Downlink resource allocation for NOMA-based hybrid spectrum access in cognitive network

Abstract

To solve the contradiction between limited spectrum resources and increasing communication demand, this paper proposes a wireless resource allocation scheme based on the Deep Q Network (DQN) to allocate radio resources in a downlink multi-user cognitive radio (CR) network with slicing. Secondary users (SUs) are multiplexed using non-orthogonal multiple access (NOMA). The SUs use the hybrid spectrum access mode to improve the spectral efficiency (SE). Considering the demand for multiple services, the enhanced mobile broadband (eMBB) slice and ultrareliable low-latency communication (URLLC) slice were established. The proposed scheme can maximize the SE while ensuring Quality of Service (QoS) for the users. This study established a mapping relationship between resource allocation and the DQN algorithm in the CR-NOMA network. According to the signal-to-interference-plus-noise ratio (SINR) of the primary users (PUs), the proposed scheme can output the optimal channel selection and power allocation. The simulation results reveal that the proposed scheme can converge faster and obtain higher rewards compared with the Q-Learning scheme. Additionally, the proposed scheme has better SE than both the overlay and underlay only modes.