This study introduces two innovative methodologies aimed at augmenting energy efficiency in satellite-to-ground communication systems through the integration of multiple Reflective Intelligent Surfaces (RISs). The primary objective of these methodologies is to optimize overall energy efficiency under two distinct scenarios. In the first scenario, denoted as Ideal Environment (IE), we enhance energy efficiency by decomposing the problem into two sub-optimal tasks. The initial task concentrates on maximizing power reception by precisely adjusting the phase shift of each RIS element, followed by the implementation of Selective Diversity to identify the RIS element delivering maximal power. The second task entails minimizing power consumption, formulated as a binary linear programming problem, and addressed using the Binary Particle Swarm Optimization (BPSO) technique. The IE scenario presupposes an environment where signals propagate without any path loss, serving as a foundational benchmark for theoretical evaluations that elucidate the systems optimal capabilities. Conversely, the second scenario, termed Non-Ideal Environment (NIE), is designed for situations where signal transmission is subject to path loss. Within this framework, the Adam algorithm is utilized to optimize energy efficiency. This non ideal setting provides a pragmatic assessment of the systems capabilities under conventional operational conditions. Both scenarios emphasize the potential energy savings achievable by the satellite RIS system. Empirical simulations further corroborate the robustness and effectiveness of our approach, highlighting its potential to enhance energy efficiency in satellite-to-ground communication systems.

This paper presents an optimization framework for an Unmanned Aerial Vehicle (UAV) assisted communication system. The goal is to determine the optimal placement of the UAV in three-dimensional space (xu, yu, zu) to minimize power consumption and maximize data rate while maintaining a desired bit error rate (BER). We first establish a mathematical model that captures the relationship between the UAV’s position, power consumption, data rate, and BER, considering free space path loss. Such a mathematical model is achieved in a static network environment and by employing the Multi-objective Particle Swarm Optimization (MOPSO) algorithm, which concurrently explores the solution space, seeking the ideal position (xu, yu, zu) that balances power consumption and data rate.We then extend the proposed framework to adapt to dynamic network environments while satisfying user demands. By adjusting the proposed system for static and dynamic network conditions, our enhanced framework ensures the UAV-assisted communication system remains efficient and effective. Theoretical analysis results, demonstrated through various examples, show that our proposed solution effectively balances delay and power consumption while maintaining the desired BER in UAV networks.

Power allocation is essential to maximise network throughput and minimise energy consumption and bit error rate performance. Thus, a new power allocation scheme for enhancing the performance of the air-based network, named Unmanned Aerial Vehicle (UAV) networks, is presented in this paper. The proposed power allocation allows the total power to be distributed optimally among the UAV network’s nodes, including source relay and destination nodes. Then, energy consumption, transmission delay, and bit error rate in UAV networks are simultaneously enhanced. The typical UAV comprises three parts: remotely controllable flight platform (fixed wing or rotary wing), transmitter and receiver wireless nodes. In the proposed UAV networks, a wireless relay is fitted with a flight platform to extend network coverage and increase transmission throughput. The downside of mobile flight communication is the delay in data transmission caused by the UAV’s movement. A consequence of this delay is increased total energy consumption, causing further degradation in bit error rate performance, especially at high SNR levels. Fortunately, delay and energy consumption can be traded off to improve communication performance in wireless communication. The proposed power allocation in this paper is achieved using the Scalarization optimisation method, which enables the best trade-off between energy consumption and data transmission delay and enhances the bit error rate.

A Reconfigurable Intelligent Surface (RIS) panel comprises many independent Reflective Elements (REs). One possible way to implement an RIS is to use a binary passive load impedance connected to an antenna element to achieve the modulation of reflected radio waves. Each RE reflects incoming waves (incident signal) by using on/off modulation between two passive loads and adjusting its phase using a Phase Shifter (PS). However, this modulation process reduces the amplitude of the reflected output signal to less than unity. Therefore, recent RIS works have employed Reflection Amplifiers (RAs) to compensate for the losses incurred during the modulation process. However, these systems only improve the reflection coefficient for a single modulation state, resulting in suboptimal RE efficacy. Thus, this paper proposes a strategy for optimising RE by continuously activating the RA regardless of the switching load state. The performance of the proposed scheme is evaluated in two scenarios: (1) In the first scenario (Sc1), the RA only operates to compensate for high-impedance loads, and (2) in the second scenario (Sc2), the RA runs continuously regardless of the RE loads. To benchmark the performance of Sc1 and Sc2, various metrics are compared, including signal-to-noise ratio, insertion loss, noise figure, communication range, and power-added efficiency. Numerical examples are provided to demonstrate the effectiveness of the proposed scheme. It is found that the proposed system in Sc2 leads to better overall performance compared to Sc1 due to the increased gain of the RIS reflection.