Data-driven baseband processing and sensing for smart & efficient virtualized RAN in 5G and beyond

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Data-driven baseband processing and sensing for smart & efficient virtualized RAN in 5G and beyond

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  • Data-Driven algorithms are transforming the wireless baseband. Using data-driven algorithm design, we can replace numerous algorithms in the 4G and 5G baseband with more efficient and accurate learned alternative which save cost, increase performance, make better use of power, heat, cooling, and antenna apertures, and ultimately provide a better experience for users and a better value proposition for operators. Beyond using data-driven solutions within current standards, AI/ML and data-driven design have begun to influence how future standards are envisioned in both 5G Advanced as well as within 6G candidate waveforms and technologies. In this latter case (and outside of standards settings), we are beginning to see AI-Native solutions, or waveform components which are designed and learned for and by AI/ML optimization from the ground up. This approach offers to provide significant performance benefits in many applications and will be an exciting area of fastmoving applied research over the coming years as 6G continues to take form.

    In this talk, we will cover key data-driven AI/ML software solutions which will make 4G/5G vRAN infrastructure more performant as well as highlighting how powerful AI/ML based spectrum sensing and broad sensing-making capabilities are becoming as an enabler for spectral efficiency, interference-free operations, and security within both public and private RAN deployments and other applications. We will highlight some of our recent work in these areas, initiatives in industry and standards bodies looking at including these technologies and discuss our vision for how these technologies will continue to evolve and shape future wireless systems. Finally, we would like to share our data-driven wideband spectrum activity recognition challenge open-dataset competition. This competition helps to provide a new broad definition and scoring metric for spectrum activity sensing making in diverse environments as both a challenge and a benchmark task to researchers in the area to help evolve the dialogue and focus beyond more simplistic tasks such as modulation classification.

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      Speaker abstract

      Deep Learning in the Physical Layer: Transforming 5G and 6G Performance with Data and AI; Wireless systems, access schemes, and band allocations are becoming increasingly complex and heterogeneous. Deep learning-based solutions within the physical layer allow for a rapid and accurate state of the art approach for both spectrum sensing as well as channel access and physical layer design. In this talk, we’ll provide an overview of the background and enablers for this trend of ML in the physical layer of communications and highlight how we are building software solutions based on these approaches and techniques at DeepSig. OpenRAN based wireless solutions are also becoming increasingly prevalent within 5G RAN deployments and provide a key opportunity for rapid deployment of these technologies and to improve their performance in real-world deployments. We’ll highlight how we are using both DL driven PHY techniques and ORAN software to bring these capabilities into ORAN deployments in order to improve energy efficiency and to improve spectral efficiency, capacity, and user experience. Finally, we’ll discuss where we believe these technologies are leading future RAN technologies and standards, and provide highlights from our own trials and partnerships, highlighting the maturity of the technology and its likelihood for adoption in next generation wireless deployments.
      Tim O’Shea
      Co-Founder & CTO
      DeepSig Inc
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    • Start date
      10 November 2021 at 15:00 CET, Geneva
    • End date
      10 November 2021 at 16:00 CET, Geneva
    • Duration
      60 minutes
    • Programme stream
    • Topics
    • UN SDGs

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