In an article published in the journal Nature, researchers explored the complexities arising from real-world applications due to swift technological progress and the integration of AI solutions. The study focuses on Intelligent Reflecting Surfaces (IRSs) in energy-constrained 6G wireless networks, enhancing communication reliability and energy efficiency.
The paper explores a practical scenario in smart ocean transportation and assesses the impact of reflecting elements and phase resolution on system performance. Results indicate that IRSs with N = 100 and b = 2 exhibit a 20% improvement in energy efficiency compared to b = 1.
Study: Boosting Energy Efficiency in 6G Networks with Intelligent Reflecting Surfaces. Image credit: Photon photo/Shutterstock
Background
The evolving landscape of Industry 5.0 prioritizes human-machine synergy, while Industry 4.0 emphasizes process automation with minimal human intervention. The collaboration between human intelligence and intelligent systems is the cornerstone of enhanced process efficiency, leading to more cost-effective and less wasteful industrial processes.
To address market demands, manufacturers create integrated, flexible, and intelligent production lines using technologies like IoT, digital twins, and edge computing. However, the increased complexity of these systems necessitates efficient communication frameworks to ensure seamless interaction and synchronization. As such, emerging technologies like AI, machine learning, and blockchain are harnessed to provide intelligence and enhance decision-making processes.
The potential of explainable AI (XAI) for advanced communication scenarios is particularly promising. Moreover, the deployment of IRSs in energy-constrained 6G wireless networks presents an innovative solution to improve communication reliability and energy efficiency. This approach holds potential for various Internet of Things (IoT) application scenarios, contributing to the evolution of efficient, secure, and sustainable Industry 5.0 systems.
The present paper illustrates a practical model using IRS in 6G wireless networks to enhance connectivity and communication reliability for diverse IoT scenarios. Contributions include introducing a wireless network model with IRS, analyzing target rate achievement, and proposing an energy efficiency model. The impact of reflecting elements and phase resolution is studied, and a performance comparison is conducted against systems without IRS and using Decode-and-Forward (DF) relay. A use case in smart ocean transportation is demonstrated, highlighting the reliability and energy efficiency of the proposed IRS-assisted framework that benefits real-time 6G applications.
Proposed methodology
In the proposed communication network, a multi-antenna base station (BS) serves a multitude of users, each equipped with a single antenna. The BS, equipped with M antenna elements, communicates with the kth user via an IRS featuring N reflecting elements deployed on a nearby high-rise building. These passive reflecting units introduce phase shifts into incoming signals to create a constructive reflected beam for the desired user, effectively reshaping signal propagation.
A power consumption model is introduced to enhance downlink transmission efficiency using the IRS. This model factors in transmit power (Pt) for data transmission, circuit power (PCKT) encompassing transmitter, receiver, and IRS circuitry, and overall power consumption (PT) as their sum. Additionally, the evaluation of energy efficiency takes into account system bandwidth (B), achievable sum rate (R), and total power consumption (PT).
Direct Transmission: In the direct transmission approach, the signal is sent directly from the BS to the kth user. The received signal by the user consists of the desired signal component and the additive noise. The achieved sum rate in this method is determined by the signal-to-noise ratio, indicating the quality of the received signal.
Relay-Assisted Transmission: In relay-assisted transmission, a cooperative DF relay comes into play. In the first phase, the BS transmits a signal to both the kth user and the relay. The relay receives the signal and adds its own noise. In the second phase, the relay processes the received signal, re-encodes it, and sends it to the kth user. The sum rate achieved in this scenario is influenced by the minimum of two factors: the signal-to-noise ratio of the relay and the combination of signal-to-noise ratios from the BS and user. This approach leverages relay nodes to enhance communication reliability and coverage.
Experimental results
Simulation in MATLAB validates the communication model presented in this study, with 104 realizations per simulation point. The setup assumes a fixed IRS location at a distance D from the base station (source), while the location of mobile users is tracked with variable d. Simulation parameters compare achievable rates across scenarios, showcasing IRS-assisted transmission (with various phase resolutions), the system without IRS, and the relay-assisted system. IRS-assisted transmission excels with 2-bit phase resolution, boosting the rate by 13.4% at d of 80 m.
The relay-based system outperforms direct transmission, achieving a target rate of 3.8 bits/s/Hz. The rate achieved through IRS varies proportionally with reflecting elements N, highlighting its data rate benefits. The impact of power consumption on IRS-assisted network transmits power decreases with N and low-bit resolution phase shifters.
Energy efficiency (EE) performance shows that IRS with 2-bit phase resolution achieves more EE for a low transmit Signal-to-Noise Ratio (SNR). In contrast, the 1-bit resolution is more energy-efficient for high transmit SNR. The use of IRS for smart ocean transportation applications is illustrated, offering benefits like intelligent freight verification, real-time communication, and reliability. IRS-enabled communication enhances climate monitoring, disaster prediction, pollution control, and more. IRS mounted on buildings, ships, Autonomous Underwater Vehicle (AUVs), or Unmanned Aerial Vehicles (UAVs) enables reliable Line of Sight (LoS) channels for underwater communication. Despite exploiting LoS, energy efficiency optimization remains a challenge for real-time applications.
Conclusion
This study explores the energy-efficient potential of 6G-enabled IRS for Industry 5.0 applications, highlighting a smart ocean transportation use case. IRSs with reflecting elements perform phase shifting to enhance communication. The model assesses achievable rates and proposes an energy-efficient power model. The impact of N and b is studied, showing that 2-bit IRS has better energy efficiency at low SNR. For N=100 with 2-bit phase resolution, energy efficiency improves by 20%. This work lays the groundwork for future studies with multiple IRSs, supporting scalable, energy-sustainable communication for evolving industries.
Machine Learning Powering Breakthroughs in Climate Forecasting and Modeling