Evaluation of Spectrum Sharing Algorithms for Networks with Heterogeneous Wireless Devices

TL;DR

This paper evaluates five dynamic spectrum access algorithms for heterogeneous 6G networks, focusing on their effectiveness in allocating frequency bands while avoiding interference, and finds that algorithms maximizing feasibility often underperform on other metrics.

Contribution

It introduces a comprehensive simulation-based comparison of five DSA algorithms, including a graph coloring approach, for spectrum sharing in heterogeneous 6G networks.

Findings

Algorithms maximizing feasibility underperform on other metrics.

Graph coloring-based algorithm shows competitive performance.

Resource utilization varies significantly across algorithms.

Abstract

As highlighted in the National Spectrum Strategy, Dynamic Spectrum Access (DSA) is key for enabling 6G networks to meet the increasing demand for spectrum from various, heterogeneous emerging applications. In this paper, we consider heterogeneous wireless networks with multiple 6G base stations (BS) and a limited number of frequency bands available for transmission. Each BS is associated with a geographical location, a coverage area, and a bandwidth requirement. We assume that clients/UEs are within the corresponding BS's coverage area. To avoid interference, we impose that BSs with overlapping coverage areas must use different frequency bands. We address the challenging problem of efficiently allocating contiguous frequency bands to BSs while avoiding interference. Specifically, we define performance metrics that capture the feasibility of the frequency allocation task, the number of BSs that can be allocated within the limited frequency bands, and the amount of resources utilized by the network. Then, we consider five different DSA algorithms that prioritize BSs based on different features -- one of these algorithms is known in the graph theory literature as Welsh-Powell graph colouring algorithm -- and compare their performance using extensive simulations. Our results show that DSA algorithms that attempt to maximize the chances of obtaining a feasible frequency allocation -- which have been widely studied in the literature -- tend to under-perform in all other metrics.