Multi-tier heterogeneous networks (HetNets) and device-to-device (D2D) communication are vastly considered in 5G networks. The interference mitigation and resource allocation in the D2D enabled multi-tier HetNets is a cumbersome and challenging task that cannot be solved by the conventional centralized resource allocation techniques proposed in the literature. In this paper, we propose a distributed multi-agent learning-based spectrum allocation scheme in which D2D users learn the wireless environment and select spectrum resources autonomously to maximize their throughput and spectral efficiency (SE) while causing minimum interference to the cellular users. We have employed the distributed learning in a stochastic geometry-based realistic multi-tier heterogeneous network to validate the performance of our scheme. The proposed scheme enables the D2D users to achieve higher throughput and SE, higher signal-to-interferenceplus-noise ratio and low outage ratio for cellular users, and better computational time efficiency and performs well in the dense multi-tier HetNets without affecting network coverage compared with the distance based resource criterion and joint-resource allocation and link adaptation schemes.INDEX TERMS D2D communication, multi-agent reinforcement learning, autonomous spectrum allocation, distributed reinforcement learning, heterogeneous networks, interference mitigation in D2D enabled HetNets.

No abstract

No abstract

No abstract

Deep reinforcement learning (DRL) is poised to revolutionise the field of artificial intelligence (AI) by endowing autonomous systems with high levels of understanding of the real world. Currently, deep learning (DL) is enabling DRL to effectively solve various intractable problems in various fields including computer vision, natural language processing, healthcare, robotics, to name a few. Most importantly, DRL algorithms are also being employed in audio signal processing to learn directly from speech, music and other sound signals in order to create audio-based autonomous systems that have many promising applications in the real world. In this article, we conduct a comprehensive survey on the progress of DRL in the audio domain by bringing together research studies across different but related areas in speech and music. We begin with an introduction to the general field of DL and reinforcement learning (RL), then progress to the main DRL methods and their applications in the audio domain. We conclude by presenting important challenges faced by audio-based DRL agents and by highlighting open areas for future research and investigation. The findings of this paper will guide researchers interested in DRL for the audio domain.

Abstract-Heterogeneous networks are a key solution to serving the exponential surge in data volume and higher quality expectations. Nonetheless, such networks require the ubiquitous presence of fiberto-the-cell to address the performance demands of 5G and fastspreading small cells. To this end, innovative ways of optimizing the usage of realistic backhaul links are being investigated. In this work, we propose a fuzzy Q-learning-based user-centric backhaul-aware user cell association scheme.The proposed scheme aims at optimizing the user-cell association process in a context-aware and backhaul-aware manner. Complementing the scheme with fuzzy-logic requires 33.3% additional storage memory. On the other hand, it increases the computational efficiency by 60% and improves the users' performance by 12%.

No abstract

No abstract