Uppsats

Monte Carlo Simulation Based NOMA-VLC Sensor Network With Imperfect SIC

Master-uppsats

Blekinge Tekniska Högskola/Institutionen för datavetenskap

Publicerad: 2025

Språk: Engelska

Sammanfattning

This thesis investigates the application of Non-Orthogonal Multiple Access (NOMA) in a Visible Light Communication (VLC) system designed for Sensor Nodes (SN). The study simulates a realistic indoor environment using MATLAB, with a central LED transmitter and two spatially distributed photodiode-based sensor receivers. On-Off Keying (OOK) modulation is employed for data transmission, enabling the use of simple yet effective intensity modulation and direct detection. A key objective is to evaluate the performance of NOMA under imperfect Successive Interference Cancellation (SIC), which more accurately reflects practical decoding capabilities at the receiver side. A Monte Carlo-based approach is used to explore power allocation strategies that minimize bit error rate (BER) while ensuring successful message decoding for both sensors. In addition, Forward Error Correction (FEC) is introduced to further improve system robustness, enabling reliable decoding even under residual interference from imperfect SIC. The system’s performance is evaluated across a range of signal-to-noise ratio (SNR) levels by analyzing bit error rate (BER), achievable data rate, and the accuracy of received messages. The far user, benefiting from higher power allocation and simpler threshold-based detection, consistently achieves reliable performance, while the near user experiences more decoding errors due to residual interference from imperfect SIC. Visual outputs of channel impulse responses, superimposed signals, and decoding flows further illustrate system behavior. The simulation results confirm the feasibility of integrating NOMA into VLC-based sensor systems and offer insight into practical design trade-offs. Challenges such as decoding complexity, power allocation sensitivity, and error propagation are discussed, and future directions are proposed, including improvements in energy efficiency, decoding robustness, and potential hardware implementation.

Information

Lärosäte / institution
Blekinge Tekniska Högskola/Institutionen för datavetenskap
Publiceringsdatum
2025
Uppsatstyp
Master-uppsats
Språk
Engelska

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