GreenMAC Protocol: A Q-Learning-Based Mechanism to Enhance Channel Reliability for WLAN Energy Savings

Abstract

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.