Digital and electronic signal processing

  • Spectrally efficient modulation formats in coherent systems

  • Advanced modulation schemes for direct detection systems

  • Channel characterization and equalization

  • Polarization, clock and carrier recovery

  • Forward error correction

  • Implementation of distribution matchers for constellation shaping

  • Orthogonal frequency-division multiplexing (OFDM)

  • Flexible and sliceable transceivers

  • Performance monitoring and signal characterization

  • Digital-to-analog and analog-to-digital converters

  • Real-time demonstration and field trials of high-speed electronic circuits and subsystems

  • Demonstration of high capacity transmission

  • Subsystems and digital signal processing for data center interconnects (DCI)

  • Subsystems and digital signal processing for space-division multiplexing (SDM)

Optical signal processing

  • Passive all-optical signal processing subsystems

  • Active all-optical signal processing subsystems

  • Microwave photonic subsystems

  • Optical signal processing with photonic integrated circuits

  • Optical buffering, bit-, and label-processing subsystems

  • Optical packet and burst switching subsystems

  • Performance monitoring and signal characterization based on optical techniques

Radio-over-fiber and free space optical communication

  • Digital, electronic and optical subsystems

  • Optical-wireless integration and multi-technology converged transmission systems

  • Visible light communication systems

  • Fronthaul systems based on analog radio signals

  • Ground-to-satellite/satellite-to-ground and inter-satellite optical communication

Passive optical networks (PON)

  • Future PON architectures (WDM-PON, TWDM-PON, OFDMA-PON, etc.)

  • Digital, electronic and optical processing for PON systems

  • Signal processing for optical backhaul/fronthaul networks

  • Signal processing for long-reach broadband access networks

Applications of machine learning (ML)in optical communication

  • Applications of ML to channel estimation and equalization

  • Applications of ML to signal characterization and performance monitoring

  • Applications of ML to component and device characterization

  • Applications of ML to physical layer and network side optimization