Resource Reservation in Backhaul and Radio Access Network with Uncertain User Demands

TL;DR

This paper addresses joint resource reservation in wireless networks by proposing an efficient algorithm that optimizes backhaul and RAN resource allocation based on demand and channel statistics, improving network performance.

Contribution

It introduces a novel BCD algorithm with multipath routing and BSUM-based distributed resource reservation for joint backhaul and RAN optimization.

Findings

The BCD algorithm converges to a KKT point.

Simulation shows the approach outperforms heuristic algorithms.

The method effectively manages uncertain user demands.

Abstract

Resource reservation is an essential step to enable wireless data networks to support a wide range of user demands. In this paper, we consider the problem of joint resource reservation in the backhaul and Radio Access Network (RAN) based on the statistics of user demands and channel states, and also network availability. The goal is to maximize the sum of expected traffic flow rates, subject to link and access point budget constraints, while minimizing the expected outage of downlinks. The formulated problem turns out to be non-convex and difficult to solve to global optimality. We propose an efficient Block Coordinate Descent (BCD) algorithm to approximately solve the problem. The proposed BCD algorithm optimizes the link capacity reservation in the backhaul using a novel multipath routing algorithm that decomposes the problem down to link-level and parallelizes the computation across backhaul links, while the reservation of transmission resources in RAN is carried out via a novel scalable and distributed algorithm based on Block Successive Upper-bound Minimization (BSUM). We prove that the proposed BCD algorithm converges to a Karush-Kuhn-Tucker solution. Simulation results verify the efficiency and the efficacy of our BCD approach against two heuristic algorithms.