Accurate and efficient estimation of Channel State Information (CSI) is critical for next-generation wireless systems operating under non-stationary conditions, where user mobility, Doppler spread, and multipath dynamics rapidly alter channel statistics. Conventional pilot aided estimators incur substantial overhead, while deep learning approaches degrade under dynamic pilot patterns and time varying fading. This paper presents a pilot-aided Flash-Attention Transformer framework that unifies model-driven pilot acquisition with data driven CSI reconstruction through patch-wise self-attention and a physics aware composite loss function enforcing phase alignment, correlation consistency, and time frequency smoothness. Under a standardized 3GPP NR configuration, the proposed framework outperforms LMMSE and LSTM baselines by approximately 13 dB in phase invariant normalized mean-square error (NMSE) with markedly lower bit-error rate (BER), while reducing pilot overhead by 16 times. These results demonstrate that attention based architectures enable reliable CSI recovery and enhanced spectral efficiency without compromising link quality, addressing a fundamental bottleneck in adaptive, low-overhead channel estimation for non-stationary 5G and beyond-5G networks.

Channel knowledge map (CKM) is a key enabler for environmental awareness in future wireless systems. However, reconstructing high-fidelity CKM from sparse and noisy measurements poses a significant challenge. While Graph Neural Networks (GNNs) have emerged as a potent tool for this task, existing methods often lack physical consistency and generalization due to reliance on single graph structures and purely data-driven approaches. To tackle this challenge, in this paper, we propose a physics-informed multi-view graph neural network (PIMV-GNN) framework. This framework innovatively integrates two mechanisms within a unified GNN backbone: a multi-view learning (MVL) module that builds a rich spatial representation of the complex environment by fusing two complementary graph structures, namely a "spatial line-of-sight" graph and a "physical proximity" graph; and a physics-informed neural network (PINN) module that enforces physical consistency by imposing constraints derived from the Helmholtz equation in the graph domain. Extensive simulations demonstrate that our proposed PIMV-GNN framework significantly outperforms baseline models under various levels of data sparsity and measurement noise. Furthermore, the results reveal a profound synergistic effect between the MVL and PINN modules, where high-quality multi-view features significantly improve the regularization efficiency of the physics-based constraints.

The rapid expansion of the Internet of Things (IoT) demands effective communication protocols that accommodate mobile and static end devices (EDs). LoRaWAN (Long Range Wide Area Network), a pioneering low-power wide-area network (LPWAN) technology, uses Adaptive Data Rate (ADR) approaches to optimize resource allocation, particularly for static EDs. However, traditional ADR approaches are ineffective in mobile contexts as they struggle to adapt to changing network conditions, resulting in significant packet loss and higher retransmission rates. Although innovative technologies, such as the Blind ADR (BADR), have been devised to improve the performance of mobile EDs, they still fall short of dealing with the unpredictable nature of mobile EDs. To address these challenges, this paper presents a novel HybridQ-ADR mechanism suitable for static and mobile EDs. This approach addresses the constraints of BADR and related methods in mobile scenarios. In particular, it provides a more efficient solution to reduce packet loss and collisions in dense and dynamic LoRa-based IoT networks. This is achieved by allocating Spreading Factors (SF) using signal orthogonality to minimize interference and provide reliable communication. Furthermore, the proposed HybridQ-ADR mechanism provides a new clustering technique based on estimated path loss. Specifically, it divides EDs into clusters and assigns different channels to each cluster, improving SF allocation and data transmission speeds. The proposed HybridQ-ADR mechanism includes a mobility-aware, Doppler-constrained SF allocation strategy, limiting each ED’s maximum SF based on its Doppler/mobility load to maintain reliable performance at high speeds. Performance evaluations using simulations and testbed implementations show that HybridQ-ADR improves latency, packet success rate, power consumption, and throughput for both static and mobile EDs.

Unmanned aerial vehicles (UAVs) in integration with reconfigurable intelligent surfaces (RIS) play a crucial role in improving wireless communication coverage and enhancing overall performance. However, optimizing the beamforming for the RIS and base station (BS) is critical in improving coverage and ensuring connectivity in densely populated areas. The primary challenge in this process arises from the diverse Quality of Service (QoS) requirements set by user equipment (UEs). To address this complexity, machine learning algorithms are employed to predict the optimal beamforming configurations for both the BS and RIS. However, the traditional supervised learning methods are becoming less effective due to the ever-changing demands of UEs, as these methods rely on fixed data patterns that struggle to adapt to the fluctuating QoS requirements of UEs. Thus, in this paper, we propose an unsupervised learning-based deep learning (DL) approach to jointly predict the optimal beamforming matrix for RIS and BS, enhancing communication coverage and maximizing QoS satisfaction of UEs. The proposed DL-based beamforming adaptively predicts the beamforming matrix, facilitates efficient data exploration during the initial learning phase, and seamlessly scales as the process advances, thereby enhancing overall performance. Numerical results demonstrate that the proposed DL-based RIS and BS beamforming outperforms by up to 89%, compared to the state-of-the-art methods.

Multi-reconfigurable intelligent surfaces (RIS) has recently gained significant interest as emerging technology for exploiting Intelligent electromagnetic environment. Inspired by opportunistic communications, a low-complexity adaptive selection and training method for multi-RISs is proposed in this paper. Firstly appropriate number of the multi-RISs is selected to assist communication. The elements of the selected RIS are grouped, and the grouped elements share a common coefficient to reduce training overhead. Secondly, the communication performance is evaluated and other RIS will be selected to assist communication if the communication performance not meet user requirement. In this way, the system performance and the training complexity of multi-RISs can be trade-off efficiently. Simulation results show that the proposed scheme outperforms the state-of-the-art benchmarks in terms of training overhead and robustness in different channel condition, and the training overhead is 30% lower compared to the centralized deployment scheme proposed in [13].

Covert communication has become a hot topic in the wireless transmission field due to the ability to secure transmitted data by hiding the wireless transmissions. Given the extensive use of drone communications and its urgent demand for security, we investigate covert communications in aerial terrestrial integrated networks (ATINs), where an unmanned aerial vehicle (UAV) tries to send private messages to a remote user via multiple terrestrial relays under the supervisions of the warden. On this foundation, one covert scheme for joint power control and relay selection has been proposed. Subsequently, we derive the detection capabilities at warden, and the effective covert rate (ECR) of link from UAV to user. Furthermore, a power optimization problem is designed to maximize ECR with covertness constraint. Finally, numerical results are presented to verify the achievable covert performance of system and prove the effectiveness of the proposed scheme.

As sixth-generation (6G) networks advance to enable massive connectivity and intelligent services, energy efficiency becomes a vital concern, particularly for battery- and edge-powered devices. Building on previous work in task-oriented semantic communication using deep reinforcement learning, this paper proposes an energy-efficient semantic communication framework based on an enhanced Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm integrated with an Adaptive Semantic Compression Policy (ASCP). In the proposed framework, we incorporate a predictive channel-aware semantic forecasting (PCSF) module, which leverages lightweight long short-term memory (LSTM) learning models to predict short-term fluctuations in channel conditions. By proactively anticipating variations in signal quality, the agent adjusts semantic compression levels and transmission strategies in advance, enhancing both energy efficiency and the preservation of task-relevant semantic information, particularly under rapidly changing network dynamics. Simulation results show that our energy-aware TD3-ASCP framework, enhanced with PCSF, significantly improves transmission efficiency and accuracy by up to 32% and 26%, respectively, compared to state-of-the-art algorithms.

NR-V2X Mode 2 is introduced by the third generation partnership project (3GPP) to support vehicle-to-everything (V2X) communication. In NR-V2X Mode 2, vehicles select resources for the exchange of cooperative awareness messages (CAM) in a decentralized manner based on their local observation using semi-persistent scheduling. Resources are distributed over the 2-D frequency and time domain, following the long-term evolution frame structure. Since vehicles select resources based on their local observations and due to spectrum scarcity, this may lead to contention. Hence, selecting a resource is challenging, and as each vehicle strives to select a resource, it becomes a consensus problem. To resolve resource contention, in this article, we propose a knowledge-empowered distributed multiagent deep reinforcement learning (K-MADRL) approach. Based on traffic flow information, long short-term memory (LSTM) is employed to deploy Unmanned Internet of Aerial Agents (UIAAs) to collect vehicle state information. UIAAs gather vehicle state knowledge and train the local deep reinforcement learning (DRL) model. The locally trained model at the UIAA is shared and aggregated at the gNB for the global model update. The trained policy is then sent to the vehicles over system synchronization blocks for distributed execution. Moreover, the vehicles select the resource based on the joint action, i.e., by anticipating the actions of the neighboring vehicles. Our scheme is compared with other methods, such as DRL, optimization techniques, the SPS method, and random allocation methods, used in the NR-V2X environment. The results of the simulations show that our scheme outperforms the other methods.

Unmanned aerial vehicle (UAV)-assisted communication is becoming a streamlined technology in providing improved coverage to the internet-of-things (IoT) based devices. Rapid deployment, portability, and flexibility are some of the fundamental characteristics of UAVs, which make them ideal for effectively managing emergency-based IoT applications. This paper studies a UAV-assisted wireless IoT network relying on non-orthogonal multiple access (NOMA) to facilitate uplink connectivity for devices spread over a disaster region. The UAV setup is capable of relaying the information to the cellular base station (BS) using decode and forward relay protocol. By jointly utilizing the concepts of unsupervised machine learning (ML) and solving the resulting non-convex problem, we can maximize the total energy efficiency (EE) of IoT devices spread over a disaster region. Our proposed approach uses a combination of k-medoids and Silhouette analysis to perform resource allocation, whereas, power optimization is performed using iterative methods. In comparison to the exhaustive search method, our proposed scheme solves the EE maximization problem with much lower complexity and at the same time improves the overall energy consumption of the IoT devices. Moreover, in comparison to a modified version of greedy algorithm, our proposed approach improves the total EE of the system by $19 \%$ for a fixed 50 k target number of bits.

The research investigates the data rates of a wide-band multi-user system by optimizing the phases of the reconfigurable intelligent surfaces (RISs) elements and performing fair power allocation for subcarriers. A practical beamforming codebook is developed to select the RIS configuration that maximizes the signal-to-noise ratio (SNR). This guides the iterative power and semidefinite relaxation (SDR) algorithms for finding the optimal RIS configuration. The iterative power method is validated by comparing the achievable data rate and the computational complexity with the existing techniques in the literature. Simulation results revealed that the data rate performance of our proposed iterative power method is comparatively close to the well-known approaches such as the SDR and the strongest tap maximization (STM). Notably, our method exhibits superior performance to STM in non-line-of-sight (NLoS) channels, while also maintaining lower computational complexity compared to SDR.

The beginning of cellular communication (next-gen, such as 5G and 6G) promises an extreme leap in connectivity, introducing intelligent, adaptive solutions that integrate communication, artificial intelligence, and emerging technologies. Our approach combines reinforcement learning with Medium Access Control (MAC) protocols to dynamically optimize resource allocation and enhance network performance. In this work, we explore the integration of the adaptive frame size adjusting approach similar to the IEEE 802.1CB to ensure the efficient handling of seamless redundancy. The proposed solutions are validated through simulation, ensuring robustness and real-world applicability. Results indicate significant improvements in redundancy rate detection and delay in the network. This work contributes to achieving intelligent, adaptive, and seamless connectivity in the next generation of communication systems.

Deforestation poses a significant global environmental challenge with far-reaching consequences for biodiversity, climate change, and livelihoods. In this context, applying advanced technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI) holds immense promise. This paper aims to comprehensively review and analyze the role of IoT, AI, and remote sensing technologies in monitoring, detecting, predicting, and preventing deforestation. By providing real-time data and enabling early detection, these technologies contribute to addressing activities like illegal logging, plant diseases, and forest fires. This review presents an overview of the advantages and limitations of these technologies, accompanied by an analysis of their current state and future potential. Key technologies covered include IoT, satellite imagery, drones, and AI algorithms, with each offering unique applications. Importantly, this paper underscores the significance of these technologies in protecting forests and the diverse species they support. The findings discussed herein aim to inform ongoing debates and provide a foundation for further research in this crucial domain. Ultimately, the knowledge gained from this research has the potential to guide practical interventions and policies for effective forest conservation.

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

Reconfigurable intelligent surfaces (RISs) play a crucial role in improving the coverage and efficiency of millimeter-wave (mmWave) communication systems for Internet of Things (IoT) networks by enhancing signal strength and reducing interference. However, to fully exploit their potential, mathematical models and optimization algorithms are needed to optimize the RIS configuration and control. In this work, we present a mathematical model and optimization algorithm for RIS-aided mmWave communication systems. This paper proposes a mathematical model to capture the effects of RISs on mmWave communication channels, including equations for channel estimation and prediction. We also develop an opti-mization problem to maximize the system throughput, along with a model for optimizing the RIS phase shift using the alternating projection phase shift algorithm. The power allocation algorithm for the mmWave base station is presented using the Karush-Kuhn-Tucker (KKT) approach. Finally, we perform simulations to validate the proposed solution and compare it with ideal and random phase shift solutions. The results show that our proposed solution performs close to the ideal solution, demonstrating the effectiveness of the proposed mathematical model and optimization algorithm.

In wireless communications, various study findings have shown that a reconfigurable intelligent surface (RIS) may successfully alter wireless wave parameters like phase and amplitude without requiring sophisticated signal processing and decoding at the receiver. However, it is necessary to take into account designing the surface under a realistic frequency selective fading channel. Because of this, we chose a wideband OFDM multi-user communication system based on an actual RIS setup that considers mutual coupling (MC) and electromagnetic interference (EMI). We used Hadamard matrix in the pilot transmissions to estimate the uncontrollable and the controllable channels. The best pilot configuration was selected to initialize the gradient descent method in order to calculate the optimal reflection coefficient that maximize the data rate for each user in the presence of EMI and MC. Simulation results revealed that the data rate has been degraded when considering EMI and MC for around 30 Mbits/s for each user. This confirms that both EMI and MC must be given considerable attention in our research due to their inevitable effects on the system performance.

Abstract Using reconfigurable intelligent surfaces (RISs) to improve the coverage and the data rate of future wireless networks is a viable option. These surfaces are constituted of a significant number of passive and nearly passive components that interact with incident signals in a smart way, such as by reflecting them, to increase the wireless system's performance as a result of which the notion of a smart radio environment comes to fruition. In this survey, a study review of RIS‐assisted wireless communication is supplied starting with the principles of RIS which include the hardware architecture, the control mechanisms, and the discussions of previously held views about the channel model and pathloss; then the performance analysis considering different performance parameters, analytical approaches and metrics are presented to describe the RIS‐assisted wireless network performance improvements. Despite its enormous promise, RIS confronts new hurdles in integrating into wireless networks efficiently due to its passive nature. Consequently, the channel estimation for, both full and nearly passive RIS and the RIS deployments are compared under various wireless communication models and for single and multi‐users. Lastly, the challenges and potential future study areas for the RIS aided wireless communication systems are proposed.

Abstract It is anticipated that sixth‐generation (6G) systems would present new security challenges while offering improved features and new directions for security in vehicular communication, which may result in the emergence of a new breed of adaptive and context‐aware security protocol. Physical layer security solutions can compete for low‐complexity, low‐delay, low‐footprint, adaptable, extensible, and context‐aware security schemes by leveraging the physical layer and introducing security controls. A novel physical layer security scheme that employs the concept of radio frequency fingerprinting (RF‐FP) for location estimation is proposed, wherein the RF‐FP values are collected at different points with in the cell. Then, based on the estimated location, the nearest possible road‐side unit for sending the information signal is located. After this, the effects on secrecy capacity (SC) and secrecy outage probability (SOP) in the presence of multiple eavesdropper per unit time are analysed. It has been shown via simulations that the proposed RF‐FP scheme increases SC by up to 25% for the same signal‐to‐noise ratio (SNR) values as those of the benchmarks, while the SOP tends to decrease by up to 30% as compared to the benchmark scheme for the same SNR value. Thus, the proposed RF‐FP‐based location estimation provides much better results as compared to the existing physical layer security schemes.

Automotive-Industry 5.0 will use beyond fifth-generation (B5G) technologies to provide robust, computationally intelligent, and energy-efficient data sharing among various onboard sensors, vehicles, and other devices. Recently, ambient backscatter communications (AmBC) have gained significant interest in the research community for providing battery-free communications. AmBC can modulate useful data and reflect it towards near devices using the energy and frequency of existing RF signals. However, obtaining channel state information (CSI) for AmBC systems would be very challenging due to no pilot sequences and limited power. As one of the latest members of multiple access technology, non-orthogonal multiple access (NOMA) has emerged as a promising solution for connecting large-scale devices over the same spectral resources in B5G wireless networks. Under imperfect CSI, this paper provides a new optimization framework for energy-efficient transmission in AmBC enhanced NOMA cooperative vehicle-to-everything (V2X) networks. We simultaneously minimize the total transmit power of the V2X network by optimizing the power allocation at BS and reflection coefficient at backscatter sensors while guaranteeing the individual quality of services. The problem of total power minimization is formulated as non-convex optimization and coupled on multiple variables, making it complex and challenging. Therefore, we first decouple the original problem into two sub-problems and convert the nonlinear rate constraints into linear constraints. Then, we adopt the iterative sub-gradient method to obtain an efficient solution. For comparison, we also present a conventional NOMA cooperative V2X network without AmBC. Simulation results show the benefits of our proposed AmBC enhanced NOMA cooperative V2X network in terms of total achievable energy efficiency.

Industry 4.0 and 5.0 are paradigms of digitalization and intelligentization. The huge available bandwidth and the least spectral interference in the millimeter-wave (mmWave) band can pave the way for a wide range of new industrial automation capabilities. Sophisticated industrial applications such as industrial Internet of Things, time-sensitive networking (TSN), intelligent logistics, product tracking, remote visual monitoring and surveillance, image-guided automated assembly and automatic fault detection require high bandwidth, reliability and low latency which can be ensured using the mmWave band. In this paper, we address the physical layer (PHY) requirements of industrial communication from the viewpoint of Industry 4.0 and beyond, while highlighting the key performance indicators. This paper proposes a 60 GHz mmWave antenna system with high reliability and low latency, making it ideally suited for industrial IoT and communication. The proposed novel antenna system is designed to cover the entire 9 GHz bandwidth of the 60 GHz standard spectrum (57 to 66 GHz) with a single element peak gain of 8.2 dBi. It provides high gain and efficiency across all four channels of 60 GHz communication from 57.24 GHz to 65.88 GHz, each channel with 2.16 GHz of bandwidth. Moreover, the simulated achieved beamforming gain of the proposed antenna system reaches 16.1 dBi, which satisfies the high gain requirement for 60 GHz multi-gigabit industrial communication. The proposed antenna system is a promising physical layer candidate suitable for communication standards such as WiGig IEEE802.11ay, IEEE802.11ad, IEEE802.15.3c, ECMA-387 and WirelessHD to ensure multi-gigabit wireless communication at 60 GHz ISM band for factory automation and industrial applications.

In the last decade, a sharp surge in the number of user proximity wireless devices (UPWDs) has been observed. This has increased the level of electromagnetic field (EMF) exposure of the users substantially and hence, the possible physiological effects. Ambient backscatter communications (ABC) has appeared to be a promising solution to reduce the power consumption of UPWDs by converting ambient radio frequency (RF) signals into useful signals while non-orthogonal multiple access (NOMA) is a compelling multiplexing scheme for enhanced spectral efficiency. This paper utilises a novel combination of ABC and NOMA to reduce the EMF in the uplink of wireless communication systems. This contemporary approach of EMF-aware resource optimization is based on k-medoids and Silhouette analysis. To curtail the uplink EMF, a power allocation strategy is also derived by converting a non-convex problem to a convex one and solving accordingly. The numerical results exhibit that the proposed ABC, NOMA, and unsupervised learning based scheme achieves a reduction in the EMF by at least 75% in comparison to the existing solutions.

Backscatter communications (BC) has emerged as a promising technology for providing low-powered transmissions in nextG (i.e., beyond 5G) wireless networks. The fundamental idea of BC is the possibility of communications among wireless devices by using the existing ambient radio frequency signals. Non-orthogonal multiple access (NOMA) has recently attracted significant attention due to its high spectral efficiency and massive connectivity. This paper proposes a new optimization framework to minimize total transmit power of BC-NOMA cooperative vehicle-to-everything networks (V2XneT) while ensuring the quality of services. More specifically, the base station (BS) transmits a superimposed signal to its associated roadside units (RSUs) in the first time slot. Then the RSUs transmit the superimposed signal to their serving vehicles in the second time slot exploiting decode and forward protocol. A backscatter device (BD) in the coverage area of RSU also receives the superimposed signal and reflect it towards vehicles by modulating own information. Thus, the objective is to simultaneously optimize the transmit power of BS and RSUs along with reflection coefficient of BDs under perfect and imperfect channel state information. The problem of energy efficiency is formulated as non-convex and coupled on multiple optimization variables which makes it very complex and hard to solve. Therefore, we first transform and decouple the original problem into two sub-problems and then employ iterative sub-gradient method to obtain an efficient solution. Simulation results demonstrate that the proposed BC-NOMA V2XneT provides high energy efficiency than the conventional NOMA V2XneT without BC.

Non-orthogonal multiple access (NOMA) is a promising radio access technique that enables massive connectivity and increased spectral efficiency. The deployment of aerial base stations (ABSs) as a relay is also an optimistic goal that fairly serves a large number of internet of things (IoT) devices. On one side, ABS-assisted communication leverages effective communication services for secondary IoT devices in smart cities. On the other hand, NOMA allows several IoT devices to concurrently acquire the same frequency-time resource. To this end, weighted sum-rate (WSR) is an essential goal because it allows numerous trade-offs between user fairness and sum-rate efficiency. Therefore, this work aims to investigate the WSR for an integrated aerial terrestrial network subject to cellular power and delay constraints in downlink NOMA. Herein, a theoretical insight-based low-complexity iterative solution is provided for optimal power and blocklength allocation to achieve maximum sum-rate. For this purpose, the mixed-integer non-linear problem is formulated and a low-complexity near-optimal solution is proposed. Numerical results show that the proposed scheme achieves a near-optimal solution and outperforms baseline techniques, i.e., the performance gain of 5.18% over the legacy OMA system for NOMA with two IoT devices per subcarrier.

Heterogeneous backscatter networks are emerging as a promising solution to address the proliferating coverage and capacity demands of next-generation vehicular networks. However, despite its rapid evolution and significance, the optimization aspect of such networks has been overlooked due to their complexity and scale. Motivated by this discrepancy in the literature, this work sheds light on a novel learning-based optimization framework for heterogeneous backscatter vehicular networks. More specifically, the article presents a resource allocation and user association scheme for large-scale heterogeneous backscatter vehicular networks by considering a collaboration centric spectrum sharing mechanism. In the considered network setup, multiple network service providers (NSPs) own the resources to serve several legacy and backscatter vehicular users in the network. For each NSP, the legacy vehicle user operates under the macro cell, whereas, the backscatter vehicle user operates under small private cells using leased spectrum resources. A joint power allocation, user association, and spectrum sharing problem has been formulated with an objective to maximize the utility of NSPs. In order to overcome challenges of high dimensionality and non-convexity, the problem is divided into two subproblems. Subsequently, a reinforcement learning and a supervised deep learning approach have been used to solve both subproblems in an efficient and effective manner. To evaluate the benefits of the proposed scheme, extensive simulation studies are conducted and a comparison is provided with benchmark techniques. The performance evaluation demonstrates the utility of the presented system architecture and learning-based optimization framework.

On the one hand, the number of wireless personal devices (WPDs) owned by individuals has soared in the last five years. On the other hand, WPDs exposed their users to electromagnetic field (EMF) radiation that has been linked with possible adverse physiological effects. In this paper, we first provide a generic analytical framework for modelling the exposure generated by WPDs having two transmit antennas. Our model is based on reliable exposure data for different types of human dielectric properties; its accuracy is showcased via simulations. We then integrate this model in an optimization framework for minimizing the exposure, while meeting spectral efficiency (SE) requirements, by means of beamforming. Results show the existence of a trade-off between the specific absorption rate (SAR) and SE, such that our exposure minimization beamforming approach can reduce the exposure by at least 27% by trading-off only 1% of the SE when compared to the optimal SE-based beamforming approach. In addition, they also indicate that increasing the number of antennas at the receiver side can help to further reduce the exposure generated by WPDs.

Low latency is a critical requirement of beyond 5G services. Previously, the aspect of latency has been extensively analyzed in conventional and modern wireless networks. With the rapidly growing research interest in wireless-powered ambient backscatter communications, it has become ever more important to meet the delay constraints, while maximizing the achievable data rate. Therefore, to address the issue of latency in backscatter networks, this paper provides a deep Q-learning based framework for delay constrained ambient backscatter networks. To do so, a Q-learning model for ambient backscatter scenario has been developed. In addition, an algorithm has been proposed that employ deep neural networks to solve the complex Q-network. The simulation results show that the proposed approach not only improves the network performance but also meets the delay constraints for a dense backscatter network.

To enable the internet of things (IoT) devices with increased coverage and optimized power consumption, the 3rdgeneration partnership (3GPP) standardizes the idea of narrowband IoT (NB-IoT) technology in the fifth generation (5G) of cellular communication. Re-transmission of control and data packets due to a poor link between user equipment (UE) and the base station (BS), is considered as one of the key feature in NB-IoT to ensure the data delivery of delay-sensitive applications, e.g. ambulance services and body sensor networks (BSN). This phenomenon degrades the energy efficiency of the already resource constrained systems. One key solution for NB-IoT UE is to exploit the device-to-device (D2D) communication using two hops instead of transmitting on a direct uplink, due to which the system performance increases. In an attempt to transmit the NB-IoT UE uplink data packet, splendid researchers have focused towards developing a D2D communication based strategy, which typically optimizes the expected packet delivery ratio (EDR) and end-to-end delay (EED) through an opportunistic method. However, such methodology imposes an additional delay due to the unavailability of active relaying nodes and increases the overall energy consumption of the system. This necessitates us to design an intelligent deterministic D2D (2D2D) relay selection strategy for delay sensitive NB-IoT UEs. The EDR and EED have been improved using deterministic programming based algorithm. Simulations with various parameters are carried out, and results are presented. Simulation results show that the deterministic algorithm gives better performance with a 10% increase in EDR and overcomes the additional delay.