Machine Learning : Proceedings of the 13 th International Conference , 1996 . On - Line Adaptation of a Signal Predistorterthrough Dual Reinforcement
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چکیده
Several researchers have demonstrated how neural networks can be trained to compensate for nonlinear signal distortion in e.g. digital satellite communications systems. These networks, however, require that both the original signal and its distorted version are known. Therefore, they have to be trained oo-line, and they cannot adapt to changing channel characteristics. In this paper, a novel dual reinforcement learning approach is proposed that can adapt on-line while the system is performing. Assuming that the channel characteristics are the same in both directions , two predistorters at each end of the communication channel co-adapt using the output of the other predistorter to determine their own reinforcement. Using the common Volterra Series model to simulate the channel, the system is shown to successfully learn to compensate for distortions up to 30%, which is signiicantly higher than what might be expected in an actual channel.
منابع مشابه
In Proceedings of ICML ' 96 : The 13 th International Conference on Machine Learning
Developing the ability to recognize a landmark from a visual image of a robot's current location is a fundamental problem in robotics. We describe a way in which the landmark matching problem can be mapped to that of learning a one-dimensional geometric pattern. We present an eecient noise-tolerant algorithm (designed using the statistical query model) to PAC-learn the class of one-dimensional ...
متن کاملOn-Line Adaptation of a Signal Predistorter through Dual Reinforcement Learning
Several researchers have demonstrated how neural networks can be trained to compensate for nonlinear signal distortion in e.g. digital satellite communications systems. These networks, however, require that both the original signal and its distorted version are known. Therefore, they have to be trained o -line, and they cannot adapt to changing channel characteristics. In this paper, a novel du...
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