Eren Balevi

E-mail

ebalevi@metu.edu.tr

Department

Department of Electrical and Electronics Engineering

Unfolded Hybrid Beamforming With GAN Compressed Ultra-Low Feedback Overhead Balevi, Eren; Andrews, Jeffrey G. (2021-12-01) Optimizing a hybrid beamforming transmitter is a non-convex problem and requires channel state information, leading in most cases to nontrivial feedback overhead. We propose a methodology relying on the principles of deep ...
Wideband Channel Estimation With a Generative Adversarial Network Balevi, Eren; Andrews, Jeffrey G. (2021-05-01) Communication at high carrier frequencies such as millimeter wave (mmWave) and terahertz (THz) requires channel estimation for very large bandwidths at low SNR. Hence, allocating an orthogonal pilot tone for each coherence...
High Dimensional Channel Estimation Using Deep Generative Networks Balevi, Eren; Doshi, Akash; Jalal, Ajil; Dimakis, Alexandros; Andrews, Jeffrey G. (2021-01-01) This paper presents a novel compressed sensing (CS) approach to high dimensional wireless channel estimation by optimizing the input to a deep generative network. Channel estimation using generative networks relies on the ...
Autoencoder-Based Error Correction Coding for One-Bit Quantization Balevi, Eren; Andrews, Jeffrey G. (2020-06-01) This paper proposes a novel deep learning-based error correction coding scheme for AWGN channels under the constraint of one-bit quantization in receivers. Specifically, it is first shown that the optimum error correction ...
Compressed Representation of High Dimensional Channels using Deep Generative Networks Doshi, Akash; Balevi, Eren; Andrews, Jeffrey G. (2020-05-01) © 2020 IEEE.This paper proposes a novel compressed representation for high dimensional channel matrices obtained by optimization of the input to a deep generative network. Channel estimation using generative networks const...
High Rate Communication over One-Bit Quantized Channels via Deep Learning and LDPC Codes Balevi, Eren; Andrews, Jeffrey G. (2020-05-01) This paper proposes a method for designing error correction codes by combining a known coding scheme with an autoencoder. Specifically, we integrate an LDPC code with a trained autoencoder to develop an error correction co...
Massive MIMO Channel Estimation With an Untrained Deep Neural Network Balevi, Eren; Doshi, Akash; Andrews, Jeffrey G. (2020-03-01) This paper proposes a deep learning-based channel estimation method for multi-cell interference-limited massive MIMO systems, in which base stations equipped with a large number of antennas serve multiple single-antenna us...
Spatial Indexing for System-Level Evaluation of 5G Heterogeneous Cellular Networks Amiri, Roohollah; Balevi, Eren; Andrews, Jeffrey G.; Mehrpouyan, Hani (2020-01-01) System level simulations of large 5G networks are essential to evaluate and design algorithms related to network issues such as scheduling, mobility management, interference management, and cell planning. In this paper, we...
Online Antenna Tuning in Heterogeneous Cellular Networks With Deep Reinforcement Learning Balevi, Eren; Andrews, Jeffrey G. (2019-12-01) We aim to jointly optimize antenna tilt angle, and vertical and horizontal half-power beamwidths of the macrocells in a heterogeneous cellular network (HetNet). The interactions between the cells, most notably due to their...
Deep learning-based encoder for one-bit quantization Balevi, Eren; Andrews, Jeffrey G. (2019-12-01) © 2019 IEEE.This paper proposes a deep learning-based error correction coding for AWGN channels under the constraint of one-bit quantization in receivers. An autoencoder is designed and integrated with a turbo code that ac...

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