Energy-Efficient Multicast/Unicast Edge Caching for Dense Small Cell Networks with Graph Theory

Mrad, Safa; Hamouda, Soumaya; Maharaj, B. T.

doi:10.1109/vtcspring.2018.8417604

Cited by 1 publication

(2 citation statements)

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“…In the Equation , the “ ‐i ” refers to the RRHs other than the considered RRH i . Also, more details about the computation of the transmit power

p_{i j}

can be found in Mrad et al The condition in Equation imposes that the total cache size of the contents, which are placed at a given RRH, is bounded within an upper threshold

S_{m a x}

(we assume that all the RRHs storage capacity, ie,

S_{m a x}

S_{i}

for all i ). The constraint in Equation is imposed to guarantee that each group of users requesting the same content j is served by at most one RRH.…”

Section: Proposed Noncooperative Proactive Caching Placement Gamementioning

confidence: 99%

“…Content delivery phase refers to the stage of transmitting the required contents through wireless channels from access points to mobile users. In our previous work, we have studied the delivery phase to optimize the energy efficiency in dense small cell using the graph theory. On the other hand, caching placement also known as proactive caching resides in determining what, when, and how to cardinally cache content files in the different base stations of the network.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic theoretic proactive caching placement game for energy saving in H‐CRAN

Mrad

Hamouda

2020

Int J Communication

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Summary Edge caching is an effective feature of the next 5G network to guarantee the availability of the service content and a reduced time response for the user. However, the placement of the cache content remains an issue to fully take advantage of edge caching. In this paper, we address the proactive caching problem in Heterogeneous Cloud Radio Access Network (H‐CRAN) from a game theoretic point of view. The problem is formulated as a bargaining game where the remote radio heads (RRHs) dynamically negotiate and decide which content to cache in which RRH under energy saving and cache capacity constraints. The Pareto optimal equilibrium is proved for the cooperative game by the iterative Nash bargaining algorithm. We compare between cooperative and noncooperative proactive caching games and demonstrate how the selfishness of different players can affect the overall system performance. We also showed that our cooperative proactive caching game improves the energy consumption of 40% as compared with noncooperative game and of 68% to no‐game strategy. Moreover, the number of satisfied requests at the RRHs with the proposed cooperative proactive caching scheme is significantly increased.

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“…In the Equation , the “ ‐i ” refers to the RRHs other than the considered RRH i . Also, more details about the computation of the transmit power

p_{i j}

can be found in Mrad et al The condition in Equation imposes that the total cache size of the contents, which are placed at a given RRH, is bounded within an upper threshold

S_{m a x}

(we assume that all the RRHs storage capacity, ie,

S_{m a x}

S_{i}

for all i ). The constraint in Equation is imposed to guarantee that each group of users requesting the same content j is served by at most one RRH.…”

Section: Proposed Noncooperative Proactive Caching Placement Gamementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%