Content-Centric Multicast Beamforming in Cache-Enabled Cloud Radio Access Networks

TL;DR

This paper introduces a joint multicast beamforming and caching strategy in cloud RANs, optimizing network cost by leveraging content caching and cooperative transmission to improve efficiency.

Contribution

It proposes a novel optimization framework for joint multicast beamforming and BS clustering considering cache placement and backhaul constraints in cloud RANs.

Findings

Multicasting reduces network power and backhaul usage.

Cache size and policies significantly impact network performance.

The proposed algorithms effectively solve the non-convex optimization problem.

Abstract

Multicast transmission and wireless caching are effective ways of reducing air and backhaul traffic load in wireless networks. This paper proposes to incorporate these two key ideas for content-centric multicast transmission in a cloud radio access network (RAN) where multiple base stations (BSs) are connected to a central processor (CP) via finite-capacity backhaul links. Each BS has a cache with finite storage size and is equipped with multiple antennas. The BSs cooperatively transmit contents, which are either stored in the local cache or fetched from the CP, to multiple users in the network. Users requesting a same content form a multicast group and are served by a same cluster of BSs cooperatively using multicast beamforming. Assuming fixed cache placement, this paper investigates the joint design of multicast beamforming and content-centric BS clustering by formulating an optimization problem of minimizing the total network cost under the quality-of-service (QoS) constraints for each multicast group. The network cost involves both the transmission power and the backhaul cost. We model the backhaul cost using the mixed $\ell_0/\ell_2$-norm of beamforming vectors. To solve this non-convex problem, we first approximate it using the semidefinite relaxation (SDR) method and concave smooth functions. We then propose a difference of convex functions (DC) programming algorithm to obtain suboptimal solutions and show the connection of three smooth functions. Simulation results validate the advantage of multicasting and show the effects of different cache size and caching policies in cloud RAN.