This paper presents a deep neural network (DNN) based symbol detection and modulation classification detector (SDMCD) for mixed blind signals detection. Unlike conventional methods that employ symbol detection after modulation classification, the proposed SDMCD can perform symbol recovery and modulation identification simultaneously. A cumulant and moment feature vector is presented in conjunction with a low complexity sparse autoencoder architecture to complete mixed signals detection. Numerical results show that SDMCD scheme has remarkable symbol error rate performance and modulation classification accuracy for various modulation formats in AWGN and Rayleigh fading channels. Furthermore, the proposed detector has robust performance under the impact of frequency and phase offsets.

Network coding on the physical-layer has recently been widely discussed as a potentially promising solution to the wireless access problem in a relay network. However, the existing research on physical-layer network coding (PNC), usually assumes that the symbol timing of the nodes is fully synchronized and hardly investigates the unavoidable symbol timing errors. Similar to many telecommunication systems, symbol timing plays a critical role in PNC and precise alignment has to be provided for the encoding. In this work, we propose a novel symbol timing algorithm with a low oversampling factor (samples per symbol) based on the a priori knowledge of the transmitted pulse shape. The proposed algorithm has the dual advantages of the low oversampling rate and high precision. The mean square error (MSE) performance is verified by simulations to be at least one order of magnitude better than that of the conventional optimum phase (OP) algorithm for a signal noise ratio (SNR) greater than 5dB.

A three-stage padding configuration providing a larger continuous virtual aperture and achieving more degrees-of-freedom (DOFs) for the direction-of-arrival (DOA) estimation is presented. The improvement is realized by appropriately cascading three-stages of an identical inter-element spacing. Each stage advantageously exhibits a continuous virtual array, which subsequently produces a hole-free resulting uniform linear array. The geometrical approach remains applicable for any existing sparse array structures with a hole-free coarray, as well as designed in the future. In addition to enlarging the continuous virtual aperture and DOFs, the proposed design offers flexibility so that it can be realized for any given number of antennas. Moreover, a special padding configuration is demonstrated, which further increases the number of continuous virtual sensors. The precise antenna locations and the number of continuous virtual positions are benefited from the closed-form expressions. Experimental works are carried out to demonstrate the effectiveness of the proposed configuration.