arXiv subjects

Jaewon Yun

Publications and source records attributed to Jaewon Yun.

3 recordsLinked to original sources

arXiv2025

Anti-Jamming Modulation for OFDM Systems under Jamming Attacks

Orthogonal frequency division multiplexing (OFDM) systems are inherently vulnerable to jamming attacks due to the independent transmission of data symbols across subcarriers. In this paper, we propose a novel anti-jamming OFDM scheme to ensure robust communication even under severe jamming attacks while maintaining high spectral efficiency. The core idea is to utilize a spreading matrix that transforms a data symbol vector into a higher-dimensional modulated vector, thereby exploiting both the spreading gain and the frequency diversity gain to mitigate jamming attacks. To recover the transmitted data symbols, we develop an efficient maximum likelihood detection (MLD) method that achieves optimal detection performance with significantly reduced computational complexity. Furthermore, we derive the theoretical bit error rate (BER) upper bound and the optimal modulation order that minimizes the BER while preserving spectral efficiency according to the jamming environment. To address practical scenarios where jamming attacks are unknown and dynamic, we establish a jamming-adaptive communication framework. This framework enables the system to estimate the jamming parameters and adapt to the dynamic environment with the optimal modulation order. Simulation results demonstrate that the proposed scheme significantly outperforms existing OFDM schemes in both BER and effective throughput, validating its robustness under various and dynamic jamming scenarios.

cs.IT↗

arXiv2023

Prior-Aware Robust Beam Alignment for Low-SNR Millimeter-Wave Communications

This paper presents a robust beam alignment technique for millimeter-wave communications in low signal-to-noise ratio (SNR) environments. The core strategy of our technique is to repeatedly transmit the most probable beam candidates to reduce beam misalignment probability induced by noise. Specifically, for a given beam training overhead, both the selection of candidates and the number of repetitions for each beam candidate are optimized based on channel prior information. To achieve this, a deep neural network is employed to learn the prior probability of the optimal beam at each location. The beam misalignment probability is then analyzed based on the channel prior, forming the basis for an optimization problem aimed at minimizing the analyzed beam misalignment probability. A closed-form solution is derived for a special case with two beam candidates, and an efficient algorithm is developed for general cases with multiple beam candidates. Simulation results using the DeepMIMO dataset demonstrate the superior performance of our technique in dynamic low-SNR communication environments when compared to existing beam alignment techniques.

cs.IT↗

arXiv2023

Communication-Efficient Federated Learning over Capacity-Limited Wireless Networks

In this paper, a communication-efficient federated learning (FL) framework is proposed for improving the convergence rate of FL under a limited uplink capacity. The central idea of the proposed framework is to transmit the values and positions of the top-$S$ entries of a local model update for uplink transmission. A lossless encoding technique is considered for transmitting the positions of these entries, while a linear transformation followed by the Lloyd-Max scalar quantization is considered for transmitting their values. For an accurate reconstruction of the top-$S$ values, a linear minimum mean squared error method is developed based on the Bussgang decomposition. Moreover, an error feedback strategy is introduced to compensate for both compression and reconstruction errors. The convergence rate of the proposed framework is analyzed for a non-convex loss function with consideration of the compression and reconstruction errors. From the analytical result, the key parameters of the proposed framework are optimized for maximizing the convergence rate for the given capacity. Simulation results on the MNIST and CIFAR-10 datasets demonstrate that the proposed framework outperforms state-of-the-art FL frameworks in terms of classification accuracy under the limited uplink capacity.

eess.SP↗