Zobrazeno 1 - 10
of 73
pro vyhledávání: '"Doshi, Akash"'
Autor:
Hehn, Thomas M., Orekondy, Tribhuvanesh, Shental, Ori, Behboodi, Arash, Bucheli, Juan, Doshi, Akash, Namgoong, June, Yoo, Taesang, Sampath, Ashwin, Soriaga, Joseph B.
Estimating path loss for a transmitter-receiver location is key to many use-cases including network planning and handover. Machine learning has become a popular tool to predict wireless channel properties based on map data. In this work, we present a
Externí odkaz:
http://arxiv.org/abs/2310.04570
Autor:
Doshi, Akash, Andrews, Jeffrey G.
As future wireless systems trend towards higher carrier frequencies and large antenna arrays, receivers with one-bit analog-to-digital converters (ADCs) are being explored owing to their reduced power consumption. However, the combination of large an
Externí odkaz:
http://arxiv.org/abs/2211.08635
Future wireless systems are trending towards higher carrier frequencies that offer larger communication bandwidth but necessitate the use of large antenna arrays. Existing signal processing techniques for channel estimation do not scale well to this
Externí odkaz:
http://arxiv.org/abs/2205.12445
Autor:
Doshi, Akash, Andrews, Jeffrey G.
The increasing number of wireless devices operating in unlicensed spectrum motivates the development of intelligent adaptive approaches to spectrum access that go beyond traditional carrier sensing. We develop a novel distributed implementation of a
Externí odkaz:
http://arxiv.org/abs/2111.09420
Publikováno v:
IEEE Journal on Selected Areas in Communications, vol. 39, no. 8, pp. 2526-2540, Aug. 2021
The increasing number of wireless devices operating in unlicensed spectrum motivates the development of intelligent adaptive approaches to spectrum access. We consider decentralized contention-based medium access for base stations (BSs) operating on
Externí odkaz:
http://arxiv.org/abs/2110.02736
Combining Contention-Based Spectrum Access and Adaptive Modulation using Deep Reinforcement Learning
Autor:
Doshi, Akash, Andrews, Jeffrey G.
Publikováno v:
2022 56th Asilomar Conference on Signals, Systems, and Computers, pp. 189-193
The use of unlicensed spectrum for cellular systems to mitigate spectrum scarcity has led to the development of intelligent adaptive approaches to spectrum access that improve upon traditional carrier sensing and listen-before-talk methods. We study
Externí odkaz:
http://arxiv.org/abs/2109.11723
Autor:
Zhang, Yi, Doshi, Akash, Liston, Rob, Tan, Wai-tian, Zhu, Xiaoqing, Andrews, Jeffrey G., Heath, Robert W.
In this work, we develop DeepWiPHY, a deep learning-based architecture to replace the channel estimation, common phase error (CPE) correction, sampling rate offset (SRO) correction, and equalization modules of IEEE 802.11ax based orthogonal frequency
Externí odkaz:
http://arxiv.org/abs/2010.09268
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
Publikováno v:
arXiv preprint arXiv:2006.04992
Forecasting stock prices can be interpreted as a time series prediction problem, for which Long Short Term Memory (LSTM) neural networks are often used due to their architecture specifically built to solve such problems. In this paper, we consider th
Externí odkaz:
http://arxiv.org/abs/2006.04992
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