Cihan Tepedelenlioglu
Arizona State University
StatisticsAlgorithmMathematical optimizationTopologyTelecommunications linkChannel state informationCommunication channelEstimation theoryEstimatorMIMOElectronic engineeringWireless sensor networkComputer networkMathematicsOrthogonal frequency-division multiplexingComputer scienceControl theorySignal-to-noise ratioTelecommunicationsReal-time computingFading
268Publications
28H-index
4,336Citations
Publications 236
Newest
Partial ordering of communication channels has applications in performance analysis, and goes beyond comparisons of channels just on the basis of their Shannon capacity or error probability. Shannon defined a partial order of channel inclusion based on convex mixture of input/output degradations of a discrete memoryless channel (DMC). In this paper, extensions to channels other than DMCs are considered. In particular, additive noise channels and phase degraded channels, and multiple input multip...
A relationship between the growth-rate of logoptimal portfolios and capacity of fading single-input multiple output (SIMO) channels are established. Using this relation, stock vector stochastic processes that model the investment environments are stochastically ordered using different criteria. The presence of side information (SI) is considered, and a bound on the gains in the growth-rate due to SI is derived along with data processing inequality and convexity properties. A statistical test on ...
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...
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It is well-known that time-varying channels can provide time diversity and improve error rate performance compared to time-invariant fading channels. However, exploiting time diversity requires very accurate channel estimates at the receiver. In order to reduce the number of unknown channel coefficients while estimating the time-varying channel, basis expansion models can be used along with long transmission frames that contain multiple orthogonal frequency division multiplexing (OFDM) symbols t...
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We consider a single BS serving G groups (flows) of users each with N real-time (RT) users. Each group is requesting to download some data by a hard-deadline at the end of a T -slot-frame. In addition, the BS is serving multiple non-real-time (NRT) users with no deadlines. The problem is to schedule the BS’s transmission to the RT and NRT users to maximize the network’s sum throughput, for users that are cooperating in the presence of interference. We present a scheduler with complexity of O(G2)...
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Abstract The efficiency of solar energy farms requires detailed analytics and information on each panel regarding voltage, current, temperature, and irradiance. Monitoring utility-scale solar array...
1 Citations
#1Henry Braun (UMN: University of Minnesota)H-Index: 7
#2Sameeksha Katoch (ASU: Arizona State University)H-Index: 4
Last. Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 28
view all 5 authors...
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#1Jongmin Lee (ASU: Arizona State University)H-Index: 6
#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 28
Last. Gowtham Muniraju (ASU: Arizona State University)H-Index: 6
view all 4 authors...
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#1Jie Fan (ASU: Arizona State University)H-Index: 3
#2Sunil Rao (ASU: Arizona State University)H-Index: 5
Last. Andreas Spanias (ASU: Arizona State University)H-Index: 32
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In this paper, we address the problem of fault classification in PhotoVoltaic (PV) arrays using a semi-supervised graph signal processing approach. Traditional fault detection and classification methods require large amounts of labeled data for training. In utility scale solar arrays, obtaining labeled data for different fault classes is resource intensive. We propose a graph based classification technique that relies on a limited amount of labeled data. We compare our results with the well know...
2 CitationsSource
#1Gowtham Muniraju (ASU: Arizona State University)H-Index: 6
#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 28
Last. Andreas Spanias (ASU: Arizona State University)H-Index: 32
view all 3 authors...
A consensus based distributed algorithm to compute the spectral radius of a network is proposed. The spectral radius of the graph is the largest eigenvalue 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. Our distributed algorithm uses a simple update rule to reach consensus on the spectral radius, usi...
3 CitationsSource