WiFi Physical Layer Signaling Mechanisms and Enhanced Security Techniques

ABSTRACT

This work presents a MATLAB-based implementation of a secure and efficient Physical (PHY)-layer signaling framework for MIMO-OFDM Wi-Fi systems. The proposed approach enhances conventional preamble functionality by embedding user-defined signaling bits within the Short Training Field (STF), enabling improved coordination without increasing overhead. An adaptive modulation scheme ranging from BPSK to higher-order QAM is employed to optimize performance across varying channel conditions. To improve reliability, convolutional coding with Viterbi decoding is integrated, offering flexible error correction compatible with dynamic OFDM frame structures. The system is evaluated over a MIMO Rayleigh fading channel using Zero-Forcing (ZF) and MMSE equalization techniques, with performance analyzed in terms of Bit Error Rate (BER) versus SNR. Additionally, a machine learning-based attack detection module using Support Vector Machine (SVM) is incorporated to identify anomalies such as jamming and spoofing. Results demonstrate improved spectral efficiency through reduced signaling overhead, enhanced robustness against channel impairments, and effective detection of malicious interference, making the proposed framework suitable for next-generation Wi-Fi PHY-layer design.

Keywords: MIMO-OFDM, PHY-layer signaling, Short Training Field (STF), Convolutional coding, Machine learning-based attack detection.