Utility Maximization for Multihop Wireless Networks Employing BATS Codes

TL;DR

This paper develops a network utility maximization framework for multihop wireless networks using BATS codes, optimizing code parameters and scheduling for multiple flows with adaptive recoding, validated through simulations.

Contribution

It introduces a novel NUM formulation for multi-flow BATS-coded networks with adaptive recoding and proposes a two-step solution algorithm.

Findings

The proposed algorithm effectively optimizes BATS code parameters and scheduling.

Numerical evaluations demonstrate the algorithm's efficiency and effectiveness.

Adaptive recoding improves network performance compared to nonadaptive schemes.

Abstract

BATS (BATched Sparse) codes are a class of efficient random linear network coding variation that has been studied for multihop wireless networks mostly in scenarios of a single communication flow. Towards sophisticated multi-flow network communications, we formulate a network utility maximization (NUM) problem that jointly optimizes the BATS code parameters of all the flows and network scheduling. The NUM problem adopts a batch-wise packet loss model that can be obtained from the network local statistics without any constraints on packet loss patterns. Moreover, the NUM problem allows a different number of recoded packets to be transmitted for different batches in a flow, which is called adaptive recoding. Due to both the probably nonconcave objective and the BATS code-related variables, the algorithms developed for the existing flow optimization problems cannot be applied directly to solve our NUM problem. We introduce a two-step algorithm to solve our NUM problem, where the first step solves the problem with nonadaptive recoding schemes, and the second step optimizes adaptive recoding hop-by-hop from upstream to downstream in each flow. We perform various numerical evaluations and simulations to verify the effectiveness and efficiency of the algorithm.