# A CNN-MPSK Demodulation Architecture with Ultra-Light Weight and Low-Complexity for Communications
###### Abstract
To improve modulation recognition accuracy at low SNRs, an algorithm for pre-denoising was proposed in [14] before modulation recognition. The pre-denoising algorithm consists of a fully CNN, which is similar to an auto-encoder. A residual learning is also used to speed up the learning process. Eye diagram measurements were further used to estimate coherent channel performance with deep learning [15]. The experimental results show that the proposed technique provides high accuracy in determining the modulation format, optical SNR, roll-off factor, and timing skew of a quadrature amplitude modulation. In [16], the modulation signals are transformed into two image representations of cyclic spectra and constellation diagram, respectively. To integrate the features, a gradient descent strategy and a multi-feature fusion technique were exploited along with a two-branch CNN model. The novel framework was proposed for low-cost link adaptation for spatial modulation MIMO (SM-MIMO). Simulations demonstrate that the supervised-learning classifiers and DNN-based adaptive SM-MIMO outperform a variety of conventional optimization-driven designs [17]. The detection of modulations was presented for multi-relay cooperative MIMO systems of 5G communications in the presence of spatially correlated channels and imperfect channel state information. The simulation results show that the machine learning techniques provide gain in terms of both the modulation detection and complexity [18]. To blindly detect the modulation order of interference signals in downlink non-orthogonal multiple access systems, a machine learning algorithm based on Anderson-Darling test was investigated [19]. DNNs and machine learning were used to develop methods for monitoring optical performance, identifying modulation formats, multipath fading channels and orthogonal frequency-division multiplexing supported by compressed sensing assisted index modulation [20; 21; 22; 23]. The previous related studies have yielded positive results. However, these studies utilize deep learning networks, which are deep and have a large output latency [24; 25]. As the deep networks have a high degree of complexity, they are difficult to train and generate a large number of parameters, which is unsuitable for small embedded hardware systems. Several studies require the input data of a system to be in an image format. In such case, the received binary data have to be converted into images, and then feature extraction and other operations are carried out. Finally, the image is converted back into binary data. The exchange of binary data and images increases the delay and complexity. There are concerns regarding the system ability to process the received data in real time. Other studies perform pre-processing operations, which increases the amount of parameters and the complexity of the overall system [26; 27]. Thus, it is hard to apply these studies for practical hardware implementations. Consequently, based on CNN and pooling techniques, we propose a shallow CNN-MPSK demodulation with ultra-light parameters to achieve a low complexity architecture. The goal of using CNNs with MPSK is to provide an alternative method for demodulation with the affordable computation complexity. The sections of the study are organized as follows. In Section 2, we analyze the modulation principle of MPSK and the coherent demodulation process. In addition, the theoretical bit-error-rate (BER) formula for coherent demodulation is derived. Section 3 presents the architecture of CNN-MPSK and the computation consumed by each component. We then show the number of parameters generated by the CNN-MPSK architecture. In Section 4, we give a specific CNN-MPSK demodulation example to illustrate parameters training and perform BER tests under different SNRs. Afterwards, we discuss the multiplications and additions involved in CNN-MPSK and coherent demodulation, and conclude this paper in Section 5.