Deep Learning Based Detection for Spectrally Efficient FDM Systems

IMNet：用于频谱高效FDM系统中集成感知与通信的载波间干扰抑制网络

IMNet: Intercarrier Interference Mitigation Network for Integrated Sensing and Communication in Spectrally Efficient FDM Systems

针对SEFDM用于ISAC时的S-ICI与D-ICI干扰问题，提出IMNet及IMNet-LR框架，实现了高精度的距离与速度估计，且执行时间远低于传统方法。

Rate Analysis and Deep Neural Network Detectors for SEFDM FTN Systems

In this work we compare the capacity and achievable rate of uncoded faster than Nyquist (FTN) signalling in the frequency domain, also referred to as spectrally efficient FDM (SEFDM). We propose a deep residual convolutional neural network detector for SEFDM signals in additive white Gaussian noise channels, that allows to approach the Mazo limit in systems with up to 60 subcarriers. Notably, the deep detectors achieve a loss less than 0.4-0.7 dB for uncoded QPSK SEFDM systems of 12 to 60 subcarriers at a 15% spectral compression.

Classification of Common Waveforms Including a Watchdog for Unknown Signals

In this paper, we examine the use of a deep multi-layer perceptron model architecture to classify received signal samples as coming from one of four common waveforms, Single Carrier (SC), Single-Carrier Frequency Division Multiple Access (SC-FDMA), Orthogonal Frequency Division Multiplexing (OFDM), and Linear Frequency Modulation (LFM), used in communication and radar networks. Synchronization of the signals is not needed as we assume there is an unknown and uncompensated time and frequency offset. An autoencoder with a deep CNN architecture is also examined to create a new fifth classificatio…