Network-Coded Content Delivery in Femtocaching-Assisted Cellular Networks

Shnaiwer, Yousef N.; Sorour, Sameh; Aboutorab, Neda; Sadeghi, Parastoo; Al-Naffouri, Tareq Y.

doi:10.1109/glocom.2015.7417276

Cited by 21 publications

(9 citation statements)

References 8 publications

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“…They also analyze asymptotic scaling laws. Caching methods based on network coding (coded-multicast) are investigated in [13] and [14], as examples. The former considers femto-caching in conjunction with opportunistic network coding and derives the optimal cache placement.…”

Section: A Related Workmentioning

confidence: 99%

A Non-Stationary Bandit-Learning Approach to Energy-Efficient Femto-Caching with Rateless-Coded Transmission

Maghsudi,

van der Schaar

2020

Preprint

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The ever-increasing demand for media streaming together with limited backhaul capacity renders developing efficient file-delivery methods imperative. One such method is femto-caching, which, despite its great potential, imposes several challenges such as efficient resource management. We study a resource allocation problem for joint caching and transmission in small cell networks, where the system operates in two consecutive phases: (i) cache placement, and (ii) joint file-and transmit power selection followed by broadcasting. We define the utility of every small base station in terms of the number of successful reconstructions per unit of transmission power. We then formulate the problem as to select a file from the cache together with a transmission power level for every broadcast round so that the accumulated utility over the horizon is maximized. The former problem boils down to a stochastic knapsack problem, and we cast the latter as a multi-armed bandit problem. We develop a solution to each problem and provide theoretical and numerical evaluations. In contrast to the state-of-the-art research, the proposed approach is especially suitable for networks with time-variant statistical properties. Moreover, it is applicable and operates well even when no initial information about the statistical characteristics of the random parameters such as file popularity and channel quality is available.

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Section: A Related Workmentioning

confidence: 99%

A Non-Stationary Bandit-Learning Approach to Energy-Efficient Femto-Caching with Rateless-Coded Transmission

Maghsudi,

van der Schaar

2020

Preprint

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“…It is assumed that there is no overlap of the users in each proximity set; that is, the users in each proximity network that are "geographically close" can communicate locally but not outside this network. Unlike previous works [22,23] that consider ideal transmissions amongst the users and from the servers, in this manuscript, we have made the more realistic assumption that the users-to-users and serversto-users' communications links are subject to random erasure.…”

Section: System Modelmentioning

confidence: 99%

“…The problem is first modelled using graphical representation proposed in [22], namely, the IDNC graph with induced subgraphs. This graph representation overcomes the limitations of the conventional graphical representation [23], as it is not compatible to implement with a system model having fullduplex communications and the additional constraints that arise from D2D-enabled communications. With this graph modelling of the system, the optimal solution is formulated using the SSP technique and shown to be NP-hard and not applicable for real-time applications [24].…”

Section: Introductionmentioning

confidence: 99%

Network Coding for Backhaul Offloading in D2D Cooperative Fog Data Networks

Quinton

Aboutorab

2018

Wireless Communications and Mobile Computing

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Future distributed data networks are expected to be assisted by users cooperation and coding schemes. Given the explosive increase in the end-users’ demand for download of the content from the servers, in this paper, the implementation of instantly decodable network coding (IDNC) is considered in full-duplex device-to-device (D2D) cooperative fog data networks. In particular, this paper is concerned with designing efficient transmission schemes to offload traffic from the expensive backhaul of network servers by employing IDNC and users cooperation. The generalized framework where users send request for multiple packets and the transmissions are subject to erasure is considered. The optimal problem formulation is presented using the stochastic shortest path (SSP) technique over the IDNC graph with induced subgraphs. However, as the optimal solution suffers from the intractability of being NP-hard, it is not suitable for real-time communications. The complexity of the problem is addressed by presenting a greedy heuristic algorithm used over the proposed graph model. The paper shows that by implementing IDNC in a full-duplex cooperative D2D network model significant reduction in the number of downloads required from the servers can be achieved, which will result in offloading of the backhaul servers and thus saving valuable servers’ resources. It is also shown that the performance of the proposed heuristic algorithm is very close to the optimal solution with much lower computational complexity.

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“…Indeed, their inclusion/exclusion produce the same objective function. This section proposes using a method similar to the one in [29] to select one of the points that achieve the optimum of theorem (4). The fundamental concept of the heuristic is to arrange users in multiple layers in descending order of criticality and to heuristically solve the maximum weight independent set in each layer.…”

Section: Proposed Solutionmentioning

confidence: 99%

“…With the emergence of smart phones, the 4G and 5G cellular network architectures, the data download problem has become more attractive in the wireless scenarios. Consequently, the data download problem was modified from a classical full package download problem into a download completion problem [3,4]. The large storage capabilities of modern user equipment enable these devices to benefit from the already downloaded messages and request only the missed ones.…”

Section: Introductionmentioning

confidence: 99%