Energy-Efficient Terahertz Communication for Sustainable 6G Networks

Abstract

The evolution of wireless systems toward sixth-generation (6G) networks demands data rates in the order of terabits per second, sub-millisecond latency, and massive device connectivity that existing millimeter-wave (mmWave) infrastructure cannot fully support. Terahertz (THz) communication, spanning the 0.1–10 THz band, has emerged as a promising enabler of this vision because of its enormous available bandwidth. However, the practical deployment of THz networks is constrained by severe propagation losses, high molecular absorption, and the substantial power consumption of THz-band hardware components such as power amplifiers, mixers, and high-resolution data converters, raising serious concerns for the energy sustainability of future networks. This paper presents a comprehensive investigation into energy-efficient THz communication for sustainable 6G networks. We first characterize the propagation and hardware-level factors that govern THz energy consumption, and then propose an integrated energy-efficient framework combining hybrid analog-digital beamforming, intelligent reflecting surfaces (IRS), artificial intelligence (AI)-driven adaptive resource allocation, and renewable energy-aware base station sleep scheduling. A system-level energy efficiency model is formulated, and simulation studies are carried out to evaluate the proposed framework against conventional fully-digital THz architectures. Results show that the proposed scheme achieves up to 58% improvement in energy efficiency (bits/Joule/Hz) at moderate transmit power levels and demonstrates favourable scaling with increasing IRS array size. The findings offer practical design guidance for building THz-enabled 6G infrastructure that balances ultra-high throughput with the sustainability goals of next-generation wireless networks.

Introduction

This paper investigates energy-efficient terahertz (THz) communication for 6G wireless networks, addressing the growing energy demands of future applications such as extended reality (XR), holographic communication, autonomous systems, and the Industrial Internet of Things (IIoT). While the THz spectrum (0.1–10 THz) offers extremely high bandwidth capable of supporting terabit-per-second data rates, it also presents significant challenges, including severe path loss, molecular absorption, signal blockage, and high hardware power consumption.

The study identifies major sources of energy inefficiency in THz systems, including:

• High power consumption of THz RF hardware (power amplifiers, ADCs/DACs, oscillators, and cooling systems).
• Energy-intensive beamforming and signal processing required by ultra-massive MIMO antenna arrays.
• Increased energy usage due to dense deployment of small cells needed to overcome the limited communication range of THz signals.

To overcome these challenges, the paper proposes an integrated energy-efficient framework built on four key components:

1. Hybrid analog–digital beamforming to reduce the number of active RF chains while maintaining beamforming performance.
2. Intelligent Reflecting Surface (IRS)-assisted communication to improve signal coverage using passive reflecting surfaces with minimal power consumption.
3. AI-driven adaptive resource allocation, where reinforcement learning dynamically adjusts transmit power, beam directions, RF chain activation, and IRS configurations according to traffic and channel conditions.
4. Renewable-aware sleep scheduling, which uses locally harvested renewable energy and intelligently switches underutilized THz access points into low-power sleep modes.

The paper also develops a mathematical energy efficiency model that evaluates system performance by considering data throughput, transmit power, circuit power, IRS control power, and renewable energy harvesting.

Simulation results for a 300 GHz indoor THz network demonstrate that the proposed framework significantly outperforms conventional fully digital beamforming and standard hybrid beamforming approaches. The proposed system:

• Achieves higher energy efficiency at lower transmit power.
• Benefits substantially from IRS deployment, with performance improving until approximately 512 reflecting elements, after which gains diminish.
• Reduces power consumption of RF chains, power amplifiers, and data converters through hybrid beamforming and passive IRS support.

Conclusion

This paper presented a comprehensive framework for energy-efficient terahertz communication in support of sustainable 6G networks. By systematically identifying the propagation- and hardware-level sources of energy inefficiency in THz systems, and by integrating hybrid beamforming, intelligent reflecting surfaces, AI-driven adaptive resource allocation, and renewable-aware sleep scheduling into a unified architecture, the proposed approach demonstrates substantial energy efficiency gains — up to 58% over conventional fully-digital designs — without compromising the ultra-high throughput that motivates THz adoption in the first place. As 6G standardization progresses, such energy-aware design principles will be essential to ensuring that the pursuit of terabit-scale wireless connectivity remains compatible with the sustainability goals of next-generation telecommunications infrastructure.

References

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Copyright

Copyright © 2026 Prof. Tejas Siddharth Nikalje. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.