Adaptive Acquisition for Compressed Sensing via Reinforcement Learning
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Solution Overview
Problem
Conventional compressed sensing methods face limitations, including reliance on discrete measurement spaces, locally optimal solutions, and fixed reconstruction approaches that fail to account for domain shifts, leading to inefficiencies and inaccuracies in signal reconstruction.
Innovation Solution
The proposed solution employs reinforcement learning-based compressed sensing using adaptive sensing operations, enabling end-to-end learning of both reconstruction and acquisition strategies, and incorporating probabilistic formulations to improve measurement selection and signal reconstruction in both continuous and discrete spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional compressed sensing methods are used, then signal reconstruction can be achieved, but the number of observations required is high and computational expense is large
Solution Approach 1:
The patent applies dynamics by replacing fixed, conventional sensing matrices with adaptive sensing matrices that are dynamically generated through reinforcement learning. The sensing matrix is no longer static but evolves based on the signal characteristics and reconstruction requirements, allowing the system to optimize the number of observations needed while reducing computational burden through learned patterns.
Solution Approach 2:
The patent changes the parameters of the sensing matrix from fixed conventional values to adaptive parameters generated by neural networks. By transforming the sensing matrix elements into learnable parameters that can be optimized through reinforcement learning, the system achieves more efficient signal reconstruction with fewer observations and reduced computational expense.
2Device complexity
If fixed reconstruction approaches are used, then the method is simple, but it cannot account for domain shift and results in inaccurate reconstruction
Solution Approach 1:
The patent applies dynamics by replacing fixed reconstruction approaches with adaptive neural network-based reconstruction methods. The reconstruction model dynamically adjusts to different signal domains and characteristics through reinforcement learning, enabling accurate reconstruction across varying conditions without requiring complex manual intervention.
Solution Approach 2:
The patent changes the reconstruction parameters from fixed conventional methods to learnable parameters optimized through reinforcement learning. This allows the reconstruction model to adapt to domain shifts and different signal characteristics, significantly improving accuracy while maintaining manageable complexity through automated learning.
3Ease of operation
If discrete measurement spaces are used, then the measurement process is simplified, but it cannot handle continuous measurements like angles
Solution Approach 1:
The patent changes the measurement space from discrete to continuous by using neural networks that can process continuous values. The reinforcement learning framework operates in continuous parameter spaces, allowing the system to handle continuous measurements such as angles while maintaining operational simplicity through automated adaptive sensing.
Solution Approach 2:
The patent substitutes conventional discrete measurement mechanisms with neural network-based continuous measurement approaches. By replacing mechanical/discrete measurement processes with learned continuous transformations, the system can handle continuous measurements like angles while maintaining ease of operation through automated processing.
4Device complexity
If greedy policy gradients are used, then the optimization process is simple, but it results in locally optimal solutions rather than globally optimal solutions
Solution Approach 1:
The patent applies feedback by implementing reinforcement learning with reward signals that provide continuous feedback on reconstruction quality. This feedback mechanism guides the optimization process toward globally optimal solutions by rewarding actions that lead to better reconstructions, overcoming the limitations of greedy approaches that lack such feedback.
Solution Approach 2:
The patent changes the optimization parameters from simple greedy gradients to reinforcement learning-based policies that consider long-term rewards. This transformation allows the system to escape local optima by exploring the solution space more effectively, guided by reward signals that encourage globally optimal reconstructions.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for reinforcement-learning-based compressed sensing. An observed signal tensor comprising a plurality of elements is accessed, and a subset of elements of a sensing matrix is generated based on processing, from among the plurality of elements, a subset of elements of the observed signal tensor using an acquisition neural network. A subset of elements of a reconstructed signal tensor is generated based on processing a second subset of elements of the observed signal tensor and the subset of elements of the sensing matrix using a reconstruction neural network. At least the first subset of elements of the reconstructed signal tensor is output.