Communications

Wireless and optical communications.

We conduct advanced research in wireless and optical communications from the network layer down to the physical layer, including highly reliable machine-to-machine wireless networks, routing and scheduling for IoT networks, massive MIMO for 5G systems, flexible wideband RF front-end architectures, advanced error correction coding, and novel modulation and transmission techniques with adaptive signal processing for coherent fiber-optic and free-space optical communications.

  • Researchers

  • Awards

    •  AWARD   Excellent Presentation Award
      Date: January 25, 2021
      Awarded to: Takenori Sumi, Yukimasa Nagai, Jianlin Guo, Philip Orlik, Tatsuya Yokoyama, Hiroshi Mineno
      MERL Contacts: Jianlin Guo; Philip Orlik
      Research Areas: Communications, Machine Learning, Signal Processing
      Brief
      • MELCO and MERL researchers have won "Excellent Presentation Award" at the IPSJ/CDS30 (Information Processing Society of Japan/Consumer Devices and Systems 30th conferences) held on January 25, 2021. The paper titled "Sub-1 GHz Coexistence Using Reinforcement Learning Based IEEE 802.11ah RAW Scheduling" addresses coexistence between IEEE 802.11ah and IEEE 802.15.4g systems in the Sub-1 GHz frequency bands. This paper proposes a novel method to allocate IEEE 802.11 RAW time slots using a Q-Learning technique. MERL and MELCO have been leading IEEE 802.19.3 coexistence standard development and this paper is a good candidate for future standard enhancement. The authors are Takenori Sumi, Yukimasa Nagai, Jianlin Guo, Philip Orlik, Tatsuya Yokoyama and Hiroshi Mineno.
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    •  AWARD   Outstanding Presentation Award at the 28th Conference of Information Processing Society of Japan/Consumer Device & Systems
      Date: October 20, 2020
      Awarded to: Yukimasa Nagai, Takenori Sumi, Jianlin Guo, Philip Orlik, Hiroshi Mineno
      MERL Contacts: Jianlin Guo; Philip Orlik
      Research Areas: Communications, Optimization, Signal Processing
      Brief
      • MELCO and MERL researchers have won "Outstanding Presentation Award" at 28th Conference of Information Processing Society of Japan (IPSJ)/Consumer Device & Systems held on September 29-30, 2020. The paper titled "IEEE 802.19.3 Standardization for Coexistence of IEEE 802.11ah and IEEE 802.15.4g Systems in Sub-1 GHz Frequency Bands" reports IEEE 802.19.3 standard development on coexistence between IEEE 802.11ah and IEEE 802.15.4g systems in the Sub-1 GHz frequency bands. MERL and MELCO have been leading this standard development and made major technical contributions, which propose methods to mitigate interference in smart meter systems. The authors are Yukimasa Nagai, Takenori Sumi, Jianlin Guo, Philip Orlik and Hiroshi Mineno.
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    •  AWARD   Best Paper AWARD at International Workshop on Informatics (IWIN) 2020
      Date: September 11, 2020
      Awarded to: Yukimasa Nagai, Jianlin Guo, Takenori Sumi, Philip Orlik, Hiroshi Mineno
      MERL Contact: Jianlin Guo
      Research Areas: Communications, Signal Processing
      Brief
      • MELCO and MERL researchers have won one of two Best Paper Awards at International Workshop on Informatics (IWIN) 2020. The paper titled 'Hybrid CSMA/CA for Sub-1 GHz Frequency Band Coexistence of IEEE 802.11ah and IEEE 802.15.4g', reports research on the severity of interference between IEEE 802.11ah and IEEE 802.15.4g based networks and also proposes methods to mitigate this interference in smart meter systems. This research reported in this paper has also informed several of MELCO/MERL's contributions to the IEEE P802.19.3 task group which is developing standards to allow for improved coexistence in outdoor metering systems. Authors are Yukimasa Nagai, Jianlin Guo, Takenori Sumi, Philip Orlik and Hiroshi Mineno.
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  • News & Events

    •  NEWS   Research on Intelligent Power Amplifier is Cover Story of Microwave Journal
      Date: April 15, 2021
      MERL Contacts: Mouhacine Benosman; Rui Ma; Koon Hoo Teo
      Research Areas: Communications, Electronic and Photonic Devices, Machine Learning
      Brief
      • The cover article in the April issue of Microwave Journal features MERL and MELCO's invited paper entitled "A New Frontier for Power Amplifiers Enabled by Machine Learning". Our recent research applying ML for optimizing operating conditions of advanced power amplifier designs is highlighted.

        Since 1958, Microwave Journal has been the leading source for information about RF and Microwave technology, design techniques, news, events and educational information. Microwave Journal reaches 50,000 qualified readers monthly with a print magazine that has a global reach.
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    •  TALK   Prof. Pere Gilabert gave an invited talk at MERL on Machine Learning for Digital Predistortion Linearization of High Efficient Power Amplifier
      Date & Time: Tuesday, February 16, 2021; 11:00-12:00
      Speaker: Prof. Pere Gilabert, Universitat Politecnica de Catalunya, Barcelona, Spain
      MERL Host: Rui Ma
      Research Areas: Communications, Electronic and Photonic Devices, Machine Learning, Signal Processing
      Brief
      • Digital predistortion (DPD) linearization is the most common and spread solution to cope with power amplifiers (PA) inherent linearity versus efficiency trade-off. The use of new radio 5G spectrally efficient signals with high peak-to-average power ratios (PAPR) occupying wider bandwidths only aggravates such compromise. When considering wide bandwidth signals, carrier aggregation or multi-band configurations in high efficient transmitter architectures, such as Doherty PAs, load-modulated balanced amplifiers, envelope tracking PAs or outphasing transmitters, the number of parameters required in the DPD model to compensate for both nonlinearities and memory effects can be unacceptably high. This has a negative impact in the DPD model extraction/adaptation, because it increases the computational complexity and drives to over-fitting and uncertainty.
        This talk will discuss the use of machine learning techniques for DPD linearization. The use of artificial neural networks (ANNs) for adaptive DPD linearization and approaches to reduce the coefficients adaptation time will be discussed. In addition, an overview on several feature-extraction techniques used to reduce the number of parameters of the DPD linearization system as well as to ensure proper, well-conditioned estimation for related variables will be presented.
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  • Research Highlights

  • Internships

    • SP1512: Mutual Interference Mitigation

      The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in mutual interference mitigation for automotive radar. Previous experience in waveform design, radar detection under interference, joint communication and sensing, interference mitigation, and deep learning for radar is highly preferred. Knowledge about automotive radar schemes (MIMO and waveform modulation, e.g., FMCW, PMCW, and OFDM) is a plus. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments using MERL in-house testbed, and prepare results for patents and publication. Senior Ph.D. students with research focuses on signal processing, machine learning, optimization, applied mathematics, or related areas are encouraged to apply. The expected duration of the internship is 3 months with a flexible start date.

    • SP1537: Machine Learning for Wireless Communications

      MERL is seeking an intern to work on machine learning for wireless communication systems. The ideal candidate would be an experienced PhD student or post-graduate researcher working in wireless communications with a focus on machine learning methods. The candidate should have a detailed knowledge of wireless communications, with some experience in machine learning, MIMO, and/or channel equalization preferred. Strong programming skills in Python and machine learning frameworks are essential. The expected duration of the internship is 3-6 months with flexible start date and length. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    • SP1582: Source & Channel Coding

      MERL is seeking a highly motivated, qualified individual to join our internship program of research on applied coding for data science. The ideal candidate is expected to possess an excellent background in channel coding, source coding, information theory, coding theory, coded modulation design, signal processing, deep learning, quantum computing, and molecular computing. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.


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  • Recent Publications

    •  Tang, Y., Kojima, K., Koike-Akino, T., Wang, Y., Jha, D., Parsons, K., Qi, M., "Nano-Optic Broadband Power Splitter Design via Cycle-Consistent Adversarial Deep Learning", Conference on Lasers and Electro-Optics (CLEO), May 2021.
      BibTeX TR2021-045 PDF Presentation
      • @inproceedings{Tang2021may3,
      • author = {Tang, Yingheng and Kojima, Keisuke and Koike-Akino, Toshiaki and Wang, Ye and Jha, Devesh and Parsons, Kieran and Qi, Minghao},
      • title = {Nano-Optic Broadband Power Splitter Design via Cycle-Consistent Adversarial Deep Learning},
      • booktitle = {Conference on Lasers and Electro-Optics (CLEO)},
      • year = 2021,
      • month = may,
      • url = {https://www.merl.com/publications/TR2021-045}
      • }
    •  Fujihashi, T., Koike-Akino, T., Watanabe, T., Orlik, P.V., "HoloCast+: Hybrid Digital-Analog Transmission for Graceful Point Cloud Delivery with Graph Fourier Transform", IEEE Transactions on Multimedia, DOI: 10.1109/​TMM.2021.3077772, May 2021.
      BibTeX TR2021-043 PDF
      • @article{Fujihashi2021may,
      • author = {Fujihashi, Takuya and Koike-Akino, Toshiaki and Watanabe, Takashi and Orlik, Philip V.},
      • title = {HoloCast+: Hybrid Digital-Analog Transmission for Graceful Point Cloud Delivery with Graph Fourier Transform},
      • journal = {IEEE Transactions on Multimedia},
      • year = 2021,
      • month = may,
      • doi = {10.1109/TMM.2021.3077772},
      • url = {https://www.merl.com/publications/TR2021-043}
      • }
    •  Kalabic, U., Weiss, A., Chiu, M., "Orbit Verification of Small Sat Constellations", IEEE International Conference on Blockchain and Cryptocurrency (ICBC), May 2021.
      BibTeX TR2021-040 PDF
      • @inproceedings{Kalabic2021may,
      • author = {Kalabic, Uros and Weiss, Avishai and Chiu, Michael},
      • title = {Orbit Verification of Small Sat Constellations},
      • booktitle = {IEEE International Conference on Blockchain and Cryptocurrency (ICBC)},
      • year = 2021,
      • month = may,
      • url = {https://www.merl.com/publications/TR2021-040}
      • }
    •  Nagai, Y., Sumi, T., Guo, J., Orlik, P.V., Mineno, H., "IEEE 802.19.3 Standardization for Coexistence of IEEE 802.11ah and IEEE 802.15.4g Systems in Sub-1 GHz Frequency Bands", Information Processing Society of Japan/Consumer Device and System Transaction, Vol. 11, No. 5, May 2021.
      BibTeX TR2021-035 PDF
      • @article{Nagai2021may,
      • author = {Nagai, Yukimasa and Sumi, Takenori and Guo, Jianlin and Orlik, Philip V. and Mineno, Hiroshi},
      • title = {IEEE 802.19.3 Standardization for Coexistence of IEEE 802.11ah and IEEE 802.15.4g Systems in Sub-1 GHz Frequency Bands},
      • journal = {Information Processing Society of Japan/Consumer Device and System Transaction},
      • year = 2021,
      • volume = 11,
      • number = 5,
      • month = may,
      • url = {https://www.merl.com/publications/TR2021-035}
      • }
    •  Kalabic, U., Weiss, A., Chiu, M., "Distributed Small Sat Location Verification", Integrated Communications Navigation and Surveillance (ICNS) Conference, April 2021.
      BibTeX TR2021-033 PDF
      • @inproceedings{Kalabic2021apr,
      • author = {Kalabic, Uros and Weiss, Avishai and Chiu, Michael},
      • title = {Distributed Small Sat Location Verification},
      • booktitle = {Integrated Communications Navigation and Surveillance (ICNS) Conference},
      • year = 2021,
      • month = apr,
      • url = {https://www.merl.com/publications/TR2021-033}
      • }
    •  Wang, F., Wang, P., Zhang, X., Li, H., Himed, B., "An Overview of Parametric Modeling and Methods for Radar Target Detection with Limited Data", IEEE Access, DOI: 10.1109/​ACCESS.2021.3074063, Vol. 9, pp. 60459 - 60469, April 2021.
      BibTeX TR2021-049 PDF
      • @article{Wang2021apr,
      • author = {Wang, Fangzhou and Wang, Perry and Zhang, Xin and Li, Hongbin and Himed, Braham},
      • title = {An Overview of Parametric Modeling and Methods for Radar Target Detection with Limited Data},
      • journal = {IEEE Access},
      • year = 2021,
      • volume = 9,
      • pages = {60459 -- 60469},
      • month = apr,
      • doi = {10.1109/ACCESS.2021.3074063},
      • issn = {2169-3536},
      • url = {https://www.merl.com/publications/TR2021-049}
      • }
    •  Skvortcov, P., Phillips, I., Forysiak, W., Koike-Akino, T., Kojima, K., Parsons, K., Millar, D.S., "Huffman-Coded Sphere Shaping for Extended-Reach Single-Span Links", IEEE Journal of Selected Topics in Quantum Electronics, DOI: 10.1109/​JSTQE.2021.3055476, Vol. 27, No. 3, February 2021.
      BibTeX TR2021-007 PDF
      • @article{Skvortcov2021feb,
      • author = {Skvortcov, Pavel and Phillips, Ian and Forysiak, Wladek and Koike-Akino, Toshiaki and Kojima, Keisuke and Parsons, Kieran and Millar, David S.},
      • title = {Huffman-Coded Sphere Shaping for Extended-Reach Single-Span Links},
      • journal = {IEEE Journal of Selected Topics in Quantum Electronics},
      • year = 2021,
      • volume = 27,
      • number = 3,
      • month = feb,
      • doi = {10.1109/JSTQE.2021.3055476},
      • issn = {1558-4542},
      • url = {https://www.merl.com/publications/TR2021-007}
      • }
    •  Kojima, K., TaherSima, M., Koike-Akino, T., Jha, D., Tang, Y., Wang, Y., Parsons, K., "Deep Neural Networks for Inverse Design of Nanophotonic Devices", IEEE Journal of Lightwave Technology, DOI: 10.1109/​JLT.2021.3050083, January 2021.
      BibTeX TR2021-001 PDF
      • @article{Kojima2021jan,
      • author = {Kojima, Keisuke and TaherSima, Mohammad and Koike-Akino, Toshiaki and Jha, Devesh and Tang, Yingheng and Wang, Ye and Parsons, Kieran},
      • title = {Deep Neural Networks for Inverse Design of Nanophotonic Devices},
      • journal = {IEEE Journal of Lightwave Technology},
      • year = 2021,
      • month = jan,
      • doi = {10.1109/JLT.2021.3050083},
      • issn = {1558-2213},
      • url = {https://www.merl.com/publications/TR2021-001}
      • }
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