Zhixiang Deng
ABSTRACT
In this letter, a transmit power control architecture based on the recurrent neural network (RNN) is proposed to solve the power allocation problem for the interference channel to maximize the weighted sum-rate. Compared with the conventional power control schemes, such as weighted minimization mean squared error (WMMSE), which require a considerable number of computations, the RNN-based scheme not only requires much lower computational complexity, but also can well learn and characterize inter-user relationship in the interference channel. The simulation results show that the proposed scheme achieves the sum-rate close to the WMMSE-based scheme with much lower complexity and obtains higher sum-rate than the deep neural network (DNN)-based scheme with much less model parameters.
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Index Terms
- Application of deep learning for power control in the interference channel: a RNN-based approach
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