Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

Abstract

Abstract-Spectrum inference, also known as spectrum prediction in the literature, is a promising technique of inferring the occupied/free state of radio spectrum from already known/measured spectrum occupancy statistics by effectively exploiting the inherent correlations among them. In the past few years, spectrum inference has gained increasing attention owing to its wide applications in cognitive radio networks (CRNs), ranging from adaptive spectrum sensing, and predictive spectrum mobility, to dynamic spectrum access and smart topology control, to name just a few. In this paper, we provide a comprehensive survey and tutorial on the recent advances in spectrum inference. Specifically, we first present the preliminaries of spectrum inference, including the sources of spectrum occupancy statistics, the models of spectrum usage, and characterize the predictability of spectrum state evolution. By introducing the taxonomy of spectrum inference from a time-frequency-space perspective, we offer an in-depth tutorial on the existing algorithms. Furthermore, we provide a comparative analysis of various spectrum inference algorithms and discuss the metrics of evaluating the efficiency of spectrum inference. We also portray the various potential applications of spectrum inference in CRNs and beyond, with an outlook to the 5th generation mobile communications (5G) and next generation high frequency (HF) communications systems. Last but not least, we highlight the critical research challenges and open issues ahead.

Efficient placement of edge computing devices for vehicular applications in smart cities

“…To this end, the WSDB must accurately predict the availability of spectrum channels and provide the operating parameters (such as transmission power) that can be used by the devices [28,45]. The WSDB makes these decisions based on spatio-temporal data of historical spectrum usage by licensed users [45], signal propagation models [46,47] and reliable terrain data [46,48]. However, experimental evaluations of WSDBs show that the information they provide may be inaccurate [36,49], for instance, due to malicious users reporting incorrect locations [50].…”

Spectrum Handoff Based on DQN Predictive Decision for Hybrid Cognitive Radio Networks

“…Cognitive radio networks (CRNs) have received great attention due to their potential to provide an efficient solution to the contradiction between spectrum scarcity and inefficient spectrum utilization, and improve system capacity via dynamic spectrum access (DSA) and spectrum management techniques [1,2]. Therefore, efficient spectrum management and resource allocation are crucial for CRNs to solve the shortage of spectrum resources and improve spectrum utilization [3,4].…”

Wideband Spectrum Sensing Method Based on Channels Clustering and Hidden Markov Model Prediction

“…In frequency domain, the adjacent or related channels states are inferred based on channels correlation. In order to take best use of the spectrum data, more and more spectrum prediction methods are made in joint time-frequency domain or even multi-dimension [9].…”