TDM-PON and LTE-A Based Cost-Efficient FiWi Access Network Deployment

Chatur, Nilesh; Adhya, Aneek

doi:10.1109/lcomm.2022.3198516

Cited by 4 publications

(6 citation statements)

References 13 publications

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“…19 presents the downstream latency obtained when there exist voids between successive gNB coverages, and the eNB and the gNBs cooperate to allocate caching and computing resources for the contents. Here, we consider the content size to be uniformly distributed between 1 GB and 5 GB, i.e., d l ∼ U [1,5] GB. The length of the void and the other parameters related to eNB are taken from Table . IX.…”

Section: Numerical Analysis Of Downstream Latencymentioning

confidence: 99%

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Caching and Computing Resource Allocation in Cooperative Heterogeneous 5G Edge Networks Using Deep Reinforcement Learning

Bose,

Chatur,

Baberwal

et al. 2024

IEEE Trans. Netw. Serv. Manage.

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In this work, we explore a framework for a 5G non-standalone (NSA) heterogeneous network, to meet heterogeneous content requests for users moving in vehicles. We consider that an enhanced NodeB (eNB) acts as a macrocell and next-generation NodeBs (gNBs) act as the small cells. To reduce the downstream latency, entire (or part) of the popular contents are fetched from the core network and cached (stored) at the eNB and gNBs. The computing resources are required at the eNB and gNBs along with the caching resources, for content compression and decompression, leading to a reduced requirement for the caching resources. The eNB and gNBs cooperatively decide on the resources (caching and computing) to be allocated. In this network planning approach, first we compute the optimal coverage radius of the eNB and gNBs. Thereafter, we identify the optimal number of non-overlapping gNBs under the coverage area of the eNB. Finally, we propose a novel deep-Q network (DQN)-based algorithm to train the centralized controller agent so as to identify an optimal policy for caching and computing resource allocation. In case the content popularity and road traffic conditions change, the agent can be trained again so as to identify a new optimal policy. We also explore the resource allocation policy using other optimization techniques, such as pattern search, genetic algorithm, and multi-start search. The results show that the proposed DQN-based algorithm is scalable and requires less computation time to identify an optimal policy for caching and computing resource allocation as compared to other existing techniques.

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Section: Numerical Analysis Of Downstream Latencymentioning

confidence: 99%

“…1). The PON consists of an optical line terminal (OLT) located at the central office, optical network units (ONUs) placed along with eNB and gNBs, and a passive splitter (PS) placed in-between the OLT and ONUs [5].…”

mentioning

confidence: 99%

Caching and Computing Resource Allocation in Cooperative Heterogeneous 5G Edge Networks Using Deep Reinforcement Learning

Bose,

Chatur,

Baberwal

et al. 2024

IEEE Trans. Netw. Serv. Manage.

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show abstract

“…In [17], a stochastic optimization model is formulated for HetNets to discover the optimal content delivery policy in view of reducing the power cost in the network. The authors in [5] present an integer linear programming (ILP)-based optimization method to reduce the deployment cost for LTE-A-based time division multiplexing PON (TDM-PON) fiber-wireless access network. In [18], a Q-learning based power allocation scheme for self organizing HetNet is discussed.…”

Section: B Related Workmentioning

confidence: 99%

“…17 presents the downstream latency obtained when there exists voids between succesive gNB coverages, and the eNB and the gNBs cooperate to allocate caching and computing resources for the contents. Here, we consider the content size to be uniformly distributed between 1 GB and 5 GB, i.e., d l ∼ U [1,5] GB. The length of the void and the other parameters related to eNB are taken from Table.…”

Section: Numerical Analysis Of Downstream Latencymentioning

confidence: 99%