Tailless Convolutional Coding With Known-Start Trellis Decoding
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Solution Overview
Problem
Existing convolutional codes, such as tail-biting convolutional codes, face challenges with high decoder complexity due to multiple starting and ending states, which limits decoding performance and resource efficiency in wireless communication systems.
Innovation Solution
The introduction of Tailless Convolutional Codes (TLCCs) with a known start state eliminates the need for tail bits and reduces decoder complexity by limiting starting states to a single known state, enabling efficient decoding and reduced resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tail-biting convolutional codes are used, then error correction capability is provided, but decoder complexity increases due to multiple starting and ending states
Solution Approach 1:
The patent extracts and removes the tail-biting requirement from the convolutional code structure. By eliminating the constraint that the encoder must return to the initial state, the code simplifies the decoding process while maintaining error correction capability through the known start state and back-trace mechanism
Solution Approach 2:
Instead of starting decoding from multiple possible states and trying to find the correct path, the patent inverts the approach by starting from a known end state and back-tracing to a known start state. This reversal simplifies the search space and reduces decoder complexity
2Ease of manufacture
If tail bits are appended to satisfy tail-biting requirement, then convolutional coding is completed, but transmission resources are wasted
Solution Approach 1:
The patent removes the unnecessary tail bits that were previously required to satisfy the tail-biting constraint. By eliminating this redundant component, the system achieves coding completion without wasting transmission resources on non-information-bearing bits
Solution Approach 2:
The patent changes the fundamental parameter of the convolutional code from requiring the encoder to return to the initial state (tail-biting) to allowing the encoder to end in any state. This parameter change eliminates the need for tail bits while maintaining the integrity of the coding process
3Measurement precision
If warm-up iterations are performed for tail-biting convolutional codes, then decoding accuracy is improved, but energy consumption increases
Solution Approach 1:
The patent applies preliminary action by establishing a known start state before decoding begins. This pre-established reference point eliminates the need for warm-up iterations to estimate the starting state, thereby reducing energy consumption while maintaining decoding accuracy through the deterministic back-trace process
Data Source
AI summary
Certain aspects of the present disclosure relate to techniques and apparatus for increasing decoding performance and/or reducing decoding complexity. An exemplary method generally includes receiving, via a wireless medium, a codeword encoded using a tailless convolutional code (TLCC) with a known start state, evaluating a set of decoding candidate paths through a trellis decoder that originate at the known start state of the TLCC, performing, for each of a plurality of the decoding candidate paths, a back trace from a respective end state to the known start state, and selecting one of the decoding candidate paths based, at least in part, on path metrics generated while performing the back trace. Other aspects, embodiments, and features are also claimed and described.