{'fraction_non_alphanumeric': 0.02346041055718475, 'fraction_numerical': 0.0, 'mean_word_length': 5.469194312796208, '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}}
Decentralized MISO Cooperative Beamforming Design for Multi-Cell URLLC Network

Achieving low-latency for ultra-reliable and low- latency communications (URLLC) is one of the major challenge for the fifth-generation cellular networks. To address this challenge, the cooperation among base stations (BSs) has the potential to reduce latency and improve data speed. However, it requires a great quantity information interaction at cost of magnanimous utilization of the backhaul. In this paper, the author analyzes the duality between virtual uplink and downlink optimization problems. then, we propose an iterative decentralized algorithm to solve a max-min achievable rate problem based on the joint of the alternating direction method of multipliers(ADMMs) and successive convex approximation (SCA) method. Specifically, we can iteratively approximate the nonconvex problem into a convex problem by using the SCA method. In the second step, the ADMM method is used to transform the centralized optimization problem into a decentralized optimization problem that solves the problem locally. Each BS is regarded as a subproblem to work the problem out locally in parallel. By allowing BSs to exchange the required information until ADMM converges, the resulting formulation is optimized. Finally, simulation results show the effectiveness of our proposed algorithm.