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1. Physical Layer Encryption for Wireless OFDM Communication Systems

   https://doi.org/10.1007/s41635-020-00097-8  

   Jacovic, Marko; Juretus, Kyle; Kandasamy, Nagarajan; Savidis, Ioannis; Dandekar, Kapil R. (July 2020, Journal of Hardware and Systems Security)

   Our everyday lives are impacted by the widespread adoption of wireless communication systems integral to residential, industrial, and commercial settings. Devices must be secure and reliable to support the emergence of large scale heterogeneous networks. Higher layer encryption techniques such as Wi-Fi Protected Access (WPA/WPA2) are vulnerable to threats, including even the latest WPA3 release. Physical layer security leverages existing components of the physical or PHY layer to provide a low-complexity solution appropriate for wireless devices. This work presents a PHY layer encryption technique based on frequency induction for Orthogonal Frequency Division Multiplexing (OFDM) signals to increase security against eavesdroppers. The secure transceiver consists of a key to frequency shift mapper, encryption module, and modified synchronizer for decryption. The system has been implemented on a Virtex-7 FPGA. The additional hardware overhead incurred on the Virtex-7 for both the transmitter and the receiver is low. Both simulation and hardware evaluation results demonstrate that the proposed system is capable of providing secure communication from an eavesdropper with no decrease in performance as compared with the baseline case of a standard OFDM transceiver. The techniques developed in this paper provide greater security to OFDM-based wireless communication systems. 

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2. Clipping-Enhanced Optical OFDM for IM/DD Communication Systems

   https://doi.org/10.1109/ICCW.2018.8403725  

   Lian, Jie; Brandt-Pearce, Maite (May 2018, 2018 IEEE International Conference on Communications Workshops (ICC Workshops))

   Orthogonal frequency division multiplexing (OFDM) is a candidate technique to provide high-speed data transmissions for optical communication systems. For intensity modulation and direct detection (IM/DD) optical communication systems, the peak transmitted power limitation of light sources and nonnegative transmitted signal constraints can result in nonlinear distortions from clipping. In this paper, we propose a clipping enhanced optical OFDM (CEO-OFDM) for IM/DD communication systems to reduce the clipping effects. CEO-OFDM transmits the information that results from clipping the peak power, which allows the use of a higher modulation index to improve the signal to noise ratio in exchange for a larger bandwidth. For the same transmitted data rate, CEO-OFDM can achieve a lower bit error rate than DC-biased optical OFDM (DCO-OFDM), asymmetrically clipped optical OFDM (ACO-OFDM) and unipolar OFDM (U-OFDM). By using a larger modulation constellation size, the proposed CEO-OFDM can support a higher throughput than other techniques when the same bit error rate is achieved. 

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3. A Machine Learning Approach for Detecting and Classifying Jamming Attacks Against OFDM-based UAVs

   https://doi.org/10.1145/3468218.3469049  

   Pawlak, Jered; Li, Yuchen; Price, Joshua; Wright, Matthew; Al Shamaileh, Khair; Niyaz, Quamar; Devabhaktuni, Vijay (June 2021, Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning)

   null (Ed.)

   In this paper, a machine learning (ML) approach is proposed to detect and classify jamming attacks on unmanned aerial vehicles (UAVs). Four attack types are implemented using software-defined radio (SDR); namely, barrage, single-tone, successive-pulse, and protocol-aware jamming. Each type is launched against a drone that uses orthogonal frequency division multiplexing (OFDM) communication to qualitatively analyze its impacts considering jamming range, complexity, and severity. Then, an SDR is utilized in proximity to the drone and in systematic testing scenarios to record the radiometric parameters before and after each attack is launched. Signal-to-noise ratio (SNR), energy threshold, and several OFDM parameters are exploited as features and fed to six ML algorithms to explore and enable autonomous jamming detection/classification. The algorithms are quantitatively evaluated with metrics including detection and false alarm rates to evaluate the received signals and facilitate efficient decision-making for improved reception integrity and reliability. The resulting ML approach detects and classifies jamming with an accuracy of 92.2% and a false-alarm rate of 1.35%. 

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4. MTC: Multiresolution Tensor Completion from Partial and Coarse Observations

   https://doi.org/10.1145/3447548.3467261  

   Yang, Chaoqi; Singh, Navjot; Xiao, Cao; Qian, Cheng; Solomonik, Edgar; Sun, Jimeng (August 2021, KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining)

   null (Ed.)

   Existing tensor completion formulation mostly relies on partial observations from a single tensor. However, tensors extracted from real-world data often are more complex due to: (i) Partial observation: Only a small subset of tensor elements are available. (ii) Coarse observation: Some tensor modes only present coarse and aggregated patterns (e.g., monthly summary instead of daily reports). In this paper, we are given a subset of the tensor and some aggregated/coarse observations (along one or more modes) and seek to recover the original fine-granular tensor with low-rank factorization. We formulate a coupled tensor completion problem and propose an efficient Multi-resolution Tensor Completion model (MTC) to solve the problem. Our MTC model explores tensor mode properties and leverages the hierarchy of resolutions to recursively initialize an optimization setup, and optimizes on the coupled system using alternating least squares. MTC ensures low computational and space complexity. We evaluate our model on two COVID-19 related spatio-temporal tensors. The experiments show that MTC could provide 65.20% and 75.79% percentage of fitness (PoF) in tensor completion with only 5% fine granular observations, which is 27.96% relative improvement over the best baseline. To evaluate the learned low-rank factors, we also design a tensor prediction task for daily and cumulative disease case predictions, where MTC achieves 50% in PoF and 30% relative improvements over the best baseline. 

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5. Design and Implementation of a Software Defined Radio-Based Radiometer Operating from a Small Unmanned Aircraft Systems

   https://doi.org/10.23919/USNC-URSI52669.2022.9887529  

   Farhad, Md Mehedi; Biswas, Sabyasachi; Rafi, Mohammad Abdus; Kurum, Mehmet; Gurbuz, Ali C. (July 2022, 2022 IEEE USNC-URSI Radio Science Meeting (Joint with AP-S Symposium))

   Passive remote sensing services are indispensable in modern society as they provide crucial information for Earth science and climate studies. In parallel, modern society also depends heavily on active wireless communication technologies for daily routines, with emerging technologies such as 5G further increasing this dependence. Unfortunately, the growth of active wireless systems often increases radio frequency interference (RFI) experienced by passive systems. This necessitates development of coexistence techniques and creation of new technology that enhances the existing and future wireless infrastructure. To study this problem, we are developing a unique testbed for collecting remote sensing datasets with ground truth in real-world settings, which will enable training, optimization, and benchmarking the coexistence solutions. The testbed includes (1) a software defined radio (SDR) based radiometer, incorporated with a dual-polarized microwave antenna operating in the L-band (1400 MHz–1427 MHz) and (2) prototyping SDR-based communication systems. This paper presents design and implementation of such radiometer from an unmanned aircraft system (UAS) for supporting different scenarios and geometries. 

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