Low-power channel coding via dynamic reconfiguration

Abstract

Presented in this paper are energy-optinium reconfiguration strutegies for channel codecs. These strategies are derived by solving an optimization problem, which has energy consumption as the objective function and a constraint on the bit error-rate (BER). Energy consumption models for a reconfigurable Reed-Solomon (RS) codec are derived via gate-level simulation of the finite field arithmetic modules. Theseenergy models along with the B E R expressions are then employed to derive the energy-optimum reconfiguration strategies. The energy savings are computed by comparing the energy consumption of the reconfigurablecodec with that of the static codec. The energy savings range from 0%-83% for channel signal-to-noise ratio ( S N R ) variations from 7dB-lOdB. On an average 55% energy savings are achieved.

A High Energy Efficiency Link Layer Adaptive Error Control Mechanism for Wireless Sensor Networks

“…According to AEC-RSSI algorithm, the energy efficiency of AEC-RSSI is concerned with FEC, ARQ and HARQ, which then can be expressed by formula (14). …”

“…According to [14], the energy consumption of decoding using RS (n, v) decoder (i.e. E dec ) is given by formula (5).…”

Low-power approach for decoding convolutional codes with adaptive viterbi algorithm approximations

“…Examples of other applications in which system characteristics have been successfully exploited to reduce energy consumption include Reed-Solomon channel coding [4] and an encryption processor [5]. The goal of the approach proposed in this paper is to reduce energy consumption while decoding high memory order punctured convolutional codes in a system where channel bandwidth availability, channel signal-to-noise ratio (SNR), and/or maximum acceptable bit error rate (BER) vary realtime.…”

A game theoretic approach to dynamic energy minimization in wireless transceivers

“…In [5,6], the authors exploit the system characteristics to reduce energy consumption of a Reed-Solomon encryption processor. More recently, in [7], power consumption of a high memory-order punctured convolutional decoder has been reduced by using an adaptive algorithm based on the channel bandwidth and the received SNR, thereby, reducing the required energy for decoding a single bit of information.…”