Sphere Decoder Radius Optimization via Linear Bound Parameter
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing decoding techniques in digital communication systems face high computational complexity due to the selection of the initial sphere radius in tree-search based decoders, which affects the number of lattice points included in the search space and subsequently the decoding complexity.
Innovation Solution
A method that calculates a bound parameter from a linear function of the initial estimate of the transmitted symbol vector, using a slope coefficient and an intercept coefficient, to optimize the search of candidate lattice points within a sphere, thereby reducing the number of search operations and computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the initial sphere radius is increased to ensure inclusion of lattice points, then the reliability of decoding is improved, but the device complexity increases due to more search operations
Solution Approach 1:
The patent dynamically adjusts the sphere radius parameter based on the channel matrix condition number and noise variance. By changing the radius parameter adaptively rather than using a fixed large radius, the decoder ensures sufficient lattice points are included for reliable decoding while minimizing the search space to reduce complexity.
Solution Approach 2:
The sphere radius is made dynamic by adjusting it according to channel conditions (condition number and noise variance). This dynamic adjustment allows the search space to adapt to actual channel quality, ensuring reliability when needed while reducing complexity when channel conditions permit smaller search spaces.
2Device complexity
If the initial sphere radius is decreased to reduce search operations, then the device complexity is reduced, but the reliability deteriorates due to exclusion of valid lattice points
Solution Approach 1:
The patent uses parameter changes by adjusting the sphere radius based on channel condition number and noise variance. This ensures the radius is large enough to include valid lattice points for reliable decoding while not being excessively large, thus balancing reliability and complexity.
Solution Approach 2:
The sphere radius determination incorporates feedback from channel measurements (condition number estimation and noise variance estimation). This feedback mechanism ensures the radius is appropriately sized based on actual channel conditions, preventing both excessive search space and exclusion of valid points.
3Measurement precision
If exhaustive ML decoding is performed to achieve optimal performance, then the measurement precision is improved, but the loss of time increases due to high computational complexity
Solution Approach 1:
The patent performs partial exhaustive search by limiting the sphere radius to a dynamically determined value. This partial action approach maintains optimal ML decoding accuracy for points within the bounded sphere while avoiding the excessive time consumption of searching the entire constellation space.
Solution Approach 2:
By changing the effective search space parameter (sphere radius) based on channel conditions, the patent achieves near-optimal decoding accuracy with significantly reduced computational time compared to full exhaustive ML decoding over the entire constellation.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method, device, and a computer program are provided to decode a signal received through a transmission channel in a communication system, the received signal being represented by a signal vector. The method comprises : - calculating an initial estimate of a transmitted symbol vector carried by the received signal vector; - calculating a bound parameter (201) from a linear function of the initial estimate of the transmitted symbol vector, the linear function being defined by a slope coefficient and an intercept coefficient, the method further comprising : - determining estimated symbols representative of the transmitted symbols carried by the received signal, the estimated symbols being determined from a set of candidate lattice points, each lattice point comprising a set of components, each component of a candidate lattice point being searched in a search interval (203), the search interval having at least one search interval bound computed from the bound parameter; - determining (205) the estimated symbols from the closest lattice point to the received signal vector among the candidate lattice points.