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greenMAC Protocol: A Q-Learning-Based Mechanism to Enhance Channel Reliability for WLAN Energy Savings
Electronics ( IF 2.6 ) Pub Date : 2020-10-19 , DOI: 10.3390/electronics9101720
Rashid Ali , Muhammad Sohail , Alaa Omran Almagrabi , Arslan Musaddiq , Byung-Seo Kim

We have seen a promising acceptance of wireless local area networks (WLANs) in our day-to-day communication devices, such as handheld smartphones, tablets, and laptops. Energy preservation plays a vital role in WLAN communication networks. The efficient use of energy remains one of the most substantial challenges to WLAN devices. Several approaches have been proposed by the industrial and institutional researchers to save energy and reduce the overall power consumption of WLAN devices focusing on static/adaptive energy saving methods. However, most of the approaches save energy at the cost of throughput degradation due to either increased sleep-time or reduced number of transmissions. In this paper, we recognize the potentials of reinforcement learning (RL) techniques, such as the Q-learning (QL) model, to enhance the WLAN’s channel reliability for energy saving. QL is one of the RL techniques, which utilizes the accumulated reward of the actions performed in the state-action model. We propose a QL-based energy-saving MAC protocol, named greenMAC protocol. The proposed greenMAC protocol reduces the energy consumption by utilizing accumulated reward value to optimize the channel reliability, which results in reduced channel collision probability of the network. We assess the degrees of channel congestion in collision probability as a reward function for our QL-based greenMAC protocol. The comparative results show that greenMAC protocol achieves enhanced system throughput performance with additional energy savings compared to existing energy-saving mechanisms in WLANs.

中文翻译:

greenMAC协议:一种基于Q学习的机制,可提高信道可靠性以节省WLAN能源

我们已经看到,在日常通信设备(例如手持智能手机,平板电脑和笔记本电脑)中,无线局域网(WLAN)的前景令人鼓舞。节能在WLAN通信网络中起着至关重要的作用。能源的有效利用仍然是WLAN设备面临的最重大挑战之一。工业和机构研究人员已经提出了几种方法来节约能源并减少WLAN设备的总体功耗,这些方法集中于静态/自适应节能方法。但是,由于增加了睡眠时间或减少了传输次数,大多数方法都以吞吐量降低为代价来节省能源。在本文中,我们认识到强化学习(RL)技术的潜力,例如Q学习(QL)模型,增强WLAN的信道可靠性以节省能源。QL是RL技术之一,它利用状态动作模型中执行的动作的累积奖励。我们提出了一种基于QL的节能MAC协议,名为绿色MAC协议。提出的绿色MAC协议通过利用累积的奖励值来优化信道可靠性来降低能耗,从而降低了网络的信道冲突概率。我们评估冲突概率中信道拥塞的程度,以此作为基于QL的绿色MAC协议的奖励函数。比较结果表明,与WLAN中的现有节能机制相比,绿色MAC协议可实现更高的系统吞吐性能,并具有更多的节能效果。
更新日期:2020-10-19
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