{'fraction_non_alphanumeric': 0.02467232074016962, 'fraction_numerical': 0.010023130300693909, 'mean_word_length': 5.555555555555555, '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}}
Radio Resource Allocation for D2D-Enabled Massive Machine Communication in the 5G Era

Device-to-Device (D2D) and Massive Machine Communication (MMC) are believed to be the cornerstones of future 5th generation (5G) cellular technologies. As a method to increase spectrum utilization, extend cellular coverage, and offload backhaul traffic, D2D has been recently incorporated into Release 12 of 3rd Generation Partnership Project (3GPP) Long Term Evolution Advanced (LTE-A) specifications. Devices in physical proximity can thus discover each other and communicate via a direct path using licensed LTE spectrums. Leveraging on the ubiquity of cellular coverage and harnessing LTE-A D2D for networks with massive number of machine-type communications, such as large-scale sensor networks and vehicular networks, introduces a paradigm shift and opens up new opportunities for proximity-based services. In this paper, we propose a novel radio resource allocation method for D2D discovery in clustered MMC networks. With a large number of nodes, the proposed method can still maintain the signaling overhead at a reasonable level while achieving high discovery rate. Experimental results show that the proposed approach significantly outperforms the existing 3GPP random resource allocation mechanism.