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[Author] Kota YAMASHITA(2hit)

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  • Frequency Division Multiplexed Radio-on-Fiber Link Employing an Electro-Absorption Modulator Integrated Laser Diode for a Cube Satellite Earth Station

    Seiji FUKUSHIMA  Takayuki SHIMAKI  Kota YAMASHITA  Taishi FUNASAKO  Tomohiro HACHINO  

     
    PAPER

      Vol:
    E99-C No:2
      Page(s):
    212-218

    Recent small cube satellites use higher frequency bands such as Ku-band for higher throughput communications. This requires high-frequency link in an earth radio station as well. As one of the solutions, we propose usage of bidirectional radio-on-fiber link employing a wavelength multiplexing scheme. It was numerically shown that the response linearity of the electro-absorption modulator integrated laser (EML) is sufficient and that the spurious emissions are lower enough or can be reduced by the radio-frequency filters. From the frequency response and the single-sideband phase noise measurements, the EML was proved to be used in a radio-on-fiber system of the cube satellite earth station.

  • Penalized and Decentralized Contextual Bandit Learning for WLAN Channel Allocation with Contention-Driven Feature Extraction

    Kota YAMASHITA  Shotaro KAMIYA  Koji YAMAMOTO  Yusuke KODA  Takayuki NISHIO  Masahiro MORIKURA  

     
    PAPER-Terrestrial Wireless Communication/Broadcasting Technologies

      Pubricized:
    2022/04/11
      Vol:
    E105-B No:10
      Page(s):
    1268-1279

    In this study, a contextual multi-armed bandit (CMAB)-based decentralized channel exploration framework disentangling a channel utility function (i.e., reward) with respect to contending neighboring access points (APs) is proposed. The proposed framework enables APs to evaluate observed rewards compositionally for contending APs, allowing both robustness against reward fluctuation due to neighboring APs' varying channels and assessment of even unexplored channels. To realize this framework, we propose contention-driven feature extraction (CDFE), which extracts the adjacency relation among APs under contention and forms the basis for expressing reward functions in disentangled form, that is, a linear combination of parameters associated with neighboring APs under contention). This allows the CMAB to be leveraged with a joint linear upper confidence bound (JLinUCB) exploration and to delve into the effectiveness of the proposed framework. Moreover, we address the problem of non-convergence — the channel exploration cycle — by proposing a penalized JLinUCB (P-JLinUCB) based on the key idea of introducing a discount parameter to the reward for exploiting a different channel before and after the learning round. Numerical evaluations confirm that the proposed method allows APs to assess the channel quality robustly against reward fluctuations by CDFE and achieves better convergence properties by P-JLinUCB.