Spread-Spectrum Coding Reducing Receiver Processing Complexity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current spread-spectrum coding techniques require complex processing at the receiver, leading to high latency, which is computationally intensive and inefficient.
Innovation Solution
The method involves partitioning a data stream into sub-blocks and spreading each sub-block using a matrix with M orthogonal rows, repeating each sub-block M times, and applying each row of the matrix to generate a spread data stream, which is then transmitted, facilitating simpler processing at the receiver.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If spread-spectrum coding is applied at the transmitter to minimize interference, then interference to other transmissions is reduced, but processing complexity and latency at the receiver become prohibitively high
Solution Approach 1:
The data stream is divided into sub-blocks of K data symbols each, and spreading is applied to each sub-block separately using different rows of the orthogonal matrix. This segmentation allows the receiver to process sub-blocks independently, reducing overall processing complexity while maintaining the interference-minimizing benefits of spread-spectrum coding.
Solution Approach 2:
The orthogonal matrix is pre-generated at the transmitter with M orthogonal rows, and the spreading operation is performed beforehand before transmission. This preliminary spreading action embeds the interference-minimizing properties into the transmitted signal structure, allowing the receiver to simply correlate and combine signals without performing complex real-time spread-spectrum processing.
2Object-generated harmful factors
If traditional spread-spectrum coding is used, then signal bandwidth is expanded to reduce power density, but receiver latency becomes prohibitively high
Solution Approach 1:
By segmenting the data into sub-blocks and applying spreading to each sub-block independently with different orthogonal matrix rows, the receiver can process and decode sub-blocks in parallel or sequentially without waiting for complete signal processing, thereby reducing latency while maintaining the low power density benefit of spread-spectrum.
Solution Approach 2:
The orthogonal matrix rows are applied in a periodic or systematic manner to different sub-blocks, creating a structured transmission pattern that allows the receiver to anticipate and efficiently process incoming signals at regular intervals, reducing processing latency.
3Reliability
If data is spread using orthogonal matrix rows, then resistance to multipath fading is improved, but computational complexity at the receiver increases
Solution Approach 1:
The use of orthogonal matrix rows for spreading different sub-blocks creates diverse signal paths that are inherently resistant to multipath fading. The segmentation approach allows the receiver to combine these orthogonal sub-blocks using simple correlation operations rather than complex computational algorithms, maintaining reliability while reducing complexity.
Solution Approach 2:
The orthogonal matrix provides a set of predetermined spreading codes with specific correlation properties that enhance resistance to multipath fading. By changing the spreading parameter (using different orthogonal rows for different sub-blocks), the system achieves diversity gain against fading while the receiver only needs to perform straightforward correlation and combining operations.
Data Source
AI summary
Certain aspects of the present disclosure relate to a method for generating spread-spectrum coded signals for transmission in a wireless communication system, and particularly for generating spread sequences of data with spreading codes that facilitate computationally efficient frequency-domain processing at a receiver.