[Paper Review] Energy-Neutral Source-Channel Coding with Battery and Memory Size Constraints

Paolo Castiglione, Gerald Matz|arXiv (Cornell University)|Sep 1, 2013

Energy Harvesting in Wireless Networks14 references4 citations

TL;DR

This paper proposes a joint source-channel coding policy for energy-harvesting sensor nodes with finite battery and data buffers, using large deviation analysis to achieve energy-neutral operation and minimize average distortion. The method dynamically adapts source and channel coding rates based on energy and channel state, achieving distortion scaling that approaches the theoretical lower bound with O((ln A/A)² + (ln B/B)²) convergence, avoiding energy leakage seen in prior policies.

ABSTRACT

We study energy management policies for the compression and transmission of source data collected by an energy-harvesting sensor node with a finite energy buffer (e.g., rechargeable battery) and a finite data buffer (memory) between source encoder and channel encoder. The sensor node can adapt the source and channel coding rates depending on the observation and channel states. In such a system, the absence of precise information about the amount of energy available in the future is a key challenge. We provide analytical bounds and scaling laws for the average distortion that depend on the size of the energy and data buffers. We furthermore design a resource allocation policy that achieves almost optimal distortion scaling. Our results demonstrate that the energy leakage of state of art energy management policies can be avoided by jointly controlling the source and channel coding rates.

Motivation & Objective

• To address the challenge of energy management in energy-harvesting sensor nodes with finite energy and data buffers.
• To eliminate energy leakage in existing resource allocation policies by jointly controlling source and channel coding rates.
• To derive analytical bounds and scaling laws for average distortion as a function of buffer sizes.
• To design a resource allocation policy that asymptotically achieves the distortion lower bound.

Proposed method

• Uses large deviation tools from Tse (2006) to analyze system behavior, avoiding the curse of dimensionality of dynamic programming.
• Models the sensor node with a finite battery (energy buffer) and finite data buffer (memory), with energy and data flow governed by Markov processes.
• Introduces a joint energy management policy that adaptively sets source and channel coding rates based on real-time energy and channel states.
• Employs a drift-plus-penalty framework with decremental drifts δ_d = β₁ ln A / A and δ_e = β₂ ln B / B to stabilize buffers and minimize distortion.
• Derives bounds on the probability of buffer overflow (FQ) and empty battery (EB), showing p_FQ^π° = o(1/A²) and p_EB^π° = o(1/B²).
• Uses Taylor expansion of the distortion function around zero drift to analyze convergence, showing D^op = D_T(0,0) + O((ln A/A)²) + O((ln B/B)²).

Experimental results

Research questions

• RQ1 How does the average distortion scale with increasing energy and data buffer sizes in an energy-harvesting sensor system?
• RQ2 Can joint adaptation of source and channel coding rates eliminate energy leakage and improve distortion performance compared to independent allocation?
• RQ3 What is the fundamental distortion lower bound for such systems under finite buffer constraints?
• RQ4 How does the proposed policy achieve near-optimal distortion scaling without requiring knowledge of future energy states?
• RQ5 What is the convergence rate of the average distortion to the optimal value under the proposed policy?

Key findings

• The proposed joint energy management policy achieves average distortion that converges to the theoretical lower bound with a rate of O((ln A/A)² + (ln B/B)²), where A and B are data and energy buffer sizes.
• The probability of data buffer overflow and energy buffer emptiness both scale as o(1/A²) and o(1/B²), respectively, under the proposed policy.
• The first-order terms in the distortion Taylor expansion vanish due to buffer stability, leading to second-order convergence behavior.
• The method avoids energy leakage by jointly controlling source and channel coding rates, unlike prior policies that treat source and channel energy allocation independently.
• The analytical bounds and scaling laws for distortion are derived using large deviation techniques, providing a scalable alternative to dynamic programming.
• Numerical validation confirms that the policy asymptotically achieves optimal distortion scaling, outperforming existing approaches that do not jointly optimize source and channel coding.