Statistical QoS Provisioning for Underwater Magnetic Induction Communication

TL;DR

This paper introduces a statistical QoS framework for underwater magnetic induction communication, employing effective capacity theory and convex optimization to enhance data rates while meeting delay and queue constraints.

Contribution

It proposes a novel current control strategy based on convex optimization to maximize effective capacity under underwater MI communication constraints.

Findings

The proposed control strategy significantly improves achievable data rates.

It differs markedly from conventional statistical QoS frameworks.

Simulation results validate the effectiveness of the new strategy.

Abstract

Magnetic induction (MI) communication, with stable channel conditions and small antenna size, is considered as a promising solution for underwater communication network. However, the narrowband nature of the MI link can cause significant delays in the network. To comprehensively ensure the timeliness and effectiveness of the MI network, in this paper we introduce a statistical quality of service (QoS) framework for MI communication, aiming to maximize the achievable rate while provisioning delay and queue-length requirements. Specifically, we employ effective capacity theory to model underwater MI communication. Based on convex optimization theory, we propose a current control strategy that maximizes the effective capacity under the constraints of limited channel capacity and limited power. Simulations demonstrate that the current control strategy proposed for MI communication differs significantly from that in the conventional statistical QoS provisioning framework. In addition, compared to other current control strategies, the proposed strategy substantially improves the achievable rate under various delay QoS requirements.