Abstract
Deep learning (DL) based autoencoder (AE) has been proposed recently as a promising, and potentially disruptive approach to design the physical layer of beyond-5G communication systems. Compared to a traditional communication system with a multiple-block structure, the DL based AE approach provides a new paradigm to physical layer design with a pure data-driven and end-to-end learning based solution. In this article, we address the dynamic interference in a multi-user Gaussian interference channel. We show that standard constellation are not optimal for this context, in particular, for a high interference condition. We propose a novel adaptive DL based AE to overcome this problem. With our approach, dynamic interference can be learned and predicted, which updates the learning processing for the decoder. Compared to other machine learning approaches, our method does not rely on a fixed training function, but is adaptive and applicable to practical systems. In comparison with the conventional system using $n$ -psk or $n$ -QAM modulation schemes with zero force (ZF) and minimum mean square error (MMSE) equalizer, the proposed adaptive deep learning (ADL) based AE demonstrates a significant achievable BER in the presence of interference, especially in strong and very strong interference scenarios. The proposed approach has laid the foundation of enabling adaptable constellation for 5G and beyond communication systems, where dynamic and heterogeneous network conditions are envisaged.
Highlights
Artificial intelligence (AI) is becoming increasingly present in all aspects of our lives, and it has the capability to manage more complex, data-intensive tasks
An adaptive deep learning (ADL) algorithm based AE is proposed for a m-user interference channel with unknown interference
With the proposed ADL algorithm, interference can be estimated and predicted, which is subsequently used for updating the DNN based decoding processing
Summary
Artificial intelligence (AI) is becoming increasingly present in all aspects of our lives, and it has the capability to manage more complex, data-intensive tasks. An in-depth analysis of the symbol constellation is studied, and we apply the ZF and MMSE equalizers for the interference channel and analyze the performance when it compares with the proposed ADL based AE approach. We propose an ADL algorithm for a m-user interference Gaussian channel, to enhance the robustness of the link by estimating the uncertain interference via learning. An ADL algorithm based AE is proposed for a wireless communication interference channel with m-user. The received n-dimensional signal y noised by a channel represented as a conditional probability density function p(y|x), and the DNNs receiver subsequently learns it with multiple dense layers. By substituting the estimated interference ‘status’ back into the learning, we update the training function for the decoder and obtain a more robust communication link. We train the AE in an end-to-end manner using the Adam optimizer, on the set of all possible messages si∈ M, using the cross-entropy loss function
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