Soft-Decision Interference Cancellation With SINR Feedback Weighting
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Solution Overview
Problem
In wireless communication systems, multipath environments cause co-channel and cross-channel interference, leading to degraded communication quality, increased error rates, reduced capacity, and decreased coverage, as receivers struggle to differentiate between intended and interfering signals.
Innovation Solution
The development of a method to form a composite interference signal for interference cancellation, which involves characterizing the signal-to-interference-and-noise ratio (SINR) and using adaptive weights to construct an interference-cancelled signal, employing modules like SINR measurement and threshold comparison to synthesize a composite interference vector for cancelling multipath components in received signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional interference cancellation methods are used in multipath environments, then interference reduction is achieved, but signal differentiation capability deteriorates and error rates increase
Solution Approach 1:
The received signal is segmented into multiple path components through multipath resolution. The interference cancellation process is divided into separate stages: initial signal acquisition, iterative interference cancellation, and composite signal formation. This segmentation allows the system to handle different interference components independently, improving signal differentiation while reducing overall interference.
Solution Approach 2:
The system performs preliminary signal acquisition and path identification before main interference cancellation. By pre-processing the received signal to identify multipath components and their characteristics, the system prepares accurate reference signals for subsequent interference subtraction, enhancing both interference reduction and signal differentiation capability.
2Reliability
If interference cancellation processing is applied, then communication quality improves, but system complexity increases
Solution Approach 1:
The interference cancellation system employs dynamic iterative processing where the number of cancellation iterations adapts based on signal conditions. The composite signal formation dynamically weights different path components based on their reliability. This dynamic approach achieves high communication quality while avoiding excessive complexity by adjusting processing depth to actual needs.
Solution Approach 2:
The system implements feedback mechanisms where cancellation results are fed back to refine subsequent cancellation operations. The composite signal formation uses feedback from SINR measurements to adjust weighting of different paths. This feedback loop achieves superior communication quality through iterative refinement without requiring proportionally increased system complexity.
3Object-affected harmful factors
If soft-decision estimates are used for interference cancellation, then interference reduction effectiveness improves, but error propagation risk increases
Solution Approach 1:
The composite signal formation applies local quality weighting to different path components based on their individual reliability. Paths with higher SINR or better estimation accuracy receive higher weights, while unreliable paths are down-weighted. This local quality assessment allows effective use of soft-decision estimates for interference cancellation while mitigating error propagation from unreliable estimates.
Solution Approach 2:
The system changes the weighting parameter of soft-decision estimates based on measured signal quality metrics. When SINR is high, full soft-decision estimates are used for maximum interference reduction. When SINR is low, the weighting is reduced to prevent error propagation. This parameter adaptation optimizes the trade-off between interference reduction effectiveness and error propagation risk.
Data Source
AI summary
A receiver produces optimal weights for cancelling multipath interference. An SINR measurement module generates SINR measurements corresponding to soft symbol estimates produced by a baseband receiver from a received multipath signal. Each soft symbol estimate is replaced with either a hard estimate or a weighted soft estimate based on how each corresponding SINR measurement compares to a predetermined threshold. The received multipath signal and estimated interference signals generated from the hard symbol estimates and/or the weighted soft symbol estimates are combined to produce interference cancelled signals that may be combined via maximum ratio combining to produce an interference-cancelled MRC signal.