@article{ozturk_anjinappa_erden_chowdhury_guvenc_dai_bhuyan_2023, title={Channel Rank Improvement in Urban Drone Corridors Using Passive Intelligent Reflectors}, ISSN={["1095-323X"]}, DOI={10.1109/AERO55745.2023.10115741}, abstractNote={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 multi path environment, taking into account that the drones are restricted to flying 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 with no IRSs present, and IRS-aided channels perform close to each other at sub-6 and mmWave bands.}, journal={2023 IEEE AEROSPACE CONFERENCE}, author={Ozturk, Ender and Anjinappa, Chethan K. and Erden, Fatih and Chowdhury, Md Moin Uddin and Guvenc, Ismail and Dai, Huaiyu and Bhuyan, Arupjyoti}, year={2023} }
 @article{ezuma_anjinappa_semkin_guvenc_2022, title={Comparative Analysis of Radar-Cross-Section-Based UAV Recognition Techniques}, volume={22}, ISSN={["1558-1748"]}, url={https://doi.org/10.1109/JSEN.2022.3194527}, DOI={10.1109/JSEN.2022.3194527}, abstractNote={This work investigates the problem of unmanned aerial vehicle (UAV) recognition using their radar cross section (RCS) signature. The RCS of six commercial UAVs is measured at 15 and 25 GHz in an anechoic chamber for both vertical–vertical (VV) polarization and horizontal–horizontal (HH) polarization. The RCS signatures are used to train 15 different recognition algorithms, each belonging to one of three different categories: statistical learning (SL), machine learning (ML), and deep learning (DL). The study shows that, while the average accuracy of all the algorithms increases with the signal-to-noise ratio (SNR), the ML algorithm achieved better accuracy than the SL and DL algorithms. For example, the classification tree ML achieves an accuracy of 98.66% at 3-dB SNR using the 15-GHz VV-polarized RCS test data from the UAVs. We investigate the recognition accuracy using the Monte Carlo analysis with the aid of boxplots, confusion matrices, and classification plots. On average, the accuracy of the classification tree ML model performed better than the other algorithms, followed by Peter Swerling’s statistical models and the discriminant analysis ML model. In general, the accuracy of the ML and SL algorithms outperformed the DL algorithms (Squeezenet, Googlenet, Nasnet, and Resnet 101) considered in the study. Furthermore, the computational time of each algorithm is analyzed. The study concludes that, while the SL algorithms achieved good recognition accuracy, the computational time was relatively long when compared to the ML and DL algorithms. Also, the study shows that the classification tree achieved the fastest average recognition time of about 0.46 ms.}, number={18}, journal={IEEE SENSORS JOURNAL}, author={Ezuma, Martins and Anjinappa, Chethan Kumar and Semkin, Vasilii and Guvenc, Ismail}, year={2022}, month={Sep}, pages={17932–17949} }
 @article{maeng_anjinappa_guvenc_2022, title={Coverage Probability Analysis of Passive Reflectors in Indoor Environments}, volume={26}, ISSN={["1558-2558"]}, url={https://doi.org/10.1109/LCOMM.2022.3193810}, DOI={10.1109/LCOMM.2022.3193810}, abstractNote={Since millimeter wave (mmWave) and sub-terahertz bands are highly vulnerable to blockage and penetration loss effects, wireless coverage enhancement is one of the critical challenges in indoor mmWave deployments. In particular, when the line-of-sight (LoS) link is blocked, a strong non-LoS (NLoS) path can provide a stable link quality. One of the efficient ways to improve the NLoS link is the use of strategically placed passive reflectors. In this letter, we study the indoor coverage improvement by using a transparent passive reflector attached on a wall. We consider an indoor open-door scenario, where the LoS link is blocked by the walls for receivers inside the room, and the coverage can only be achieved via an NLoS link through a passive reflector. We analytically derive closed-form equations of the reflection visibility probability and the coverage probability. By simulation and analytical results, we show the coverage dependency on the location and size of the reflector.}, number={10}, journal={IEEE COMMUNICATIONS LETTERS}, author={Maeng, Sung Joon and Anjinappa, Chethan K. and Guvenc, Ismail}, year={2022}, month={Oct}, pages={2287–2291} }
 @article{anjinappa_ganesh_ozdemir_ridenour_khawaja_guvenc_nomoto_ide_2022, title={Indoor Propagation Measurements with Transparent Reflectors at 28/39/120/144 GHz}, ISSN={["2164-7038"]}, DOI={10.1109/ICCWorkshops53468.2022.9814550}, abstractNote={One of the critical challenges of operating with the terahertz or millimeter-wave wireless networks is the necessity of at least a strong non-line-of-sight (NLoS) reflected path to form a stable link. Recent studies have shown that an economical way of enhancing/improving these NLoS links is by using passive metal-lic reflectors that provide strong reflections. However, despite its inherent radio advantage, metals can dramatically influence the landscape's appearance - especially the indoor environment. A conceptual view of escaping this is by using transparent reflectors. In this work, for the very first time, we evaluate the wireless propagation characteristics of passive transparent reflectors in an indoor environment at 28 GHz, 39 GHz, 120 GHz, and 144 GHz bands. In particular, we investigate the penetration loss and the reflection characteristics at different frequencies and compare them against the other common indoor materials such as ceiling tile, clear glass, drywall, plywood, and metal. The measurement results suggest that the transparent reflector, apart from an obvious advantage of transparency, has a higher penetration loss than the common indoor materials (excluding metal) and performs similarly to metal in terms of reflection. Our experimental results directly translate to better reflection performance and preserving the radio waves within the environ-ment than common indoor materials, with potential applications in controlled wireless communication.}, journal={2022 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS (ICC WORKSHOPS)}, author={Anjinappa, Chethan K. and Ganesh, Ashwini P. and Ozdemir, Ozgur and Ridenour, Kris and Khawaja, Wahab and Guvenc, Ismail and Nomoto, Hiroyuki and Ide, Yasuaki}, year={2022}, pages={1118–1123} }
 @article{ezuma_anjinappa_funderburk_guvenc_2022, title={Radar Cross Section Based Statistical Recognition of UAVs at Microwave Frequencies}, volume={58}, ISSN={["1557-9603"]}, url={https://doi.org/10.1109/TAES.2021.3096875}, DOI={10.1109/TAES.2021.3096875}, abstractNote={This article presents a radar cross-section (RCS)-based statistical recognition system for identifying/classifying unmanned aerial vehicles (UAVs) at microwave frequencies. First, the article presents the results of the vertical (VV) and horizontal (HH) polarization RCS measurement of six commercial UAVs at 15 and 25 GHz in a compact range anechoic chamber. The measurement results show that the average RCS of the UAVs depends on shape, size, material composition of the target UAV as well as the azimuth angle, frequency, and polarization of the illuminating radar. Afterward, radar characterization of the target UAVs is achieved by fitting the RCS measurement data to 11 different statistical models. From the model selection analysis, we observe that the lognormal, generalized extreme value, and gamma distributions are most suitable for modeling the RCS of the commercial UAVs while the Gaussian distribution performed relatively poorly. The best UAV radar statistics forms the class conditional probability densities for the proposed UAV statistical recognition system. The performance of the UAV statistical recognition system is evaluated at different signal noise ratio (SNR) with the aid of Monte Carlo analysis. At an SNR of 10 dB, the average classification accuracy of 97.60% or better is achievable.}, number={1}, journal={IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS}, author={Ezuma, Martins and Anjinappa, Chethan Kumar and Funderburk, Mark and Guvenc, Ismail}, year={2022}, month={Feb}, pages={27–46} }
 @article{ozturk_erden_du_anjinappa_ozdemir_guvenc_2022, title={Ray Tracing Analysis of Sub-6 GHz and mmWave Indoor Coverage with Reflecting Surfaces}, ISSN={["2164-2958"]}, DOI={10.1109/RWS53089.2022.9719917}, abstractNote={Indoor coverage and channel modelling is crucial for network planning purposes at mmWave bands. In this paper, we analyzed received power patterns and connectivity in an indoor office environment for sub-6 GHz and mmWave bands using ray tracing simulations and theoretical models over different scenarios. We discussed the effect of using metallic walls instead of regular drywall, base station (BS) location, and open/shut doors. Our results showed that ray tracing solutions are consistent with theoretical calculations, and using reflective walls significantly improves average received power and connectivity at mmWave bands, e.g., for the given floor plan, coverage increases from 86% to 97.5% at 60 GHz band.}, journal={2022 IEEE RADIO AND WIRELESS SYMPOSIUM (RWS)}, author={Ozturk, Ender and Erden, Fatih and Du, Kairui and Anjinappa, Chethan K. and Ozdemir, Ozgur and Guvenc, Ismail}, year={2022}, pages={160–163} }
 @article{chowdhury_anjinappa_guvenc_sichitiu_ozdemir_bhattacherjee_dutta_marojevic_floyd_2021, title={A Taxonomy and Survey on Experimentation Scenarios for Aerial Advanced Wireless Testbed Platforms}, volume={2021-March}, ISSN={["1095-323X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85111405299&partnerID=MN8TOARS}, DOI={10.1109/AERO50100.2021.9438449}, abstractNote={There are various works in the recent literature on fundamental research and experimentation on unmanned aerial vehicle (UAV) communications. On the other hand, to our best knowledge, there is no taxonomy and survey on experimentation possibilities with a software-defined aerial wireless platform. The goal of this paper is first to have a brief overview of large-scale advanced wireless experimentation platforms broadly available to the wireless research community, including also the Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW). We then provide a detailed taxonomy and a comprehensive survey of experimentation possibilities that can be carried out in a platform such as AERPAW. In particular, we conceptualize and present eleven different classes of advanced and aerial wireless experiments, provide several example experiments for each class, and discuss some of the existing related works in the literature. The paper will help to develop a better understanding of the equipment and software resources that can be available for experimentation in mid-scale wireless platforms, as well as the capabilities and limitations of such platforms.}, journal={2021 IEEE AEROSPACE CONFERENCE (AEROCONF 2021)}, author={Chowdhury, Md Moin Uddin and Anjinappa, Chethan K. and Guvenc, Ismail and Sichitiu, Mihail and Ozdemir, Ozgur and Bhattacherjee, Udita and Dutta, Rudra and Marojevic, Vuk and Floyd, Brian}, year={2021} }
 @article{anjinappa_erden_guvenc_2021, title={Base Station and Passive Reflectors Placement for Urban mmWave Networks}, volume={70}, ISSN={["1939-9359"]}, url={https://doi.org/10.1109/TVT.2021.3065221}, DOI={10.1109/TVT.2021.3065221}, abstractNote={The use of millimeter-wave (mmWave) bands in 5G networks introduces a new set of challenges to network planning. Vulnerability to blockages and high path loss at mmWave frequencies require careful planning of the network to achieve a desired service quality. In this paper, we propose a novel 3D geometry-based framework for deploying mmWave base stations (gNBs) in urban environments by considering first-order reflection effects. We also provide a solution for the optimum deployment of passive metallic reflectors (PMRs) to extend radio coverage to non-line-of-sight (NLoS) areas. In particular, we perform visibility analysis to find the direct and indirect visibility regions, and using these, we derive a geometry-and-blockage-aided path loss model. We then formulate the network planning problem as two independent optimization problems, placement of gNB(s) and PMRs, to maximize the coverage area, minimize the deployment cost, and maintain a desired quality-of-service level. We test the efficacy of our proposed approach using a generic map and compare our simulation results with the ray tracing solution. Our simulation results show that considering the first-order reflections in planning the mmWave network helps reduce the number of PMRs required to cover the NLoS area. Moreover, the gNB placement aided with PMRs require fewer gNBs to cover the same area, which in turn reduces the deployment cost.}, number={4}, journal={IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Anjinappa, Chethan Kumar and Erden, Fatih and Guvenc, Ismail}, year={2021}, month={Apr}, pages={3525–3539} }
 @article{anjinappa_guvenc_2021, title={Coverage Hole Detection for mmWave Networks: An Unsupervised Learning Approach}, volume={25}, ISSN={["1558-2558"]}, url={https://doi.org/10.1109/LCOMM.2021.3106251}, DOI={10.1109/LCOMM.2021.3106251}, abstractNote={The utilization of millimeter-wave (mmWave) bands in 5G networks poses new challenges to network planning. Vulnerability to blockages at mmWave bands can cause coverage holes (CHs) in the radio environment, leading to radio link failure when a user enters these CHs. Detection of the CHs carries critical importance so that necessary remedies can be introduced to improve coverage. In this letter, we propose a novel approach to identify the CHs in an unsupervised fashion using a state-of-the-art manifold learning technique: uniform manifold approximation and projection. The key idea is to preserve the local-connectedness structure inherent in the collected unlabelled channel samples, such that the CHs from the service area are detectable. Our results on the DeepMIMO dataset scenario demonstrate that the proposed method can learn the structure within the data samples and provide visual holes in the low-dimensional embedding while preserving the CH boundaries. Once the CH boundary is determined in the low-dimensional embedding, channel-based localization techniques can be applied to these samples to obtain the geographical boundaries of the CHs.}, number={11}, journal={IEEE COMMUNICATIONS LETTERS}, author={Anjinappa, Chethan K. and Guvenc, Ismail}, year={2021}, month={Nov}, pages={3580–3584} }
 @article{eroglu_anjinappa_guvenc_pala_2021, title={Slow Beam Steering and NOMA for Indoor Multi-User Visible Light Communications}, volume={20}, ISSN={["1558-0660"]}, url={https://doi.org/10.1109/TMC.2019.2960495}, DOI={10.1109/TMC.2019.2960495}, abstractNote={Visible light communication (VLC) is an emerging technology that enables broadband data rates using the visible spectrum. In this paper, considering slow beam steering where VLC beam directions are assumed to be fixed during a transmission frame, we find the steering angles that simultaneously serve multiple users within the frame duration and maximize the data rates. This is achieved by solving a non-convex optimization problem using a grid-based search and majorization-minimization (MM) procedure. Subsequently, we consider multiple steerable beams with a larger number of users in the network and propose an algorithm to cluster users and serve each cluster with a separate beam. We optimize the transmit power of each beam to maximize the data rates. Finally, we propose a non-orthogonal multiple access (NOMA) scheme for the beam steering and user clustering scenario, to further increase the data rates of the users. The simulation results show that the proposed beam steering method can efficiently serve a high number of users, and with power optimization, a sum rate gain up to thirteen times is possible. The simulation results for NOMA suggests an additional 10 Mbps sum rate gain for each NOMA user pair.}, number={4}, journal={IEEE TRANSACTIONS ON MOBILE COMPUTING}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Eroglu, Yusuf Said and Anjinappa, Chethan Kumar and Guvenc, Ismail and Pala, Nezih}, year={2021}, month={Apr}, pages={1627–1641} }
 @article{sayeed_vouras_gentile_weiss_quimby_cheng_modad_zhang_anjinappa_erden_et al._2020, title={A Framework for Developing Algorithms for Estimating Propagation Parameters from Measurements}, ISSN={["2166-0069"]}, DOI={10.1109/GCWkshps50303.2020.9367404}, abstractNote={A framework is proposed for developing and evaluating algorithms for extracting multipath propagation components (MPCs) from measurements collected by sounders at millimeter-wave (mmW) frequencies. To focus on algorithmic performance, an idealized model is proposed for the spatial frequency response of the propagation environment measured by a sounder. The input to the sounder model is a pre-determined set of MPC parameters that serve as the “ground truth”. A three-dimensional angle-delay (beamspace) representation of the measured spatial frequency response serves as a natural domain for implementing and analyzing MPC extraction algorithms. Metrics for quantifying the error in estimated MPC parameters are introduced. Initial results are presented for a greedy matching pursuit algorithm that performs a least-squares (LS) reconstruction of the MPC path gains within the iterations. The results indicate that the simple greedy-LS algorithm has the ability to extract MPCs over a large dynamic range, and suggest several avenues for further performance improvement through extensions of the greedy-LS algorithm as well as by incorporating features of other algorithms, such as SAGE and RIMAX.}, journal={2020 IEEE GLOBECOM WORKSHOPS (GC WKSHPS)}, author={Sayeed, Akbar and Vouras, Peter and Gentile, Camillo and Weiss, Alec and Quimby, Jeanne and Cheng, Zihang and Modad, Bassel and Zhang, Yuning and Anjinappa, Chethan and Erden, Fatih and et al.}, year={2020} }
 @article{ezuma_erden_anjinappa_ozdemir_guvenc_2020, title={Detection and Classification of UAVs Using RF Fingerprints in the Presence of Wi-Fi and Bluetooth Interference}, volume={1}, ISSN={["2644-125X"]}, url={https://doi.org/10.1109/OJCOMS.2019.2955889}, DOI={10.1109/OJCOMS.2019.2955889}, abstractNote={This paper investigates the problem of detection and classification of unmanned aerial vehicles (UAVs) in the presence of wireless interference signals using a passive radio frequency (RF) surveillance system. The system uses a multistage detector to distinguish signals transmitted by a UAV controller from the background noise and interference signals. First, RF signals from any source are detected using a Markov models-based naïve Bayes decision mechanism. When the receiver operates at a signal-to-noise ratio (SNR) of 10 dB, and the threshold, which defines the states of the models, is set at a level 3.5 times the standard deviation of the preprocessed noise data, a detection accuracy of 99.8% with a false alarm rate of 2.8% is achieved. Second, signals from Wi-Fi and Bluetooth emitters, if present, are detected based on the bandwidth and modulation features of the detected RF signal. Once the input signal is identified as a UAV controller signal, it is classified using machine learning (ML) techniques. Fifteen statistical features extracted from the energy transients of the UAV controller signals are fed to neighborhood component analysis (NCA), and the three most significant features are selected. The performance of the NCA and five different ML classifiers are studied for 15 different types of UAV controllers. A classification accuracy of 98.13% is achieved by k-nearest neighbor classifier at 25 dB SNR. Classification performance is also investigated at different SNR levels and for a set of 17 UAV controllers which includes two pairs from the same UAV controller models.}, journal={IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Ezuma, Martins and Erden, Fatih and Anjinappa, Chethan Kumar and Ozdemir, Ozgur and Guvenc, Ismail}, year={2020}, pages={60–76} }
 @article{ozdemir_anjinappa_hamila_al-dhahir_guvenc_2019, title={Joint Frame Synchronization and Channel Estimation: Sparse Recovery Approach and USRP Implementation}, volume={7}, ISSN={["2169-3536"]}, DOI={10.1109/ACCESS.2019.2905761}, abstractNote={Correlation-based techniques used for frame synchronization can suffer significant performance degradation over multi-path frequency-selective channels. In this paper, we propose a joint frame synchronization and channel estimation (JFSCE) framework as a remedy to this problem. This framework, however, increases the size of the resulting combined channel vector which should capture both the channel impulse response vector and the frame boundary offset and, therefore, its estimation becomes more challenging. On the other hand, because the combined channel vector is sparse, sparse channel estimation methods can be applied. We propose several JFSCE methods using popular sparse signal recovery algorithms which exploit the sparsity of the combined channel vector. Subsequently, the sparse channel vector estimate is used to design a sparse equalizer. Our simulation results and experimental measurements using software defined radios show that in some scenarios our proposed method improves the overall system performance significantly, in terms of the mean square error between the transmitted and the equalized symbols compared to the conventional method.}, journal={IEEE ACCESS}, author={Ozdemir, Ozgur and Anjinappa, Chethan Kumar and Hamila, Ridha and Al-Dhahir, Naofal and Guvenc, Ismail}, year={2019}, pages={39041–39053} }