Sparse Code Multiple Access Encoding for Wireless Networks
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Solution Overview
Problem
Traditional CDMA encoding techniques face limitations in achieving high coding rates, particularly in next-generation wireless networks, as they require additional mechanisms to support the growing demands for data transmission.
Innovation Solution
The implementation of Sparse Code Multiple Access (SCMA) encoding, which directly maps binary data to multi-dimensional codewords, bypassing QAM symbol mapping, and uses distinct codebooks for each multiplexed layer to achieve multiple access, allowing for reduced baseband processing complexity through low complexity message passing algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional CDMA encoding with QAM symbol mapping is used, then the system can provide relatively high coding rates, but it cannot meet the ever-growing demands of next-generation wireless networks for higher coding rates
Solution Approach 1:
The patent transitions from traditional one-dimensional QAM symbol mapping to multi-dimensional sparse code mapping. By mapping binary data directly to multi-dimensional codewords (e.g., 4-dimensional, 8-dimensional), the system achieves higher coding rates and better spectral efficiency, directly addressing the limitation of traditional CDMA encoding
Solution Approach 2:
The patent changes the fundamental encoding parameter from QAM modulation order to sparse code dimensionality and sparsity pattern. By varying the codebook dimension and sparsity level, the system can adapt to different network demands and achieve higher coding rates while maintaining robust performance
2Adaptability or versatility
If distinct codebooks are assigned to each multiplexed layer for SCMA encoding, then multiple access capability is achieved, but baseband processing complexity increases
Solution Approach 1:
The patent divides the encoding process into separate codebooks for different multiplexed layers, with each codebook assigned to a specific layer. This segmentation enables multiple access capability while allowing independent optimization of each layer's codebook, managing the complexity through modular design
Solution Approach 2:
The patent uses predefined codebooks that are agreed upon by both transmitter and receiver. The receiver has copies of these codebooks and uses low complexity message passing algorithms (MPA) to identify and decode codewords by matching received signals against the known codebook structures, significantly reducing baseband processing complexity
3Reliability
If SCMA encoding with multi-dimensional codewords is implemented, then coding gains over conventional CDMA are achieved, but the system requires reduced receiver complexity through low complexity message passing algorithms
Solution Approach 1:
The patent replaces traditional complex receiver processing (such as sequential interference cancellation or maximum likelihood detection) with low complexity message passing algorithms (MPA). The MPA uses iterative belief propagation over a factor graph representation of the codebook structure, achieving near-optimal performance with much reduced computational complexity
Solution Approach 2:
The patent changes the detection approach from direct multi-dimensional signal processing to iterative probability message passing. By transforming the detection problem into a message passing framework with predefined factor graphs, the system achieves coding gains while maintaining reduced receiver complexity
Data Source
AI summary
Coding gains can be achieved by encoding binary data directly to multi-dimensional codewords, which circumvents QAM symbol mapping employed by conventional CDMA encoding techniques. Further, multiple access can be achieved by assigning different codebooks to different multiplexed layers. Moreover, sparse codewords can be used to reduce baseband processing complexity on the receiver-side of the network, as sparse codewords can be detected within multiplexed codewords in accordance with message passing algorithms (MPAs).