Throughput Maximization in Cloud Radio Access Networks using Network Coding

TL;DR

This paper introduces a novel network coding-based scheduling scheme for cloud radio access networks, significantly improving throughput by intelligently mixing user flows in each radio resource block.

Contribution

It proposes a joint scheduling and coding scheme using instantly decodable network coding to maximize throughput in CRANs, a novel approach compared to traditional single-user RRB allocations.

Findings

Significant throughput gains over classical coding methods.

Effective joint scheduling and coding strategy demonstrated via simulations.

Graph-based formulation enables optimal user-RRB association.

Abstract

This paper is interested in maximizing the total throughput of cloud radio access networks (CRANs) in which multiple radio remote heads (RRHs) are connected to a central computing unit known as the cloud. The transmit frame of each RRH consists of multiple radio resources blocks (RRBs), and the cloud is responsible for synchronizing these RRBS and scheduling them to users. Unlike previous works that consider allocating each RRB to only a single user at each time instance, this paper proposes to mix the flows of multiple users in each RRB using instantly decodable network coding (IDNC). The proposed scheme is thus designed to jointly schedule the users to different RRBs, choose the encoded file sent in each of them, and the rate at which each of them is transmitted. Hence, the paper maximizes the throughput which is defined as the number of correctly received bits. To jointly fulfill this objective, we design a graph in which each vertex represents a possible user-RRB association, encoded file, and transmission rate. By appropriately choosing the weights of vertices, the scheduling problem is shown to be equivalent to a maximum weight clique problem over the newly introduced graph. Simulation results illustrate the significant gains of the proposed scheme compared to classical coding and uncoded solutions.