Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Balevi, Eren"'
Autor:
Balevi, Eren, Andrews, Jeffrey G.
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 bandwidth leads to excessiv
Externí odkaz:
http://arxiv.org/abs/2012.12063
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 assumption that the reconstr
Externí odkaz:
http://arxiv.org/abs/2006.13494
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 look back to the idea of sp
Externí odkaz:
http://arxiv.org/abs/2005.11289
Autor:
Balevi, Eren, Andrews, Jeffrey G.
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 code for intractable nonlinear
Externí odkaz:
http://arxiv.org/abs/2003.00081
Autor:
Balevi, Eren, Andrews, Jeffrey G.
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 generative deep learning models. The first one is a supervised learning
Externí odkaz:
http://arxiv.org/abs/1911.12461
Autor:
Balevi, Eren, Andrews, Jeffrey G.
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 first shown that the optimum error correction code that minimizes the
Externí odkaz:
http://arxiv.org/abs/1909.12120
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 users. The proposed estimator
Externí odkaz:
http://arxiv.org/abs/1908.00144
Autor:
Balevi, Eren, Andrews, Jeffrey G.
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 to channel estimation for multicarrier signals with any number of ante
Externí odkaz:
http://arxiv.org/abs/1904.09346
Autor:
Balevi, Eren, Andrews, Jeffrey G.
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 coupled interference render
Externí odkaz:
http://arxiv.org/abs/1903.06787
Autor:
Balevi, Eren, Andrews, Jeffrey G.
This paper develops novel deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization greatly reduces compl
Externí odkaz:
http://arxiv.org/abs/1811.00971