Non‐orthogonal multiple access (NOMA) along with cooperative communications have been recognised as promising candidates for the fifth generation (5G) wireless networks and have attracted many researchers. Every networked device however has its own limited power supply. To this extent, this study investigates a power‐splitting relaying (PSR) protocol for wireless energy harvesting (EH) and information processing in the NOMA systems to prolong the lifetime of the energy constrained relay nodes in wireless networks so as to avail the ambient radio‐frequency (RF) signal as well as to simultaneously harvest the energy and process the information. Decode‐and‐forward relaying is employed at the relay node where the energy from the received RF signal is harvested and exploited to forward the information to the destination. Specifically, the outage probability and ergodic rate of the PSR protocol are derived to realise the impacts of EH time, EH efficiency, power splitting ratio, source data rate, and the distance between nodes. It is shown that an outperformance in terms of the energy efficiency is achieved with an increased EH efficiency and the employment of the NOMA when compared to the conventional orthogonal multiple access.

In this paper, we consider an integration of software-defined edge techniques into unmanned aerial vehicles (UAV) to deliver multimedia contents in Internet of Things (IoT) networks. The UAV is integrated with caching, resource allocation, multicasting transmission, and 3D flight trajectory (3DF) techniques, to serve the IoT devices and users (IDU) in different clusters. By further utilising many aspects of multimedia contents, IDU features, UAV-to-clusters association, and diverse characteristics of wireless propagation environments, we formulate a service time-aware caching, power allocation, and 3DF (TCP-3DF) optimisation problem. The TCP-3DF problem is solved by genetic algorithms (GA) for optimally finding 1) the multimedia contents to cache, 2) the powers allocated to multicast each content and to fly over each hop, and 3) where to stop in 3D coordinates and stop sequence. The optimal results are found under the constraints not only on the storage and power resources but also on the service time required by different multimedia applications and services. Due to the complexity of various optimisation variables and constraints, the GA is modified to ensure the accurate and stable convergence by applying penalty function and truncated chromosome scheme. Simulation results are shown to demonstrate the feasibility of GA and the benefits of the proposed TCP-3DF method in terms of sum rate, service time, and flight distance while conserving the storage and power resources.

In this paper, we evaluate end-to-end outage probability of a multi-hop decode-and-forward relaying protocol in underlay cognitive radio network. In the proposed protocol, named COOP, secondary nodes including source and relays have to harvest radio-frequency energy from multiple secondary power beacons, and adjust their transmit power, follows a pre-determined interference threshold given by multiple primary users. To enhance the outage performance for the secondary network under an joint constraint of the interference threshold, Rayleigh fading channel and hardware noises caused by imperfect transceiver hardware, the secondary relays on the source-destination path cooperate to forward the source data to the destination. Particularly, they attempt to receive the source data from their previous nodes, and forward it to the secondary destination if requested. Moreover, whenever the destination cannot receive the source data successfully, a successful relay that has the shortest distance to the destination is selected for retransmission. Due to usage of the cooperative transmission, the proposed COOP protocol obtains better performance, as compared with the corresponding multi-hop relaying one (denoted DIRECT) which only uses direct transmission at each hop. We evaluate the outage performance of COOP and DIRECT via both simulation and theory. The obtained results present a significant performance enhancement, as comparing COOP with DIRECT.

Compact spectrum sensing (SS) is crucial for optimizing wireless network performance, particularly within dynamic 5 G NR and LTE environments. TThe effectiveness of traditional SS methods in real-world scenarios is often limited by challenges such as low signal-to-noise ratio, multipath fading, and high computational complexity. Deep learning-based approaches have emerged as promising solutions by offering superior adaptability and performance. However, existing DL models often suffer from computational costs and excessive storage requirements, thus constraining deployment on resource-constrained devices. To address these challenges, this paper introduces a novel Resolution-Preserving Network (RPNet), integrated with a Convolutional Layer Reuse (CLRu) technique, to enhance SS efficiency while reducing model size significantly. RPNet is designed to maintain the resolution of spectrogram input throughout its architecture, preserving critical spatial details for segmentation. CLRu optimizes memory usage by reusing convolutional layers, effectively reducing the number of trainable parameters with very slightly compromising performance. Extensive simulations on a synthetic 5G-LTE dataset demonstrate that RPNet+CLRu achieves a greater than $9 \times$ reduction in model size while maintaining 87.45% segmentation accuracy. In addition, the proposed model outperforms existing deep models in storage efficiency and computational feasibility, hence presenting it as a promising candidate for real-time SS in next-generation wireless networks.