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In this paper, a novel modulation and multiplexing technology for reducing out-of-band leakage is proposed for multicarrier schemes based on CP-OFDM. The proposed method is to spread data of N layers with N selected spreading codes, then multiplex them into a data sequence and transmit it on 2K subcarriers. The N spreading codes, which can suppress out-of-band leakage, are selected out from a complete set of mutual orthogonal spreading codes. The simulation results show that the data modulation and multiplexing technology effectively reduces interference with neighboring frequency bands or adjacent sub-bands for different numerologies and so significantly improves spectrum efficiency

Graphene has great application potential in radio frequency (RF) electronics such as low-noise amplifier (LNA) due to its amazing electrical properties. In this work, a graphene-transfer after process was developed to fabricate two-stage graphene LNA monolithic microwave integrated circuits (MMICs) on sapphire substrate to reduce possible damage to the graphene material in the device fabrication process. Using microstrip lines, inductances, and capacitances as input–output, and interstage impedance match networks, the two-stage Ku-band graphene LNA MMIC was obtained with a 10.2 dB maximum gain at 12.7 GHz, and 4.2 dB minimum noise figure at 12.5 GHz. [ABSTRACT FROM AUTHOR]

Bayesian Personalized Ranking (BPR) is a representative pairwise learning method for optimizing recommendation models. It is widely known that the performance of BPR depends largely on the quality of negative sampler. In this paper, we make two contributions with respect to BPR. First, we find that sampling negative items from the whole space is unnecessary and may even degrade the performance. Second, focusing on the purchase feedback of E-commerce, we propose a negative sampler for BPR by leveraging the additional view data. In our proposed sampler, users’ viewed interactions are considered as an intermediate feedback between the purchased and unobserved interactions. We jointly learn the pairwise rankings of user preference among these three types of interactions and design a user-oriented weighting strategy during learning process, which is more effective and flexible. Compared to the vanilla BPR that applies a uniform sampler on all candidates, our view-enhanced sampler enhances BPR with a relative improvement over 36.64 and 16.40 percent on Beibei and Tmall datasets, respectively. Empirical studies demonstrate the importance of considering users’ additional feedback when modeling their preference on different items, which can effectively improve the quality of sampled negative items towards learning a better personalized ranking function. Our implementation is available at https://github.com/dingjingtao/NegativeSamplerBPR. [ABSTRACT FROM AUTHOR]

The most attractive application of graphene in the RF area is low-noise amplifier (LNA) due to its amazing properties. But, design and fabrication of graphene LNA Monolithic Microwave Integrated Circuits (MMICs) are still blank. In this work, 200 nm gate-length chemical vapor deposition (CVD) monolayer graphene transistors were fabricated on a sapphire substrate. The graphene transistor shows an intrinsic gain of ${g} _{\text{m}}/{g} _{\text{ds}}= {1.1}$ , and high extrinsic maximum oscillation frequency with ${f} _{\text{max}}$ (48 GHz) > cutoff frequency ${f} _{\text{T}}$ (43 GHz). The graphene transistors show low extrinsic minimum noise figure of 2–7 dB in the measured frequency range of 6–50 GHz. A C-band graphene LNA MMIC was designed and fabricated and shows a maximum gain of 8.34 dB at 5.5 GHz, and minimum noise figure of 4.96 dB at 5.8 GHz. This work demonstrates the application potential of graphene for future RF high-speed electronics. [ABSTRACT FROM AUTHOR]

Predicting and utilizing the evolution trend of hot topics is critical for contingency management and decision-making purposes of government bodies and enterprises. This paper proposes a model named online opinion dynamics (OODs) where any node in a social network has its unique confidence threshold and influence radius. The nodes in the OOD are mainly affected by their neighbors and are also randomly influenced by unfamiliar nodes. In the traditional opinion model, however, each node is affected by all other nodes, including its friends. Furthermore, many traditional opinion evolution approaches are reviewed to see if all nodes (participants) can eventually reach a consensus. On the contrary, OOD is more focused on such details as concluding the overall trend of events and evaluating the support level of each participant through numerical simulation. Experiments show that OOD is superior to the improvement of the original Hegselmann–Krause (HK) model, HK-13 and HK-17, with respect to qualitative predictions of the evolution trend of an event. The quantitative predictions of the HK model cannot be used to make decisions, whereas the results of the OOD model are proved to be acceptable. [ABSTRACT FROM AUTHOR]

Non-orthogonal transmission, although not entirely new to the wireless industry, is gaining more attention due to its promised throughput gain and unique capability to support a large number of simultaneous transmissions within limited resources. In this article, several key techniques for non-orthogonal transmission are discussed. The downlink technique is featured by MUST, which is being specified in 3GPP for mobile broadband services. In the uplink, grantfree schemes such as multi-user shared access and sparse code multiple access, are promising in supporting massive machine-type communication services. The multi-antenna aspect is also addressed in the context of MUST, showing that MIMO technology and non-orthogonal transmission can be used jointly to provide combined gain. [ABSTRACT FROM PUBLISHER]