Adaptive Pseudo-Noise Sequences for Relay Network Signal Reliability
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Solution Overview
Problem
Wireless communication systems face challenges with multipath fading, especially in relay networks, where existing techniques like OFDM and CDMA fail to maintain signal quality due to selective fading and frequency offsets, and conventional pseudo-noise sequences do not adequately secure signals against interference.
Innovation Solution
The method involves generating and using non-binary spreading pseudo-noise sequences based on channel state information (CSI) to modulate and demodulate signals in wireless communication networks, enhancing signal transmission reliability by dynamically adapting to channel conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If OFDM techniques are used to combat multipath fading, then signal reliability is improved, but the system completely fails under frequency-offset environments (Doppler frequency shifts)
Solution Approach 1:
The patent employs dynamic pseudo-noise sequences that adapt to channel conditions rather than using fixed OFDM structures. The spreading sequences are generated based on current channel state information, allowing the system to dynamically adjust to frequency offsets and multipath conditions, resolving the contradiction between reliability and adaptability
Solution Approach 2:
The system changes the parameters of the spreading sequences (non-binary properties, dynamic generation) to maintain performance under varying channel conditions. By modifying the sequence generation parameters based on channel state, the system achieves both reliability in multipath environments and tolerance to frequency offsets
2Measurement precision
If CDMA schemes with fixed orthogonal user sequences are used to combat multiple-access interference, then cross-correlation properties are improved, but orthogonality is destroyed by multi-path fading and multi-access interference
Solution Approach 1:
Instead of fixed orthogonal sequences, the patent uses dynamically generated pseudo-noise sequences that adapt to the channel conditions. This dynamic approach maintains the desired cross-correlation properties while being robust against the destruction of orthogonality caused by multi-path fading and multi-access interference
Solution Approach 2:
The patent converts the harmful effects of multi-path fading and interference into beneficial information by using channel state information to generate the spreading sequences. The channel conditions that normally destroy orthogonality are instead used to create optimized sequences that perform well under those specific conditions
3Reliability
If maximum eigenvalue principle is used to obtain pseudo-noise sequences, then signal-to-interference-plus-noise ratio is improved, but the approach does not converge for relay systems
Solution Approach 1:
The patent performs preliminary channel estimation and uses this information to generate the spreading sequences before transmission. By preparing the sequences in advance based on channel state information, the system achieves high SINR without requiring iterative convergence during operation, eliminating the convergence problem in relay systems
Data Source
AI summary
Fully connected uplink and downlink fully connected relay network systems using pseudo-noise spreading and despreading sequences subjected to maximizing the signal-to-interference-plus-noise ratio. The relay network systems comprise one or more transmitting units, relays, and receiving units connected via a communication network. The transmitting units, relays, and receiving units each may include a computer for performing the methods and steps described herein and transceivers for transmitting and/or receiving signals. The computer encodes and/or decodes communication signals via optimum adaptive PN sequences found by employing Cholesky decompositions and singular value decompositions (SVD). The PN sequences employ channel state information (CSI) to more effectively and more securely computing the optimal sequences.