Adaptive Wireless Waveform Generation for Per-Packet Channel Feedback

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Solution Overview

Problem

Existing wireless communications networks struggle to efficiently support a diverse range of devices with varying data traffic profiles and requirements, such as reduced complexity devices, high-definition video streaming, and autonomous vehicle communications, due to limitations in link adaptation and channel condition awareness.

Innovation Solution

A method involving data partitioning, channel information feedback, and dynamic waveform generation using machine learning-based neural networks to adapt communications parameters for optimal transmission based on real-time channel conditions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If a single waveform generation approach is used for all devices, then device complexity is reduced, but network efficiency and adaptability to different traffic profiles deteriorate

Engineering Contradiction:

Improvewaveform generation complexityVSAvoidadaptability to different traffic profiles

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The system dynamically selects waveform generation approaches based on device type and traffic profile characteristics. Different devices receive different waveform generation treatments - some use neural network-based dynamic waveform generation while others use conventional methods, allowing the system to adapt to varying requirements without increasing overall complexity unnecessarily

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

Different parts of the network (different device types and traffic profiles) receive different waveform generation qualities. High-performance devices with specific traffic profiles receive sophisticated neural network-generated waveforms, while other devices use standard waveforms, optimizing resources locally rather than uniformly across the entire network

Inventive Principle:

Principle #3Local quality

2Reliability

If dynamic waveform generation using neural networks is implemented, then network efficiency and reliability are improved, but device complexity and processing requirements increase

Engineering Contradiction:

Improvetransmission reliabilityVSAvoidwaveform generation system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The network is segmented into different device categories and traffic profile groups. Neural network-based waveform generation is applied selectively to specific segments (device types and traffic profiles) that benefit most from it, rather than implementing it universally. This reduces overall system complexity while maintaining reliability where needed

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The neural network acts as an intermediary between channel conditions and waveform generation. It processes channel state information and translates it into optimized waveform parameters, bridging the gap between raw channel data and actionable transmission parameters while managing complexity through the intermediary processing layer

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If channel information feedback is collected for each data portion, then transmission accuracy is improved, but communication overhead and processing time increase

Engineering Contradiction:

Improvechannel condition awarenessVSAvoidcommunication overhead time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system collects channel information feedback at selective intervals and for selective data portions rather than continuously for all data. This partial action approach provides sufficient channel awareness to generate accurate waveforms while avoiding the time loss and overhead associated with continuous full-feedback collection

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

Channel information feedback is collected and processed in advance before waveform generation occurs. By having channel conditions assessed beforehand, the system can prepare optimized waveforms proactively, reducing the time penalty associated with real-time feedback processing and enabling more efficient transmission

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20260095357A1Communications devices, infrastructure equipment and methods

Publication Date: 2026.04.02 SONY GROUP CORP

AI summary

A method of transmitting data by a transmitter to a receiver in a wireless communications network. The method comprises receiving data for transmission to the receiver via a communications channel, dividing the data into portions for transmission, receiving, for each of the portions of data, an indication of channel information from the receiver for use by the transmitter in determining values for one or more communications parameters with which the portion of data should be transmitted, determining the values for the one or more communications parameters based on the received channel information, dynamically generating, for each of the portions of data, a waveform representative of the portion of data, and transmitting each of the portions of data, using the generated waveform representations and in accordance with the values of the one or more communications parameters, to the receiver.