Robust Adaptive Modulation and Coding (AMC) selection in LTE systems using reinforcement learning
| Autor: | Andrea Passarella, Raffaele Bruno, Antonino Masaracchia |
|---|---|
| Přispěvatelé: | Bruno, R, Masaracchia, A, Passarella A |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Engineering
reinforcement learning Settore ING-INF/03 - Telecomunicazioni business.industry Link adaptation channel quality Channel models LTE channel quality adaptive modulation and coding (AMC) reinforcement learning performance evaluation performance evaluation LTE Robustness (computer science) Electronic engineering Reinforcement learning Decision process business Reinforcement learning algorithm Coding (social sciences) adaptive modulation and coding (AMC) |
| Zdroj: | IEEE 80th Vehicular Technology Conference: VTC2014-Fall, Vancouver, Canada, 2014 info:cnr-pdr/source/autori:Bruno R.; Masaracchia A.; Passarella A./congresso_nome:IEEE 80th Vehicular Technology Conference: VTC2014-Fall/congresso_luogo:Vancouver, Canada/congresso_data:2014/anno:2014/pagina_da:/pagina_a:/intervallo_pagine VTC Fall ResearcherID |
| Popis: | Adaptive Modulation and Coding (AMC) in LTE networks is commonly employed to improve system throughput by ensuring more reliable transmissions. Most of existing AMC methods select the modulation and coding scheme (MCS) using pre-computed mappings between MCS indexes and channel quality indicator (CQI) feedbacks that are periodically sent by the receivers. However, the effectiveness of this approach heavily depends on the assumed channel model. In addition CQI feedback delays may cause throughput losses. In this paper we design a new AMC scheme that exploits a reinforcement learning algorithm to adjust at run-time the MCS selection rules based on the knowledge of the effect of previous AMC decisions. The salient features of our proposed solution are: $i)$ the low-dimensional space that the learner has to explore, and $ii)$ the use of direct link throughput measurements to guide the decision process. Simulation results obtained using ns3 demonstrate the robustness of our AMC scheme that is capable of discovering the best MCS even if the CQI feedback provides a poor prediction of the channel performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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