Iterative Interference Cancellation with Stabilizing Step Sizes
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Solution Overview
Problem
Current wireless communication systems face challenges in effectively canceling intra-channel and inter-channel interference in coded, multiple-access spread-spectrum transmissions, leading to degraded communication quality, reduced data rates, and increased error floors due to the complexity of existing interference cancellation methods.
Innovation Solution
A generalized interference-canceling receiver employing soft-weighting subtractive cancellation with stabilizing step-sizes and mixed-decision symbol estimation is implemented, which uses iterative algorithms to mitigate interference, stabilize processing, and adapt to various constellation sizes, thereby reducing complexity and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If iterative interference cancellation is implemented to reduce interference and improve communication quality, then communication quality and data rates are improved, but computational complexity increases
Solution Approach 1:
The interference cancellation process is divided into multiple iterations, where each iteration progressively reduces interference. The algorithm segments the cancellation task into manageable steps, with each iteration refining the estimate of interfering signals and subtracting them from the received signal, thereby reducing overall computational complexity while improving communication quality
Solution Approach 2:
The algorithm employs dynamic step sizes that adapt during iterations to stabilize convergence. The step size parameter is adjusted based on the current state of interference cancellation, allowing the system to converge efficiently without requiring excessive computational resources, thus balancing communication quality improvement with complexity management
2Reliability
If complex interference cancellation algorithms are used to achieve optimal performance, then interference suppression is improved, but convergence stability deteriorates
Solution Approach 1:
The algorithm incorporates feedback mechanisms where the output of each iteration is fed back into the next iteration. The estimated interference signals from previous iterations are used to refine subsequent cancellation steps, creating a closed-loop system that stabilizes convergence while maintaining effective interference suppression through continuous refinement
Solution Approach 2:
The algorithm changes parameters dynamically during iteration, specifically adjusting step sizes based on the convergence state. By modifying the step size parameter adaptively, the system maintains stability during convergence while still achieving optimal interference suppression performance, preventing oscillations and ensuring reliable convergence
3Device complexity
If fixed step sizes are used in iterative cancellation, then algorithm simplicity is maintained, but convergence performance and stability worsen
Solution Approach 1:
The algorithm transitions from fixed to dynamic step sizes, where the step size parameter changes adaptively during iterations. This dynamic adjustment allows the system to optimize convergence performance for different interference conditions and iteration stages, significantly improving convergence reliability while maintaining reasonable algorithm complexity through a systematic adaptation rule
Data Source
AI summary
A receiver is configured for canceling intra-cell and inter-cell interference in coded, multiple-access, spread-spectrum transmissions that propagate through frequency-selective communication channels. The receiver employs iterative symbol-estimate weighting, subtractive cancellation with a stabilizing step-size, and mixed-decision symbol estimate. Receiver embodiments may be implemented explicitly in software of programmed hardware, or implicitly in standard Rake-based hardware either within the Rake (i.e., at the finger level) or outside the Rake (i.e., at the user of subchannel symbol level).