Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

Abstract

Spectrum scarcity due to inefficient utilisation has ignited a plethora of dynamic spectrum access solutions to accommodate the expanding demand for future wireless networks. Dynamic spectrum access systems allow secondary users to utilise spectrum bands owned by primary users if the resulting interference is kept below a pre-designated threshold. Primary and secondary user spectrum occupancy patterns determine if minimum interference and seamless communications can be guaranteed. Thus, spectrum occupancy prediction is a key component of an optimised dynamic spectrum access system. Spectrum occupancy prediction recently received significant attention in the wireless communications literature. Nevertheless, a single consolidated literature source on statistical spectrum occupancy prediction is not yet available in the open literature. Our main contribution in this paper is to provide a statistical prediction classification framework to categorise and assess current spectrum occupancy models. An overview of statistical sequential prediction is presented first. This statistical background is used to analyse current techniques for spectrum occupancy prediction. This review also extends spectrum occupancy prediction to include cooperative prediction. Finally, theoretical and implementation challenges are discussed.

An Underwater Cooperative Spectrum Sharing Protocol for a Centralized Underwater Cognitive Acoustic Network

“…The receiver CU also allocates the sender CUs into the channel and transmits power to maximize their transmission rate; In [ 21 ], a receiver-viewed dynamic borrowing (RvDV) algorithm, a heuristic spectrum decision method among cluster heads in a cluster-based underwater sensor network was proposed. In this algorithm, a cluster head can borrow additional spectrum resources for data transmission from neighboring cluster heads by informing them of its traffic information in the control channel; In [ 22 ], a dynamic spectrum access considering the CR concept was proposed to utilize the limited acoustic frequency resources more efficiently. Under the assumption that the number of CUs is the same as the number of channels, a heuristic algorithm that determines the CU–channel pairs to maximize the minimum channel capacity per CU by applying graph theory was proposed.…”

“…In [ 22 ], a dynamic spectrum access considering the CR concept was proposed to utilize the limited acoustic frequency resources more efficiently. Under the assumption that the number of CUs is the same as the number of channels, a heuristic algorithm that determines the CU–channel pairs to maximize the minimum channel capacity per CU by applying graph theory was proposed.…”

A Survey on Spectrum Sensing and Learning Technologies for 6G

“…The unsupervised learning classification algorithm is also known as the clustering algorithm [79]. It can automatically divide samples into multiple disjoint clusters according to their inherent properties without requiring labelled training data set.…”