Neural Network Detection for Bandwidth-Constrained ISI Channels
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Solution Overview
Problem
Bandwidth-constrained communication systems face limitations in achieving high spectral efficiency and reliability due to inter-symbol interference (ISI) and channel impairments like fading, which existing technologies struggle to overcome effectively without increasing complexity and power consumption.
Innovation Solution
The implementation of a bandwidth-constrained equalized transport (BCET) system that introduces memory into signals through pulse-shaping filters and utilizes neural networks for detection, combining error control coding with frequency domain processing to mitigate ISI and enhance spectral efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional modulation formats without memory are used, then system implementation is simpler, but spectral efficiency cannot approach the Shannon limit
Solution Approach 1:
The patent changes the fundamental parameter of the modulation format by introducing memory through pulse-shaping filters, transitioning from memoryless modulation to modulation with memory. This allows the system to approach Shannon capacity while maintaining practical implementability through structured pulse-shaping designs.
Solution Approach 2:
The patent adds the dimension of time-memory to the traditional modulation scheme by using pulse-shaping filters that create inter-symbol interference in a controlled manner. This temporal dimension allows the system to achieve higher spectral efficiency without proportionally increasing implementation complexity.
2Reliability
If error control coding with iterative decoding is used, then reliability improves, but system complexity and power consumption increase
Solution Approach 1:
The patent merges the pulse-shaping filter design with the error control coding scheme, creating a joint optimization approach. The pulse-shaping filter is specifically designed to work conjunction with the error control code, allowing the receiver to exploit the structured interference pattern and reduce decoding complexity while maintaining reliability.
Solution Approach 2:
The patent applies preliminary pulse-shaping filtering at the transmitter that intentionally creates a known interference pattern. This preliminary action structures the inter-symbol interference in a predictable way that facilitates simpler iterative decoding at the receiver, as the decoder can anticipate and compensate for the structured interference.
3Productivity
If bandwidth constraint is imposed to increase spectral efficiency, then information rate improves, but inter-symbol interference increases
Solution Approach 1:
The patent converts the harmful inter-symbol interference into a beneficial structured pattern through careful pulse-shaping filter design. By intentionally creating controlled ISI that follows a predictable structure, the system can exploit this interference pattern to achieve higher spectral efficiency while the receiver can efficiently compensate for it using the known structure.
Solution Approach 2:
The patent changes the bandwidth parameter by imposing a strict bandwidth constraint and compensating through pulse-shaping. Instead of allowing unlimited bandwidth expansion to avoid ISI, the system maintains a constrained bandwidth and uses tailored pulse-shaping filters to manage the resulting ISI, thereby achieving high spectral efficiency within the bandwidth limit.
4Productivity
If sophisticated encoding schemes are used to approach Shannon limit, then spectral efficiency improves, but system complexity increases
Solution Approach 1:
The patent changes the modulation format parameter to include memory through pulse-shaping, which fundamentally alters the system's ability to approach Shannon capacity. This parameter change enables simpler encoding schemes to achieve near-capacity performance, as the structured pulse-shaping provides implicit error protection and facilitates more efficient decoding.
Data Source
AI summary
The technology relates to bandwidth constrained communication systems with neural network based detection. In some embodiments, a bandwidth constrained equalized transport (BCET) communication system comprises: a transmitter comprising an error control code encoder, a pulse-shaping filter, and a first interleaver; a communication channel; and a receiver comprising a neural network processing block that processes a received signal. The error control code encoder can append redundant information onto the signal. The pulse-shaping filter can intentionally introduce memory into the signal in the form of inter-symbol interference. The first interleaver can change a temporal order of the symbols in the signal. The neural network can be trained with positive mappings between transmitted and decoded training signals, or negative mappings between training signals and erroneous decoded signals that are known to contain errors.