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The surge of mobile devices such as smartphone and tablets requires additional capacity. To achieve ubiquitous and high data rate Internet connectivity, effective spectrum sharing and utilization of the wireless spectrum carry critical importance. In this paper, we consider the use of unlicensed LTE (LTE-U) technology in the 3.5 GHz Citizens Broadband Radio Service (CBRS) band and develop a multiarmed bandit (MAB) based spectrum sharing technique for a smooth coexistence with WiFi. In particular, we consider LTE-U to operate as a General Authorized Access (GAA) user; hereby MAB is used to adaptively optimize the transmission duty cycle of LTE-U transmissions. Additionally, we incorporate downlink power control which yields a high energy efficiency and interference suppression. Simulation results demonstrate a significant improvement in the aggregate capacity (approximately 33%) and cell-edge throughput of coexisting LTE-U and WiFi networks for different base station densities and user densities.

Emerging vertical applications enabled by connected devices and smart infrastructures have created an ever-increasing demand for high data rates over 5th-Generation (5G) and beyond wireless networks. Deployment of dense small cells (SCs) and millimeter wave (mmWave) communication systems have become inevitable in future wireless networks. Consequently, it is more accurate to model such networks in the 3-Dimensional (3D) space due to the spatially distributed nature of the SCs, locations of the devices, radio resources and propagation environment. Accurate estimation of location-specific path loss parameters is then essential for efficient utilization of radio resources and management of dynamic coverage in 3D SC networks. In the paper, a framework for location-specific path loss estimation is developed for efficient radio resource management, based on the principle of crowdsensing together with Linear Algebra (LA) and machine learning (ML) techniques considering 2.5 GHz and 28 GHz bands. The corresponding procedure for capturing dynamic coverage of a SC base station (BS) serving to an arbitrary cluster is proposed and examined based on its 3D propagation characteristics. Results show that the accuracy of 3D channel parameter estimation using gradient descent ML techniques is superior compared to LA technique and can achieve over 98% estimation accuracy. It is shown that using the proposed process, parameters can be extrapolated for the slightly extended 3D communication distances from the cluster boundary for the worst-case locations of devices based on already estimated propagation parameters with accuracy over 74% for certain distances. Although numerical results are presented for a single amorphous 3D cell of a wireless network, the framework given in the paper can be extended to any arbitrary 3D wireless cellular network.INDEX TERMS 3D cells, clusters, crowdsensing, cell coverage, machine learning.

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