Millimeter-Wave ISAC Testbed Using Programmable Digital Coding Dynamic Metasurface Antenna: Practical Design and Implementation

Dynamic Metasurface Antennas (DMAs) are transforming reconfigurable antenna technology by enabling energy-efficient, cost-effective beamforming through programmable meta-elements, eliminating the need for traditional phase shifters and delay lines. This breakthrough technology is emerging to revolutionize beamforming for next-generation wireless communication and sensing networks. In this paper, we present the design and real-world implementation of a DMA-assisted wireless communication platform operating in the license-free 60 GHz millimeter-wave (mmWave) band. Our system employs high-speed binary-coded sequences generated via a field-programmable gate array (FPGA), enabling real-time beam steering for spatial multiplexing and independent data transmission. A proof-of-concept experiment successfully demonstrates high-definition quadrature phase-shift keying (QPSK) modulated video transmission at 62 GHz. Furthermore, leveraging the DMA's multi-beam capability, we simultaneously transmit video to two spatially separated receivers, achieving accurate demodulation. We envision the proposed mmWave testbed as a platform for enabling the seamless integration of sensing and communication by allowing video transmission to be replaced with sensing data or utilizing an auxiliary wireless channel to transmit sensing information to multiple receivers. This synergy paves the way for advancing integrated sensing and communication (ISAC) in beyond-5G and 6G networks. Additionally, our testbed demonstrates potential for real-world use cases, including mmWave backhaul links and massive multiple-input multiple-output (MIMO) mmWave base stations.

Exploiting Deep Learning for Persian Sentiment Analysis

The rise of social media is enabling people to freely express their opinions about products and services. The aim of sentiment analysis is to automatically determine subject's sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as topic, product, movie, news etc. Deep learning has recently emerged as a powerful machine learning technique to tackle a growing demand of accurate sentiment analysis. However, limited work has been conducted to apply deep learning algorithms to languages other than English, such as Persian. In this work, two deep learning models (deep autoencoders and deep convolutional neural networks (CNNs)) are developed and applied to a novel Persian movie reviews dataset. The proposed deep learning models are analyzed and compared with the state-of-the-art shallow multilayer perceptron (MLP) based machine learning model. Simulation results demonstrate the enhanced performance of deep learning over state-of-the-art MLP.