Content-Centric Sparse Multicast Beamforming for Cache-Enabled Cloud RAN

TL;DR

This paper introduces a content-centric multicast beamforming approach in cache-enabled cloud RAN, optimizing cluster formation and transmission to reduce backhaul and power costs while ensuring quality of service.

Contribution

It formulates a novel joint caching and beamforming optimization problem and proposes an effective solution using sparse beamforming and convex-concave procedures.

Findings

Significant reduction in backhaul cost and transmit power.

Effective content-centric clustering regardless of channel conditions.

Performance gains over heuristic caching strategies.

Abstract

This paper presents a content-centric transmission design in a cloud radio access network (cloud RAN) by incorporating multicasting and caching. Users requesting a same content form a multicast group and are served by a same cluster of base stations (BSs) cooperatively. Each BS has a local cache and it acquires the requested contents either from its local cache or from the central processor (CP) via backhaul links. We investigate the dynamic content-centric BS clustering and multicast beamforming with respect to both channel condition and caching status. We first formulate a mixed-integer nonlinear programming problem of minimizing the weighted sum of backhaul cost and transmit power under the quality-of-service constraint for each multicast group. Theoretical analysis reveals that all the BSs caching a requested content can be included in the BS cluster of this content, regardless of the channel conditions. Then we reformulate an equivalent sparse multicast beamforming (SBF) problem. By adopting smoothed $\ell_0$-norm approximation and other techniques, the SBF problem is transformed into the difference of convex (DC) programs and effectively solved using the convex-concave procedure algorithms. Simulation results demonstrate significant advantage of the proposed content-centric transmission. The effects of three heuristic caching strategies are also evaluated.