Embedded DRL Framework for IoT Wireless Transceiver Adaptation
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Solution Overview
Problem
Existing IoT wireless protocols are inflexible and unable to self-optimize and adapt to unpredictable or adversarial spectrum conditions, posing a challenge for the next-generation IoT devices, which may lead to severe delays in IoT development.
Innovation Solution
A general-purpose, hybrid software/hardware deep reinforcement learning (DRL) framework tailored for embedded IoT wireless devices, incorporating a supervised DRL model selection and bootstrap process that leverages transfer learning and high-level synthesis circuit design to provide a neural network architecture that satisfies hardware and application throughput constraints, enabling real-time DRL execution with improved convergence and energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep reinforcement learning algorithms are implemented in resource-challenged embedded IoT devices, then real-time spectrum sensing and adaptive reaction capabilities are improved, but device complexity and resource consumption increase
Solution Approach 1:
The patent segments the DRL system into two distinct components: a training neural network (TNN) that operates remotely with full computational resources for algorithm training and optimization, and a lightweight operative neural network (ONN) that runs locally on the embedded IoT device for real-time inference. This segmentation allows the complex training operations to be separated from the resource-constrained embedded device, enabling real-time adaptation without overwhelming the device's computational resources.
Solution Approach 2:
The patent introduces an intermediary architecture where the TNN acts as a remote training server that generates and updates neural network models, which are then deployed to the embedded device's ONN. This intermediary approach allows the embedded device to benefit from sophisticated DRL algorithms without directly hosting the heavy training computations, effectively mediating between the computational demands of DRL and the limited resources of embedded IoT devices.
2Speed
If DRL algorithms run in real-time on embedded devices, then response speed to spectrum changes is improved, but energy consumption increases
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network algorithms remotely using the TNN before deployment to the embedded device. The ONN receives pre-computed weights and biases that have already been optimized through extensive training, allowing it to perform real-time inference with minimal computational effort and energy consumption. This preliminary training phase separates the energy-intensive learning process from the energy-constrained embedded operation.
Solution Approach 2:
The patent changes the operational parameters of the neural network by distinguishing between training mode (handled remotely by TNN) and inference mode (handled locally by ONN). The ONN operates with fixed parameters (weights and biases) received from the TNN, eliminating the need for local gradient computations and parameter updates during real-time operation. This parameter change from dynamic training to static inference dramatically reduces energy consumption while maintaining real-time responsiveness.
3Productivity
If hardware-implemented operative neural networks are used, then data processing throughput is improved, but hardware resource requirements increase
Solution Approach 1:
The patent extracts the heavy hardware implementation requirements from the embedded IoT device by relocating the training neural network to a remote server. The embedded device only needs to host a simplified operative neural network with fixed parameters, extracting the computationally intensive training functions from the resource-constrained environment. This allows the embedded device to achieve real-time processing with minimal hardware overhead.
Solution Approach 2:
The patent uses copying by deploying a simplified version of the trained neural network from the TNN to the ONN on the embedded device. The ONN copies the essential inference functionality with pre-computed parameters, allowing real-time processing without replicating the full training capabilities. This copying approach enables the embedded device to achieve high-speed inference with a fraction of the hardware resources required for full DRL training.
Data Source
AI summary
A networking device, such as an Internet of Things (IoT) device, implements an operative neural network (ONN) to optimize an internal wireless transceiver based on detected radio frequency (RF) spectrum conditions. The wireless transceiver detects the RF spectrum conditions local to the networking device and generates a representation of the RF spectrum conditions. The ONN determines transceiver parameters based on the RF spectrum conditions. A controller causes the representation of the RF spectrum conditions to be transmitted to a network node. Independent of the networking device, a training neural network (TNN) is trained based on the representation of the RF spectrum conditions, and neural network (NN) parameters are generated via the training a function of the representation of the RF spectrum conditions. The controller then reconfigures the ONN based on the NN parameters.