Chaotic Spread Spectrum Symbol Estimation with Energy Normalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In spread spectrum communications, non-stationary chaotic spreading sequences lead to degraded bit error rates and unreliable data delivery due to dynamic symbol energy variations, especially when combined with amplitude modulation schemes like QAM, which increase susceptibility to noise and corruption.
Innovation Solution
A method for improving symbol estimation by generating and synchronizing discrete-time chaotic samples at the receiver, obtaining normalization factor values based on comparing received symbol energy with expected symbol energy, and adjusting the de-spread signal to normalize symbol energy, thereby reducing the likelihood of improper symbol estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If non-stationary chaotic spreading sequences are used to improve security and robustness, then signal security and interference resistance are improved, but symbol energy becomes dynamic and non-stationary causing degraded bit error rates
Solution Approach 1:
The patent applies dynamics by transitioning from stationary signal models to dynamic adaptive normalization. The receiver continuously tracks and compensates for time-varying symbol energy in chaotic spread spectrum signals using adaptive algorithms that adjust normalization factors in real-time, allowing the system to handle non-stationary characteristics while maintaining reliable symbol detection.
Solution Approach 2:
The patent changes the parameter of symbol energy normalization by introducing adaptive normalization factors that dynamically adjust based on received signal characteristics. Instead of assuming constant energy, the system estimates and compensates for varying symbol energy levels, transforming the non-stationary signal into an effectively stationary representation for processing.
2Productivity
If amplitude modulation with multiple symbols is used to increase data capacity, then user data capacity is improved, but Hamming distance between symbols decreases making symbols more susceptible to noise
Solution Approach 1:
The patent implements feedback mechanisms where the receiver continuously monitors signal quality and uses this information to adjust normalization factors and detection thresholds. This feedback loop allows the system to compensate for reduced Hamming distance effects by adapting to actual signal conditions, maintaining reliable detection even at higher data capacities.
Solution Approach 2:
The patent applies preliminary action by performing energy normalization and signal conditioning before symbol detection. By pre-processing the signal to establish stable energy levels and remove dynamic variations, the system creates optimized conditions for subsequent symbol detection, reducing the impact of noise susceptibility inherent in high-capacity modulation schemes.
3Productivity
If non-stationary spreading sequences are combined with amplitude modulation, then data capacity and security are improved, but symbol energy variations cause degraded reception
Solution Approach 1:
The patent introduces an intermediary normalization process between the chaotic spreading and amplitude modulation operations. This intermediary step of energy normalization acts as a mediator that decouples the non-stationary characteristics from the symbol detection process, allowing high-capacity modulation to benefit from both security and reliable reception by removing the harmful effects of dynamic energy variations.
Data Source
AI summary
A communications system includes RF hardware (104) configured for receiving an input data signal includes a modulated carrier encoded with information symbols and modulated using a sequence of discrete time chaotic samples. The system also includes a chaotic sequence generator (340) configured for generating the sequence of discrete-time chaotic samples and a correlator (328). The correlator is configured for synchronizing the input data signal with the sequence of discrete-time chaotic samples and obtaining normalization factor values for each of the information symbols based on comparing a received symbol energy for the information symbols and a symbol energy of the discrete-time chaotic samples associated with the duration of transmission of the information symbols.