Blind adaptive multi-user detection for under-ice acoustic communications with mobile interfering users.

Area of Science:

• Underwater Acoustic Communications
• Signal Processing
• Adaptive Filtering

Background:

• Under-ice environments pose unique challenges for acoustic communication networks.
• Interference from rising/diving users degrades signal quality.
• Blind adaptive multi-user detection is essential for mitigating interference.

Purpose of the Study:

• To investigate the performance of three blind adaptive multi-user detection algorithms.
• To evaluate their effectiveness in under-ice communication networks with dynamic users.
• To assess their ability to combat inter-symbol and multiple access interference.

Main Methods:

• Implementation and experimental validation of Least Mean Squares (LMS), Recursive Least Squares (RLS), and Kalman Filter (KF) blind adaptive multi-user detection algorithms.
• Conducting under-ice random asynchronous multi-user communication experiments.
• Utilizing 1.2 km under-ice acoustic channels in the Bohai Sea with 7 users.

Main Results:

• The experimental results demonstrated the feasibility and effectiveness of all three proposed algorithms.
• Significant mitigation of inter-symbol interference (ISI) and multiple access interference (MAI) was achieved.
• The algorithms proved robust in the presence of rising and diving interfering users.

Conclusions:

• The investigated blind adaptive algorithms are highly effective for under-ice acoustic communication.
• These methods offer a viable solution for enhancing communication reliability in challenging underwater scenarios.
• The study validates the practical application of advanced signal processing techniques in extreme environments.