Wi-Fi Backscatter System With Tag Sensors Using Multi-Antennas for Increased Data Rate and Reliability

Area of Science:

• Wireless Communication
• Internet of Things (IoT)
• Signal Processing

Background:

• Existing Wi-Fi backscatter systems face reliability issues due to power level modulation and reduced symbol distance.
• Multi-antenna systems offer potential for improved channel diversity but increase computational complexity.

Purpose of the Study:

• To propose a multi-antenna Wi-Fi backscatter system for tag sensors to enhance data rate and signal reliability.
• To develop low-complexity demodulation methods for multi-antenna systems.

Main Methods:

• Implemented a Wi-Fi backscatter system utilizing multiple antennas to achieve channel diversity.
• Introduced two backscatter methods and three low-complexity demodulation schemes: subcarrier energy-based threshold (SET), tag's channel state-based threshold (TCST), and similar Euclidean distance (SED).
• Validated performance using the 802.11 TGn channel model through simulations.

Main Results:

• The proposed multi-antenna tag sensor achieved approximately 10 dB better bit error rate (BER) and throughput compared to existing methods.
• The low-complexity demodulation schemes demonstrated BER performance comparable to existing methods (within 1 dB difference).
• Computational complexity was reduced by up to 60% compared to the standard Euclidean distance method.

Conclusions:

• The multi-antenna Wi-Fi backscatter system significantly improves tag sensor communication performance.
• The proposed low-complexity demodulation methods effectively reduce computational overhead without compromising BER performance.
• This research offers a more reliable and efficient solution for Wi-Fi backscatter tag sensors.