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 …

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.

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 …

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 …