A Novel Distributed Q-Learning Based Resource Reservation Framework for Facilitating D2D Content Access Requests in LTE-A Networks

Kumar, Naveen; Swain, Siba Narayan; Murthy, C. Siva Ram

doi:10.1109/tnsm.2018.2807594

Cited by 26 publications

(10 citation statements)

References 28 publications

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“…Q-learning is an RL technique that allows a proxy to learn the optimal policy to follow in a known environment [17,38,40,41]. A set of possible states X=x1,x2, x3,…, xn, and a set of actions A=a1 ,a2 ,a3,…,an, describing the environment is defined in this model.…”

Section: Design Of the Proposed Schemementioning

confidence: 99%

An Effective Spectrum Handoff Based on Reinforcement Learning for Target Channel Selection in the Industrial Internet of Things

Oyewobi

Hancke

Abu‐Mahfouz

et al. 2019

Sensors

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The overcrowding of the wireless space has triggered a strict competition for scare network resources. Therefore, there is a need for a dynamic spectrum access (DSA) technique that will ensure fair allocation of the available network resources for diverse network elements competing for the network resources. Spectrum handoff (SH) is a DSA technique through which cognitive radio (CR) promises to provide effective channel utilization, fair resource allocation, as well as reliable and uninterrupted real-time connection. However, SH may consume extra network resources, increase latency, and degrade network performance if the spectrum sensing technique used is ineffective and the channel selection strategy (CSS) is poorly implemented. Therefore, it is necessary to develop an SH policy that holistically considers the implementation of effective CSS, and spectrum sensing technique, as well as minimizes communication delays. In this work, two reinforcement learning (RL) algorithms are integrated into the CSS to perform channel selection. The first algorithm is used to evaluate the channel future occupancy, whereas the second algorithm is used to determine the channel quality in order to sort and rank the channels in candidate channel list (CCL). A method of masking linearly dependent and useless state elements is implemented to improve the convergence of the learning. Our approach showed a significant reduction in terms of latency and a remarkable improvement in throughput performance in comparison to conventional approaches.

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Section: Design Of the Proposed Schemementioning

confidence: 99%

An Effective Spectrum Handoff Based on Reinforcement Learning for Target Channel Selection in the Industrial Internet of Things

Oyewobi

Hancke

Abu‐Mahfouz

et al. 2019

Sensors

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“…In recent years, deep learning, has been promisingly applied to wireless networks optimization and big data processing [21,29,30]. In [31], the authors considered solving a resource allocation problem for D2D communication using a Q-learning-based method. In [32], the authors applied deep learning to reduce the complexity of solving a wireless networks optimization problem.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach for Maximum Activity Links in D2D Communications

Zhang

Palmieri

et al. 2019

Sensors

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Mobile cellular communications are experiencing an exponential growth in traffic load on Long Term Evolution (LTE) eNode B (eNB) components. Such load can be significantly contained by directly sharing content among nearby users through device-to-device (D2D) communications, so that repeated downloads of the same data can be avoided as much as possible. Accordingly, for the purpose of improving the efficiency of content sharing and decreasing the load on the eNB, it is important to maximize the number of simultaneous D2D transmissions. Specially, maximizing the number of D2D links can not only improve spectrum and energy efficiency but can also reduce transmission delay. However, enabling maximum D2D links in a cellular network poses two major challenges. First, the interference between the D2D and cellular communications could critically affect their performance. Second, the minimum quality of service (QoS) requirement of cellular and D2D communication must be guaranteed. Therefore, a selection of active links is critical to gain the maximum number of D2D links. This can be formulated as a classical integer linear programming problem (link scheduling) that is known to be NP-hard. This paper proposes to obtain a set of network features via deep learning for solving this challenging problem. The idea is to optimize the D2D link schedule problem with a deep neural network (DNN). This makes a significant time reduction for delay-sensitive operations, since the computational overhead is mainly spent in the training process of the model. The simulation performed on a randomly generated link schedule problem showed that our algorithm is capable of finding satisfactory D2D link scheduling solutions by reducing computation time up to 90% without significantly affecting their accuracy.

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“…[39] proposes a distributed game-theoretic scheme to solve the uplink RA problem in a multi-cell scenario. In [40], the uplink RB assignment between cellular and DUEs is performed based on a Q-learning solution. Paper [41] evaluates the resource allocation in a cognitive D2D network, whereas many other papers consider approximations such as stochastic geometry to manage the uplink spectral resources [42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Joint uplink and downlink resource allocation for device to device communications

Selmi

Bouallègue²

2021

IET Communications

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With the ever-increasing number of intelligent devices, extensive research efforts have been spent towards developing efficient resource allocation in underlying device to device communications. Centralised and distributed approaches have been proposed to improve the network performances through uplink and downlink schemes. However, the full-duplex resource allocation remains challenging. Here, a joint uplink-downlink resource block assignment for device to device communications is proposed. The device's geometric distribution via the Poisson point process is formulated and the optimization problem as a mixed strategy non-cooperative game is modelled. The proposed resource allocation scheme allows many device to device communication pairs to share the same cellular resource block but does not restrict their number in advance; it is determined dynamically according to the channel's capacity and interference condition. Inversely, a device to device communication transmitter can simultaneously be allocated many resource blocks according to its bandwidth requirement and the number of its antennas. Furthermore, the optimal amount of data and power transmitted by each device is calculated. The efficiency of this algorithm is evaluated through computer simulations, and the results demonstrate good performances in the spectral and energy efficiency with relatively low complexity and convergence time.

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A Novel Distributed Q-Learning Based Resource Reservation Framework for Facilitating D2D Content Access Requests in LTE-A Networks

Cited by 26 publications

References 28 publications

An Effective Spectrum Handoff Based on Reinforcement Learning for Target Channel Selection in the Industrial Internet of Things

An Effective Spectrum Handoff Based on Reinforcement Learning for Target Channel Selection in the Industrial Internet of Things

A Deep Learning Approach for Maximum Activity Links in D2D Communications

Joint uplink and downlink resource allocation for device to device communications

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