Browsing by Author "Erpek, Tugba"

Now showing 1 - 5 of 5
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    Adversarial Machine Learning for NextG Covert Communications Using Multiple Antennas
    Kim, Brian; Sagduyu, Yalin; Davaslioglu, Kemal; Erpek, Tugba; Ulukus, Sennur (MDPI, 2022-07-29)
    This paper studies the privacy of wireless communications from an eavesdropper that employs a deep learning (DL) classifier to detect transmissions of interest. There exists one transmitter that transmits to its receiver in the presence of an eavesdropper. In the meantime, a cooperative jammer (CJ) with multiple antennas transmits carefully crafted adversarial perturbations over the air to fool the eavesdropper into classifying the received superposition of signals as noise. While generating the adversarial perturbation at the CJ, multiple antennas are utilized to improve the attack performance in terms of fooling the eavesdropper. Two main points are considered while exploiting the multiple antennas at the adversary, namely the power allocation among antennas and the utilization of channel diversity. To limit the impact on the bit error rate (BER) at the receiver, the CJ puts an upper bound on the strength of the perturbation signal. Performance results show that this adversarial perturbation causes the eavesdropper to misclassify the received signals as noise with a high probability while increasing the BER at the legitimate receiver only slightly. Furthermore, the adversarial perturbation is shown to become more effective when multiple antennas are utilized.
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    Encoding and decoding of information for wireless transmission using multi-antenna transceivers
    (United States Patent and Trademark Office, 2019-05-28)
    Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned communication over multi-input-multi-output (MIMO) channels. One of the methods includes: determining a transmitter and a receiver, at least one of which implements a machine-learning network; determining a MIMO channel model; determining first information; using the transmitter to process the first information and generate first RF signals representing inputs to the MIMO channel model; determining second RF signals representing outputs of the MIMO channel model, each second RF signal representing aggregated reception of the first RF signals altered by transmission through the MIMO channel model; using the receiver to process the second RF signals and generate second information as a reconstruction of the first information; calculating a measure of distance between the second and first information; and updating the machine-learning network based on the measure of distance between the second and first information.
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    How to Attack and Defend NextG Radio Access Network Slicing With Reinforcement Learning
    Shi, Yi; Sagduyu, Yalin E.; Erpek, Tugba; Gursoy, M. Cenk (IEEE, 2023)
    In this paper, reinforcement learning (RL) for network slicing is considered in next generation (NextG) radio access networks, where the base station (gNodeB) allocates resource blocks (RBs) to the requests of user equipments and aims to maximize the total reward of accepted requests over time. Based on adversarial machine learning, a novel over-the-air attack is introduced to manipulate the RL algorithm and disrupt NextG network slicing. The adversary observes the spectrum and builds its own RL based surrogate model that selects which RBs to jam subject to an energy budget with the objective of maximizing the number of failed requests due to jammed RBs. By jamming the RBs, the adversary reduces the RL algorithm's reward. As this reward is used as the input to update the RL algorithm, the performance does not recover even after the adversary stops jamming. This attack is evaluated in terms of both the recovery time and the (maximum and total) reward loss, and it is shown to be much more effective than benchmark (random and myopic) jamming attacks. Different reactive and proactive defense schemes such as suspending the RL algorithm's update once an attack is detected, introducing randomness to the decision process in RL to mislead the learning process of the adversary, or manipulating the feedback (NACK) mechanism such that the adversary may not obtain reliable information are introduced to show that it is viable to defend NextG network slicing against this attack, in terms of improving the RL algorithm's reward.
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    Multi-Hop User Equipment (UE) to UE Relays for MANET/Mesh Leveraging 5G NR Sidelink
    Shyy, DJ; Luu, Cuong; Xu, John D.; Liu, Lingjia; Erpek, Tugba; Gabay, David; Bate, David (ACM, 2023-12-06)
    This paper provides use cases to adapt 5G sidelink technology to enable multi-hop User Equipment (UE)-to-UE (U2U) and UE-to- Network relaying in 3GPP standards. Such a capability could enable groups of users to communicate with each other when operating at the periphery or outside a network’s coverage area, with commercial and public safety benefits. This paper compares routing protocols to enable sidelink with U2U relay to support a Mobile Ad hoc Network (MANET). A gap analysis of current 3rd Generation Partnership Project (3GPP) Release 18 (R-18) specifications is performed to determine the missing procedures to enable multi-hop U2U relaying, along with a proposed candidate protocol to fill the gap. The candidate protocol can be submitted as a contribution to 3GPP TSG Service and System Aspects (SA) Working Group 2 (WG2) as proposed changes to the 5G architecture in 3GPP Release 19 (R-19).
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    Multi-layer Optimization Aspects of Deep Learning and MIMO-based Communication Systems
    Erpek, Tugba (Virginia Tech, 2019-09-20)
    This dissertation addresses multi-layer optimization aspects of multiple input multiple output (MIMO) and deep learning-based communication systems. The initial focus is on the rate optimization for multi-user MIMO (MU-MIMO) configurations; specifically, multiple access channel (MAC) and interference channel (IC). First, the ergodic sum rates of MIMO MAC and IC configurations are determined by jointly integrating the error and overhead effects due to channel estimation (training) and feedback into the rate optimization. Then, we investigated methods that will increase the achievable rate for parallel Gaussian IC (PGIC) which is a special case of MIMO IC where there is no interference between multiple antenna elements. We derive a generalized iterative waterfilling algorithm for power allocation that maximizes the ergodic achievable rate. We verified the sum rate improvement with our proposed scheme through extensive simulation tests. Next, we introduce a novel physical layer scheme for single user MIMO spatial multiplexing systems based on unsupervised deep learning using an autoencoder. Both transmitter and receiver are designed as feedforward neural networks (FNN) and constellation diagrams are optimized to minimize the symbol error rate (SER) based on the channel characteristics. We first evaluate the SER in the presence of a constant Rayleigh-fading channel as a performance upper bound. Then, we quantize the Gaussian distribution and train the autoencoder with multiple quantized channel matrices. The channel is provided as an input to both the transmitter and the receiver. The performance exceeds that of conventional communication systems both when the autoencoder is trained and tested with single and multiple channels and the performance gain is sustained after accounting for the channel estimation error. Moreover, we evaluate the performance with increasing number of quantization points and when there is a difference between training and test channels. We show that the performance loss is minimal when training is performed with sufficiently large number of quantization points and number of channels. Finally, we develop a distributed and decentralized MU-MIMO link selection and activation protocol that enables MU-MIMO operation in wireless networks. We verified the performance gains with the proposed protocol in terms of average network throughput.