Adaptive CELP Bit Allocation for Periodic and Non-Stationary Signals
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Solution Overview
Problem
Conventional Code Excited Linear Prediction (CELP) techniques in wireless communication assign a fixed number of bits to pulse sequences, leading to code errors and inefficient quality improvement, as they do not adapt bit allocation based on signal periodicity and stationarity.
Innovation Solution
An encoding and decoding method that dynamically switches the number of bits assigned to noise or pulse sequences based on the level of periodicity and stationarity of time series signals, using prediction analysis to determine optimal bit allocation for improved encoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a fixed number of bits is assigned to pulse sequences in conventional CELP techniques, then device complexity is reduced and ease of operation is improved, but encoding quality and compression efficiency deteriorate due to inability to adapt to signal characteristics
Solution Approach 1:
The patent implements dynamic bit allocation by switching between first and second decoding modes based on signal periodicity detection. The system dynamically adjusts the number of bits assigned to pulse sequence codes according to whether the signal is periodic or non-periodic, rather than using a fixed bit allocation. This resolves the contradiction by making the bit allocation adaptive to signal characteristics, improving encoding quality without requiring complex manual configuration.
Solution Approach 2:
The patent changes the parameter of bit allocation based on signal periodicity. By detecting whether the signal is periodic or non-periodic, the system switches between different bit allocation schemes (first decoding mode for periodic signals, second decoding mode for non-periodic signals). This parameter change approach allows the system to optimize encoding quality for different signal types while maintaining operational simplicity through automated detection.
2Manufacturing precision
If more bits are allocated to pulse sequences, then encoding quality improves for non-stationary signals, but compression efficiency deteriorates due to increased bit rate
Solution Approach 1:
The patent applies different bit allocation strategies to different signal types locally. Instead of uniformly allocating bits to all pulse sequences, the system detects signal periodicity and allocates more bits only when the signal is periodic (requiring higher precision), while using fewer bits for non-periodic signals. This local quality approach optimizes the balance between encoding quality and compression efficiency by matching bit allocation to actual signal requirements.
Solution Approach 2:
The patent uses partial action by allocating additional bits only when necessary (for periodic signals) rather than always allocating maximum bits. The system selectively increases bit allocation for pulse sequence codes based on periodicity detection, avoiding unnecessary bit consumption for non-periodic signals where full precision is not required. This resolves the contradiction by applying enhanced precision only where it provides actual benefit.
3Productivity
If fewer bits are allocated to pulse sequences, then compression efficiency improves, but encoding quality deteriorates for periodic signals requiring higher precision
Solution Approach 1:
The system dynamically adjusts bit allocation based on real-time signal periodicity detection. When periodic signals are detected, the system switches to the first decoding mode with higher bit allocation to maintain encoding quality. When non-periodic signals are detected, it switches to the second decoding mode with lower bit allocation to improve compression efficiency. This dynamic adaptation resolves the contradiction by matching bit allocation to signal characteristics.
Solution Approach 2:
The patent changes the bit allocation parameter based on signal periodicity detection. By switching between different decoding modes (with different bit requirements) according to whether the signal is periodic or non-periodic, the system optimizes the balance between compression efficiency and encoding quality. This parameter change strategy ensures sufficient precision for periodic signals while achieving better compression for non-periodic signals.
Data Source
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AI summary
In encoding, the number of bits to be assigned to codes corresponding to noise or a pulse sequence obtained according to prediction analysis applied to time series signals included in a predetermined time interval is switched according to whether an index that indicates a level of periodicity and/or stationarity of input time series signals satisfies a condition that indicates high periodicity and/or high stationarity or a condition that indicates low periodicity and/or low stationarity, to acquire the codes corresponding to the noise and the pulse sequence. In decoding, a decoding mode for codes corresponding to noise or a pulse sequence included in codes corresponding to a predetermined time interval is switched according to the same criterion as that described above to decode the codes corresponding to the noise or the pulse sequence to acquire noise or a pulse sequence corresponding to the predetermined time interval.