# An Unsupervised Learning Approach for Spectrum Allocation in Terahertz Communication Systems

arXiv: https://arxiv.org/abs/2208.03618 · HTML: https://mlsanity.com/paper/2208.03618/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 7 Aug 2022.

## Builds on (most cited first, 10 of 10)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Learning Optimal Resource Allocations in Wireless Systems](https://mlsanity.com/paper/1807.08088/) (2018)
- [Towards 6G Networks: Use Cases and Technologies](https://mlsanity.com/paper/1903.12216/) (2019)
- [Next Generation Terahertz Communications: A Rendezvous of Sensing, Imaging, and Localization](https://mlsanity.com/paper/1909.10462/) (2019)
- [Toward End-to-End, Full-Stack 6G Terahertz Networks](https://mlsanity.com/paper/2005.07989/) (2020)
- [Terahertz Communications for 6G and Beyond Wireless Networks: Challenges, Key Advancements, and Opportunities](https://mlsanity.com/paper/2207.11021/) (2022)
- [IEEE 802.15.3d: First Standardization Efforts for Sub-Terahertz Band Communications towards 6G](https://mlsanity.com/paper/2011.01683/) (2020)
- [Coverage Analysis for 3D Terahertz Communication Systems](https://mlsanity.com/paper/2104.09913/) (2021)
- [Spectrum Allocation with Adaptive Sub-band Bandwidth for Terahertz Communication Systems](https://mlsanity.com/paper/2111.05629/) (2021)
- [Novel Spectrum Allocation Among Multiple Transmission Windows for Terahertz Communication Systems](https://mlsanity.com/paper/2207.02401/) (2022)

## Abstract (arXiv)

We propose a new spectrum allocation strategy, aided by unsupervised learning, for multiuser terahertz communication systems. In this strategy, adaptive sub-band bandwidth is considered such that the spectrum of interest can be divided into sub-bands with unequal bandwidths. This strategy reduces the variation in molecular absorption loss among the users, leading to the improved data rate performance. We first formulate an optimization problem to determine the optimal sub-band bandwidth and transmit power, and then propose the unsupervised learning-based approach to obtaining the near-optimal solution to this problem. In the proposed approach, we first train a deep neural network (DNN) while utilizing a loss function that is inspired by the Lagrangian of the formulated problem. Then using the trained DNN, we approximate the near-optimal solutions. Numerical results demonstrate that comparing to existing approaches, our proposed unsupervised learning-based approach achieves a higher data rate, especially when the molecular absorption coefficient within the spectrum of interest varies in a highly non-linear manner.