arXiv subjects

Arupjyoti Bhuyan

Publications and source records attributed to Arupjyoti Bhuyan.

At least 19 recordsLinked to original sources

arXiv2026

Battlefield 5G: Dual-PKI and TPM-Based UE Attestation for Tactical 5G Standalone Networks

The standardized 5G Authentication and Key Agreement (5G-AKA) authenticates a subscriber credential stored on a Universal Subscriber Identity Model (USIM) but does not authenticate the physical device that holds that credential or verify its boot state. This gap is significant in tactical 5G deployments, where user equipment may be captured, modified, returned to service, or used with transplanted subscriber credentials. We present Battlefield 5G, a pre-authentication framework for 5G Standalone networks that combines dual X.509 device-certificate checks with Trusted Platform Module (TPM) -based boot attestation before standard registration is accepted. The design places an outer certificate challenge on the 5G base-station called gNB, an independent inner certificate challenge on the Access and Mobility Management Function (AMF) in the 5G core network, and a TPM PCR (Platform Configuration Register) quote verified by an attestation proxy on the 5G core network side. A gNodeB (gNB) side Radio Resource Control (RRC) forwarding gate and an AMF-side save-and-replay mechanism enable multi-round certificate and attestation challenge-response exchanges to be inserted into the registration path without modifying any 3GPP Non-Access Stratum (NAS) message structures or adding new NAS message types. We implement these capabilities by extending the Radio Access Network of the Software Radio System (srsRAN), gNB, User Equipment of the Software Radio System (srsUE) and Open5GS in a B210-based Universal Radio Peripheral (USRP) testbed with a hardware TPM 2.0 in the UE. The prototype blocks SIM-transplant, rogue-certificate, firmware-tampering, and replay attacks. Across six trials, Battlefield 5G increases average onboarding latency from 1886 ms to 2260 ms, adding 373.4 ms of pre-authentication overhead while preserving standard 5G-AKA, security mode, and packet data unit (PDU) session procedures.

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arXiv2026

UAV-Based 3D Spectrum Sensing: Insights on Altitude, Bandwidth, Trajectory, and Effective Antenna Patterns on REM Reconstruction

Spectrum sensing and the generation of 3D Radio Environment Maps (REMs) are essential for enabling spectrum sharing within cognitive radio networks. While Uncrewed Aerial Vehicles (UAVs) offer high-mobility 3D sensing, REM accuracy is challenged by dynamic flight behaviors, where fluctuations in UAV speed and direction introduce measurement inconsistencies. Furthermore, the airframe itself impacts the onboard antenna's radiation characteristics. In this paper, using real-world data, we systematically analyze how REM reconstruction accuracy is shaped by three key pillars: physical sensing parameters like altitude and bandwidth, environmental shadowing, and distortions caused by the UAV airframe. First, we benchmark diverse spatial prediction models, including simple Kriging (SK), ordinary Kriging (OK), trans-Gaussian Kriging, and Gaussian process regression (GPR). We demonstrate that while SK and its trans-Gaussian variant are highly accurate at extreme sample sparsity, OK improves as sample size increases, and GPR serves as the most stable overall baseline. Building on this, we propose a novel matrix completion (MC)-assisted GPR framework that enhances REM reconstruction in the presence of non-uniform spatial smoothness. The method operates by decomposing the REM into two distinct layers: a global smooth component and a highly varying local component. Our analysis based on real-world measurements reveals three key findings: 1) REM accuracy and shadowing variance follow a distinct tri-phasic trend as the UAV altitude increases; 2) REM accuracy significantly improves with increased spectrum bandwidth; and 3) antenna pattern calibration from in-field measurements significantly enhances REM accuracy by accounting for the effect of the UAV airframe.

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arXiv2026

TransfoREM: Transformer aided 3D Radio Environment Mapping

Providing reliable cellular connectivity to Unmanned Aerial Vehicles (UAV) is a key challenge, as existing terrestrial networks are deployed mainly for ground-level coverage. The cellular network coverage may be available for a limited range from the antenna side lobes, with poor connectivity further exacerbated by UAV flight dynamics. In this work, we propose TransfoREM, a 3D Radio Environment Map (REM) generation method that combines deterministic channel models and real-world data to map terrestrial network coverage at higher altitudes. At the core of our solution is a transformer model that translates radio propagation mapping into a sequence prediction task to construct REMs. Our results demonstrate that TransfoREM offers improved interpolation capability on real-world data compared against conventional Kriging and other machine learning (ML) techniques. Furthermore, TransfoREM is designed for holistic integration into cellular networks at the base station (BS) level, where it can build REMs, which can then be leveraged for enhanced resource allocation, interference management, and spatial spectrum utilization.

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arXiv2025

Elevation- and Tilt-Aware Shadow Fading Correlation Modeling for UAV Communications

Future wireless networks demand a more accurate understanding of channel behavior to enable efficient communication with reduced interference. Uncrewed Aerial Vehicles (UAVs) are poised to play an integral role in these networks, offering versatile applications and flexible deployment options. However, accurately characterizing the shadow fading (SF) behavior in UAV communications remains a challenge. Traditional SF correlation models rely on spatial distance and neglect the UAV's 3D orientation and elevation angle. Yet even slight variations in pitch angle (5 to 10 degrees) can significantly affect the signal strength observed by a UAV. In this study, we investigate the impact of UAV pitch and elevation geometry on SF and propose an elevation- and tilt-aware spatial correlation model. We use a real-world fixed-altitude UAV measurement dataset collected in a rural environment at 3.32 GHz with a 125 kHz bandwidth. Results show that a 10-degree tilt-angle separation and a 20-degree elevation-angle separation can reduce the SF correlation by up to 15% and 40%, respectively. In addition, integrating the proposed correlation model into the ordinary Kriging (OK) framework for signal strength prediction yields an approximate 1.5 dB improvement in median RMSE relative to the traditional correlation model that ignores UAV orientation and elevation.

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arXiv2025

GPS Spoofing Attacks on Automated Frequency Coordination System in Wi-Fi 6E and Beyond

The 6 GHz spectrum, recently opened for unlicensed use under Wi-Fi 6E and Wi-Fi 7, overlaps with frequencies used by mission-critical incumbent systems such as public safety communications and utility infrastructure. To prevent interference, the FCC mandates the use of Automated Frequency Coordination (AFC) systems, which assign safe frequency and power levels based on Wi-Fi Access Point (AP)-reported locations. In this work, we demonstrate that GPS-based location reporting, which Wi-Fi APs use, can be spoofed using inexpensive, off-the-shelf radio equipment. This enables attackers to manipulate AP behavior, gain unauthorized spectrum access, cause harmful interference, or disable APs entirely by spoofing them into foreign locations. We validate these attacks in a controlled lab setting against a commercial AP and evaluate a commercial AFC system under spoofed scenarios. Our findings highlight critical gaps in the security assumptions of AFC and motivate the need for stronger location integrity protections.

cs.NI↗

arXiv2025

Platform-Aware Channel Knowledge Mapping via Mutual Antenna Pattern Learning in 3D Wireless Links

This letter proposes a platform-aware framework to characterize wireless links by empirically modeling the `near-platform' scattering and reflections induced by the hardware mounting structures of both endpoints. We model the link characteristics as a novel mutual antenna pattern: a joint function of the angle of arrival (AoA) and angle of departure (AoD). We demonstrate that while individual platform-aware patterns are mathematically unidentifiable from power measurements, the coupled mutual pattern can be effectively estimated in a least-squares sense. Our framework is evaluated using noisy measurement data, revealing that as few as 10 measurements per joint-angular bin are sufficient. The proposed methodology is validated through cross-validation of experimental subsets, demonstrating that the learned mutual radiation pattern reduces path loss estimation errors by up to 10 dB compared to traditional models using isolated anechoic chamber antenna gains.

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arXiv2022

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

The exponential growth of internet connected systems has generated numerous challenges, such as spectrum shortage issues, which require efficient spectrum sharing (SS) solutions. Complicated and dynamic SS systems can be exposed to different potential security and privacy issues, requiring protection mechanisms to be adaptive, reliable, and scalable. Machine learning (ML) based methods have frequently been proposed to address those issues. In this article, we provide a comprehensive survey of the recent development of ML based SS methods, the most critical security issues, and corresponding defense mechanisms. In particular, we elaborate the state-of-the-art methodologies for improving the performance of SS communication systems for various vital aspects, including ML based cognitive radio networks (CRNs), ML based database assisted SS networks, ML based LTE-U networks, ML based ambient backscatter networks, and other ML based SS solutions. We also present security issues from the physical layer and corresponding defending strategies based on ML algorithms, including Primary User Emulation (PUE) attacks, Spectrum Sensing Data Falsification (SSDF) attacks, jamming attacks, eavesdropping attacks, and privacy issues. Finally, extensive discussions on open challenges for ML based SS are also given. This comprehensive review is intended to provide the foundation for and facilitate future studies on exploring the potential of emerging ML for coping with increasingly complex SS and their security problems.

cs.CR↗

arXiv2021

Ensuring Reliable Connectivity to Cellular-Connected UAVs with Up-tilted Antennas and Interference Coordination

To integrate unmanned aerial vehicles (UAVs) in future large-scale deployments, a new wireless communication paradigm, namely, the cellular-connected UAV has recently attracted interest. However, the line-of-sight dominant air-to-ground channels along with the antenna pattern of the cellular ground base stations (GBSs) introduce critical interference issues in cellular-connected UAV communications. In particular, the complex antenna pattern and the ground reflection (GR) from the down-tilted antennas create both coverage holes and patchy coverage for the UAVs in the sky, which leads to unreliable connectivity from the underlying cellular network. To overcome these challenges, in this paper, we propose a new cellular architecture that employs an extra set of co-channel antennas oriented towards the sky to support UAVs on top of the existing down-tilted antennas for ground user equipment (GUE). To model the GR stemming from the down-tilted antennas, we propose a path-loss model, which takes both antenna radiation pattern and configuration into account. Next, we formulate an optimization problem to maximize the minimum signal-to-interference ratio (SIR) of the UAVs by tuning the up-tilt (UT) angles of the up-tilted antennas. Since this is an NP-hard problem, we propose a genetic algorithm (GA) based heuristic method to optimize the UT angles of these antennas. After obtaining the optimal UT angles, we integrate the 3GPP Release-10 specified enhanced inter-cell interference coordination (eICIC) to reduce the interference stemming from the down-tilted antennas. Our simulation results based on the hexagonal cell layout show that the proposed interference mitigation method can ensure higher minimum SIRs for the UAVs over baseline methods while creating minimal impact on the SIR of GUEs.

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arXiv2021

A Q-Learning-based Approach for Distributed Beam Scheduling in mmWave Networks

We consider the problem of distributed downlink beam scheduling and power allocation for millimeter-Wave (mmWave) cellular networks where multiple base stations (BSs) belonging to different service operators share the same unlicensed spectrum with no central coordination or cooperation among them. Our goal is to design efficient distributed beam scheduling and power allocation algorithms such that the network-level payoff, defined as the weighted sum of the total throughput and a power penalization term, can be maximized. To this end, we propose a distributed scheduling approach to power allocation and adaptation for efficient interference management over the shared spectrum by modeling each BS as an independent Q-learning agent. As a baseline, we compare the proposed approach to the state-of-the-art non-cooperative game-based approach which was previously developed for the same problem. We conduct extensive experiments under various scenarios to verify the effect of multiple factors on the performance of both approaches. Experiment results show that the proposed approach adapts well to different interference situations by learning from experience and can achieve higher payoff than the game-based approach. The proposed approach can also be integrated into our previously developed Lyapunov stochastic optimization framework for the purpose of network utility maximization with optimality guarantee. As a result, the weights in the payoff function can be automatically and optimally determined by the virtual queue values from the sub-problems derived from the Lyapunov optimization framework.

cs.IT↗

arXiv2021

60 GHz Outdoor Propagation Measurements and Analysis Using Facebook Terragraph Radios

The high attenuation of millimeter-wave (mmWave) would significantly reduce the coverage areas, and hence it is critical to study the propagation characteristics of mmWave in multiple deployment scenarios. In this work, we investigated the propagation and scattering behavior of 60 GHz mmWave signals in outdoor environments at a travel distance of 98 m for an aerial link (rooftop to rooftop), and 147 m for a ground link (light-pole to light-pole). Measurements were carried out using Facebook Terragraph (TG) radios. Results include received power, path loss, signal-to-noise ratio (SNR), and root mean square (RMS) delay spread for all beamforming directions supported by the antenna array. Strong line-of-sight (LOS) propagation exists in both links. We also observed rich multipath components (MPCs) due to edge scatterings in the aerial link, while only LOS and ground reflection MPCs in the other link.

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arXiv2021

Channel Rank Improvement in Urban Drone Corridors Using Passive Intelligent Reflectors

Multiple-input multiple-output (MIMO) techniques can help in scaling the achievable air-to-ground (A2G) channel capacity while communicating with drones. However, spatial multiplexing with drones suffers from rank deficient channels due to the unobstructed line-of-sight (LoS), especially in millimeter-wave (mmWave) frequencies that use narrow beams. One possible solution is utilizing low-cost and low-complexity metamaterial-based intelligent reflecting surfaces (IRS) to enrich the multipath environment, taking into account that the drones are restricted to fly only within well-defined drone corridors. A hurdle with this solution is placing the IRSs optimally. In this study, we propose an approach for IRS placement with a goal to improve the spatial multiplexing gains, and hence to maximize the average channel capacity in a predefined drone corridor. Our results at 6 GHz, 28 GHz and 60 GHz show that the proposed approach increases the average rates for all frequency bands for a given drone corridor, when compared with the environment where there are no IRSs present, and IRS-aided channels perform close to each other at sub-6 and mmWave bands.

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arXiv2021

A Non-cooperative Game-based Distributed Beam Scheduling Framework for 5G Millimeter-Wave Cellular Networks

This paper studies the problem of distributed beam scheduling for 5G millimeter-Wave (mm-Wave) cellular networks where base stations (BSs) belonging to different operators share the same spectrum without centralized coordination among them. Our goal is to design efficient distributed scheduling algorithms to maximize the network utility, which is a function of the achieved throughput by the user equipment (UEs), subject to the average and instantaneous power consumption constraints of the BSs. We propose a Media Access Control (MAC) and a power allocation/adaptation mechanism utilizing the Lyapunov stochastic optimization framework and non-cooperative games. In particular, we first decompose the original utility maximization problem into two sub-optimization problems for each time frame, which are a convex optimization problem and a non-convex optimization problem, respectively. By formulating the distributed scheduling problem as a non-cooperative game where each BS is a player attempting to optimize its own utility, we provide a distributed solution to the non-convex sub-optimization problem via finding the Nash Equilibrium (NE) of the game whose weights are determined optimally by the Lyapunov optimization framework. Finally, we conduct simulation under various network settings to show the effectiveness of the proposed game-based beam scheduling algorithm in comparison to that of several reference schemes.

cs.IT↗

arXiv2021

Precoder Design for Physical-Layer Security and Authentication in Massive MIMO UAV Communications

Supporting reliable and seamless wireless connectivity for unmanned aerial vehicles (UAVs) has recently become a critical requirement to enable various different use cases of UAVs. Due to their widespread deployment footprint, cellular networks can support beyond visual line of sight (BVLOS) communications for UAVs. In this paper, we consider cellular connected UAVs (C-UAVs) that are served by massive multiple-input-multiple-output (MIMO) links to extend coverage range, while also improving physical layer security and authentication. We consider Rician channel and propose a novel linear precoder design for transmitting data and artificial noise (AN). We derive the closed-form expression of the ergodic secrecy rate of C-UAVs for both conventional and proposed precoder designs. In addition, we obtain the optimal power splitting factor that divides the power between data and AN by asymptotic analysis. Then, we apply the proposed precoder design in the fingerprint embedding authentication framework, where the goal is to minimize the probability of detection of the authentication tag at an eavesdropper. In simulation results, we show the superiority of the proposed precoder in both secrecy rate and the authentication probability considering moderate and large number of antenna massive MIMO scenarios.

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arXiv2021

Base Station Antenna Uptilt Optimization for Cellular-Connected Drone Corridors

The concept of drone corridors is recently getting more attention to enable connected, safe, and secure flight zones in the national airspace. To support beyond visual line of sight (BVLOS) operations of aerial vehicles in a drone corridor, cellular base stations (BSs) serve as a convenient infrastructure, since such BSs are widely deployed to provide seamless wireless coverage. However, antennas in the existing cellular networks are down-tilted to optimally serve their ground users, which results in coverage holes if they are also used to serve drones. In this letter, we consider the use of additional uptilted antennas at cellular BSs and optimize the uptilt angle to minimize outage probability for a given drone corridor. Our numerical results show how the beamwidth and the maximum drone corridor height affect the optimal value of the antenna uptilt angle.

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arXiv2021

Uncoordinated Spectrum Sharing in Millimeter Wave Networks Using Carrier Sensing

We propose using Carrier Sensing (CS) for distributed interference management in millimeter-wave (mmWave) cellular networks where spectrum is shared by multiple operators that do not coordinate among themselves. In addition, even the base station sites can be shared by the operators. We describe important challenges in using traditional CS in this setting and propose enhanced CS protocols to address these challenges. Using stochastic geometry, we develop a general framework for downlink coverage probability analysis of our shared mmWave network in the presence of CS and derive the downlink coverage probability expressions for several CS protocols. To the best of our knowledge, our work is the first to investigate and analyze (using stochastic geometry) CS for mmWave networks with spectrum and BS sites shared among non-coordinating operators. We evaluate the downlink coverage probability of our shared mmWave network using simulations as well as numerical examples based on our analysis. Our evaluations show that our proposed enhancements lead to an improvement in downlink coverage probability, compared to the downlink coverage probability with no CS, for higher values of signal-to-interference and noise ratio (SINR). Interestingly, our evaluations also reveal that for lower values of SINR, not using any CS is the best strategy in terms of the downlink coverage probability.

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arXiv2020

Physical Layer Security for NOMA Transmission in mmWave Drone Networks

The non-orthogonal multiple access (NOMA) and millimeter-wave (mmWave) transmission enable the unmanned aerial vehicle (UAV) assisted wireless networks to provide broadband connectivity over densely packed urban areas. The presence of malicious receivers, however, compromise the security of the UAV-to-ground communications link, thereby degrading secrecy rates. In this work, we consider a NOMA-based transmission strategy in a mmWave UAV-assisted wireless network, and investigate the respective secrecy-rate performance rigorously. In particular, we propose a protected-zone approach to enhance the secrecy-rate performance by preventing the most vulnerable subregion (outside the user region) from the presence of malicious receivers. The respective secrecy rates are then derived analytically as a function of the protected zone, which verifies great secrecy rate improvements through optimizing shape of the protected zone in use. Furthermore, we show that the optimal protected zone shape for mmWave links appears as a compromise between protecting the angle versus distance dimension, which would otherwise form to protect solely the distance dimension for sub-6GHz links. We also numerically evaluate the impact of transmission power, protected-zone size, and UAV altitude on the secrecy-rate performance improvements as practical considerations.

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arXiv2020

Who is in Control? Practical Physical Layer Attack and Defense for mmWave based Sensing in Autonomous Vehicles

With the wide bandwidths in millimeter wave (mmWave) frequency band that results in unprecedented accuracy, mmWave sensing has become vital for many applications, especially in autonomous vehicles (AVs). In addition, mmWave sensing has superior reliability compared to other sensing counterparts such as camera and LiDAR, which is essential for safety-critical driving. Therefore, it is critical to understand the security vulnerabilities and improve the security and reliability of mmWave sensing in AVs. To this end, we perform the end-to-end security analysis of a mmWave-based sensing system in AVs, by designing and implementing practical physical layer attack and defense strategies in a state-of-the-art mmWave testbed and an AV testbed in real-world settings. Various strategies are developed to take control of the victim AV by spoofing its mmWave sensing module, including adding fake obstacles at arbitrary locations and faking the locations of existing obstacles. Five real-world attack scenarios are constructed to spoof the victim AV and force it to make dangerous driving decisions leading to a fatal crash. Field experiments are conducted to study the impact of the various attack scenarios using a Lincoln MKZ-based AV testbed, which validate that the attacker can indeed assume control of the victim AV to compromise its security and safety. To defend the attacks, we design and implement a challenge-response authentication scheme and a RF fingerprinting scheme to reliably detect aforementioned spoofing attacks.

cs.CR↗

arXiv2020

Power Allocation for Fingerprint-Based PHY-Layer Authentication with mmWave UAV Networks

Physical layer security (PLS) techniques can help to protect wireless networks from eavesdropper attacks. In this paper, we consider the authentication technique that uses fingerprint embedding to defend 5G cellular networks with unmanned aerial vehicle (UAV) systems from eavesdroppers and intruders. Since the millimeter wave (mmWave) cellular networks use narrow and directional beams, PLS can take further advantage of the 3D spatial dimension for improving the authentication of UAV users. Considering a multi-user mmWave cellular network, we propose a power allocation technique that jointly takes into account splitting of the transmit power between the precoder and the authentication tag, which manages both the secrecy as well as the achievable rate. Our results show that we can obtain optimal achievable rate with expected secrecy.

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