Proactive channel access in cognitive radio networks using statistical radio environment maps

Abstract

Inclusion of statistical knowledge of the primary user (PU) channel usage had shown to be beneficial in dynamic spectrum access. Motivated by this fact, this paper investigated the importance of collecting and using statistics on neighboring secondary users (SUs) in selecting channels in addition to the knowledge of PU channel usage. The paper assumed that PU traffic characteristics of the channels are included in the radio environment map in the form of probabilistic suffix trees, which is a sequence predictor based on Markov property. In the proposed method, an intelligent sequence hopping-based common control channel and a carrier sense multiple access (CSMA)/collision avoidance (CA)-based medium access control (MAC) protocol were introduced. As shown in the paper, selecting channels using statistics of both the neighboring SUs and PUs reduced the number of packet collisions compared to a scheme which only uses PU statistics. Furthermore, the simulation results showed that the scheme proposed had better throughput performance with respect to both the random channel selection scheme and the scheme which only uses PU statistics while having less training complexity.

A QoS-aware framework for spectrum characterization and switching decision in cognitive radio networks

“…This has resulted in reconsideration of spectrum usage regulation by the government including the technology of spectrum access itself. Many research have considered overcoming this problem (Asuquo et al, 2020;Dhivya, and Murugesh, 2017;Zheng and Hua, 2016;Christian et al 2012;Ghasemi and Sousa, 2008), using reactive measures whereas few works exist on the use of proactive strategies (Ozturk et al, 2019;Devanarayana and Alfa, 2015;Dhivya et al 2013). With the current massive connectivity of mobile devices for IoTbased applications (Devaraj et al 2022;Tarek et al 2020), it becomes necessary to deploy appropriate channel allocation schemes to ensure fast end-to-end transmission of data packets, especially with resource-constrained devices.…”

Cross‐layer perspective for channel assignment in cognitive radio networks: A survey

“…After the channel selection, desirable number of handshake trials were performed, and when handshake trail was successful, only then handoff was assumed to be successful; otherwise, next channel was selected. Devanarayana and Alfa collected and used statistics of neighboring SUs while selecting channels along with history of PU's channel usage. The authors used the radio environment map and assumed that the characteristics of PU traffic in a channel are included in it.…”