Researcher:
Khan, Nasir

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PhD Student

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Nasir

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Khan

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Khan, Nasir

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2021 - 20222

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Researcher Of Publications: search.filters.isAuthorOfPublication.ca04a479-c001-4789-aa68-80fd7e017a6f

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    Resource allocation for ultra-reliable low-latency vehicular networks in finite blocklength regime
    (Institute of Electrical and Electronics Engineers Inc., 2022) Department of Electrical and Electronics Engineering; N/A; Ergen, Sinem Çöleri; Khan, Nasir; Faculty Member; PhD Student; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; 7211; N/A
    Ensuring ultra-reliable low-latency communication (URLLC) is crucial in the timely delivery of safety-critical messages in vehicle-to-vehicle (V2V) communications. The stringent latency requirement in URLLC requires the usage of finite block length information theory. Previously proposed resource allocation schemes for V2V communication rely on Shannon rate and do not incorporate spectrum allocation into the blocklength and power optimization while relying solely on slow-varying large-scale channel statistics. This paper investigates the combined spectrum, blocklength, and power allocation to minimize the worst-case decoding-error probability in the finite blocklength (FBL) regime for a URLLC-based V2V communication scenario. We first formulate the problem as a non-convex mixed-integer nonlinear programming problem (MINLP). To solve this challenging problem, we decompose the original problem into two interrelated subproblems. First, the spectrum allocation is performed by clustering vehicles into distinct zones. Second, an iterative block coordinate descent (BCD) based algorithm is developed for the blocklength and transmit power optimization. Via extensive simulations, we demonstrate that the proposed scheme outperforms the benchmark scheme based on a path-following iterative strategy and yields substantially higher network reliability for different network parameters.
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    Deep neural network based minimum length scheduling in wireless powered communication networks
    (Institute of Electrical and Electronics Engineers (IEEE), 2021) Department of Electrical and Electronics Engineering; Ergen, Sinem Çöleri; Khan, Nasir; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; 7211; N/A
    Minimization of schedule length is key in ensuring the delay performance of wireless powered communication networks (WPCNs) demanding strict timing and reliability guarantees. Previous solution methodologies proposed for these wireless networks suffer from high run-time complexity, making it very difficult to solve the problem in real time. This paper considers a run-time efficient deep learning based approach for solving minimum length scheduling problem in a full-duplex WPCN. Leveraging upon the universal approximation capability of neural networks, a multi-output feed forward deep neural network based framework is proposed where inputs are the channel coefficients and outputs are the optimal power, transmission length and schedule of users. Simulation results indicate that the proposed deep learning based approach can very well approximate the true outputs with a percentage error below 1% for different network configurations while maintaining a very low run-time complexity.