Cihan Tepedelenlioglu

Arizona State University

Mathematical optimizationCommunication channelMathematicsComputer scienceFading

257Publications

26H-index

4,017Citations

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Publications 234

Newest

#1Gowtham MunirajuH-Index: 3

#2Bhavya KailkhuraH-Index: 14

Last. Andreas SpaniasH-Index: 29

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Sampling one or more effective solutions from large search spaces is a recurring idea in machine learning (ML), and sequential optimization has become a popular solution. Typical examples include data summarization, sample mining for predictive modeling, and hyperparameter optimization. Existing solutions attempt to adaptively trade off between global exploration and local exploitation, in which the initial exploratory sample is critical to their success. While discrepancy-based samples have bec...

#1Gowtham MunirajuH-Index: 3

#2Cihan TepedelenliogluH-Index: 26

Last. Andreas SpaniasH-Index: 29

view all 3 authors...

#1Gowtham Muniraju (ASU: Arizona State University)H-Index: 3

#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26

Last. Andreas Spanias (ASU: Arizona State University)H-Index: 29

view all 3 authors...

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...

#1Gowtham Muniraju (ASU: Arizona State University)H-Index: 3

#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26

Last. Andreas Spanias (ASU: Arizona State University)H-Index: 29

view all 3 authors...

A distributed algorithm to compute the spectral radius of the graph in the presence of additive channel noise is proposed. The spectral radius of the graph is the eigenvalue with the largest magnitude of the adjacency matrix, and is a useful characterization of the network graph. Conventionally, centralized methods are used to compute the spectral radius, which involves eigenvalue decomposition of the adjacency matrix of the underlying graph. We devise an algorithm to reach consensus on the spec...

#2Xiaofeng LiH-Index: 4

Last. Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26

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We study a system with a full-duplex (FD) amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay and a single antenna at the source and the destination. The residual self-interference (RSI) is shown to behave as inter-symbol interference (ISI) at the destination. Assuming Rayleigh fading on all the channels, we derive the end-to-end impulse response and consider diversity combining schemes including Equal Gain Transmission (EGT), Maximal Ratio Transmission (MRT) and Antenna Selecti...

#1Ahmed Ewaisha (ASU: Arizona State University)H-Index: 5

#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26

We consider one non-real-time (NRT) and N realtime (RT) cellular users interested in downloading delay-tolerant and delay-sensitive packets from the base station (BS), respectively. Each RT packet is a multicast packet and RT user i is satisfied if more than q i % of the data is received before the deadline. At each slot, one of the RT users is allowed to retransmit the data it has received from the BS to its RT neighbors. This reduces the load on the BS but causes interference to the NRT user. ...

#1Jie Fan (ASU: Arizona State University)H-Index: 1

#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26

Last. Andreas Spanias (ASU: Arizona State University)H-Index: 29

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Using graphs to represent data sets that reside on irregular and complex structures can bring special advantages. Graph signal processing (DSP G ) converts traditional DSP operators, such as time shift, linear filters and Fourier transform, from time and frequency domain to the graph domain. In machine learning applications, DSP G provides an approach for semi-supervised classification. Different from conventional graph-filter-based classifiers, we propose a new graph filter with multiple graph ...

A Large-Deviation Upperbound on Directed Last-Passage Percolation Growth Rate Based on Entropy of Direction Vector.

#1Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26

This short note provides a large-deviation-based upper bound on the growth rate of directed last passage percolation (LPP) using the entropy of the normalized direction vector.

#1Blaine Ayotte (Clarkson University)H-Index: 2

#2Justin Au-Yeung (Clarkson University)

Last. Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26

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In this innovative practice work-in-progress paper, we discuss novel methods to teach machine learning concepts to undergraduate students. Teaching machine learning involves introducing students to complex concepts in statistics, linear algebra, and optimization. In order for students to better grasp concepts in machine learning, we provide them with hands-on exercises. These types of immersive experiences will expose students to the different stages of the practical uses of machine learning. Th...

#1Ruochen Zeng (ASU: Arizona State University)H-Index: 4

#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26

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...

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