Joint Optimization of Cloud and Edge Processing for Fog Radio Access Networks

Document Type

Article

Publication Date

11-1-2016

Abstract

This paper studies the joint design of cloud and edge processing for the downlink of a fog radio access network (F-RAN). In an F-RAN, as in cloud-RAN (C-RAN), a baseband processing unit (BBU) can perform joint baseband processing on behalf of the remote radio heads (RRHs) that are connected to the BBU by means of the fronthaul links. In addition to the minimal functionalities of conventional RRHs in C-RAN, the RRHs in an F-RAN may be equipped with local caches, in which frequently requested contents can be stored, as well as with baseband processing capabilities. They are hence referred to as enhanced RRH (eRRH). This paper focuses on the design of the delivery phase for an arbitrary pre-fetching strategy used to populate the caches of the eRRHs. Two fronthauling modes are considered, namely, a hard-Transfer mode, whereby non-cached files are communicated over the fronthaul links to a subset of eRRHs, and a soft-Transfer mode, whereby the fronthaul links are used to convey quantized baseband signals as in a C-RAN. Unlike the hard-Transfer mode in which baseband processing is traditionally carried out only at the eRRHs, the soft-Transfer mode enables both centralized precoding at the BBU and local precoding at the eRRHs based on the cached contents, by means of a novel superposition coding approach. To attain the advantages of both approaches, a hybrid design of soft-and hard-Transfer modes is also proposed. The problem of maximizing the delivery rate is tackled under fronthaul capacity and per-eRRH power constraints. Numerical results are provided to compare the performance of hard-and soft-Transfer fronthauling modes, as well as of the hybrid scheme, for different baseline pre-fetching strategies.

Identifier

85027072092 (Scopus)

Publication Title

IEEE Transactions on Wireless Communications

External Full Text Location

https://doi.org/10.1109/TWC.2016.2605104

ISSN

15361276

First Page

7621

Last Page

7632

Issue

11

Volume

15

Grant

694630

Fund Ref

Horizon 2020 Framework Programme

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