ML Demodulation for Energy-Detecting MIMO Receivers

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

MIMO communication systems face challenges in increasing data rate due to spatial interference and nonlinearities introduced by amplitude detection, requiring complex demodulation algorithms and precise channel estimation, especially in high-frequency bands like the millimeter spectrum.

Innovation Solution

A receiver using energy detection and a machine-learning algorithm, specifically artificial neural networks, for demodulation that considers nonlinearities and spatial interference without needing a precise channel estimate, combined with an algebraic error correction code for improved performance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If frequency multiplexing is performed to increase data rate, then transmission data rate increases by factor equal to number of frequency channels, but filter banks introduce losses and inter-channel interference

Engineering Contradiction:

Improvetransmission data rateVSAvoidsignal losses in filter banks

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The patent segments the frequency spectrum into multiple orthogonal frequency channels and processes them independently through filter banks, allowing parallel transmission while maintaining spectral efficiency. Each frequency channel is treated as a separate transmission path, enabling data rate multiplication without excessive interference between channels.

Inventive Principle:

Principle #1Segmentation

2Measurement precision

If coherent reception architecture is implemented for frequency multiplexing, then frequency selectivity is improved, but phase noise sensitivity increases and demodulation complexity increases

Engineering Contradiction:

Improvefrequency selectivityVSAvoiddemodulation algorithm complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent implements feedback mechanisms where channel state information is estimated and used to adjust equalization parameters dynamically. This feedback loop allows the system to compensate for phase noise and frequency offsets adaptively, reducing the complexity of the demodulation algorithm while maintaining frequency selectivity.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system dynamically changes key parameters including equalization filter coefficients, frequency offset correction values, and phase compensation parameters based on real-time channel conditions. This adaptive parameter adjustment simplifies the demodulation process by automatically tracking channel variations without requiring complex algorithms.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If spatial multiplexing using multiple antennas is implemented, then data rate increases by factor equal to number of transmit antennas, but spatial interference between signals increases

Engineering Contradiction:

Improvedata rateVSAvoidspatial interference

Core Design Contradiction:

ProductivityVSObject-generated harmful factors

Solution Approach 1:

The patent extends the signal processing from two-dimensional spatial domain to three-dimensional space by incorporating time-domain equalization alongside spatial processing. This additional temporal dimension allows the system to separate spatially multiplexed signals by exploiting both spatial and temporal characteristics, effectively reducing spatial interference while maintaining high data rates.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Solution Approach 2:

The patent introduces channel equalization as an intermediary processing stage between signal reception and demodulation. This equalization layer acts as a mediator that pre-processes the spatially multiplexed signals, separating interfering components before they reach the demodulator, thereby reducing the burden on subsequent processing stages.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Reliability

If amplitude detection is implemented at reception, then nonlinearities are introduced into transmission chain, but demodulation algorithm complexity increases due to need for precise channel estimate

Engineering Contradiction:

Improvesignal detection accuracyVSAvoiddemodulation algorithm complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent performs preliminary channel estimation and equalization before the final demodulation stage. By pre-processing the signals to compensate for nonlinearities introduced by amplitude detection, the system simplifies the subsequent demodulation algorithm, reducing its complexity while maintaining detection accuracy.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11463289B2Machine learning-based demodulation method for energy-detecting MIMO receivers

Publication Date: 2022.10.04 COMMISSARIAT A LENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES

AI summary

A method for receiving a plurality of separate signals transmitted respectively by a plurality of transmit antennas, includes the steps of: receiving a plurality of respective signals on a plurality of receive antennas, applying energy detection to each of the received signals, jointly demodulating the received signals by way of a machine-learning algorithm trained beforehand so as to learn to demodulate each modulated symbol of the transmitted signal based on the respective contributions of this modulated symbol that are received on the plurality of receive antennas.