Deep learning (DL) has recently aroused substantial concern due to its successful implementations in many fields. Currently, there are few studies on the applications of DL in the automatic modulation classification (AMC), which plays a critical role in non-cooperation communications. Besides, most previous work ignores the feature interaction and only considers spatial or temporal attributes of signals. Combining the advantages of the convolutional neural network (CNN) and the long short-term memory (LSTM), this paper addresses the AMC using CNN-LSTM based dual-stream structure, which efficiently explores the feature interaction and the spatial-temporal properties of raw complex temporal signals. Specifically, a preprocessing step is first implemented to convert signals into the temporal in-phase/ quadrature (I/Q) format and the amplitude/phase (A/P) representation, which facilitates the acquirement of more effective features for classification. To extract features from each signal pattern, each stream is composed of CNN and LSTM (denoted as CNN-LSTM). Most importantly, the features learned from two streams interact in pairs, which increases the diversity of features and thereby further improves the performance. Finally, some comparisons with previous work are performed. The experimental results not only demonstrate the superior performance of the proposed method compared with the existing state-of-the-art methods, but also reveal the potential of DL-based approaches for AMC.

Automatic modulation classification (AMC) is becoming increasingly important in spectrum monitoring and cognitive radio. However, most existing modulation classification algorithms neglect the complementarities between different features and the importance of features fusion. To remedy these flaws, this paper presents a scheme of features fusion for AMC using convolutional neural network (CNN).The approach attempts to fuse different images and handcrafted features of signals to obtain more discriminating features. First, eight handcrafted features and different images features are both extracted. In the latter, signals are converted into two kinds of time-frequency images by smooth pseudo wigner-ville distribution (SPWVD) and Born-Jordan distribution (BJD), and a fine-tuned CNN model is utilized to extract image features. Second, the joint features are formed by combined of each of image and handcrafted features, and a multimodality fusion (MMF) model is applied to fuse the joint features to yield further improvement. Finally, simulation results reveal the superior performance of the proposed scheme. It is worth mentioning that the classification accuracy can reach 92.5\% with signal-to-noise ratio at -4dB.

This correspondence paper investigates relay selection (RS) schemes for cooperative downlink nonorthogonal multiple access (NOMA) networks with multiple relays. Two optimal RS schemes, termed as the two-stage weighted-max-min (WMM) and max-weighted-harmonic-mean (MWHM) schemes, are proposed for cooperative NOMA with fixed and adaptive power allocations (PAs) at the relays, respectively. Then, the outage probabilities of the two proposed RS schemes are analyzed, and their diversity gains are also determined. The provided simulation results show that the proposed optimal two-stage WMM and MWHM schemes outperform the existing suboptimal RS schemes for cooperative NOMA networks with fixed and adaptive PAs at the relays, respectively.

This paper considers a secure non-orthogonal multiple access system, where confidential messages are transmitted from a base station to multiple legitimate destinations and wiretapped by multiple illegitimate receivers. It is assumed that all the channels experience Nakagami-m fading model and all the nodes are equipped with multiple antennas, respectively. Both non-colluding and colluding eavesdroppers are respectively considered. Max-min (MM) transmit antenna selection (TAS) strategy is adopted to improve the secrecy performance of the target system, in which both users in user paring are considered simultaneously. In particular, closed-form expressions for the cumulative distribution function of the signal-to-interference-noise ratio at the legitimate user are derived firstly. Then we obtain the exact and asymptotic analytical results in a closed form for the secrecy outage probability of MM TAS scheme. Monte-Carlo simulation results are presented to corroborate the correctness of the analysis. The results show that the secrecy diversity order is zero and non-zero for fixed and dynamic power allocations, respectively.

This paper proposes an adaptive relay selection method that exploits the social network and establishes a physical domain and social domain-based model named the SRSM(Social-based D2D relay selection model)to address relay selection failure caused by the diversity of users cooperation willingness and the instability of communication links due to human mobility. Under the premise of tolerable interference caused to cellular users, the proposed approach synthetically considers the desired physical-relation factor and the social-interaction factor for the relay user. In particular, the reliability of the D2D link is obtained by considering the transmission stability of the link, assessed by the user encounter history and cooperation willingness characterized by user social distance, which is set as the criterion of relay selection, combined with the physical channel condition. Simulation results show that compared with the traditional socially blind method, the proposed method can increase the probability of relay selection success and reduce the burden of the cellular network to improve the system performance.