Viterbi Decoding for UWB Pulse Trains Without Inverse Mapping
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Solution Overview
Problem
Existing UWB data transmission systems using the {3, 2, 5} convolutional code suffer from suboptimal decoding, particularly due to higher error rates in systematic bits and underutilization of soft-decision decoding, leading to inefficient data recovery.
Innovation Solution
Implement a communication system that maps the systematic and parity bits of the {3, 2, 5} convolutional code into two binary trains dependent on g0^g1 and g1, using a Viterbi decoder based on the {3, 7, 5} convolutional code for decoding, eliminating the need for inverse mapping and enabling soft-decision decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If inverse mapping decoding is used to recover systematic bits from received pulse trains, then decoding can be performed, but the decoding distance of systematic bits is reduced and error rate increases
Solution Approach 1:
Instead of performing inverse mapping to recover systematic bits and then decoding, the patent inverts the approach by directly feeding the received pulse trains into a Viterbi decoder configured with the original convolutional code parameters. This eliminates the inverse mapping step that degraded systematic bit reliability and allows soft-decision decoding to fully exploit the received signal information.
Solution Approach 2:
The patent changes the parameters of the Viterbi decoder to match the original convolutional encoding parameters (constraint length K=3, generator polynomials g0=[010], g1=[101]). By aligning the decoder parameters with the encoder parameters and removing the inverse mapping transformation, the systematic bits achieve the same decoding distance and error performance as the parity bits.
2Productivity
If hard-decision inverse mapping is used to decode bits from pulse trains, then decoding can be performed, but soft-decision decoding capabilities are underutilized
Solution Approach 1:
The patent extracts and removes the inverse mapping step from the decoding process. By eliminating this hard-decision transformation that converts soft information into hard bits, the system preserves the soft-decision information throughout the entire decoding chain, allowing the Viterbi decoder to fully utilize likelihood information for optimal decoding performance.
3Ease of manufacture
If systematic and parity bits are mapped into separate binary trains dependent on g0^g1 and g1, then modulation can be performed, but the decoding process becomes suboptimal
Solution Approach 1:
The patent applies a universal Viterbi decoder that handles both systematic and parity bits uniformly. By configuring the decoder with the original convolutional code parameters and feeding it the received pulse trains directly, the system achieves equal error performance for both bit types, making the decoding process as reliable as the modulation implementation.
Data Source
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AI summary
A receiver (115) receives pulse trains (r0, r1) encoding a plurality of bits (b) using a convolutional code {3, 2, 5} whose systematic bit g0 and parity bit g1 are mapped into two binary trains (21, 22) depending respectively on g0^g1 and g1. Noting that the convolutional coding and the mapping lead to modulation (bz-1^b^b.z+1, bz-1^b.z+1, bz-1^b.z+1), a demodulator demodulates the pairs of received pulse trains (r0, r1) into respective pairs of LLR values (b0, b1) and a Viterbi decoder built on a convolutional code {3, 7, 5} decodes each pair of LLR values into a decoded bit (d). Optimal decoding is thus obtained, notably showing a gain estimated at 1.8 dB (in error rate) without additional complexity.