{'fraction_non_alphanumeric': 0.022447501810282405, 'fraction_numerical': 0.002896451846488052, 'mean_word_length': 5.034934497816594, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A transfer learning framework for energy efficient Wi-Fi networks and performance analysis using real data

In the recent past, there has been an exponential increase in data intensive services over the communication networks. This trend would sustain in future communication networks as well, especially in the Wi-Fi networks. This could be attributed to rapid growth of business and institutional entities and the need for cellular data off-loading for which localized Wi-Fi networks are preferred due to higher offered data rate. In such networks, a major portion of energy consumption occurs at the access network entities making energy efficient operation of Wi-Fi access points (APs) extremely crucial. In this paper, an actor-critic (AC) reinforcement learning (RL) framework is designed to enable traffic based ON/OFF switching of APs in Wi-Fi network. Furthermore, previously estimated traffic statistics is exploited in future scenarios which speeds up the learning process and provide additional improvement in energy saving. The important feature of the present study is the validation of the proposed framework on real data collected from an institute's Wi-Fi network. The simulation results for 20 APs of a Wi-Fi network shows that the proposed framework can lead to around 75% saving in energy consumption as compared to the case when AP switching is not considered.