Department Publications

This paper addresses the urgent question of how emerging telecommunication networks, particularly beyond 5G and 6G networks, can be designed and governed to support climate action while limiting their own environmental footprint. We provide a comprehensive and cross-disciplinary analysis of the role of telecommunications in climate change adaptation, mitigation, and sectoral decarbonization, going beyond existing studies that typically focus on isolated technologies or single application domains. We review international climate and telecom policy frameworks, evaluate emerging 5G/6G capabilities, and analyze regulatory pathways for green networks to assess how next-generation connectivity, AI-based energy optimization, remote monitoring systems, and renewable integration can reduce emissions and enhance network resilience. Our findings organize these developments into a novel three-pillar …

Civilian GNSS receivers are vulnerable to spoofing due to open signal structures, while adaptive electronic-warfare platforms increasingly synthesize high-fidelity counterfeit signals. Existing detection methods depend on handcrafted features or supervised learning, limiting robustness under distribution shifts and novel attacks. This paper introduces NEMESIS, a self-supervised framework based on joint embedding predictive architectures (JEPA) in the wavelet time-frequency domain, which leverages unlabeled RF corpora via masked time-frequency prediction to learn spoofing-relevant embeddings directly from raw RF signals without labels or contrastive augmentation. Experiments demonstrate that wavelet-domain JEPA substantially outperforms time- and frequency-domain variants and achieves competitive accuracy relative to contrastive learning while significantly reducing labeling requirements.

This paper proposes a secure and cost-effective communication and management approach for distributed disaster early warning networks to ensure reliable, low-latency alert dissemination. While decentralized architectures reduce latency by enabling direct device communication, they are highly vulnerable to attacks due to their exposure in public community settings and the challenges posed by Network Address Translation (NAT) and firewalls. Traditional NAT traversal techniques often lack the built-in security required for such sensitive infrastructure. We identify Nebula, an open-source overlay networking tool, as a viable solution because it is lightweight enough for low-resource devices such as the Raspberry Pi typically found in decentralized early warning systems, supports NAT traversal, and provides endto-end encryption. Preliminary experiments conducted in an AWS environment—designed to simulate real-world Raspberry Shake sensor deployments—confirm Nebula’s ability to establish direct peer-to-peer (P2P) connectivity with a single hop and effectively encrypt traffic

Voice communication is essential in post-disaster scenarios where users may be unable to type due to injury, stress, or environmental constraints. While Meshtastic provides a decentralized LoRa-based mesh network for long-range messaging, it does not natively support voice and is limited by low data rates and strict regulatory duty-cycle requirements. This paper investigates two methods for enabling voice over such networks: waveform-based audio packetization and a semantic speech-to-text (STT)/text-to-speech (TTS) approach. Preliminary experimental evaluations demonstrate that waveform-based methods, even when using low-bitrate codecs like Codec2, are impractical for urgent communication; delivering a 3-second voice message requires hundreds of packets and several minutes due to ransmission latency and duty-cycle constraints. In contrast, the proposed offline STT/TTS method transcribes speech locally, transmits it as a standard text payload, typically in a single packet and reconstructs the audio at the receiver. This semantic approach reduces delivery time from minutes to seconds, while maintaining high reliability and regulatory compliance. The study concludes that semantic communication is the most viable solution for voice interaction in bandwidth-constrained LoRa mesh environments.

This work presents the development of an opensource standalone (SA) 5G testbed with end-to-end slicing, using open-source software on commercial off-the-shelf (COTS) hardware and software-defined radio (SDR). Key contributions include customized slice configurations in the 5G Core Network (5GC) and dedicated static resource allocation in the radio access network (RAN). The testbed is validated through slice isolation tests. Its utility is then demonstrated via a warehouse application featuring Automated Guided Vehicles (AGVs) on one slice and environmental sensors & actuators on another. Critical AGV response times are evaluated under mixed traffic conditions, showing robust slice isolation and superior performance over baseline networks. The testbed offers a scalable, cost-effective platform for 5G education, research, and application testing. It also highlights the transformative potential of such open …

The deployment of 5G networks using open-source software platforms on commodity hardware and software defined radios (SDRs) has become increasingly important for both research and prototyping. Most work is constrained to a single platform such as OpenAirInterface (OAI), inhibiting the potential for cross-platform trade-offs. This paper presents Open5GFusion, a heterogeneous 5G Standalone (SA) testbed that combines OAI, srsRAN, and Open5GS to enable flexible experimentation with different core network (CN) and radio access network (RAN) alternatives and to exploit the strengths of each. A graphical user interface (GUI) simplifies deployment management and monitoring across different CN/RAN combinations. The testbed leverages commercial off-the-shelf (COTS) hardware with USRP B210 and N310 SDRs, allowing real-time evaluation of throughput, latency, modulation schemes, network slicing …

Semi-grant-free non-orthogonal multiple access (SGF-NOMA) schemes group one grant-based (GB) user with multiple grant-free (GF) users into one time/frequency resource block (RB) to enhance spectral efficiency. Due to the sporadic traffic of GF users and the stringent quality of service (QoS) requirement of the GB user, the access collision problem becomes severe in SGF-NOMA. To solve this problem, this paper firstly designs an RB-based power pool (PP), which directs GF users to adjust their transmit power without disrupting the ongoing transmission of the internal GB user. After that, this work proposes an efficient multi-agent deep reinforcement learning (MA-DRL) framework to jointly optimize the PP and access strategy for maximizing the network throughput. In particular, this work exploits the fast-response feature of the traditional competitive MA-DRL and the increased-performance feature of the …

Fire is a serious threat to both human life and property, and fire safety is crucial. Many traditional methods have been developed to detect fires. However, inspired by past research that used Wi-Fi signals for monitoring environments and recognizing activities, this approach aims to use Wi-Fi signals to detect fire that utilize existing wireless communication systems for non-intrusive sensing. The idea is that fire and smoke can interfere with Wi-Fi signals passing through an environment, then Wi-Fi CSI could provide detailed information about the wireless channel between a Wi-Fi transmitter and a receiver. This research demonstrates the effectiveness of using Wi-Fi for fire detection, utilizing Raspberry Pi and ESP32 devices with ESP32 CSI tool. It analyses the temporal shift in Wi-Fi CSI Amplitude before, during, and after the ignition of a flame. The results showed that fire causes a noticeable variation in the CSI …

This paper characterizes the communication–sensing rate tradeoff of an active reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication system operating under residual environmental clutter. The BS beamformer and active-RIS coefficients are jointly designed while accounting for amplifier noise on the forward and return sensing paths, a global RIS power budget, and perelement amplification limits. Residual clutter introduces a beamformer-dependent term in the sensing denominator, leading to a generally indefinite, nonconvex sensing-QoS constraint in the communication-centric (C-C) design and a fractional quadratic beamformer objective in the sensing-centric (S-C) design. We develop alternating optimization procedures for both formulations based on established MM, Kronecker-vectorization, and Dinkelbach techniques. Their QoS-threshold sweeps are refined and filtered to …

Low-resolution analog-to-digital converters (ADCs) are a promising way to cut power consumption in future 6G receivers. However, they break many assumptions behind traditional communication system design. Instead of optimizing each block separately, this work uses an end-to-end autoencoder (AE) to jointly learn and optimize the transmitter and receiver for systems with phase quantization. Two key obstacles arise: (1) phase quantization is non-differentiable, which blocks gradientbased training back to the transmitter, and (2) standard crossentropy loss leads to poor block error rate (BLER) performance. To overcome these issues, we introduce a differentiable approximation of phase quantization and a new loss function that adds a distance-based regularization term to improve symbol separability. Simulations show that the proposed AE achieves near-optimal BLER for uncoded -ary phase shift keying …

This paper investigates sum-rate maximization in a multi-user downlink system assisted by a movable RIS (MRIS) under dynamic blockage conditions. Unlike conventional fixed RIS (FRIS) deployments, the RIS is modeled as a rigid panel whose anchor position can be continuously adjusted within a predefined region, introducing an additional spatial degree of freedom. In practical environments, temporary blockages arise from moving objects or environmental changes, significantly degrading communication links. To capture this effect, a geometric blockage model is incorporated into the RIS-to-user channels, where propagation paths are selectively obstructed based on line-of-sight conditions. The base station-to-RIS channel is modeled using a field-response-based multipath formulation, while RIS-to-user links follow distance-dependent path loss with phase propagation. For a fixed position, a common RIS …

Low-resolution digital-to-analog converters (DACs) and analog-to-digital converters (ADCs) are important enablers of energy-efficient wideband uplinks, but they introduce nonlinear distortions whose impact depends on the operating point and, in multi-user systems, on the time- and frequency-resource split. This paper studies quantization-aware uplink resource allocation for multi-user orthogonal multiple access (OMA), focusing on frequency division multiple access (FDMA) and time division multiple access (TDMA) with finite-resolution quantization at both the user transmitters and the base station receiver. Using Bussgang-style decompositions for power-preserving DAC quantization and mid-point ADC quantization, we derive a closed-form, conservative lower bound on the per-user achievable rate in terms of the allocation variables, channel gains, noise level, and quantization distortion factors. Under this …

Noncoherent phase shift keying receivers with coarse phase quantization require one additional bit of phase resolution than their coherent counterparts to achieve full diversity. In this letter, we propose a differential encoding scheme that applies a fixed, resolution-dependent phase shift to the transmitted symbols to remove this identified one-bit performance penalty. The impact of phase quantization on the error performance is characterized under the proposed differential encoding strategy. We show that the proposed differential encoding enables an -bit phase-quantized noncoherent receiver to match the error performance of a conventional -bit phase-quantized noncoherent receiver. Ultimately, our results demonstrate that the one-bit performance penalty previously observed in phase-quantized noncoherent receivers is not a fundamental limitation due to the lack of receiver channel state information, but …

This paper proposes a support vector machine (SVM)-based framework for signal detection in low-resolution single-input multiple-output (SIMO) systems, operating without channel state information (CSI). We consider n-bit phase quantization under M-PSK modulation and develop a CSI-free, model-free detector that directly maps quantized observations to symbol estimates. For QPSK modulation, the proposed method achieves symbol error probability (SEP) performance within 15% of the coherent maximum likelihood detector (MLD). We further extend the framework to higher-order M-PSK modulations, providing the first model-free, CSI-free SEP performance characterization for low-resolution, phase-quantized SIMO systems; addressing a regime where no general MLD solution is available.

Integrated sensing and communication (ISAC) is expected to be a key enabler for future wireless networks, improving spectral and hardware efficiency by jointly performing radar sensing and wireless communication within a unified framework. This paper proposes BeamNet , an unsupervised deep learning framework for transmit beamforming in dual-function radar-communication systems operating over general fading with imperfect channel state information (CSI). BeamNet maps noisy estimates of the communication and sensing channels to a transmit beamforming vector and is trained end-to-end by maximizing a weighted sum of the communication rate (CR) and sensing rate (SR), thereby learning the CR–SR Pareto frontier without beamforming labels or embedded optimization solvers. Using Rayleigh fading with perfect CSI, we first show that BeamNet reproduces the analytical Pareto-optimal beamforming solutions. We then use BeamNet to characterize, for Nakagami- m and Rician fading, the CR–SR trade-off across a range of fading parameters, and to assess robustness under distribution mismatch between training and test channels. Finally, under imperfect CSI, we demonstrate that BeamNet yields CR–SR trade-offs that are consistently sandwiched between the perfect-CSI and mismatched analytical baselines, outperforming the closed-form beamformer applied to imperfect CSI and recovering part of the performance loss caused by channel estimation errors. These results indicate that unsupervised learning offers a flexible and robust approach to ISAC beamforming in fading environments with imperfect channel knowledge.

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.

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 …

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 …

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 …

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 …

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 …

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 …