OFDM Communications Processing with Joint Machine Learning Equalization
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Solution Overview
Problem
Conventional communications systems face challenges in achieving low bit error rates and high computational complexity in processing digital communications signals, particularly in systems with large numbers of antennas, due to the use of separate stages for tasks like pilot estimation, interpolation, and equalization.
Innovation Solution
Implementing a machine-learning network that jointly performs tasks such as pilot estimation, interpolation, and equalization, trained through optimization techniques, to consolidate processing functions and reduce complexity while improving performance metrics like bit error rate and error vector magnitude.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate stages are used for pilot estimation, interpolation, and equalization, then processing functions can be implemented with conventional methods, but computational complexity increases and bit error rate performance deteriorates
Solution Approach 1:
The patent merges pilot estimation, interpolation, and equalization into a single joint machine learning model. This consolidation eliminates the need for separate processing stages, reducing computational complexity while improving bit error rate performance through unified optimization of all functions simultaneously.
Solution Approach 2:
The machine learning model serves multiple functions simultaneously: it performs pilot estimation, interpolation, and equalization. This multi-functional approach replaces several specialized conventional algorithms with a single universal model that handles all signal processing tasks.
2Measurement precision
If multiple processing stages are implemented for signal processing, then processing accuracy can be maintained, but system complexity and processing time increase
Solution Approach 1:
By combining multiple processing stages into a single machine learning inference operation, the patent reduces processing time while maintaining accuracy. The unified model performs all necessary transformations in one computational pass rather than through multiple sequential stages.
3Reliability
If conventional baseline methods are used for signal processing, then implementation is straightforward, but performance metrics like bit error rate and error vector magnitude are inferior
Solution Approach 1:
The patent transforms the signal processing approach by changing from fixed conventional algorithms to a machine learning model with learnable parameters. The model is trained to optimize performance metrics like error vector magnitude, achieving superior results through data-driven parameter adaptation rather than fixed computational rules.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for processing communications signals using a machine-learning network are disclosed. In some implementations, pilot and data information are generated for a data signal. The data signal is generated using a modulator for orthogonal frequency-division multiplexing (OFDM) systems. The data signal is transmitted through a communications channel to obtain modified pilot and data information. The modified pilot and data information are processed using a machine-learning network. A prediction corresponding to the data signal transmitted through the communications channel is obtained from the machine-learning network. The prediction is compared to a set of ground truths and updates, based on a corresponding error term, are applied to the machine-learning network.