SIC Receiver Decoding Order Using Reinforcement Learning
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Solution Overview
Problem
In high-speed communication networks like LTE or 5G, accurately decoding codewords in MIMO systems is challenging due to interference, and existing methods struggle to efficiently determine the optimal decoding order for codewords across multiple channels.
Innovation Solution
A reinforcement learning-based method is employed to determine the decoding order for codewords in a SIC receiver, using a decoding order determination circuit that learns from state metrics and rewards to prioritize codeword decoding, updating policies and state information iteratively to improve decoding accuracy and remove interfering channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reinforcement learning is used to determine decoding order, then decoding accuracy is improved, but device complexity increases
Solution Approach 1:
A neural network model is introduced as an intermediary component between the received signal and the decoding process. The neural network processes channel state information and signal metrics to predict optimal decoding orders, acting as a mediator that translates complex input data into actionable decoding sequences without requiring manual algorithm design
Solution Approach 2:
The system performs preliminary training of the neural network model using labeled data containing channel conditions and corresponding optimal decoding orders. This pre-training phase prepares the model in advance to make accurate predictions during actual operation, eliminating the need for real-time complex calculations and reducing operational device complexity
2Measurement precision
If iterative decoding with policy updates is performed, then decoding accuracy is improved, but processing time increases
Solution Approach 1:
The system performs a limited number of iterative decoding attempts rather than exhaustive searching. After a predetermined number of iterations or when a confidence threshold is reached, the process terminates, providing sufficient decoding accuracy without the time cost of complete convergence or exhaustive search
Solution Approach 2:
The system implements feedback mechanisms where decoding results from previous iterations are used to update the neural network policy. Successful decodings provide positive feedback that reinforces effective decoding order patterns, while failures trigger policy adjustments, enabling the system to learn and improve over time with minimal iterations
Data Source
AI summary
Methods and apparatus for discovering codeword decoding order in a serial interference cancellation receiver using reinforcement learning. In an embodiment, a method is provided for decoding codewords in a multiple-input-multiple-output (MIMO) communication network. The method includes determining a decoding order based on a state space and a decoding policy, decoding selected codewords based on the decoding order, updating the decoding policy based on the decoding results and the state space, updating the state space based on decoding results, and updating the decoding order based on the state space and the decoding policy.