Green OFDMA Resource Allocation in Cache-Enabled CRAN

TL;DR

This paper proposes an energy-efficient resource allocation scheme for cache-enabled CRANs using OFDMA, optimizing user assignment, RRH selection, and power allocation to reduce transmit power while considering caching and capacity constraints.

Contribution

It introduces a joint optimization framework for resource allocation in cache-enabled CRANs and develops an efficient Lagrange duality-based solution for it.

Findings

Caching significantly reduces transmit power in CRANs.

The proposed algorithm outperforms non-caching strategies.

Energy savings are validated through simulations.

Abstract

Cloud radio access network (CRAN), in which remote radio heads (RRHs) are deployed to serve users in a target area, and connected to a central processor (CP) via limited-capacity links termed the fronthaul, is a promising candidate for the next-generation wireless communication systems. Due to the content-centric nature of future wireless communications, it is desirable to cache popular contents beforehand at the RRHs, to reduce the burden on the fronthaul and achieve energy saving through cooperative transmission. This motivates our study in this paper on the energy efficient transmission in an orthogonal frequency division multiple access (OFDMA)-based CRAN with multiple RRHs and users, where the RRHs can prefetch popular contents. We consider a joint optimization of the user-SC assignment, RRH selection and transmit power allocation over all the SCs to minimize the total transmit power of the RRHs, subject to the RRHs' individual fronthaul capacity constraints and the users' minimum rate constraints, while taking into account the caching status at the RRHs. Although the problem is non-convex, we propose a Lagrange duality based solution, which can be efficiently computed with good accuracy. We compare the minimum transmit power required by the proposed algorithm with different caching strategies against the case without caching by simulations, which show the significant energy saving with caching.