Michel Kulhandjian

Yaakoub Berrouche, Michel Kulhandjian, Hovannes Kulhandjian

Non-orthogonal multiple access (NOMA) systems allowing multiple users sharing the same resource block offer significant gains in spectral efficiency which can enable the required massive access in future wireless systems. However, they face several challenges due to their sensitivity to power allocation coefficients, fading effects, and imperfect channel state information (CSI). To address these limitations, this paper proposes Hadamard-NOMA, an approach leveraging the Hadamard Transform (HT) at the source level prior to modulation. By introducing HT, the system mitigates the adverse impact of fading and CSI imperfections, reducing bit error rates (BER) and enhancing overall system reliability. Theoretical analysis and Monte Carlo simulations validate the effectiveness of this technique, demonstrating robust NOMA transmission in dynamic wireless environments. The proposed method offers a promising solution for next-generation wireless networks, ensuring more reliable performance under diverse transmission conditions. Simulation results confirm analytical predictions, demonstrating significant performance improvements over state-of-the-art T-NOMA and Usman-NOMA schemes. Specifically, for the Near user, a gain of 15 dB is achieved at a Bit Error Rate (BER) of $10^{-2}$, while the Far user benefits from a 10 dB gain at a BER of $10^{-1}$. Compared to Usman-NOMA, the proposed method provides an improvement of 15 dB for the Far user at BER $10^{-1}$. Additionally, in a two-user scenario with imperfect Successive Interference Cancelation (SIC), user 1 requires an SNR at least 14 dB lower than user 2 to achieve a BER of $10^{-3}$. These findings highlight the effectiveness of applying HT at the source stage, significantly mitigating CSI errors and making NOMA more resilient for next-generation wireless networks.

Narek Papyan, Michel Kulhandjian, Hovannes Kulhandjian et al.

In this survey we are focusing on utilizing drone-based systems for the detection of individuals, particularly by identifying human screams and other distress signals. This study has significant relevance in post-disaster scenarios, including events such as earthquakes, hurricanes, military conflicts, wildfires, and more. These drones are capable of hovering over disaster-stricken areas that may be challenging for rescue teams to access directly. Unmanned aerial vehicles (UAVs), commonly referred to as drones, are frequently deployed for search-and-rescue missions during disaster situations. Typically, drones capture aerial images to assess structural damage and identify the extent of the disaster. They also employ thermal imaging technology to detect body heat signatures, which can help locate individuals. In some cases, larger drones are used to deliver essential supplies to people stranded in isolated disaster-stricken areas. In our discussions, we delve into the unique challenges associated with locating humans through aerial acoustics. The auditory system must distinguish between human cries and sounds that occur naturally, such as animal calls and wind. Additionally, it should be capable of recognizing distinct patterns related to signals like shouting, clapping, or other ways in which people attempt to signal rescue teams. To tackle this challenge, one solution involves harnessing artificial intelligence (AI) to analyze sound frequencies and identify common audio signatures. Deep learning-based networks, such as convolutional neural networks (CNNs), can be trained using these signatures to filter out noise generated by drone motors and other environmental factors. Furthermore, employing signal processing techniques like the direction of arrival (DOA) based on microphone array signals can enhance the precision of tracking the source of human noises.

Jinxin Liu, Petar Djukic, Michel Kulhandjian et al.

We propose Deep Dict, a deep learning-based lossy time series compressor designed to achieve a high compression ratio while maintaining decompression error within a predefined range. Deep Dict incorporates two essential components: the Bernoulli transformer autoencoder (BTAE) and a distortion constraint. BTAE extracts Bernoulli representations from time series data, reducing the size of the representations compared to conventional autoencoders. The distortion constraint limits the prediction error of BTAE to the desired range. Moreover, in order to address the limitations of common regression losses such as L1/L2, we introduce a novel loss function called quantized entropy loss (QEL). QEL takes into account the specific characteristics of the problem, enhancing robustness to outliers and alleviating optimization challenges. Our evaluation of Deep Dict across ten diverse time series datasets from various domains reveals that Deep Dict outperforms state-of-the-art lossy compressors in terms of compression ratio by a significant margin by up to 53.66%.