An Unsupervised Learning Approach for Spectrum Allocation in Terahertz Communication Systems
This addresses spectrum efficiency for terahertz communication users, but it is incremental as it builds on existing optimization and learning methods.
The paper tackles spectrum allocation in multiuser terahertz communication systems by proposing an unsupervised learning approach with adaptive sub-band bandwidth to reduce molecular absorption loss variation, achieving higher data rates compared to existing methods, especially under non-linear absorption conditions.
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.