Department Publications

This manuscript introduces NeuroDetect, a model-free deep learning-based signal detection framework tailored for phase-modulated wireless systems with low-resolution analog-to-digital converters (ADCs). The proposed framework eliminates the need for explicit channel state information, which is typically difficult to acquire under coarse quantization. NeuroDetect utilizes a neural network architecture to learn the nonlinear relationship between quantized received signals and transmitted symbols directly from data. It achieves near-optimum performance, within a worst-case 12% margin of the maximum likelihood detector that assumes perfect channel knowledge. We rigorously investigate the interplay between ADC resolution and detection accuracy, introducing novel penalty metrics that quantify the effects of both quantization and learning errors. Our results shed light on the design trade-offs between ADC resolution and detection accuracy, providing future directions for developing energy-efficient high-speed and wideband wireless systems.

This concept paper proposes an architecture that integrates agentic AI models with digital twin representations for each subsystem component in an RF transceiver. The digital twins, constructed using machine learning techniques such as deep learning and reinforcement learning, are paired with specialized, autonomous AI agent(s) capable of independent reasoning, planning, and adaptive action. Agentic AI models go beyond traditional rule-based/symbolic AI to autonomously select optimal strategies, synthesize data from diverse sources, and interact with both virtual and real-world environments. Ideas for simulations and experimental validations are proposed for future work.

Future wireless systems are expected to rely on large antenna arrays at the base station (BS) to meet growing demands for data rates. However, the power consumption and hardware complexity of high-resolution analog-to-digital converters (ADCs) pose significant challenges to the practical deployment of such arrays. Low-resolution ADCs offer a power- and cost-effective alternative, motivating a deeper understanding of information-theoretic limits with low-resolution quantization. This letter establishes capacity scaling laws for both single-input single-output (SISO) and multi-antenna systems with low-resolution quantization. We show that, unlike the classical capacity growth in unquantized systems, the capacity of low-resolution systems approaches n at a rate for SISO and multiple-input single-output (MISO) systems, where n is the number of quantization bits and is the signal-to-noise ratio. For …

Real-time sensing and perception of the radio spectrum based on artificial intelligence (AI) is crucial for emerging intelligent wireless and electronic warfare systems. However, sensing can be greatly impacted by harmful radio frequency interference (RFI). Emerging drone warfare allows many RFI sources/jammers to be distributed across a wide field-of-view which necessitates real-time measurement, adaptation and aperture nulling to remove the RFI before AI-based sensing and perception of sources of interest can occur. This work explores algorithmic innovations that improve the computational complexity of classical Howells-Applebaum adaptive nulling algorithm to enable fast, real-time adaptive operation at significantly lower arithmetic complexity. Design examples for AI-enabled sensing and perception across a 32-element antenna receiver with 32 independent channels and a Xilinx Virtex-6 Sx475 FPGA …

This paper investigates an intelligent reflective surface (IRS) assisted secure multi-user two-way communication system. The aim of this paper is to enhance the physical layer security by optimizing the minimum secrecy-rate among all user-pairs in the presence of a malicious user. The optimization problem is converted into an alternating optimization problem consisting of two sub-problems. Transmit power optimization is handled using a fractional programming method, whereas IRS phase shift optimization is handled with semi-definite programming. The convergence of the proposed algorithm is investigated numerically. The performance gain in minimum secrecy-rate is quantified for four different user configurations in comparison to the baseline scheme. Results indicate a 3.6-fold gain in minimum secrecy rate over the baseline scheme when the IRS is positioned near a legitimate user, even when the malicious …

The demand for spectrum access continues to grow exponentially and dynamic spectrum access (DSA) is a candidate to alleviate issues with spectrum scarcity by adapting spectrum resources in real-time. Despite the benefits of intelligent DSA protocols, many algorithms are complex, and thus prohibitively difficult to implement in a real-time system. This paper proposes a contention-based access method named Guided-Aloha (or G-Aloha), that stems from the simple and well-known slotted-Aloha (S-Aloha) access method, for DSA in the context of primary users (PU) and secondary users (SU) of the network. The G-Aloha method is proposed for SU access to provide extended protection to the PU compared to the S-Aloha method by being guided by the knowledge from spectrum prediction (or spectrum forecasting), considering any potential regulatory requirements for protecting the PU. Using Markov modeling, an …

Virtual cycling is an engaging way to promote health and fitness while addressing the safety challenges of outdoor cycling. This paper introduces a Competitive Virtual Cycling (CVC) system that enhances user experience through immersive visuals, real-time competition, and low-latency networking. The system integrates virtual reality (VR) headsets, smart bicycle trainers, and real-world 360 ° videos to create a realistic gaming environment. A dynamic resistance feedback mechanism, driven by real-time sensor data from smart trainers and actuators, adjusts pedal resistance based on terrain variations, ensuring an authentic riding experience. To enhance engagement, we propose a novel avatar placement algorithm that optimizes competitor visibility in immersive, interactive races. Performance evaluations show that the system achieves a mean latency of 60 ms on a 4G network and 30 ms on a private 5G network …

This research presents a comprehensive experimental and simulation-based investigation aimed at enhancing the link distance and performance of indoor Visible Light Communication (VLC) systems. A basic VLC setup was incrementally improved by optimizing key system parameters, including LED transmit power, transimpedance amplifier (TIA) feedback resistor, lens configuration, photodiode selection, and wall color. Each enhancement was experimentally validated, and corresponding simulations were conducted to analyze system behavior and verify measurement results. The study also examines the applicability of well-established VLC channel models, such as the Masao Nakagawa model and second-reflection models, in real-world scenarios. The findings confirm that incorporating optical and environmental optimizations can significantly extend link distance, up to 3 m with high accuracy, while aligning …

Low-resolution analog-to-digital converters (ADCs) have emerged as a promising solution to reduce the power consumption of massive multiple-input-multiple-output (MIMO) receivers. However, allocating a fixed number of ADC bits uniformly across all receive antennas often results in suboptimal energy efficiency (EE), particularly when channel conditions vary significantly across antennas. This paper proposes a learning-based ADC bit allocation strategy that dynamically adjusts the resolution of each ADC based on the corresponding channel gain. The proposed method effectively allocates higher-resolution ADCs to antennas experiencing stronger channel gains while minimizing the resolution for weaker channels, thereby improving the overall EE. Simulation results demonstrate that the proposed method achieves near-optimal EE performance comparable to the brute-force approach but with significantly lower …

Real-time pedestrian sensing at crossings is essential for Intelligent Transportation Systems (ITS). Yet traditional vision-based solutions and wearable devices face privacy, cost, and deployment challenges. While Wi-Fi channel state information (CSI) has shown promise in indoor environments, its potential for dynamic outdoor environments remains largely unexplored. This paper presents a device-free, CSI-based system for detecting human movement and walking direction at crossings and alert oncoming vehicles to enhance pedestrian safety. A twostage classification framework is adopted with noise suppression and Doppler-sensitive features. Classical and deep learning models are evaluated across four outdoor locations and seven subjects, achieving 96.9% accuracy for movement detection and up to 62% accuracy for classification of movement direction. These findings demonstrate the feasibility of …

Vehicular ad-hoc networks (VANETs) face significant challenges in channel estimation, primarily due to the rapidly changing environment and the impact of Doppler frequency shifts caused by high-speed vehicle movement. This paper aims to evaluate the performance of existing channel estimation algorithms under scenarios with Doppler frequency shifts through computational experiments, with the assessment metric being the bit error rate (BER) performance of the algorithms under various signal-to-noise ratio (SNR) conditions. Based on simulation results, this paper systematically compares the effects of the least squares (LS), linear minimum mean square error (LMMSE), radial basis function (RBF), and genetic algorithm-optimized RBF (GA-RBF) in both relay-free and relayed scenarios, assessing their effectiveness in low-speed and high-speed environments. The results show that the LMMSE and GA-RBF …