Adaptive Sub-band Filtering for 5G Self-Interference Cancellation
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Solution Overview
Problem
Conventional methods struggle to effectively cancel self-interference in 5G communications due to the high instantaneous bandwidth and rapidly changing channels, which limits the efficiency of digital cancelers and makes real-time interference mitigation challenging.
Innovation Solution
The proposed method involves sub-banding and downsampling error signals using an analysis filter for multiple sub-bands, updating adaptive filter coefficients based on these signals, and employing a variable step-size to converge to a steady-state mean-square error, allowing for efficient cancellation of self-interference in full-duplex communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a digital canceler using a finite impulse response (FIR) filter with several hundred coefficients is used to suppress self-interference, then the self-interference cancellation capability is improved, but the device complexity and computational load increase significantly
Solution Approach 1:
The patent applies segmentation by dividing the frequency spectrum into multiple sub-bands using an analysis filter bank. Instead of processing the entire wideband signal with a single complex filter, the system segments the signal into narrower frequency components, processes each sub-band independently with simpler filters, and then combines the results. This reduces the complexity of individual filter coefficients while maintaining overall cancellation effectiveness across the full bandwidth.
2Productivity
If the sample rate is increased to 1.0 gigasamples per second to handle 5G bandwidth requirements, then the data transfer speed is improved, but the computational complexity and hardware requirements increase significantly
Solution Approach 1:
The system segments the high-rate sampled signal into multiple sub-bands using an analysis filter bank, allowing parallel processing of frequency components. This enables the system to handle high sample rates by distributing the computational load across multiple simpler processing channels rather than requiring a single complex high-rate processor.
Solution Approach 2:
The patent transforms the time-domain high-rate signal processing problem into a frequency-domain solution by applying the analysis filter bank. This dimensional transformation allows the system to process high sample rate data by converting it into multiple lower-rate sub-band signals that can be handled more efficiently, effectively adding a frequency dimension to the processing approach.
3Adaptability or versatility
If the coherence time is reduced due to rapidly changing channels in 5G communications, then the adaptability to channel changes is improved, but the difficulty of implementing real-time cancellation increases
Solution Approach 1:
The patent implements dynamics by using an adaptive filter that can dynamically adjust its coefficients in real-time based on the changing channel conditions. The filter continuously updates its parameters to track the time-varying channel characteristics, enabling it to adapt to rapid channel changes while maintaining effective cancellation. This dynamic adjustment capability allows the system to respond to coherence time variations without requiring complex reconfiguration.
4Productivity
If the instantaneous bandwidth is increased to 400 MHz for 5G communications, then the data transfer capacity is improved, but the difficulty of tracking rapidly changing channels increases
Solution Approach 1:
The system segments the wide 400 MHz instantaneous bandwidth into multiple narrower sub-bands using the analysis filter bank. This segmentation allows the adaptive filter to track channel variations within each narrower sub-band more effectively, reducing the tracking difficulty while maintaining the overall high bandwidth capacity for data transfer.
Data Source
AI summary
Methods and systems for identifying a signal-of-interest in an over-the-air signal that includes a self-interfering signal. The over-the-air signal and the transmitted signal are sampled and passed to an adaptive filter. The adaptive filter processes a plurality of samples in parallel. The samples are subbanded by passing through an analysis filter, downsampled, and then used to update the adaptive filter coefficients. The updated filter coefficients may be updated based on a variable step-size that decreases as the system converges or a fixed step-size.