LTE网络中基于帧的Q-learning RACH接入

2014 Australasian Telecommunication Networks and Applications Conference (ATNAC) Pub Date : 2014-11-01 DOI:10.1109/ATNAC.2014.7020894

L. Bello, P. Mitchell, D. Grace

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引用次数: 12

摘要

为了提高基于q学习的RACH访问(QL-RACH)方案的性能，提出了一种新的回退方案。提出了一种基于帧的QL-RACH退退方案(FB-QL-RACH)。该方案降低了H2H (Human-to-Human)和M2M (Machine-to-Machine)用户在初始访问和回退时共享同一帧时发生冲突的概率。同时也消除了在QL-RACH方案中由于M2M退场而导致的空闲插槽的概率。此外，本文还考虑了泊松交通模型和周期交通模型在共享RACH中的相互作用。仿真结果表明，新的回退方案将RACH的总体吞吐量提高了70%左右。结果还表明，使用QL-RACH可以控制这两种流量分布。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Frame based back-off for Q-learning RACH access in LTE networks

This paper introduces a novel back-off scheme to improve the performance of a Q-learning based RACH access (QL-RACH) scheme. A Frame-based Back-off with QL-RACH (FB-QL-RACH) scheme is proposed. The scheme reduces the probability of collision between Human-to-Human (H2H) and Machine-to-Machine (M2M) users when sharing the same frame for both the initial access and the back-off. Also the probability of idle slots caused by M2M back-off in the QL-RACH scheme is eliminated. In addition the paper also considers the interaction of Poisson and Periodic traffic models in sharing the RACH. Simulation results show that the new back-off scheme improves the overall RACH throughput by around 70%. The results also show that both traffic distributions can be controlled using QL-RACH.

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2014 Australasian Telecommunication Networks and Applications Conference (ATNAC)

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