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Statistical QoS Provisioning for Underwater Magnetic Induction Communication

Zhichao Li, Jianyu Wang, Wenchi Cheng, Yudong Fang

TL;DR

The paper addresses delays in underwater MI links by introducing a statistical QoS framework based on effective capacity $E_C(\theta)$. It models the MI channel with a stochastic misalignment $M$ and formulates a convex optimization over $\xi(M)=I_t^2(M)$ to maximize $E_C(\theta)$ under peak current and average power constraints, deriving a closed-form optimal policy with thresholds $M_1$ and $M_2$. The solution yields an explicit current-control rule $\xi_{\text{opt}}(M)$ and corresponding $E_C(\theta)$, with special cases: as $\theta \to 0$ the policy reduces to channel water-filling, and as $\theta \to \infty$ the system enforces zero outage. Numerical results show the proposed strategy outperforms traditional QoS-aware methods across varying delay requirements, distance, and QoS exponents, demonstrating practical gains for reliable MI-based underwater communication.

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.

Statistical QoS Provisioning for Underwater Magnetic Induction Communication

TL;DR

The paper addresses delays in underwater MI links by introducing a statistical QoS framework based on effective capacity . It models the MI channel with a stochastic misalignment and formulates a convex optimization over to maximize under peak current and average power constraints, deriving a closed-form optimal policy with thresholds and . The solution yields an explicit current-control rule and corresponding , with special cases: as the policy reduces to channel water-filling, and as the system enforces zero outage. Numerical results show the proposed strategy outperforms traditional QoS-aware methods across varying delay requirements, distance, and QoS exponents, demonstrating practical gains for reliable MI-based underwater communication.

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.

Paper Structure

This paper contains 9 sections, 1 theorem, 28 equations, 5 figures.

Table of Contents

  1. Introduction
  2. system Model
  3. Stochastic Misalignment Model for MI Links
  4. Signal Model
  5. Power model
  6. Statistical QoS Provisioning and Effective Capacity Model
  7. Optimal Current Control for QoS-aware Based MI Communication System
  8. Simulation Result
  9. Conclusion

Key Result

Theorem 1

The optimal current control scheme which is the solution to the optimization problem, denoted by $\xi _{opt}\left( M \right)$, is given by: where $M_1=\sqrt{\frac{R_t}{\frac{1}{\lambda _0}-a}}$, $M_2=\sqrt{R_t/\left( \frac{1}{\lambda _02^{R_{\max}\left( \beta +1 \right)}}-a \right)}$, $\lambda _0=\lambda _1/\left( b\beta \right)$, $\lambda_0$ can be solving numerically.

Figures (5)

  • Figure 1: Underwater MI networks and statistical QoS provisioning framework with finite-length queue.
  • Figure 2: The transmit currents comparison between the WFWC and the WF strategy.
  • Figure 3: The optimal current adaptation strategy with current constraint.
  • Figure 4: The optimal power-adaptation policy without current restriction.
  • Figure 5: Normalized effective capacities under our optimal current control strategy with other classic strategies.

Theorems & Definitions (2)

  • Theorem 1
  • proof