Tree-Structured Spreading Codes for Low-Complexity Multi-Stream Decoding
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Solution Overview
Problem
Existing wireless communication technologies face challenges in efficiently decoding multiple data streams due to the high computational complexity required for decoding dense pseudo-random spreading codes, particularly in scenarios involving asynchronous and non-orthogonal access to resources.
Innovation Solution
The use of sparse spreading codes with an acyclic tree graph structure reduces computational complexity by allocating data streams across resources in a manner that allows for efficient decoding using a message passing algorithm, minimizing the need for excessive computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dense pseudo-random spreading codes are used for wireless communication, then communication capacity and resource utilization are improved, but decoding computational complexity increases significantly
Solution Approach 1:
The patent segments the dense pseudo-random spreading code into a hierarchical tree structure with sparse connections. Each node in the tree represents a subset of the original code, and only specific parent-child relationships are maintained. This segmentation transforms the dense code into a sparse tree-based code that preserves communication capacity while reducing decoding complexity through the tree's inherent sparsity and hierarchical organization.
2Reliability
If traditional decoding methods are used for multiple data streams, then all data streams can be decoded, but the computational resources required become excessive
Solution Approach 1:
The patent implements dynamic decoding by allowing the receiver to adaptively select which data streams to decode based on current channel conditions, buffer states, and computational resource availability. The tree structure enables flexible navigation where the decoder can traverse only the necessary paths to retrieve required data streams, dynamically adjusting the decoding process to balance completeness with computational efficiency.
3Adaptability or versatility
If asynchronous and non-orthogonal access to resources is enabled, then network flexibility and user access are improved, but signal interference and decoding difficulty increase
Solution Approach 1:
The patent introduces the tree-structured spreading code as an intermediary between multiple users and the shared communication medium. Each user's data is encoded through a unique path in the tree, creating a structured superposition of signals. The receiver uses the known tree structure as a mediator to separate and decode individual user signals from the combined transmission, managing interference through the hierarchical organization rather than requiring orthogonal separation.
Data Source
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AI summary
Various additional and alternative aspects are described herein. In some aspects, the present disclosure provides a method of communication by an apparatus. The method includes selecting one or more resources for transmitting data of a first data stream based on an acyclic graph. The selected resources conform to the acyclic graph comprising data streams at odd levels of the acyclic graph and resources at even levels of the acyclic graph. The acyclic graph includes edges between each level of the acyclic graph. The edges connect the resources allocated to each data stream. The method further includes transmitting the data of the first data stream on the selected one or more resources.