A flexible framework based on reinforcement learning for adaptive modulation and coding in OFDM wireless systems

Leite, João; Carvalho, Paulo S.; Vieira, Robson D.

doi:10.1109/wcnc.2012.6214482

Cited by 33 publications

(30 citation statements)

References 12 publications

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“…RL based solutions have been extensively used in wireless communications to estimate the dynamic system model on the fly [98,99,100,101,102,103,104,105,106]. In the context of the physical layer, RL based solutions can extensively improve the system data rate, bit error rate, goodput (i.e., the amount of useful information that successfully arrived at the destination over the time-varying channel) and energy efficiency [107,108,109,110].…”

Section: Adaptive Rate and Power Controlmentioning

confidence: 99%

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

et al. 2019

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The Internet of Things (IoT) is expected to require more effective and efficient wireless communications than ever before. For this reason, techniques such as spectrum sharing, dynamic spectrum access, extraction of signal intelligence and optimized routing will soon become essential components of the IoT wireless communication paradigm. In this vision, IoT devices must be able to not only learn to autonomously extract spectrum knowledge on-the-fly from the network but also leverage such knowledge to dynamically change appropriate wireless parameters (e.g., frequency band, symbol modulation, coding rate, route selection, etc.) to reach the network's optimal operating point. Given that the majority of the IoT will be composed of tiny, mobile, and energy-constrained devices, traditional techniques based on a priori network optimization may not be suitable, since (i) an accurate model of the environment may not be readily available in practical scenarios; (ii) the computational requirements of traditional optimization techniques may prove unbearable for IoT devices. To address the above challenges, much research has been devoted to exploring the use of machine learning to address problems in the IoT wireless communications domain. The reason behind machine learning's popularity is that it provides a general framework to solve very complex problems where a model of the phenomenon being learned is too complex to derive or too dynamic to be summarized in mathematical terms.This work provides a comprehensive survey of the state of the art in the application of machine learning techniques to address key problems in IoT wireless communications with an emphasis on its ad hoc networking aspect. First, we present extensive background notions of machine learning techniques. Then, by adopting a bottom-up approach, we examine existing work on machine learning for the IoT at the physical, data-link and network layer of the protocol stack. Thereafter, we discuss directions taken by the community towards hardware implementation to ensure the feasibility of these techniques. Additionally, before concluding, we also provide a brief discussion of the application of machine learning in IoT beyond wireless communication. Finally, each of these discussions is accompanied by a detailed analysis of the related open problems and challenges.

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Section: Adaptive Rate and Power Controlmentioning

confidence: 99%

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

et al. 2019

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“…Juggling-like ARQ (J-ARQ) as it illustrated in in Fig.2, is effective to deal with long delayed communication [6], which utilizes the idle time between waiting for the acknowledgement signals from the receiver, to keep sending packets with a periodic interval.…”

Section: Rl Combined With Juggling-like Arqmentioning

confidence: 99%

“…But only a few modulations and coding schemes are taken into consideration. In [6] [7], RL is proposed as an online learning algorithm to optimize the link adaptation with minimal assumption of the operating environment. It is inadequate under current tuning configurations for IEEE 802.11ac standard, which concludes guard interval, frame aggregation and multiple bandwidth [4] [8].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning-based link adaptation in long delayed underwater acoustic channel

et al. 2019

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In this paper, we apply reinforcement learning, a significant area of machine learning, to formulate an optimal self-learning strategy to interact in an unknown and dynamically variable underwater channel. The dynamic and volatile nature of the underwater channel environment makes it impossible to employ pre-knowledge. In order to select the optimal parameters to transfer data packets, by using reinforcement learning, this problem could be resolved, and better throughput could be achieved without any environmental pre-information. The slow sound velocity in an underwater scenario, means that the delay of transmitting packet acknowledgement back to sender from the receiver is material, deteriorating the convergence speed of the reinforcement learning algorithm. As reinforcement learning requires a timely acknowledgement feedback from the receiver, in this paper, we combine a juggling-like ARQ (Automatic Repeat Request) mechanism with reinforcement learning to minimize the long-delayed reward feedback problem. The simulation is accomplished by OPNET.

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“…In such case, when perfect CSI is used for resource allocation, the system overhead can be as large as about 25% of the system capacity. The authors in [16] use the reinforcement learning, a machine learning technique, for adaptive modulation and coding in orthogonal frequency division multiplex-multiple input, multiple output (OFDM-MIMO) based 5G systems. In our previous work [3], we used the random forests algorithm as a binary classifier for allocating resources to users present in CRAN-based 5G system.…”

Section: Related Workmentioning

confidence: 99%

Random forests for resource allocation in 5G cloud radio access networks based on position information

Imtiaz

Koudouridis

Ghauch

et al. 2018

J Wireless Com Network

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Next generation 5G cellular networks are envisioned to accommodate an unprecedented massive amount of Internet of things (IoT) and user devices while providing high aggregate multi-user sum rates and low latencies. To this end, cloud radio access networks (CRAN), which operate at short radio frames and coordinate dense sets of spatially distributed radio heads, have been proposed. However, coordination of spatially and temporally denser resources for larger sets of user population implies considerable resource allocation complexity and significant system signalling overhead when associated with channel state information (CSI)-based resource allocation (RA) schemes. In this paper, we propose a novel solution that utilizes random forests as supervised machine learning approach to determine the resource allocation in multi-antenna CRAN systems based primarily on the position information of user terminals. Our simulation studies show that the proposed learning based RA scheme performs comparably to a CSI-based scheme in terms of spectral efficiency and is a promising approach to master the complexity in future cellular networks. When taking the system overhead into account, the proposed learning-based RA scheme, which utilizes position information, outperforms legacy CSI-based scheme by up to 100%. The most important factor influencing the performance of the proposed learning-based RA scheme is antenna orientation randomness and position inaccuracies. While the proposed random forests scheme is robust against position inaccuracies and changes in the propagation scenario, we complement our scheme with three approaches that restore most of the original performance when facing random antenna orientations of the user terminal.

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A flexible framework based on reinforcement learning for adaptive modulation and coding in OFDM wireless systems

Cited by 33 publications

References 12 publications

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

Reinforcement learning-based link adaptation in long delayed underwater acoustic channel

Random forests for resource allocation in 5G cloud radio access networks based on position information

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