QoS-Aware Resource Allocation of Two-tier HetNet: A Q-learning Approach

AlSobhi, Waleed; Aghvami, A.H.

doi:10.1109/ict.2019.8798829

Cited by 10 publications

(77 citation statements)

References 8 publications

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“…However, for efficient implementation of QL, design of an appropriate and effective reward function (RF) is crucial which considers the constraints in the optimization problem. Authors in [26]- [34] proposed multiple RFs claiming to optimize the power allocation problem of SBSs to improve sum capacity and provide QoS to the UEs. The QL can be implemented either through cooperative learning (CL) or independent learning (IL) for RRM in HetNets.…”

Section: Rtmentioning

confidence: 99%

“…Furthermore, the RFs presented in [26]- [28], [30] are certainly not designed to fulfill the QoS requirements of the MUEs and FUEs, while the RF in [29], [31] was able to allocate fair power to the FBSs through QL due to FBS proximity consideration as a factor to MUE. However, the RF in [29], [31], [33], [34] was not designed by keeping the density of femtocells in mind therefore, could not support a large number of femtocells. These techniques failed to maintain QoS for FUEs due to a lack of consideration towards their minimum threshold capacity, thus increasing the interference with increased density.…”

Section: Rtmentioning

confidence: 99%

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Interference Mitigation in HetNets to Improve the QoS Using Q-Learning

2021

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Small cells (SCs) based ultra-dense heterogeneous networks (HetNets) are one of the promising solutions for increased coverage and capacity in 5G cellular networks. However, in multi-tiered architecture, co-tier and cross-tier interferences are both performance-limiting factors. Efficient resource allocation techniques can handle interferences effectively, however, their complexity linearly increases with the density of the HetNets resulting from dynamic and unplanned deployment of SCs. Therefore, HetNets can be implemented only through an algorithm that is self-organizing and adaptive to the dynamic conditions. In this research paper, a Q-Learning (QL) based adaptive resource allocation scheme is proposed and evaluated for SC-based ultra-dense HetNets. This QL scheme allocates optimal power to the small cell base station (SBS) to meet the minimum required capacity of macrocell user equipment (MUEs) and the small cell user equipment (SUEs) to provide quality of service (QoS). The proposed QL scheme not only maintains the minimum required capacities of the MUEs and SUEs but has also shown a significant improvement in the capacities of MUEs and SUEs in high interference scenarios as compared to the prior works. In the high co-tier and cross-tier interference scenario, where the state of the art schemes fail to maintain the minimum required capacity of the MUE, the proposed scheme provides a minimum MUE capacity of 2 b/s/Hz, which is twice the minimum required QoS threshold. In a similar way, the proposed solution guarantees QoS up to 16 SCs which are 37.5% more SC than the previously reported works in high interference scenario while maintaining a minimum SUE capacity of 1.5 b/s/Hz, which is 33% higher than the minimum required QoS threshold. By simultaneously mitigating co-tier and cross-tier interferences in ultra-dense HetNets, the proposed solution not only improved the minimum capacities of MUEs and SUEs but also sets a new benchmark for minimum QoS threshold.

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Section: Rtmentioning

confidence: 99%

Section: Rtmentioning

confidence: 99%

Interference Mitigation in HetNets to Improve the QoS Using Q-Learning

2021

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“…Hence, the exit function can be expressed as Algorithm 3 is used to select the appropriate exit path, which is a variant of the forward propagation algorithm. (16)…”

Section: Multipath Dnn Trainingmentioning

confidence: 99%

“…Most of the current research on RL methods in wireless resource allocation is based on the action-value function to approximate the optimal action selection [14][15][16]. In [14], the authors present a software-defined satellite-terrestrial network framework, which jointly considered the networking, caching, and computing resources in satelliteterrestrial networks.…”

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confidence: 99%

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Reinforcement learning-based hybrid spectrum resource allocation scheme for the high load of URLLC services

Huang

Xie

Cheriet

2020

J Wireless Com Network

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Ultra-reliable and low-latency communication (URLLC) in mobile networks is still one of the core solutions that require thorough research in 5G and beyond. With the vigorous development of various emerging URLLC technologies, resource shortages will soon occur even in mmWave cells with rich spectrum resources. As a result of the large radio resource space of mmWave, traditional real-time resource scheduling decisions can cause serious delays. Consequently, we investigate a delay minimization problem with the spectrum and power constraints in the mmWave hybrid access network. To reduce the delay caused by high load and radio resource shortage, a hybrid spectrum and power resource allocation scheme based on reinforcement learning (RL) is proposed. We compress the state space and the action space by temporarily dumping and decomposing the action. The multipath deep neural network and policy gradient method are used, respectively, as the approximater and update method of the parameterized policy. The experimental results reveal that the RL-based hybrid spectrum and the power resource allocation scheme eventually converged after a limited number of iterative learnings. Compared with other schemes, the RL-based scheme can effectively guarantee the URLLC delay constraint when the load does not exceed 130%.

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Resource allocation trends for ultra dense networks in 5G and beyond networks: A classification and comprehensive survey

Sharma

Kumar

2021

Physical Communication

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QoS-Aware Resource Allocation of Two-tier HetNet: A Q-learning Approach

Cited by 10 publications

References 8 publications

Interference Mitigation in HetNets to Improve the QoS Using Q-Learning

Interference Mitigation in HetNets to Improve the QoS Using Q-Learning

Reinforcement learning-based hybrid spectrum resource allocation scheme for the high load of URLLC services

Resource allocation trends for ultra dense networks in 5G and beyond networks: A classification and comprehensive survey

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