Gain and power optimization of the wireless optical system with multilevel modulation

Area of Science:

• Optical Communications
• Wireless Networking
• Signal Processing

Background:

• Wireless optical communication systems are susceptible to performance degradation from transmitter sway in outdoor environments.
• While power efficiency is a key design consideration, bandwidth efficiency is also critical for these systems.
• Multilevel modulation, specifically multilevel pulse amplitude modulation (ML-PAM), is a natural approach to enhance bandwidth efficiency.

Purpose of the Study:

• To develop and analyze a model for four-level pulse amplitude modulation (4-PAM) in intensity modulation and direct detection (IM/DD) wireless optical systems.
• To formulate the system design as an optimization problem considering transmitter power, bit error probability, and system gains.
• To address the analytical and numerical challenges associated with modeling improper integrals involving Gaussian and complementary error functions (erfc).

Main Methods:

• Development of a mathematical model based on four-level pulse amplitude modulation (4-PAM).
• Formulation of the system design as an optimization problem incorporating key performance metrics.
• Analytical and numerical treatment of complex integrals for accurate performance evaluation.

Main Results:

• The study successfully models the performance of 4-PAM in wireless optical communication systems.
• The optimization problem formulation provides insights into the trade-offs between power, error probability, and gains.
• A power penalty of approximately 3 dB is observed to achieve doubled bandwidth efficiency at optimal design points.

Conclusions:

• The proposed model effectively analyzes the performance of 4-PAM for enhancing bandwidth efficiency in wireless optical systems.
• The findings quantify the power penalty associated with doubling bandwidth, offering crucial data for system designers.
• The research highlights the importance of addressing complex mathematical challenges for accurate system modeling and optimization.