one is based on symmetric-paired designs while the second technique maps the complex orthogonal designs (CODs) to QODs directly. With these schemes, QODs for any number of transmit antennas can be constructed. Moreover, a low-complexity maximum-likelihood (ML) decoder for the proposed construction techniques has been presented that provides optimal decoupled decoding with phenomenal complexity reduction. Simulation results show that the diversity order of the first QOD construction is higher than the second design given the number of transmit antennas are same.

Passive multistatic radars (PMRs) are becoming mature in recent times and different kinds of practical systems are being studied and developed. Whereas various issues related to passive radars are still under active research, an important aspect is that the performance of a PMR is greatly dependent on the geometry and physical locations of the transceivers. A PMR designer generally has no control on the position of the transmitters, however, locating the passive receiver to obtain best target detection capabilities remain an interesting research problem. In this paper, we study the optimization of the position of a passive radar receiver in an indoor environment that uses WiFi as an illuminator of opportunity. Furthermore, the proposed technique also provides a method to select a subset of optimal transmitters, among many, for the detection of a target in an indoor area. The subject optimization is performed on a case study where different constraints are applied to find the optimal location of the receiver.

Millimeter wave (mmWave) links have the potential to offer high data rates and capacity needed in fifth generation (5G) networks, however they have very high penetration and path loss. A solution to this problem is to bring the base station closer to the end-user through heterogeneous networks (HetNets). HetNets could be designed to allow users to connect to different base stations (BSs) in the uplink and downlink. This phenomenon is known as downlink-uplink decoupling (DUDe). This paper explores the effect of DUDe in a three tier HetNet deployed in two different real-world environments. Our simulation results show that DUDe can provide improvements with regard to increasing the system coverage and data rates while the extent of improvement depends on the different environments that the system is deployed in.

Millimeter wave (mmWave) technology is gaining momentum because of its ability to provide high data rates. However, in addition to other challenges in the operation of mmWave systems, developing cell search algorithms is a challenge due to high path loss, directional transmission, and excessive sensitivity to blockage at mmWave frequencies. Thus, the cell search schemes of long term evolution (LTE) cannot be used with mmWave networks. Exhaustive and iterative search algorithms have been proposed in literature for carrying out cell search in mmWave systems. The exhaustive search offers high probability of detection with high discovery delay while the iterative approach offers low probability of detection with low discovery delay. In this paper, we propose a hybrid algorithm that combines the strengths of exhaustive and iterative methods. We compare the three algorithms in terms of misdetection probability and discovery delay and show that hybrid search is a smarter algorithm that achieves a desired balance between probability of detection performance and discovery delay.

This paper presents a two-dimensional phase extraction system using passive WiFi sensing to monitor three basic elderly care activities including breathing rate, essential tremor and falls. Specifically, a WiFi signal is acquired through two channels where the first channel is the reference one, whereas the other signal is acquired by a passive receiver after reflection from the human target. Using signal processing of cross-ambiguity function, various features in the signal are extracted. The entire implementations are performed using software defined radios having directional antennas. We report the accuracy of our system in different conditions and environments and show that breathing rate can be measured with an accuracy of 87% in the absence of obstacles. We also show a 98% accuracy in detecting falls and 93% accuracy in classifying tremor. The results indicate that passive WiFi systems show great promise in replacing typical invasive health devices as standard tools for health care.

Cooperative spatial diversity allows for more robust wireless networks with higher capacity and data rates. These diversity gains, however, diminish with the multiple carrier frequency offsets (CFOs) that arise due to distributed transmissions over independent channels. A number of techniques have been proposed for multiple CFO estimation, however, their empirical performance remains to be analyzed. This paper seeks to experimentally analyze the performance of the iterative MUltiple SIgnal Characterization (I-MUSIC) algorithm for decode-and-forward (DF) relaying on the universal software radio peripheral (USRP) platforms. The results show that the aforementioned algorithm performs reasonably well, in both line-of-sight (LoS) and non-line-of-sight (NLoS) channels, in terms of mean squared error (MSE) of the estimated CFOs. However, the performance margin greatly depends on the type of environment and the number of data symbols used for estimation.

This paper presents a novel approach to cooperative non orthogonal multiple access (NOMA) using distributed space time block coding (STEG) known as STEG-NOMA. In conventional NOMA, the strong users detect the messages of weak users through successive interference cancellation (SIG). In cooperative NOMA, these copies are then forwarded by strong users to weak users at the expense of extra time slots. However, the proposed scheme exploits this feature of cooperation using STEGs to enable cooperation among the users. The STEGNOMA renders less complexity as lesser number of SIGs are performed at each user. To this end, we derive the outage probability of the STEG-NOMA scheme which involves finding the distribution of signal-to-interference ratio at the receiving terminals. The numerical results show that STEG-NOMA outperforms conventional NOMA and conventional cooperative NOMA in terms of outage probability and average sum rate.

In this paper, we consider the problem of cyclic block construction, for frequency-domain equalization (FDE) of SOQPSK, using an intrafix segment in addition to the cyclic prefix. We propose a novel procedure to compute the intrafix symbols for SOQPSK-TG waveforms used in aeronautical telemetry. The proposed method is employed to generate an exact cyclic signal, which is a requirement for implementation of FDE at the receiver. We identify that the required number of symbols to generate an exact cyclic signal are unreasonably large for SOQPSK-TG (9 symbols per block). Thus, we also propose an approximate technique that utilizes only dominant Laurent pulses to help reduce the length of intrafix upto 2 symbols. A detailed numerical evaluation of this approximate technique is performed to study the trade-off between the reduction in the intrafix length and the ideal properties of the exact signal. Bit-error rate (BER) results are presented for minimum-mean squared error (MMSE) equalizer when the cyclic part of the received signal is made by approximation. Simulation results show that MMSE equalizer shows similar level of performance with original and approximate signal.

The existing cellular networks are being modified under the umbrella of fifth generation (5G) networks to provide high data rates with optimum coverage. Current cellular systems operating in ultra high frequency (UHF) bands suffer from severe bandwidth congestion hence 5G enabling technologies such as millimeter wave (mmWave) networks focus on significantly higher data rates. In this paper, we explore the impact of co-existance of massive Multiple-Input Multiple-Output (MIMO) that provides large array gains and mmWave small cells on coverage. We investigate the downlink performance in terms of coverage and rate of a three tier network where a massive MIMO macro base stations (MBSs) are overlaid with small cells operating at sub-6GHz and mmWave frequency bands. Based on the existing stochastic models, we investigate user association, coverage probability and data rate of the network. Numerical results clearly show that massive MIMO enabled MBSs alongside mmWave small cells enhance the performance of heterogeneous networks (HetNets) significantly.

Smart meters are being deployed globally on a trial basis and are expected to enable remote reading and demand response among other advanced functions, by setting up a two-way communication network. However, it remains to be determined as to how these meters will transmit their data to an aggregation point. An elegant solution to this problem is the use of cooperative communication in a neighborhood area network. This work experimentally compares cooperative networks, deployed in disparate environments, in terms of range extension and energy consumption of the overall network. Data transmissions take place through the universal software radio peripheral platforms. The method has been implemented in both indoor and outdoor environments, with cooperative transmission (CT) taking place over a multihop network, employing the binary phase shift keying scheme. The results indicate that CT can be used to effectively and reliably relay data in a network such as that in a smart grid.

Millimeter wave (mmWave) technology is considered as a key enabler for fifth generation (5G) networks to achieve higher data rates with low transmission power by offloading the users with low signal-to-noise-ratios. Millimeter wave networks operating at E and W frequency bands have available bandwidth of 1 GHz or more to provide higher data rates whereas their propagation characteristics differ greatly from the conventional Ultra High Frequency (UHF) networks operating at sub 6 GHz frequency band. The purpose of this paper is to investigate the performance in terms of coverage and rate, of hybrid cellular networks where base stations (BSs) operating at mmWave and sub 6 GHz bands coexist in suburban environment such as a university campus. The actual building locations within a suburban university campus are modeled as blockages and the analysis is carried out for different densities of UHF and mmWave BSs for different densities of outdoor users. Our analysis also highlight the fact that mmWave cellular networks are predominantly noise-limited due to larger available bandwidth in comparison to the interference limited conventional UHF networks. Extensive simulation results demonstrate the effectiveness of dense deployment of mmWave BSs to achieve better coverage and rate probabilities in comparison to the stand alone UHF network.

Smart meters are being deployed worldwide on a trial basis and are expected to enable remote reading and demand response among other advanced functions, by establishing a two-way communication network. A possible technique for transmitting data to the aggregation point is the use of cooperative communication in the neighborhood area network. This paper presents an experimental comparison between cooperative networks located in disparate environments, in terms of range extension and bit error rate as the performance metrics. Transmissions have been achieved using universal software radio peripheral (USRP) platforms. The cooperative transmission (CT) tests are performed over a multiple hop network that uses the binary phase shift keying (BPSK) scheme.

This paper studies energy-efficient relay selection schemes for cooperative multi-hop device-to-device (D2D) networks. D2D networks exploit proximity gain by establishing direct links between devices instead of relying on cellular links for communication. We consider a D2D network with random deployment of relay nodes between the source and the destination. The relay nodes are grouped into clusters, which act cooperatively to enhance network reliability. We derive received power and end-to-end transmission success probability expressions for a single-hop, two-hop and multi-hop network scenario assuming a Rayleigh fading channel and path loss. Further, we compare two relay selection schemes: i) random relay selection (RRS) and ii) Signal-to-noise ratio (SNR)-based relay selection (SRS) on the basis of energy-efficiency that provides a required quality of service (QoS).

In this paper, the downlink performance of two-tier heterogenous network is investigated. We consider a scenario where the macro-tier is empowered by massive antenna-array thus allowing for Massive multiple-input multiple-output (MIMO) transmission scheduling. The small cellular network complements the macro-tier capacity. We propose a novel channel allocation mechanism which optimally splits the spectral resources to maximize network level throughput and energy efficiency. Our proposed channel allocation mechanism is robust to the topological and channel variations. More specifically, the proposed scheme is designed by capturing the random locations of the users in both tiers by a Poisson Point Process (PPP). The channel uncertainty is captured by considering Rayleigh fading complemented by large scale power law path-loss. Our analysis shows that there exists an optimal split which maximizes the network wide throughput and energy efficiency. We also demonstrate that there exists an optimal transmit power which maximizes the energy efficiency for the network. Under different scenarios, massive MIMO plays a vital role in improving sum rate capacity as compared to single antenna femtocells. Finally, using implementation parameters, we obtain the optimal configurations that improve system capacity and energy efficiency.

A Poisson point process (PPP)-based model for a single-input single-output (SISO) transmission between two randomly located nodes is developed and analyzed. The power received at a node, when a randomly deployed transmitter transmits the message signal in the presence of Rayleigh fading and path loss, is shown to be the ratio of an exponential random variable (RV) and a generalized gamma (GG) RV. The cumulative distribution function (CDF) of the received power is derived, which is used to find the outage probability at the receiver. The study is then further extended to SISO multi-hop links where the coverage probability of the network is calculated. Finally, a transmission model is proposed that saves a significant amount of energy by carefully selecting an intermediate node in a multi-hop network such that the lifetime of the network can be increased. Numerical simulations are presented to validate the theoretical models.

Smart meters are being deployed worldwide on a trial basis and are expected to enable remote reading and demand response among other advanced functions, by establishing a two-way communication network. However, it remains to be determined as to how these meters would transmit their data to an aggregation point. Our paper employs a medium access control (MAC) protocol for a cooperative network to address this problem. The said protocol has been developed on the GNU Radio platform, while the test-bed consists of universal software radio peripherals (USRPs) that serve as network nodes. The performance of the system has then been evaluated and compared with that of fully cooperative and single-input-single-output (SISO) networks. It has been shown that the proposed system outperforms the SISO network in terms packet error rates and throughput.

For the past few years, 5G heterogeneous networks (HetNets) have gain phenomenal attention in the wireless industry. In this paper, we propose a hierarchical game theoretical framework for the optimal resource allocation on the uplink of a heterogeneous network with femtocells overlaid on the edge of a macrocell. In the first game, the femtocell access points (FAPs) play a non- cooperative game to choose their access policy between open and closed in order to maximize the rate of their home subscribers. The second game of the algorithm allows macrocell user equipments (MUEs) to decide their connectivity between the FAPs and the macrocell base station (MBS) with the goal of maximizing their rates and the overall network performance; thereby, distributing intelligence and control to the users. The FAPs and the MUEs are the players of two different games that strategically decide their policies in an ordered fashion. Simulation results show that this hierarchical game approach with network- assisted user-centric design offers a significant improvement in terms of the performance of HetNets relative to an closed and only network-centric access policy schemes.

Millimeter wave (mmWave) technology integrated with heterogeneous networks (HetNets) has emerged as a new wave to overcome the thirst for higher data rates and severe shortage of spectrum. In this paper, we consider the uplink of a hybrid HetNet with femtocells overlaid on a macrocell, and formulate a two layer game theoretic framework to maximise the energy efficiency (EE) while optimising the network resources. The outer layer allows each femtocell access point (FAP) to maximise the data rate of its users by selecting the frequency band either from the sub-6 GHz and the mmWave. The solution to this non-cooperative game can be obtained by using pure strategy Nash equilibrium. The inner layer ensures the energy efficient user association method subject to the minimum rate and maximum transmission power constraints by using dual de-composition approach. Simulation results show that the proposed hybrid HetNet scheme exploiting the mmWave frequency band improves the sum-rate and EE in comparison to the scenario where all the networks operate at sub-6 GHz frequency band. The performance can further be enhanced by incorporating the power control mechanism.

Massive MIMO (multiple-input multiple-output) has been identified as a key technology for next generation cellular systems. This paper considers a multi-cellular system with large antenna arrays at the base station (BS) and single antenna user terminals (UTs), operating in a time division duplex (TDD) mode, under a composite fading- shadowing environment. In the uplink transmission, the pilot contamination occurs as the UTs transmit pilots to their respective BSs, and the serving BS estimates the channel state information using a minimum mean squared error estimation. This channel information is further used to design beamforming (BF) and regularized zero-forcing (RZF) precoders for downlink (DL) transmission. We analyze the ergodic rates for DL transmission using different precoding schemes and varying shadowing intensity. It has been observed that shadowing does not average out as we increase the number of antennas as opposed to multi-path fading, and the severity of shadowing badly affects the performance of massive MIMO systems.

Non-orthogonal multiple access (NOMA) is a major technique that is expected to lead towards the fifth generation (5G) wireless communication networks, as it involves the sharing of space resources among users in a given scenario. In this paper, we propose an uplink (UL) NOMA setup that utilizes the multiple-input multiple-output (MIMO) infrastructure. As performed in related literature, K clients are placed in 'weak' and 'strong' categories, depending on the channel state information (CSI). The base station (BS) uses successive interference cancellation (SIC) to overcome interference experienced by users forming the weak set from their counterparts in the strong set. The paper goes on to compare the performance of this NOMA system with that of a conventional orthogonal multiple access (OMA) setting, with the scope being extended to include both ultra high frequency (UHF) and millimeter wave (mmWave) networks. The performance evaluation is carried out in terms of, among other metrics, average sum- rate and outage probability.

In this paper, a pilot reuse scheme has been considered for the uplink (UL) transmissions of a massive multiple-input multiple-output (MIMO) system. Subsequently, a lower bound on the throughput of the system, that uses a maximal ratio combining (MRC) receiver, has been derived, which is applicable for any number of antennas. The throughput, along with other metrics, has been used to differentiate between the performance of ultra high frequency (UHF) and millimetre wave (mmWave) networks. The results indicate that the reuse factor plays a critical role in the least achievable throughput for UHF systems only, whereas the performance is almost independent for the mmWave networks.

Non-orthogonal multiple access (NOMA) is a key enabling technology for fifth generation (5G) wireless networks because of its ability to provide greater spectral efficiency. However, a conventional NOMA scheme offers significant interference and higher outage probability especially when the number of users in the network is large. Therefore, in this paper, we propose a suboptimal algorithm which uses the concept of successive bandwidth division (SBD) in NOMA system, which not only reduces the complexity of the receiver side to a great extent, but also enhances the overall signal-to-interference plus noise ratio (SINR) of the uplink NOMA by supporting 2 N users with just N base station (BS) antennas. The BS is assumed to have perfect channel state information (CSI) and uses a zero-forcing (ZF) postcoding matrix to recover the signals of different users. Numerical results show that the performance of the proposed scheme outperforms the conventional NOMA techniques in terms of receiver complexity and outage probability.

A spatial Poisson point process model for a multihop cooperative network with fixed hop boundaries and random number of the nodes in each hop is developed and analyzed. Transmissions over multi-hop network follow a decode-and-forward (DF) protocol that form Opportunistic Large Array (OLA). The probability density function (PDF) of the received power at a node is derived, when all the DF nodes in the previous level transmit with the same power in the presence of Rayleigh fading and path loss. The performance of the system is characterized in terms of one-hop success probability, which is calculated using the PDF of the received power at a node. The coverage range of the multi-hop network is analyzed for different set of network parameters.

In this paper, we propose a fuzzy logic-based dynamic subchannel allocation scheme for densely deployed femtocell networks affected by strong co-tier interference. The channel model includes Rayleigh fading with path loss and the femtocells use OFDMA-based channel access. The reported signal-to-interference plus noise ratio (SINR) values at femtocell base stations are used to form a fuzzy superset, which in turn is utilized to allocate subchannels in an optimal way. The results are compared with the traditional user-oriented and channel-oriented subchannel allocation schemes. The proposed scheme provides almost the same system capacity with better fairness as compared to traditional channel-oriented scheme, while it also improves the sum-rate capacity of the femtocell network as compared to the user-oriented scheme. Simulations have been performed to judge the quality of proposed algorithm for a variety of densely deployed femtocells.

This paper presents a comparison between cooperative networks with full relay participation and limited participation (LP), in terms of the energy used by the entire network and the bit error rate (BER) of the received signal. The Universal Software Radio Peripheral (USRP) platform was used to perform the transmissions while signal processing, combination and time synchronization was performed on the GNU Radio Companion (GRC). The experiments were performed in an indoor office environment with cooperative transmission (CT) taking place over a single hop network, using binary phase shift keying (BPSK) as the modulation scheme. It has been demonstrated that relay selection provides better energy efficiency for a required quality of service (QoS).

This paper presents an experimental comparison between cooperative communication and single-input single-output (SISO) system, in terms of the corresponding ranges of signal reception in each case. Transmissions have been achieved using Universal Software Radio Peripheral (USRP) platform, with the signal processing and time synchronization occurring in the GNU Radio environment. The method has been implemented in a typical office environment, with cooperative transmission (CT) taking place over a multiple hop network, using the binary phase shift keying (BPSK) scheme. Presented herein are comparisons between SISO and cooperative networks with regards to ranges and bit error rate (BER) performance.

In this paper, near-orthogonal random space-time block codes (STBCs) are used to design near-orthogonal channels to transmit information independently in a fully opportunistic multi-hop strip-shaped cooperative wireless network. This opportunistic large array (OLA) network is considered to have fixed hop boundaries having constant node density and operating on decode-and-forward (DF) relaying mode. To deal with random number of DF nodes, in each hop, directional statistical concepts are used for the randomization of underlying deterministic STBC letting each node to transmit linear combination of symbols or STBC columns. The transmissions are modeled stochastically with Markov chain. Network performance is evaluated on the basis of one-hop success probability and coverage for different number of nodes per hop, and dimensions of STBCs.

Femtocell access points (FAPs) are low-powered and small-sized base stations that provide high data rates and coverage to the indoor users. Because of their operation in the same spectrum as that of macrocells, interference becomes a major problem. Reverse frequency allocation (RFA) is one of the robust schemes, which mitigate the interference by transfering it from the macrocell base station (MBS) to the macrocell user equipments (MUEs), particularly in the downlink. In this paper, we provide a multiple region 2-tier downlink model using the RFA scheme. We derive a closed-form expression for the coverage probability of femtocell user equipment (FUE) using different RFA schemes under open access mode. Where FAPs and MUEs are modeled as independent Poisson point process (PPP). Using simulations, we show that for reasonable values of signal-to-interference ratio, multiple regions enhance the coverage probability of an FUE. It is shown that a 4-region RFA outperforms the other RFA schemes at different densities of FAPs and MUEs as well as for different values of threshold for the same set of parameters.

In this paper, the performance of a multi-hop network is investigated in which M sources have independent information to be transmitted to a far off common destination. Linear network coding technique is used by the intermediate relays to transmit the combined information of M sources. Channel model includes Rayleigh fading and path loss. The multi-hop transmission process is modeled by a quasi-stationary Markov process, whereas the relay nodes use decode and forward (DF) mechanism at each hop. By finding the outage probability of each node and studying the properties of Markov process, the network coverage is analyzed for a given signal-to-noise ratio (SNR) margin.

Shadowing and multipath fading are two fundamental channel characteristics that impact the performance of a wireless communication system. In this paper, we analyze the performance of device to device (D2D) cooperative heterogeneous networks where idle femtocell users can be engaged to provide better coverage to a macrocell user using amplify-and-forward cooperative strategy. We consider multipath fading and lognormal shadowing along with path loss and model their effects on the performance of the network. Closed-form expressions for the signal-to-noise ratio (SNR) and outage probabilities are derived and it has been shown that the downlink performance of the macrocell user can be enhanced by employing femto user cooperation. Analytical results are validated through simulations.

In this paper, a pilot reuse scheme is considered for the uplink of a massive multiple input multiple output (MIMO) system and a lower bound on the throughput of the system is derived, which is applicable for any number of antennas. A hexagonal geometry of the system is considered where each cell contains uniformly distributed users and a conventional frequency reuse pattern. The derived lower bound is shown to be limited by three types of interferences: inter-cell interference, intra- cell interference and pilot contamination. A set of conditions including the number of users, antennas, pilot reuse factor and coherence period is analyzed for which the lower bound of throughput is achieved. The results indicate that the reuse factor plays a critical role in the least achievable throughput. A minimum reuse factor is quantified for a given user density and coherence period.

Multiple single antenna terminals transmit simultaneously to an array of hundreds of antennas in the uplink of a multi-user massive multiple- input-multiple-output (MIMO) system. Under Rayleigh fading and lognormal shadowing, the expression for the probability density function (PDF) for signal-to-interference-plus-noise-ratio (SINR) does not exist in a closed-form, which is required to calculate the outage probability of a user. This paper provides approximate closed-form expressions for the outage probability of a user when the base station (BS) uses a maximum-ratio- combining (MRC) receiver in the presence of above channel impairments. It has been shown that the PDF of SINR can be well-approximated by a lognormal random variable (RV). Moreover, the effects of shadowing on the performance of the system have been quantified and it has been shown that the shadowing does not average out by increasing the number of antennas.

This paper addresses the problem of signal-to-noise ratio (SNR) estimation for a virtual multi-input single-output (MISO) communication system employing non-coherent M-ary frequency shift keying (NCMFSK) modulation scheme. The transmitted signals from L different nodes undergoing correlated Rayleigh fading with additive white Gaussian noise (AWGN) are combined at a single receiving node via equal gain combining (EGC) scheme. Maximum likelihood (ML) estimation technique is used for deriving the closed-form expressions for data aided (DA) and non-data aided (NDA) estimators. Cramer-Rao bound (CRB) has also been derived to evaluate the performance of the derived estimators. Numerical results have been shown for various parameters such as number of transmitting nodes, modulation order, and varying number of symbols.

In this letter, a virtual multiple-input single-output (VMISO) network employing non-coherent frequency shift keying (FSK) is considered. In the VMISO network, spatially separated single-antenna nodes transmit the same information cooperatively to a single receiver where each received signal is affected by an independent carrier frequency offset (CFO). The existing works in this area assume a perfect carrier synchronization between the nodes. However, in this letter, the effects of CFOs on the performance degradation of this network are analyzed. For that purpose, the expression for the probability of symbol error has been derived. The results indicate that the performance is degraded due to CFOs, which is dependent upon the magnitude of CFOs, signal-to-noise ratio (SNR), number of transmitting nodes, and the modulation order of FSK. At high SNR, the CFOs affect the system severely and the performance margin is minimum.

In this paper, we propose a mathematical model for timing synchronization errors in a cascaded virtual multi-hop multiple-input multiple-output (MIMO) system that incorporates cooperation at each hop using decode-and-forward (DF) algorithm. Specifically, the DF relays are grouped to form clusters and one cluster transmits the same data to the next cluster over orthogonal fading channels. The proposed error model is used to study the statistics of the timing error from one cluster to the next and it has been shown that the variance of the timing errors gradually increases as the data traverse the multi-hop network. The effects of the timing errors on the bit error probability (BEP) performance of the system have been quantified. For that purpose, the closed-form expressions of the BEP for the network are derived for every hop with a particular number of nodes per hop. The results indicate that the BEP performance of the system degrades as the data propagates from hop to hop. We quantify the minimum required SNR and the optimal number of relays per cluster that guarantee successful traversal of data to a specified number of hop while keeping overall BEP below a defined threshold.

In this paper, the problem of signal-to-noise ratio (SNR) estimator design for a single-input multiple-output (SIMO) communication system employing non-coherent M-ary frequency shift keying (NCMFSK) modulation scheme is considered. The transmitted signal undergoes Rayleigh fading and additive white Gaussian noise (AWGN) and is received at a receiver with L diversity branches. Closed-form expressions of data aided (DA) and non-data aided (NDA) estimators have been derived using the maximum likelihood (ML) estimation approach. Cramer-Rao bound has been evaluated to compare the performance of the designed estimators. The effect of increasing the receiver diversity branches on the performance of estimators has been quantified.

We consider a cooperative multi-hop line network, where a group of nodes cooperatively transmits the same message to another group of nodes, and model the transmission from one group to another as a discrete-time quasi-stationary Markov process. We derive the transition probability matrix of the Markov chain by considering the wireless channel exhibiting composite shadowing-fading. The sum distribution of the received power by multiple relays is approximated by a single log-normal random variable (RV) by using the moment generating function (MGF)-based technique. This MGF-based technique uses Gauss-Hermite integration to present the sum distribution in closed form. We quantify the signal-to-noise ratio (SNR) margin required to achieve a certain quality of service (QoS) under standard deviation of the shadowing. We also provide the optimal level of cooperation required for obtaining maximum coverage of a line network under a given QoS. Monte Carlo simulations are used to validate the analytical model.

This paper presents an analytical model for a multi-hop two-dimensional (2-D) network with finite density of nodes communicating with one another by forming an opportunistic large array (OLA). Transmission among these nodes is modeled via Markov Chain, where the wireless channel is considered as a composite lognormal-Rice random process. We approximate the sum distribution of the received power at a node with lognormal random variable (RV) using a moment generating function (MGF)-based approach, which is acquired by using Gauss-Hermite integration. The transition probability matrix is derived to determine the one-hop success probability. Coverage aspects for different arrangement of nodes is quantified along with the effects of wireless channel on system performance.

In this paper, deterministic space-time block codes (STBCs) are used o design orthogonal channels to transmit the information independently in a cooperative communication-based sensor network. Two topologies of two-dimensional (2D) opportunistic networks, the ditributed grid strip and the colocated groups, having same node density are considered. Orthogonal STBCs designed for deterministic number of nodes are partially randomized with the help of indicator random vector and are used for the opportunistic multi-hop network in which the number of cooperating or decode-and-forward (DF) nodes in each hop are random. Different node geometries and the effect of increasing node number in each level are compared on the basis of one-hop success probability and network coverage at various signal-to-noise (SNR) margins. The analysis for different STBCs is made on the basis of diversity and rate it ensure at a certain required quality of service (QoS).

The need for small-sized and low-powered home base stations such as femtocells has increased with an escalated data demand. Since the downlink traffic is larger in magnitude than the uplink, use of femtocells provides a good solution. Femto-cells not only increase the throughput but also the overall capacity of the system while operating in the same licensed spectrum. Because of their operation in the same spectrum, interference becomes a major problem. In this paper, we study the downlink performance of a heterogeneous network by proposing a Reverse frequency allocation (RFA) scheme by dividing the cell service area into multiple regions and assign frequencies to various cell entities in such a way that the major interference is avoided. RFA scheme not only improves the spectral efficiency by utilizing the complete spectrum within one cell but also eliminates the strong interference due to macro base station on femto users.We analyze and evaluate the achievable performance of this technique for the downlink scenario. Using simulations, we show that under reasonable signal-to-interference plus noise ratio (SINR) values, multiple regions enhance the performance of users by decreasing the overall system outage probability. It is shown that a 4-region RFA scheme provides almost double performance gain over a 2-region RFA scheme for the same set of parameters.

A linear multi‐hop cooperative network is considered where the relay nodes are deployed in a random fashion according to a Bernoulli random process. The transmission from one hop to another is modelled as a discrete‐time Markov process while taking into account the random locations of the participating nodes along with the Rayleigh fading channel model. Quasi‐stationary theory of Markov chains has been applied to study the various properties of the network under consideration, including successful probability of hops as well as network coverage. It is shown that there is a loss of one‐hop success probability when the random deployment is done compared to a regular deployment. However, an additional signal‐to‐noise ratio margin has also been quantified for obtaining a desired quality of service and one‐hop success probability.

In this paper, a strip-shaped cooperative multi-hop wireless network is modeled stochastically with quasi-stationary Markov chain. The network is considered to be a fixed boundary decode-and-forward Opportunistic Large Array (OLA), where each level is of the same size and contains the same number of nodes placed randomly. The state of the system is represented by the number of nodes that decode the message in the current level. The distribution of the received power at a node is derived to formulate the transition probability matrix. For the distribution of power, a closed-form expression of the distribution of distance between a pair of nodes in disjoint levels is derived. It is seen that the distribution of distance can be well-approximated by the Weibull distribution. The Weibull approximation is then carried forward to find the distribution of the received power at a node assuming all nodes transmit with the same power and the channel has Rayleigh fading and path loss with an arbitrary exponent. The coverage and outage characteristics for various network sizes and path loss exponents are quantified. The signal-to-noise ratio (SNR) margin required for a given network coverage is determined by using the Perron-Frobenius theorem of non-negative matrices. Numerical simulations are performed to validate the theoretical results.

In this paper, a closed-form expression for the outage probability of an ad hoc single-input single-output (SISO) network is derived when the network is subject to Rayleigh fading and arbitrary path loss model. The underlying stochastic geometry of the network assumes a simple point process in which a single node is placed randomly in a square region. A closed-form expression of the distribution of the distance between a pair of nodes is derived. This expression is then generalized for positive powers to incorporate the path loss exponent. Moment matching is used to make the model analytically tractable. It is shown that the distribution of the distance is well approximated by the Weibull distribution with the scale parameter depending on the size of the network and a constant shape parameter. The coverage and outage behaviors of the network for various network sizes and path loss exponents are quantified. Numerical simulations are performed to validate the theoretical models.

This paper studies two topologies for cooperative multi-hop linear networks; a distributed equi-distant node topology and a co-located group of nodes topology such that both topologies operate under composite shadowing-fading environment. The transmission from one hop to another in both topologies is modeled as a Markov process where the underlying channel model is drawn from a Suzuki distribution. The distribution of the sum of multiple Suzuki random variables (RVs) is obtained by a moment generating function (MGF)-based approximation technique. It is shown that the coverage of both topologies is different contingent upon the severity of shadowing and the transmit power of the radios. The optimal level of cooperation between nodes is shown to have a dependency on the path loss exponent such that a large number of cooperators is optimal for a small path loss exponent and vice versa for obtaining the maximum range of the network. Monte Carlo simulations are performed to validate the analytical model.

We consider a cooperative multi-hop line network, where a group of nodes cooperatively transmits the same message to another group of nodes, and model the transmission from one group to another as a discrete-time quasi-stationary Markov process. We derive the transition probability matrix of the Markov chain by considering the wireless channel exhibiting composite shadowing-fading. The shadowing is modeled as a log-normal random variable (RV) and the multipath fading as a Rayleigh RV, where the multiplicative model for the mixture distribution known as Suzuki (Rayleigh-lognormal) distribution has been considered. The sum distribution of the multiple Suzuki RVs is approximated by a single log-normal RV by using the moment generating function (MGF)-based technique. This MGF-based technique uses Gauss-Hermite integration to present the sum distribution in closed form. We quantify the signal-to-noise ratio (SNR) margin required to achieve a certain quality of service (QoS) under standard deviation of the shadowing. We also provide the optimal level of cooperation required for obtaining maximum coverage of a line network under a given QoS. Two topologies for linear network are considered and the performance of each topology under various system parameters is provided. The analytical results have been validated by matching with the simulation results.

A one-dimensional cooperative network is modeled stochastically, such that the nodes are randomly placed according to a Bernoulli process. A discrete time quasi-stationary Markov chain model is considered to characterize the multi-hop transmissions and its transition probability matrix has been derived. By the Perron-Frobenious theorem, the eigen-decomposition of the matrix gives useful information about the coverage of the network and signal-to-noise (SNR) margin that is required for obtaining a given quality of service or packet delivery ratio. An SNR penalty for the random placement of nodes, compared to regular placement, is quantified.

Pilot-assisted estimation of signal-to-noise ratio (SNR) is considered for an orthogonal non-coherent M-ary FSK receiver having a carrier frequency offset (CFO) at the front end. The received signal is also corrupted by symbol-rate Rayleigh fading and additive white Gaussian noise. A two-step estimation procedure is designed by observing received data in the receiver. In the first step, an asymptotically unbiased estimate of CFO is obtained using a moments based approach. This estimate is then used to derive a maximum-likelihood estimator for the SNR. The Cramer Rao Bound for SNR has been derived to compare the overall estimator performance in terms of its variance.

We consider a quasi-stationary Markov chain as a model for a decode and forward wireless multi-hop cooperative transmission system that forms successive Opportunistic Large Arrays (OLAs). This paper treats a linear network topology, where the nodes form a one-dimensional horizontal grid with equal spacing. In this OLA approach, all nodes are intended to decode and relay. We derive the transition probability matrix of the Markov chain based on the hypoexponential distribution of the received power at a given time instant assuming that all the nodes have equal transmit power and the channel has Rayleigh fading and path loss with an arbitrary exponent. The Perron-Frobenius eigenvalue and the corresponding eigenvector of the sub-stochastic matrix indicates the signal-to-noise ratio (SNR) margin that enables a given hop distance.

This paper considers how to estimate the average signal-to-noise ratio (SNR) for a communication system employing orthogonal non-coherent M-ARY frequency shift keying (NCMFSK), in white Gaussian noise (AWGN) and over both symbol-by-symbol fading channels and block fading channels. The proposed algorithm finds its application in a variety of applications including a cooperative transmission system, which is the main motivation behind this study. The maximum likelihood estimator and one using data statistics have been derived and simulated for various scenarios including data-aided, non-data aided and joint estimation using both the data and pilot sequences. We also derive the Cramer-Rao bound for the estimators in the case of Rayleigh fading channels. The results show that for a particular region of interest (e.g. high SNR or low SNR) and depending upon the availability of pilot sequence, a particular SNR estimation scheme is suitable.

We consider a quasi-stationary Markov chain as a model for a decode and forward wireless multi-hop cooperative transmission system that forms successive Opportunistic Large Arrays (OLAs). This paper treats a linear network topology, where the nodes form a one-dimensional horizontal grid with equal spacing. In this OLA approach, all nodes are intended to decode and relay. Therefore, the method has potential application as a high-reliability and low-latency approach for broadcasting in a line-shaped network, or unicasting along a pre-designated route. We derive the transition probability matrix of the Markov chain based on the hypoexponential distribution of the received power at a given time instant assuming that all the nodes have equal transmit power and the channel has Rayleigh fading and path loss with an arbitrary exponent. The state is represented as a ternary word, which indicates which nodes have decoded in the present hop, in a previous hop, or have not yet decoded. The Perron-Frobenius eigenvalue and the corresponding eigenvector of the sub-stochastic matrix indicates the signal-to-noise ratio (SNR) margin that enables a given hop distance.

We consider the irreducible discrete time Markov chain, with one absorbing state, as a potential candidate to model a wireless multi-hop transmission system that does cooperative transmission at every hop. This paper describes the modeling for a special geometry where the nodes are aligned on a one-dimensional horizontal grid with equal spacing and such that the cooperating clusters are adjacent. This model can be considered a precursor to a model for an Opportunistic Large Array broadcast for the finite density case. Assuming all the nodes have equal transmit power, the successive transmissions can be modeled as a Markov chain in discrete time. We derive the transition probability matrix of the Markov chain based on the hypoexponential distribution of the received power at a given time instant. The Perron-Frobenius eigenvalue of that sub-stochastic matrix is used in formulating a bound on how far transmissions can reach with a particular relay transmit power.

This paper deals with the problem of estimating average signal-to-noise ratio (SNR) for a communication system employing non-coherent M-ary frequency shift keying (NCMFSK) over fading channels and white Gaussian noise (AWGN). We derive two estimators; one using maximum likelihood (ML) approach and other using the data statistics. Various scenarios have been taken into account including data-aided (DA), non-data aided (NDA) and joint estimation using both the data and pilot sequences. We also derive the Cramer-Rao bound (CRB) for the estimators. The results show that for a particular region of interest (e.g. high SNR or low SNR) and depending upon the availability of pilot sequence, a particular SNR estimation scheme is suitable.

Estimation of the signal-to-noise ratio (SNR) is considered for a non-coherent M-ary frequency shift keying (NC-MFSK) receiver. It has been assumed that the transmitted symbols undergo a slow flat fading channel where a block of data is corrupted by an independent constant fade and additive white Gaussian noise. Two approaches for SNR estimation are reported in this paper: an approximate maximum likelihood approach and another using the data statistics, both for data aided and non-data aided systems. It has been shown that for a particular SNR region of interest and depending upon the availability of pilot symbols, a particular approach is suitable for SNR estimation.

This paper deals with the problem of estimating the average signal-to-noise ratio (SNR) for a communication system employing non-coherent binary frequency shift keying (NCBFSK) over fading channels with white Gaussian noise (AWGN). The maximum likelihood (ML) estimator and one using data statistics have been derived and simulated for various scenarios including data-aided (DA), non-data aided (NDA) and joint estimation using both the data and pilot sequences. We also derive the Cramer-Rao bound (CRB) for the estimators. The results show that for a particular region of interest (e.g. high SNR or low SNR) and depending upon the availability of pilot sequence, a particular SNR estimation scheme is suitable.