Source-Channel Coding under Energy, Delay and Buffer Constraints

TL;DR

This paper develops optimal transmission policies for energy-constrained wireless sensor nodes transmitting Gaussian sources over fading channels with delay and buffer constraints, considering various energy scenarios including harvesting.

Contribution

It formulates convex optimization problems for different energy constraints and characterizes the structure of optimal policies, including a novel 2D waterfilling interpretation for strict delay cases.

Findings

Optimal policies depend on energy availability, delay, and buffer size.

Energy harvesting and processing costs significantly influence transmission strategies.

Numerical results demonstrate the impact of constraints on distortion and energy efficiency.

Abstract

Source-channel coding for an energy limited wireless sensor node is investigated. The sensor node observes independent Gaussian source samples with variances changing over time slots and transmits to a destination over a flat fading channel. The fading is constant during each time slot. The compressed samples are stored in a finite size data buffer and need to be delivered in at most $d$ time slots. The objective is to design optimal transmission policies, namely, optimal power and distortion allocation, over the time slots such that the average distortion at destination is minimized. In particular, optimal transmission policies with various energy constraints are studied. First, a battery operated system in which sensor node has a finite amount of energy at the beginning of transmission is investigated. Then, the impact of energy harvesting, energy cost of processing and sampling are considered. For each energy constraint, a convex optimization problem is formulated, and the properties of optimal transmission policies are identified. For the strict delay case, $d=1$, $2D$ waterfilling interpretation is provided. Numerical results are presented to illustrate the structure of the optimal transmission policy, to analyze the effect of delay constraints, data buffer size, energy harvesting, processing and sampling costs.