Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Chang, Hao Hsuan"'
Publikováno v:
IEEE Transactions on Wireless Communications (2024)
Due to its ubiquitous and contact-free nature, the use of WiFi infrastructure for performing sensing tasks has tremendous potential. However, the channel state information (CSI) measured by a WiFi receiver suffers from errors in both its gain and pha
Externí odkaz:
http://arxiv.org/abs/2307.12126
In dynamic spectrum access (DSA) networks, secondary users (SUs) need to opportunistically access primary users' (PUs) radio spectrum without causing significant interference. Since the interaction between the SU and the PU systems are limited, deep
Externí odkaz:
http://arxiv.org/abs/2305.11237
Due to the growing volume of data traffic produced by the surge of Internet of Things (IoT) devices, the demand for radio spectrum resources is approaching their limitation defined by Federal Communications Commission (FCC). To this end, Dynamic Spec
Externí odkaz:
http://arxiv.org/abs/2106.14976
Deep reinforcement learning (DRL) has been shown to be successful in many application domains. Combining recurrent neural networks (RNNs) and DRL further enables DRL to be applicable in non-Markovian environments by capturing temporal information. Ho
Externí odkaz:
http://arxiv.org/abs/2010.05449
Akademický článek
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Reservoir computing (RC) is a special recurrent neural network which consists of a fixed high dimensional feature mapping and trained readout weights. In this paper, we introduce a new RC structure for multiple-input, multiple-output orthogonal frequ
Externí odkaz:
http://arxiv.org/abs/1907.01516
Dynamic spectrum access (DSA) is regarded as an effective and efficient technology to share radio spectrum among different networks. As a secondary user (SU), a DSA device will face two critical problems: avoiding causing harmful interference to prim
Externí odkaz:
http://arxiv.org/abs/1810.11758
Akademický článek
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Autor:
Chang, Hao-Hsuan
This dissertation considers a deep reinforcement learning (DRL) setting under the practical challenges of real-world wireless communication systems. The non-stationary and partially observable wireless environments make the learning and the convergen
Externí odkaz:
http://hdl.handle.net/10919/113863
Publikováno v:
In Energy 1 April 2018 148:90-111
