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This paper addresses the channel selection problem for Long Term Evolution Unlicensed (LTE-U). Channel selection is a frequency-domain mechanism that facilitates the coexistence of multiple networks sharing the unlicensed band. In particular, the paper considers a fully distributed approach where each small cell autonomously selects the channel to set-up an LTE-U carrier. The problem is modeled using...
The use of Long Term Evolution (LTE) in the unlicensed 5 GHz band, referred to as LTE-U, is a promising enhancement to increase the capacity of LTE networks and meet the requirements of future systems. This paper considers a Q-learning based Channel Selection strategy to decide the most appropriate channel to use for downlink traffic offloading in the unlicensed band as a mechanism to greatly facilitate...
The use of Long Term Evolution (LTE) in the unlicensed 5 GHz band, referred to as LTE-U, is a promising enhancement to increase the capacity of LTE networks and meet the requirements foreseen for future systems. Nevertheless, coexistence among several LTE-U and/or Wi-Fi systems in the same band is a key technical challenge to be resolved. In this context, this paper focuses on the channel selection...
This paper presents a novel distributed framework to decide the spectrum assignment in a primary cellular radio access network. The distributed nature of the framework allows each cell to autonomously decide (by means of machine learning procedures) the best frequencies to use in order to maximize spectral efficiency, preserve quality-of-service, and generate spectrum gaps, so that secondary cognitive...
This paper proposes a self-organized spectrum assignment strategy in the context of next generation multicell orthogonal frequency division multiple access networks. The proposed strategy is able to dynamically find spectrum assignments per cell depending on the spatial distribution of the users over the scenario, opening new spectrum access opportunities for secondary spectrum usage. Reinforcement...
In this work the feasibility of Reinforcement Learning (RL) for Dynamic Spectrum Management (DSM) in the context of next generation multicell Orthogonal Frequency Division Multiple Access (OFDMA) networks is studied. An RL-based algorithm is proposed and it is shown that the proposed scheme is able to dynamically find spectrum assignments per cell depending on the spatial distribution of the users...
This paper concentrates on the dynamic tuning of radio resource management parameters in the context of HSDPA systems coexisting with UMTS Relpsila99. In particular, an automatic tuning system is proposed taking into account the close interdependence between the cell throughput and the users channel quality indicators. From these, appropriate performance indicators are derived and a mid-term allocation...
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