Interference Cancellation Using Soft Scaling of Hard Decisions
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Solution Overview
Problem
In wireless communication systems, interference between signals complicates signal recovery due to imperfect separation caused by transmission synchronization errors and channel effects, leading to significant processing burdens for receivers, especially in low signal quality conditions.
Innovation Solution
The method employs hard decision logic for simplified estimation of interfering signals combined with soft scaling based on received signal quality to improve interference cancellation performance, particularly in low signal quality conditions, using pre-computed scaling factors stored in look-up tables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full-complexity demodulation and decoding is applied to interfering signals, then interference cancellation accuracy is improved, but receiver processing complexity increases significantly
Solution Approach 1:
The interference cancellation process is segmented into two distinct stages: (1) hard decision stage where interfering signal bits are detected as discrete 0s or 1s without full demodulation/decoding, and (2) soft scaling stage where these hard decisions are scaled by factors derived from signal quality metrics. This segmentation eliminates the need for full-complexity processing of interfering signals while maintaining effective cancellation through the combination of hard decisions and soft weighting.
Solution Approach 2:
Instead of applying full demodulation and decoding to interfering signals (excessive action), the invention applies only hard decision processing (partial action) followed by soft scaling. This partial processing approach achieves sufficient interference cancellation accuracy without the prohibitive computational burden of complete signal processing, effectively doing 'just enough' processing to solve the problem.
2Device complexity
If hard detection processing is used for interferer signal estimation, then receiver complexity is reduced, but interference cancellation performance deteriorates due to decreased estimation accuracy
Solution Approach 1:
The invention introduces soft scaling factors as an intermediary element between hard detected interfering signal bits and the final interference cancellation operation. These scaling factors, derived from signal quality metrics such as SINR or SNR, act as mediators that adjust the contribution of each hard decision based on its reliability. This intermediary mechanism allows the system to use simple hard decisions while compensating for their accuracy limitations through adaptive weighting, thereby maintaining cancellation performance without increasing complexity.
Solution Approach 2:
The invention changes the parameter of interference signal representation from soft values (high accuracy, high complexity) to hard decisions (low accuracy, low complexity), then compensates by introducing a new parameter - the soft scaling factor - that is derived from signal quality metrics. This parameter change transforms the fundamentally simple hard decisions into adaptively weighted contributions that maintain accuracy while preserving low complexity throughout the processing chain.
3Device complexity
If hard decisions are used for interferer signal bits, then processing complexity is reduced, but reliability of interference cancellation decreases in low signal quality conditions
Solution Approach 1:
The invention introduces dynamics into the interference cancellation process by making the scaling factors adaptive rather than static. The scaling factors are dynamically adjusted based on real-time signal quality measurements (SINR or SNR), allowing the system to automatically adapt its behavior to current channel conditions. In low signal quality conditions, the dynamics enable the system to reduce reliance on potentially unreliable hard decisions, thereby maintaining reliability without sacrificing the complexity benefits of hard decision processing.
Data Source
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AI summary
The teachings herein disclose interference cancellation processing that uses hard decision logic for simplified estimation of interfering signals, in combination with soft scaling of the hard decisions for better interference cancellation performance, particularly in low signal quality conditions. In one aspect, the soft scaling may be understood as attenuating the amount of interference cancellation applied by a receiver, in dependence on the dynamically changing received signal quality at the receiver. More attenuation is applied at lower signal quality because the hard decisions are less reliable at lower signal qualities, while less (or no) attenuation is applied at higher signal qualities, reflecting the higher reliability of the hard decisions at higher signal qualities. Signal quality may be quantized into ranges, with a different value of soft scaling factor used for each range, or a soft scaling factor may be calculated for the continuum of measured signal quality.