One of the major challenges facing the realization of cognitive radios (CRs) in future mobile
and wireless communications is the issue of high energy consumption. Since future network …

This paper considers uplink solar-powered cognitive radio networks (CRNs) where multiple
secondary users (SUs) transmit data to a secondary base station (SBS) by sharing a …

In this article, we propose a deep Q-learning based algorithm for optimal resource allocation
in energy harvested cognitive radio networks (EH-CRN). In EH-CRN, channel resources of …

Energy efficiency (EE) and spectrum efficiency (SE) have received significant attentions on
optimizing the network performance in cognitive radio networks. In this paper, an EE+ SE …

Cognitive radio networks (CRN) has been depicted as one of the most important driving
forces in achieving next generation wireless communication capabilities. To this end …

A new opportunistic cross‐layer MAC protocol involving channel allocation and packet
scheduling for cognitive radio networks is proposed. Cognitive radio allows secondary users …

In this paper, a reinforcement learning technique is employed to maximize the performance
of a cognitive radio network (CRN). In the presence of primary users (PUs), it is presumed …

The most prominent challenge to the wireless community is to meet the demand for radio
resources. Cognitive Radio (CR) is envisioned as a potential solution that utilizes its …

Energy efficiency, high data speeds, and effective spectrum utilization stand as the primary
factors governing 5G and beyond networks. A base station (BS) in a cognitive radio network …

This paper proposes a resource allocation strategy for multi-channel underlay cognitive
radio (CR) systems by means of an ensemble deep learning framework. The transmit power …