Training Sequence Codes With Controlled Cross-Correlation for GERAN QAM
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Solution Overview
Problem
Conventional training sequence codes (TSCs) in GERAN systems fail to effectively manage cross-correlation properties, leading to performance deterioration in cellular systems with high co-channel interference, especially when extended to high-order QAM schemes like 16-QAM and 32-QAM.
Innovation Solution
Generating TSCs with both autocorrelation and cross-correlation properties using Golay complementary sequences and quasi-complementary sequences, and employing a min-max optimization method to evaluate signal-to-noise ratio degradation, resulting in improved TSC candidates that optimize SNR and reduce interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional TSCs are used in GERAN systems, then the system operates with existing modulation schemes (GMSK, 8-PSK), but performance deteriorates in environments with high co-channel interference and cannot effectively support high-order QAM schemes
Solution Approach 1:
The patent changes the fundamental parameters of TSC generation by introducing sequences with controlled autocorrelation and cross-correlation properties. Specifically, it uses sequences where the sum of autocorrelation values equals zero and cross-correlation values are bounded, enabling the system to adapt to high-order QAM schemes while maintaining reliability in high interference environments through optimized correlation characteristics
2Reliability
If TSCs are designed to manage cross-correlation properties, then system performance improves in high co-channel interference environments, but the complexity of sequence generation and selection increases
Solution Approach 1:
The patent segments the TSC design process into distinct components: first generating base sequences with specific autocorrelation properties, then constructing extended sequences by combining these base sequences. This segmentation allows systematic control of cross-correlation properties while managing complexity through modular generation and pre-computation of sequence sets
Solution Approach 2:
The patent performs preliminary action by pre-generating and storing sets of sequences with verified autocorrelation and cross-correlation properties before actual communication operation. This pre-computation and verification process eliminates the need for real-time complex calculations, reducing operational complexity while ensuring reliable performance in high interference environments
Data Source
AI summary
A method and apparatus for generating training sequence codes in a communication system. In the method, a pair of sequences A and B having cross-correlation properties as well as autocorrelation properties are generated, and protection sequences A′ and B′ are generated by copying last L symbols of the sequences A and B, respectively. The training sequence codes are generated by locating the protection sequences A′ and B′ in the most significant positions (MSPs) of the sequences A and B. The training sequence codes can be extended and applied to 16-QAM and 32-QAM used in a GERAN system, and the use of such training sequence codes enables data to be efficiently transmitted/received without performance deterioration in a GERAN system.