Abstract
With the substantial increase in spatio-temporal mobile traffic, reducing the network-level energy consumption while satisfying various quality-of-service (QoS) requirements has become one of the most important challenges facing six-generation (6G) wireless networks. We herein propose a novel multi-agent distributed Q-learning based outage-aware cell breathing (MAQ-OCB) framework to optimize energy efficiency (EE) and user outage jointly. Through extensive simulations, we demonstrate that the proposed MAQ-OCB can achieve the EE-optimal solution obtained by the exhaustive search algorithm. In addition, MAQ-OCB significantly outperforms conventional algorithms such as no transmission-power-control (No TPC), On-Off, centralized Q-learning based outage-aware cell breathing (C-OCB), and random-action algorithms.
Highlights
Sixth-generation (6G) wireless networks have garnered significant attention from both industry and academia for supporting emerging novel mobile services such as high-fidelity holograms, immersive extended reality (XR), tactile internet, industry 4.0, smart home/city, and digital twins [1,2,3]
In [16], a joint optimization framework involving power ramping and preamble selection was considered to improve the EE of narrow-band Internet of Things (NB-IoT) systems, where users independently learn their own policies for preamble transmissions based on a distributed multi-agent reinforcement learning algorithm
Simulation setups of the proposed algorithm were implemented in Matlab R2020a, and the training is conducted on a personal PC with a CPU i7-9750 at 2.6 GHz and a RAM of 16 GB
Summary
Sixth-generation (6G) wireless networks have garnered significant attention from both industry and academia for supporting emerging novel mobile services such as high-fidelity holograms, immersive extended reality (XR), tactile internet, industry 4.0, smart home/city, and digital twins [1,2,3]. In [8], a density clustering-based BS control algorithm was proposed for energy-efficient ultra-dense cellular Internet of Things (IoT) Networks, where each BS switches to the awake/sleep mode based on user distribution to improve both the average area throughput and network EE. In [16], a joint optimization framework involving power ramping and preamble selection was considered to improve the EE of narrow-band Internet of Things (NB-IoT) systems, where users independently learn their own policies for preamble transmissions based on a distributed multi-agent reinforcement learning algorithm. We propose a novel multi-agent distributed Q-learning based outage-aware cell breathing (MAQ-OCB) technique to maximize EE while reducing the outage probability of users in ultra-dense small cell networks. We demonstrate the performance results of the proposed algorithm in accordance with the amount of SBS collaboration level
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