Advanced Photonics Congress

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