Anti-Jamming Modulation for OFDM Systems under Jamming Attacks

TL;DR

This work addresses jamming threats in OFDM by introducing a spreading-based anti-jamming modulation that distributes each QAM symbol across multiple subcarriers using a fixed unitary spreading matrix, enabling robust demodulation even when some subcarriers are jammed. It develops both a full ML detector for known jamming variance and a low-complexity variant, analyzes BER, and optimizes the modulation order to balance spreading gain and noise sensitivity. To handle unknown jamming conditions, the paper proposes a jamming-adaptive framework with a two-phase operation: a noncoherent phase for parameter estimation and a coherent phase that uses the estimated parameters to select the optimal modulation and apply a reduced-complexity detector. Simulation results demonstrate superior BER performance of the proposed framework across diverse SNR/SJR and jamming scenarios, highlighting practical robustness and adaptability for real-world OFDM systems.

Abstract

In this paper, we propose an anti-jamming communication framework for orthogonal frequency-division multiplexing (OFDM) systems under jamming attacks. To this end, we first develop an anti-jamming modulation scheme that uses a spreading matrix to distribute each symbol across multiple subcarriers, enhancing robustness against jamming. For optimal demodulation at a receiver, we devise a maximum likelihood detection (MLD) method and its low-complexity variant tailored to our anti-jamming modulation scheme in scenarios with known jamming variance. We analyze the bit error rate (BER) of our modulation scheme to optimize its modulation order according to a jamming scenario. To adapt to dynamic and unknown jamming environments, we present a jamming-adaptive communication framework consisting of two phases: (i) a jamming-noncoherent phase and (ii) a jamming-coherent phase. In the jamming-noncoherent phase, we develop an approximate MLD method that operates without prior knowledge of jamming variance and enables the estimation of jamming parameters. In the jamming-coherent phase, we use these estimated parameters to optimize the proposed modulation scheme while employing the low-complexity MLD method. Simulation results demonstrate the superior BER performance of the proposed anti-jamming framework compared to existing OFDM communication frameworks across a wide range of communication and jamming scenarios.

Figures (7)

• Figure 1: Illustration of a point-to-point OFDM communication system under jamming attacks when employing interleaving and deinterleaving processes at the transmitter and receiver, respectively.
• Figure 2: Illustration of jamming-adaptive communication framework.
• Figure 3: BER comparison of the full and low-complexity MLD methods of AJ-OFDM for different SNRs under partial band jamming with $\rho = 0.5$ and SJR = $-20$ dB. The dashed and dotted lines indicate an analytical BER derived for the full MLD method in AJ-OFDM.
• Figure 4: BER comparison of various OFDM frameworks for different SNRs, SJRs, and modulation orders under partial band jamming with $\rho = 0.5$.
• Figure 5: BER comparison of various OFDM frameworks under partial band jamming with varying $\rho$ at a fixed SNR of $20$ dB and SJR of $-20$ dB. The red stars indicate the BERs of AJ-OFDM with the optimal modulation order.