… spectrum resource becomes available in the IAB network, the solution space for spectrum
allocation … To address this issue, we will exploit latest findings in reinforcement learning (RL) …

… of dynamic spectrum allocation in real application scenarios. This paper presents a survey
on the state-of-the-art spectrum allocation algorithms based on reinforcement learning …

… a great technical challenge to spectrum allocation. Existing centralized schemes require …
distributed spectrum allocation framework based on multiagent deep reinforcement learning …

… for spectrum access in DSA networks, where power allocation is not considered in [18] and
[19]. In this article, we apply deep RL in both spectrum access and power allocation of DSA …

… desired goal of resource allocation of licensed primary user’s (PU’s) unused spectrum to the
… spectrum sensing, spectrum detection, spectrum allocation, and management. The spectrum…

… First, we introduce Q-Learning+, a Q-Learning adaptation to … a new reinforcement
learningbased allocation algorithm that … in terms of allocation than the traditional Q-Learning, …

… Next, we introduce two reinforcement learning methods that are used in the proposed
design. Q-learning [11] is a popular reinforcement learning method that learns an action value …

… , we formulate the spectrum allocation and power management as a … reinforcement learning
(DRL), ie, double deep Q-learning, to achieve an efficient policy for joint spectrum allocation …

… technological preparations for spectrum management such as higher spectral efficiency, lower
… the spectrum allocation problem concerning network capacity and spectrum efficiency as …

… a decentralized spectrum allocation approach in which each radar chooses a subband
separately to reduce the mutual interference. Although decentralized spectrum allocation has …