Iterative Interference Suppression for Wireless Multiple-Access Systems
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Solution Overview
Problem
In wireless multiple-access systems with multiple receive antennas, intra-channel and inter-channel interference degrades communication quality, reducing system capacity and coverage, and increasing error rates due to multipath effects and non-orthogonal code waveforms.
Innovation Solution
An iterative interference-suppression system using multiple receive antennas, which includes front-end processing for generating initial symbol estimates and iterative interference-suppression units that apply soft-weighting, subtraction, and stabilizing step-sizes to mitigate interference, employing RAKE receivers and combiners to combine and resolve signals across antennas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If multiple receive antennas are used to process signals, then interference reduction capability is improved, but device complexity increases
Solution Approach 1:
The interference suppression process is divided into multiple iterations, where each iteration processes a portion of the interference. The receiver segments the total interference into intra-channel and inter-channel components, and further divides each into multiple suppression stages, allowing complex interference cancellation to be achieved through simpler repeated operations
Solution Approach 2:
The system performs preliminary interference suppression before final symbol detection. Initial symbol estimates are generated first, then used to construct interference signals that are subtracted from received signals in advance of final detection, improving subsequent detection accuracy without requiring complete interference cancellation upfront
Solution Approach 3:
The interference suppression weights are dynamically adjusted across iterations based on estimated interference levels and signal characteristics. The system adapts the suppression strength and methodology in each iteration based on current channel conditions and interference measurements, rather than using fixed suppression parameters
2Measurement precision
If iterative interference suppression is applied, then symbol estimation accuracy is improved, but processing time increases
Solution Approach 1:
The interference suppression operates in periodic iterations rather than continuously. Each iteration performs a complete cycle of symbol estimation, interference reconstruction, and signal subtraction. The system can terminate after a predetermined number of iterations or when convergence criteria are met, balancing accuracy improvement against processing time
Solution Approach 2:
The system performs partial interference suppression by limiting the number of iterations or the degree of suppression applied in each iteration. Rather than attempting complete interference cancellation, the system achieves sufficient suppression to meet performance requirements while avoiding excessive processing time associated with perfect cancellation
3Object-affected harmful factors
If soft-weighting and subtractive suppression are used, then interference cancellation effectiveness is improved, but computational complexity increases
Solution Approach 1:
The system changes the weighting parameters dynamically based on signal conditions. Soft weights are assigned to different symbol estimates and interference components based on their reliability and correlation with the received signal. These weights are adjusted across iterations to optimize the balance between interference cancellation and noise introduction
Solution Approach 2:
The system creates copies of estimated symbols and reconstructed interference signals to facilitate the subtraction process. Rather than directly manipulating the original received signal, the system generates multiple copies for different processing paths (weighting, interference reconstruction, subtraction), allowing complex operations to be performed on copies while preserving the original signal
4Reliability
If multiple iterations of interference suppression are performed, then error rate is reduced, but system throughput decreases
Solution Approach 1:
The iterative process is structured with periodic termination criteria that allow the system to stop after achieving sufficient error rate improvement. The iterations are performed periodically rather than continuously, with the ability to terminate early when performance targets are met or when diminishing returns are detected, maintaining throughput while achieving reliability goals
Data Source
AI summary
This invention teaches to the details of an interference suppressing receiver for suppressing intra-cell and inter-cell interference in coded, multiple-access, spread spectrum transmissions that propagate through frequency selective communication channels to a multiplicity of receive antennas. The receiver is designed or adapted through the repeated use of symbol-estimate weighting, subtractive suppression with a stabilizing step-size, and mixed-decision symbol estimates. Receiver embodiments may be designed, adapted, and implemented explicitly in software or 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 or subchannel symbol level). Embodiments may be employed in user equipment on the forward link or in a base station on the reverse link. It may be adapted to general signal processing applications where a signal is to be extracted from interference.