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Deep learning (DL) technology is an effective tool for automatic modulation recognition (AMR) in the field of cognitive radio (CR). Most of the existing DL-based approaches usually design a deep network with many layers, in which only refined features from the final layer are used for AMR. However, rough features from other layers (i.e., features captured in a shallow network with fewer layers) can also provide useful information for modulation recognition. These rough features are not carefully exploited in previous approaches. In this paper, we propose a novel multi-path features fusion network for AMR, in which both refined and rough features are learned. The proposed approach identifies 11 signals including digital modulation and analog modulation generated by the GNU radio and compared to the classic network in all SNR. The experiment results show that the effectiveness of our approach. Especially, our approach is able to achieve 99.04% accuracy in +18dB SNR which outperforms all comparison approaches. [ABSTRACT FROM AUTHOR]

This paper presents a circularly polarized (CP) slot antenna array using substrate integrated waveguide (SIW)–feeding network at Q-band. To achieve CP radiation and high-gain performance simultaneously, a metallic radiating slanted cross slot is stacked atop an SIW coupling slot in the CP antenna element design. The fabricated CP antenna element features 14.6% (42.6–49.3 GHz) measured impedance bandwidth for |S11| ≤ −10 dB, 9.1% (42–46 GHz) measured 3-dB axial ratio (AR) bandwidth for AR ≤ 3, and 8.3 dBic measured peak gain, respectively. Then, a 1 × 8 antenna array is designed, fabricated, and measured. The measured results demonstrated that the impedance bandwidth and 3-dB AR bandwidth of the proposed 1 × 8 array are 14.8% (41.9–48.6 GHz) and 11% (43–48 GHz), respectively. The measured peak gain of the 1 × 8 array is 14.7 dBic at 44 GHz. [ABSTRACT FROM AUTHOR]

Machine to Machine technology has a broad application prospect in the 5G network, but there is a bottleneck in the energy consumption of intelligent devices powered by the battery. In this paper, we study the energy-saving strategy in the 5G millimeter-wave system. Firstly, a Discontinuous Reception scheme based on Beam Measurement (BM-DRX) is proposed to avoid unnecessary beamforming. Secondly, we modified the frame structure and optimized the beamforming to short the beamforming time and save the power consumption. Finally, based on the European Telecommunications Standards Institute data model, we used the semi-Markov process to analyze the BM-DRX by considering the beam misalignment events as a Poisson distribution. Simulation results show that the proposed scheme can not only meet the delay performance, but also save the energy consumption of the system. [ABSTRACT FROM AUTHOR]

An energy efficient protocol for routing and scheduling in wireless body area networks is proposed by considering node energy, path loss, traffic type, and other relevant factors. First, a channel competition procedure is proposed to reduce network flooding, making use of the related information that sensor nodes collected in the initialization phase. Then, energy abundant routes are selected according to the node type and energy consumption in routing setup phase. Finally, an adaptive slot assignment method is designed to meet requirements of data rate and priority in slot assignment phase. The simulation results show that the proposed protocol not only save energy, but also improve the channel utilization. [ABSTRACT FROM AUTHOR]