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Current studies that Our records indicate that Hao-Hsuan Chang is a Graduate Student Member of the IEEE. Please verify. Our records indicate that Jonathan D. Ashdown is a Member of the IEEE. Please verify. apply reinforcement learning (RL) to dynamic spectrum access (DSA) problems in wireless communications systems mainly focus on model-free RL (MFRL). However, in practice, MFRL requires a large number of samples to achieve good performance making it impractical in real-time applications such as DSA. Combining model-free and model-based RL can potentially reduce the sample complexity while achieving a similar level of performance as MFRL as long as the learned model is accurate enough. However, in a complex environment, the learned model is never perfect. In this article, we combine model-free and model-based RL, and introduce an algorithm that can work with an imperfectly learned model to accelerate the MFRL. Results show our algorithm achieves higher sample efficiency than the standard MFRL algorithm and the Dyna algorithm (a standard algorithm integrating model-based RL and MFRL) with much lower computation complexity than the Dyna algorithm. For the extreme case where the learned model is highly inaccurate, the Dyna algorithm performs even worse than the MFRL algorithm while our algorithm can still outperform the MFRL algorithm.

In this letter we apply deep learning tools to conduct channel estimation for an orthogonal frequency division multiplexing (OFDM) system based on downlink pilots. To be specific, a residual learning based deep neural network specifically designed for channel estimation is introduced. Due to the compact network size as well as the underlying network architecture, the computation cost can be greatly reduced. Furthermore, this residual network architecture is compatible with any downlink pilot patterns making it compatible for modern wireless systems. The estimation error of the introduced residual learning approach is evaluated under 3rd Generation Partnership Project (3GPP) channel models. It outperforms other deep learning based estimation method with comparable to minimum mean square error (MMSE) estimation performance.

Power control plays an important role in improving the system throughput in communication system since co-channel interference is a major limitation to the system throughput. The power control problem of maximizing the system throughput in the multiuser and multichannel communication system is a highly complicated nonconvex problem since user are interfered with one another if operating in the same wireless channel. We reformulate the nonconvex objective function of this problem as a difference of two convex functions, which is called DC (difference of convex function) programming. To reduce the computation complexity in the high dimensional space, we introduce an alternative power allocation scheme to search in the low dimensional space, where each user updates its power sequentially. A global optimal power allocation is found by utilizing the branch-and- bound algorithm for each user while taking other users' power allocation as constant value. Furthermore, we incorporate each user's maximum power and minimum data rate constraint into the optimization framework. We found that the minimum data rate constraint of each user can be turned into multiple linear inequalities and then be added to the DC programming optimization framework. The simulation results show that our introduced method achieves the highest sum data rate compared to the state-of-the-art methods, including iterative water filling and geometric programming.

Decoration of CuInS2 (CIS) quantum dots (QDs) on ZnO nanowires (NWs) with an interlayer of In2S3 as photoelectrode has been successfully fabricated on FTO via the simple solution routes for photoelectrochemical (PEC) application. Scanning electron microscopy, transmission electron microscopy, and X-ray diffraction are utilized to systematically analyze the morphology and structure of the CIS QD/In2S3/ZnO NWs heterostructure. The composition of this multilayer heterostructure and the removal of QD ligands by a thermal process are confirmed by X-ray photoelectron spectra. In comparison with CIS QDs/ZnO NWs, the CIS QD/In2S3/ZnO heterostructural photoelectrode displays an efficient charge separation and carrier transport path for photocurrent up to 2.4 mA·cm–2 that is competitive with other Cd- and Pb-free QD-based materials. In addition, Mott–Schottky analysis demonstrates the negative shift of the flat band in the CIS QD/In2S3/ZnO, which benefits the early onset potential. Significantly, this hierarchical ...

In this work, we propose to establish a mobile edge computing (MEC) network that considers computation, caching and communication jointly. Depending on the demanding categories, users in the network are partitioned into computation-driven and caching-driven users, both of which need memory resource to improve their quality of experiences (QoEs). Thus, a memory resource allocation problem is aroused to maximize the performance of the whole network. Due to the fact that the users' characterization plays an important role to the resource allocation scheme and with the help of machine learning techniques, we propose to study and predict the users' patterns by distributed learning methods which take the heterogeneity of base station type and users' mobility, etc into consideration. The proposed machine learning based distributed MEC system can maximize the efficiency of the network by optimizing the resource allocation scheme and perfectly predicting users' pattern.

Based on mathematics and engineering point of view, we aim to explore the establishment of model structure, and thus calculate the benefits and risks of a financial product. Specifically, we exploit a large amount of market data of futures options so as to address two issues in this work. The first issue is to discover an appropriate product choice and timing for profitable trading. Without loss of generality, we investigate the effectiveness of several trading constraints and technical indicators by scrutinizing and back testing with the long-term market data. The second issue is to use the spread strategies for risk control when being an options seller. Note that a spread position is constituted where one buys an option and sells another option against it. In general, we develop a scheme to simulate different trading strategies and thus identify some simple but profitable strategies. Experimental studies show that our strategies yield good profit in the TAIFEX market during 2009 to 2018.

Surface plasmon resonance ( SPR) is one of the main mechanisms of Surface Raman Enhance S cattering (SERS) and it will depend on the morphology and free carrier density of s ubstrates, in many of discussions have been proved. Recently, the semiconductor copper(I) sulphide (Cu 2 S), the natural p -type semi conductor, exhibits remarkable SPR in the near -infrared region [1] and can be regards as best candidate for active SERS substrates. In this repor t, the successive ionic layer ad sorption and reaction (SILAR) process will be used to synthesis Cu 2 S nano structures [2] from ZnO nanorods as template deposited by electrochemical reaction. To further manipulate the different carrier densities of Cu 2 S nano structures s, the adjustment of Cu vacancy in Cu 2 S can be accomplished by thermal processes under noble gas . Taking 4 -aminothiophenol (4 -ATP) as probe molecule to measure the SERS performance by Cu 2 S nano structures made in this fabrication and also examines the effect on SERS by adjust ing Cu vacancy under an excit ed wavelength of 632.8 nm and light power of 15 mW. In fact, the modulation of Cu vacancy wi ll positively correlate to th e SPR frequencies and so could get the best enhancement factor under the limited condition of excit ed source. Therefore, our results could provide a new opportunity to use SERS to explore the molecule -semiconductor interaction, a fundamental but essential question for designing novel devices. Keywords: Cu

The stroke is a very important feature for a character and is helpful for word recognition and handwriting identification. Although thinning algorithms can be applied for stroke extraction, they always suffer from the problems of bifurcation and disconnection. Moreover, since the end points of strokes cannot be preserved by thinning, the stroke length cannot be accurately determined and the start and the end parts of a stroke, which are useful for identifying the writing habit of a person, are hard to be extracted explicitly. In this paper, we proposed a very accurate stroke extraction algorithm which can well preserve the ends of strokes. Simulations on some Chinese characters show that the proposed algorithm is reliable and can precisely extract the strokes of characters.