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Cihan Tepedelenlioglu
Arizona State University
230Publications
26H-index
3,530Citations
Publications 230
Newest
Published on Jun 1, 2019in Physical Communication 1.45
Ruochen Zeng3
Estimated H-index: 3
(ASU: Arizona State University),
Cihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
Abstract Device-to-Device (D2D) communications has been proposed to provide high data rate service via direct transmissions between devices. Cooperation between the cellular user (CU) and the D2D user can be achieved using superposition coding, where the D2D transmitter (DT) allocates some of its transmission power to forward the CU’s traffic, and transmits to its own D2D receiver (DR) with the remaining power. The sum rate of the cellular and D2D networks in existing schemes are limited by allo...
Suhas Ranganath7
Estimated H-index: 7
(ASU: Arizona State University),
Jayaraman J. Thiagarajan10
Estimated H-index: 10
(ASU: Arizona State University)
+ -3 AuthorsCihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
In this paper, we present a unique Android-DSP (AJDSP) application which was built from the ground up to provide mobile laboratory and computational experiences for educational use. AJDSP provides a mobile intuitive environment for developing and running signal processing simulations in a user-friendly. It is based on a block diagram system approach to support signal generation, analysis, and processing. AJDSP is designed for use by undergraduate and graduate students and DSP instructors. Its ex...
Ahmed Ewaisha4
Estimated H-index: 4
,
Cihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
In this work we study the problem of hard-deadline constrained data offloading in cellular networks. A single-Base-Station (BS) single-frequency-channel downlink system is studied where users request the same packet from the BS at the beginning of each time slot. Packets have a hard deadline of one time slot. The slot is divided into two phases. Out of those users having high channel gain allowing them to decode the packet in the first phase, one is chosen to rebroadcast it to the remaining user...
Published on May 2, 2019in Synthesis Lectures on Signal Processing
Henry Braun4
Estimated H-index: 4
(ASU: Arizona State University),
Pavan K. Turaga21
Estimated H-index: 21
(ASU: Arizona State University)
+ 3 AuthorsCihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
Abstract Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the creation of new types of high-performance sensors includi...
Sunil Rao2
Estimated H-index: 2
,
Andreas Spanias25
Estimated H-index: 25
,
Cihan Tepedelenlioglu26
Estimated H-index: 26
Published on May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
Raksha Ramakrishna1
Estimated H-index: 1
(ASU: Arizona State University),
Anna Scaglione44
Estimated H-index: 44
(ASU: Arizona State University)
+ 1 AuthorsCihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
In this paper, we present a distributed array processing algorithm to analyze the power output of solar photo-voltaic (PV) installations, leveraging the low-rank structure inherent in the data to estimate possible faults. Our multi-agent algorithm requires near-neighbor communications only and is also capable of jointly estimating the common low rank cloud profile and local shading of panels. To illustrate the workings of our algorithm, we perform experiments to detect shading faults in solar PV...
Published on May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
Jie Fan (ASU: Arizona State University), Cihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University),
Andreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
We propose a novel graph filtering method for semi-supervised classification that adopts multiple graph shift matrices to obtain more flexibility in dealing with misleading features. The resulting optimization problem is solved with a computationally efficient alternating minimization approach. In simulation experiments, we implement both conventional and our proposed graph filters as semi-supervised classifiers on real and synthetic datasets to demonstrate advantages of our algorithms in terms ...
Published on Jan 1, 2019in arXiv: Signal Processing
Gowtham Muniraju1
Estimated H-index: 1
(ASU: Arizona State University),
Cihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University),
Andreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
A novel distributed algorithm for estimating the maximum of the node initial state values in a network, in the presence of additive communication noise is proposed. Conventionally, the maximum is estimated locally at each node by updating the node state value with the largest received measurements in every iteration. However, due to the additive channel noise, the estimate of the maximum at each node drifts at each iteration and this results in nodes diverging from the true max value. Max-plus a...
Published on Dec 1, 2018in IEEE Transactions on Communications 5.69
Xiaofeng Li3
Estimated H-index: 3
(ASU: Arizona State University),
Cihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University),
Habib Senol6
Estimated H-index: 6
(KHU: Kadir Has University)
Channel estimation and optimal training sequence design for full-duplex one-way relays are investigated. We propose a training scheme to estimate the residual self-interference (RSI) channel and the channels between nodes simultaneously. A maximum likelihood estimator is implemented with the Broyden–Fletcher–Goldfarb–Shanno algorithm. In the presence of RSI, the overall source-to-destination channel becomes an inter-symbol-interference (ISI) channel. With the help of estimates of the RSI channel...
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