2019
DOI: 10.3390/info10100315
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A Comparison of Reinforcement Learning Algorithms in Fairness-Oriented OFDMA Schedulers

Abstract: Due to large-scale control problems in 5G access networks, the complexity of radioresource management is expected to increase significantly. Reinforcement learning is seen as apromising solution that can enable intelligent decision-making and reduce the complexity of differentoptimization problems for radio resource management. The packet scheduler is an importantentity of radio resource management that allocates users’ data packets in the frequency domainaccording to the implemented scheduling rule. In this c… Show more

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Cited by 14 publications
(11 citation statements)
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“…An ML-based RRM algorithm is proposed in [27] to estimate the required resources in the network for tackling traffic on-demand traffics over HTTP. ML has been used in resource allocation by considering various QoS combinations objectives such as packet loss [23], delay [24], and user fairness [25]. However, these models are defined for improving delay or enhancing throughput for Ultra-Reliable and Low-Latency Communications (URLLC) and throughput of enhanced Mobile Broadband (eMBB) UEs, while traffic types are considered homogeneous [26].…”
Section: Related Workmentioning
confidence: 99%
“…An ML-based RRM algorithm is proposed in [27] to estimate the required resources in the network for tackling traffic on-demand traffics over HTTP. ML has been used in resource allocation by considering various QoS combinations objectives such as packet loss [23], delay [24], and user fairness [25]. However, these models are defined for improving delay or enhancing throughput for Ultra-Reliable and Low-Latency Communications (URLLC) and throughput of enhanced Mobile Broadband (eMBB) UEs, while traffic types are considered homogeneous [26].…”
Section: Related Workmentioning
confidence: 99%
“…An autonomous network resource management for QoS and QoE provisioning is proposed in [12] to predict the amount of network resources that needs to be allocated to cope with the traffic demands for live and on-demand dynamic adaptive streaming over HTTP. Machine learning is used to optimize the scheduling and resource allocation problems in 5G radio access networks focusing on different combinations of QoS objectives, such as: throughput, delay and packet loss in [18], packet loss and delay in [24], system throughput and user fairness in [25]. However, these ML-based scheduling solutions are designed for homogeneous traffic types only.…”
Section: Related Workmentioning
confidence: 99%
“…access is a common feature for Wi-Fi and 5G, which was adopted by WLANs after it was successfully applied in the cellular domain. In the following papers ML is used to the address the several problems in OFDMA-based cellular networks: fair scheduling [149], [150], carrier frequency offset (CFO) estimation for uplink transmissions [151], [152], internetwork interference control [153], and resource allocation [154]. They implement RL [149], [150], [153], supervised deep learning [151], unsupervised deep learning [152], and a genetic learning algorithm [154] to support performance optimization.…”
Section: B Multi-user Communicationmentioning
confidence: 99%
“…In the following papers ML is used to the address the several problems in OFDMA-based cellular networks: fair scheduling [149], [150], carrier frequency offset (CFO) estimation for uplink transmissions [151], [152], internetwork interference control [153], and resource allocation [154]. They implement RL [149], [150], [153], supervised deep learning [151], unsupervised deep learning [152], and a genetic learning algorithm [154] to support performance optimization. We believe that these papers may provide interesting insights and guidelines for researchers working in the Wi-Fi domain.…”
Section: B Multi-user Communicationmentioning
confidence: 99%