28 citations · 31 across the 5 of their papers we have counts for
13 papers
Wideband Channel Estimation with A Generative Adversarial Network
Eren Balevi, Jeffrey G. Andrews
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 a…
High Dimensional Channel Estimation Using Deep Generative Networks
Eren Balevi, Akash Doshi, Ajil Jalal +2
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 estimatio…
Spatial Indexing for System-Level Evaluation of 5G Heterogeneous Cellular Networks
Roohollah Amiri, Eren Balevi, Jeffrey G. Andrews +1
System level simulations of large 5G networks are essential to evaluate and design algorithms related to network issues such as scheduling, mobility management, interference manage…
High Rate Communication over One-Bit Quantized Channels via Deep Learning and LDPC Codes
Eren Balevi, Jeffrey G. Andrews
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 aut…
Two-Stage Learning for Uplink Channel Estimation in One-Bit Massive MIMO
Eren Balevi, Jeffrey G. Andrews
We develop a two-stage deep learning pipeline architecture to estimate the uplink massive MIMO channel with one-bit ADCs. This deep learning pipeline is composed of two separate ge…
Autoencoder-Based Error Correction Coding for One-Bit Quantization
Eren Balevi, Jeffrey G. Andrews
This paper proposes a novel deep learning-based error correction coding scheme for AWGN channels under the constraint of one-bit quantization in the receivers. Specifically, it is…
Massive MIMO Channel Estimation with an Untrained Deep Neural Network
Eren Balevi, Akash Doshi, Jeffrey G. Andrews
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…
Deep Learning-Based Channel Estimation for High-Dimensional Signals
Eren Balevi, Jeffrey G. Andrews
We propose a novel deep learning-based channel estimation technique for high-dimensional communication signals that does not require any training. Our method is broadly applicable…
Online Antenna Tuning in Heterogeneous Cellular Networks with Deep Reinforcement Learning
Eren Balevi, Jeffrey G. Andrews
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 be…
One-Bit OFDM Receivers via Deep Learning
Eren Balevi, Jeffrey G. Andrews
This paper develops novel deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-b…
Enabling Aloha-NOMA for Massive M2M Communication in IoT Networks
Mohamed Elkourdi, Asim Mazin, Eren Balevi +1
The Internet of things (IoT), which is the network of physical devices embedded with sensors, actuators, and connec- tivity, is being accelerated into the mainstream by the emergen…
ALOHA-NOMA for Massive Machine-to-Machine IoT Communication
Eren Balevi, Faeik T. Al Rabee, Richard D. Gitlin
This paper proposes a new medium access control (MAC) protocol for Internet of Things (IoT) applications incorporating pure ALOHA with power domain non-orthogonal multiple access (…
Optimizing the Number of Fog Nodes for Cloud-Fog-Thing Networks
Eren Balevi, Richard D. Gitlin
Going from theory to practice in fog networking raises the question of the optimum number of fog nodes that will be upgraded from the existing nodes. This paper finds the optimum n…