Deep contextual bandits for fast neighbor-aided initial access in mmWave cell-free networks
| Autor: | Samad Ali, Insaf Ismath, Matti Latva-aho, Nandana Rajatheva |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Signal Processing (eess.SP)
Scheme (programming language) deep reinforcement learning mmWave Computer science Distributed computing user-centric initial access Throughput Control and Systems Engineering FOS: Electrical engineering electronic engineering information engineering Beam search Electrical Engineering and Systems Science - Signal Processing Electrical and Electronic Engineering Latency (engineering) deep contextual bandits Host (network) computer Selection (genetic algorithm) 5G Sleep mode computer.programming_language |
| Popis: | Access points (APs) in millimeter-wave (mmWave) and sub-THz-based user-centric (UC) networks will have sleep mode functionality. As a result of this, it becomes challenging to solve the initial access (IA) problem when the sleeping APs are activated to start serving users. In this paper, a novel deep contextual bandit (DCB) learning method is proposed to provide instant IA using information from the neighboring active APs. In the proposed approach, beam selection information from the neighboring active APs is used as an input to neural networks that act as a function approximator for the bandit algorithm. Simulations are carried out with realistic channel models generated using the Wireless Insight ray-tracing tool. The results show that the system can respond to dynamic throughput demands with negligible latency compared to the standard baseline 5G IA scheme. The proposed fast beam selection scheme can enable the network to use energy-saving sleep modes without compromising the quality of service due to inefficient IA Comment: Submitted to IEEE Wireless Communication Letters |
| Databáze: | OpenAIRE |
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