In this paper, we investigate a deep learning (DL)-based resource allocation strategy for an
underlay cognitive radio network with multiple-input-single-output interference channels …

In this paper, we develop and analyze a novel performance metric, called interference
efficiency, which shows the number of transmitted bits per unit of interference energy …

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 …

In this paper, we consider joint beamforming, power, and channel allocation in a multiuser
and multichannel underlay multiple-input-single-output (MISO) cognitive radio network …

In this paper, a multi-efficiency based scheme is considered, for not only the energy
efficiency (EE) and spectrum efficiency (SE) of the primary users (PUs) and the secondary …

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 …

Energy efficiency (EE) optimization is investigated for a multi-user cognitive radio network
(CRN) over multiple-input-multiple-output (MIMO) interference channels (ICs). To reduce the …

In this letter, a resource allocation strategy based on a deep neural network (DNN) is
proposed for multi-channel cognitive radio networks, where the secondary user (SU) …

We address the problem of power allocation and secondary user (SU) selection in the
downlink from a secondary base station (SBS) equipped with a large number of antennas in …

In this paper, we develop a resource allocation framework to optimize the downlink
transmission of a backhaul-aware multi-cell cognitive radio network (CRN) which is enabled …