This paper presents a novel framework for cognitive radio (CR)-enabled integrated terrestrial and non-terrestrial networks (ITNTNs), comprising a primary low-Earth-orbit (LEO) satellite network and a secondary terrestrial network. In particular, a beyond-diagonal reconfigurable intelligent surface (BD-RIS) mounted secondary base station (BS) reuses the same spectrum to communicate with the secondary user. The proposed framework improves the energy efficiency of the secondary network while ensuring that the interference temperature threshold of the primary LEO network is not violated. The joint optimization of BS power allocation and BD-RIS phase shifts is considered, which results in a highly nonconvex problem. To address this challenge, the Dinkelbach method is first employed to transform the fractional objective function, followed by the development of an alternating optimization strategy. Specifically, the BS power allocation is optimized using the Lagrangian method with Karush-Kuhn-Tucker (KKT) conditions, while the BD-RIS phase shifts are updated through manifold optimization techniques. Numerical results demonstrate that the proposed BD-RIS framework performs better than conventional diagonal RIS (D-RIS) configurations in terms of energy and spectral efficiency, highlighting its potential to enable green, adaptive, and high-capacity 6G ITNTN deployments.

Vehicular Ad Hoc Networks (VANETs) form the foundational communication framework of intelligent transportation systems, facilitating low-latency, vehicle-to-everything data exchange for enhanced traffic efficiency and safety. Accordingly, ensuring secure, efficient, and scalable authentication is essential to maintain communication trustworthiness, especially in highly dynamic and dense traffic scenarios. While traditional public key cryptography (PKC)-based solutions offer strong security guarantees, they are computationally intensive and struggle to scale under VANET workloads. To address these challenges, this paper proposes a novel lightweight handover authentication scheme that integrates pairing-based cryptography with symmetric key primitives to ensure message integrity, anonymity, and unlinkability. The proposed solution is deployed within a real-world Reconfigurable Intelligent Surface (RIS)-assisted communication environment, enhancing the robustness and feasibility of the authentication process during handover. Furthermore, a comprehensive evaluation is conducted, comparing the computational and communication overhead of the proposed scheme with existing cryptographic protocols. Results demonstrate the superior scalability and efficiency of the proposed approach, making it well-suited for next-generation VANET applications.

This paper introduces a novel strategy for radio resource scheduling in a Coordinated Multi-Point (CoMP) Open Radio Access Network (O-RAN) environment, particularly under the constraints of a non-ideal fronthaul. We propose a comprehensive three-phase scheme, which includes the selection of Cell Edge Users (CEUEs), DPS-based clustering and switching, and a weighted mechanism for Proportional Fair (PF)-based radio resource allocation. Using a modified Vienna 5G system-level simulator, we conducted a series of detailed simulations to validate our approach in a real-world scenario. The simulation results reveal significant enhancements in both the throughput performance of CEUEs and the overall user average throughput. Furthermore, our approach demonstrates substantial improvements in the fairness index, thereby underscoring its potential to significantly improve the efficiency and user experience within 5G networks.

Intelligent transportation systems increasingly depend on wireless communication for broadcasting traffic messages and facilitating real-time vehicular communication. In this context, message authentication is crucial for establishing secure and reliable communication. However, security solutions must consider the dynamic nature of vehicular communication links, which fluctuate between line-of-sight (LoS) and non-line-of-sight (NLoS) due to obstructions. This paper proposes a lightweight cross-layer authentication scheme that employs public-key infrastructure (PKI)-based authentication for initial legitimacy detection/handshaking while using key-based physical-layer re-authentication for message verification. This approach reduces signature generation and signaling overheads associated with each transmission, thereby enhancing network scalability. However, the receiver operating characteristic (ROC;Pd: detection vs.PFA: false alarm probabilities) of the latter decreases with lower signal-to-noise ratio (SNR). To address this, we investigate the use of reconfigurable intelligent surfaces (RISs) to strengthen the SNR directed toward the designated vehicle in shadowed areas (i.e., NLoS scenarios), thereby improving the ROC. Theoretical analysis and practical implementation are conducted using a 1-bit RIS consisting of 64×64 reflective metasurfaces. Experimental results show a significant improvement inPd, increasing from 0.82 to 0.96 at SNR = −6 dB for an orthogonal frequency-division multiplexing (OFDM) system with 128 subcarriers. We also conducted informal and formal security analyses using Burrows-Abadi-Needham (BAN) logic to prove the scheme’s ability to resist passive and active attacks. Furthermore, the proposed scheme reduces computational and communication overheads by 43% and 13%, respectively, compared to traditional cryptographic methods, demonstrating its superiority for real-time, challenging communication scenarios.

Life on Earth depends on healthy oceans, which supply a large percentage of the planet’s oxygen, food, and energy. However, the oceans are under threat from climate change, which is devastating the marine ecosystem and the economic and social systems that depend on it. The Internet of Underwater Things (IoUT), a global interconnection of underwater objects, enables round-the-clock monitoring of the oceans. It provides high-resolution data for training machine learning (ML) algorithms for rapidly evaluating potential climate change solutions and speeding up decision making. The sensors in conventional IoUT are battery powered, which limits their lifetime, and constitutes environmental hazards when they die. In this article, we propose a sustainable scheme to improve the throughput and enable wireless charging of underwater networks, enabling them to potentially operate indefinitely. The scheme is based on simultaneous wireless information and power transfer (SWIPT) from an autonomous underwater vehicle (AUV) used for data collection. We model the problem of jointly maximizing throughput and harvested power as a Markov decision process (MDP), and develop a model-free reinforcement learning (RL) solution. The model’s reward function incentivises the AUV to find optimal trajectories that maximize throughput and power transfer to the underwater nodes while minimising its own energy consumption. To the best of our knowledge, this is the first attempt at using RL for this application. The scheme is implemented in an open 3-D RL environment specifically developed in MATLAB for this study. The performance results show up 207% improvement in energy efficiency compared to those of a random trajectory scheme used as a baseline model.

The cellular Internet of Things envisions the connection of billions of devices with the Internet through cellular networks. These Internet-of-Things Devices (IoDs) are normally energy constrained. In order to provide network services to these energy-constrained IoDs, this article proposes a cellular architecture, whereby IoDs utilize resources of the cellular network for energy harvesting and information transmission. The radio resources of the cellular network are divided by time and frequency, and each time-frequency block is called a cellular resource block (CRB). At the beginning of each time slot, IoDs select CRBs randomly. IoDs receive the control information from the base station (BS) and extract the resource allocation information about the selected CRB. When selected CRBs are busy, IoDs harvest energy from the radio-frequency signals of the BS, and then they use that stored energy to transmit their information to the cluster head (CH) when selected CRBs are idle. In order to obtain the average harvested energy, the random distance between cellular BS and randomly deployed IoD has been statistically calculated. Using the finite-state Markov chain model, the transmission probability of each IoD is calculated. Analytical expressions for the collision probability and average throughput of IoDs are also derived. To verify the derived analytical expressions, system-level simulations are carried out, and it is shown that the analytical results match the simulation results. Moreover, the overall performance of the considered architecture is studied for different system parameters.

In order to enhance the capacity of the Fifth Generation and beyond (5GB) cellular networks, third generation partnership project (3GPP) has provided guidelines for enabling device-to-device (D2D) communication. An essential requirement for successful D2D communication is the users' ability to discover each other, specially in the out-of-network scenarios. While there has been some research effort in the area of D2D discovery, most of the works assume that the devices are static. In a typical mobile environment, the constraints like time-to-discovery, etc, are more rigid and deserve investigation. This paper formulates a mathematical model for a mobile device's neighborhood discovery as it moves across an area that has other D2D users. In particular, we determine the expected number of users discovered by a mobile device while moving in a straight line between two way-points following random waypoint mobility. The model is verified using Monte-Carlo simulation and can be used for future analysis of mobile D2D-discovery.

In this paper, we propose an ambient backscatter (AB)-enabled decode-and-forward (DF) cognitive relay network with wireless energy-harvesting capabilities. In our proposed scheme, a source node communicates with its destination node via a radio frequency-powered DF relay. It is assumed that the relay node is equipped with two different interfaces and can concurrently harvest/decode and backscatter the received source signals. A power-splitting-based approach is adopted for the information processing and the energy harvesting at the relay. The analytical expressions for the outage probability at all of the receiving nodes are derived. It has been shown that the analytical results match the simulation results. It has also been shown that using the AB for secondary communications can significantly improve the overall network performance in terms of the achievable throughput and the energy efficiency.

Wireless energy harvesting has emerged as an efficient solution to prolong the lifetime of wireless networks composed of energy‐constrained nodes. In this paper, we consider a multipoint‐to‐multipoint relay network, where multiple source nodes communicate with their respective destination nodes via intermediate energy‐constrained decode‐and‐forward (DF) relay. The performance of two different transmission modes, namely, delay tolerant and delay nontolerant, is studied. Based on power‐splitting relaying protocol (PSR), optimal energy harvesting and distribution schemes for both transmission modes are provided. In addition, for more realistic and practical analysis, we consider a nonlinear energy conversion model for energy harvesting at the relay node. Our numerical results provide useful insights into different system parameters of a nonlinear energy harvesting‐based multipair DF relay network.

Wireless energy harvesting is an efficient way to prolong the lifetime of energy constrained networks. In this paper, a two-way amplify-and-forward (AF) based relay network is considered, where two communicating nodes concurrently transmit there information signals to a relay node using time switching based relaying protocol (TSR). The relay node is energy constrained and therefore it first harvests the energy from the received radio frequency (RF) signals. After successful energy harvesting the relay utilizes the harvested energy to amplify and forward the received information signal to its destination. We derive the analytical expression for outage probability and achievable throughput at the receiving nodes. Our numerical results verify our analytical derivation and shows the effect of different system parameters on achievable throughput at receiving nodes.