Throughput Maximization of Network-Coded and Multi-Level Cache-Enabled Heterogeneous Network

TL;DR

This paper proposes a novel approach to maximize throughput in multi-level cache-enabled heterogeneous networks by jointly optimizing network-coded user scheduling and power allocation, addressing fronthaul constraints.

Contribution

It introduces a two-layered rate-aware network coding graph and an iterative optimization method to improve throughput in cache-enabled heterogeneous networks.

Findings

Significant throughput gains over existing solutions.

Effective joint optimization of scheduling and power allocation.

Validation through extensive simulations.

Abstract

One of the paramount advantages of multi-level cache-enabled (MLCE) networks is pushing contents proximity to the network edge and proactively caching them at multiple transmitters (i.e., small base-stations (SBSs), unmanned aerial vehicles (UAVs), and cache-enabled device-to-device (CE-D2D) users). As such, the fronthaul congestion between a core network and a large number of transmitters is alleviated. For this objective, we exploit network coding (NC) to schedule a set of users to the same transmitter. Focusing on this, we consider the throughput maximization problem that optimizes jointly the network-coded user scheduling and power allocation, subject to fronthaul capacity, transmit power, and NC constraints. Given the intractability of the problem, we decouple it into two separate subproblems. In the first subproblem, we consider the network-coded user scheduling problem for the given power allocation, while in the second subproblem, we use the NC resulting user schedule to optimize the power levels. We design an innovative \textit{two-layered rate-aware NC (RA-IDNC)} graph to solve the first subproblem and evaluate the second subproblem using an iterative function evaluation (IFE) approach. Simulation results are presented to depict the throughput gain of the proposed approach over the existing solutions.