Distributed Resource Management in Downlink Cache-Enabled Multi-Cloud Radio Access Networks

TL;DR

This paper proposes a distributed resource management algorithm for multi-cloud radio access networks with caching, optimizing energy efficiency under fronthaul and power constraints, addressing interference in dense B5G scenarios.

Contribution

It introduces a novel distributed optimization algorithm for user-cloud association and beamforming in cache-enabled multi-cloud RANs, handling non-convex mixed-integer problems.

Findings

The algorithm effectively alleviates interference in dense MC-RANs.

It improves energy efficiency under fronthaul and power constraints.

Distributed implementation reduces communication overhead.

Abstract

In light of the premises of beyond fifth generation (B5G) networks, the need for better exploiting the capabilities of cloud-enabled networks arises, so as to cope with the large-scale interference resulting from the massive increase of data-hungry systems. A compound of several clouds, jointly managing inter-cloud and intra-cloud interference, constitutes a practical solution to account for the requirements of B5G networks. This paper considers a multi-cloud radio access network model (MC-RAN), where each cloud is connected to a distinct set of base stations (BSs) via limited capacity fronthaul links. The BSs are equipped with local cache storage and baseband processing capabilities, as a means to alleviate the fronthaul congestion problem. The paper then investigates the problem of jointly assigning users to clouds and determining their beamforming vectors so as to maximize the network-wide energy efficiency (EE) subject to fronthaul capacity and transmit power constraints. This paper solves such a mixed discrete-continuous, non-convex optimization problem using fractional programming (FP) and successive inner-convex approximation (SICA) techniques to deal with the non-convexity of the continuous part of the problem, and $l_0$-norm approximation to account for the binary association part. A highlight of the proposed algorithm is its capability of being implemented in a distributed fashion across the network's multiple clouds through a reasonable amount of information exchange. The numerical simulations illustrate the pronounced role the proposed algorithm plays in alleviating the interference of large-scale MC-RANs, especially in dense networks.