This article investigates the slicing concept in the 5G Radio Access Network (RAN) with the related challenges and research problems. The objective is to identify the plausible options for implementing the slicing concept at the RAN level by the Mobile Network Operator (MNO) to respond to the needs of verticals. We start by identifying the different slice granularity options, i.e., how to define slices by combining customer and service needs. We then present how the 5G New Radio (NR) features can be used for facilitating slice implementation and provide typical configurations for different slice types from technology and RAN architecture perspectives. The main challenges for RAN slicing are then discussed, with a special attention to the resource allocation problem between slices sharing the same spectrum band. We also investigate the multi-tenant slicing implementation in terms of the openness of the network to third parties which is regarded as a key issue that may encourage vertical players to use operators' networks rather than deploying their own infrastructure.

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The global Beyond 3G system will consist of several coexisting and cooperating access technologies. One of the key concepts of this global technology is the Reconfigurability, that allows different network elements to dynamically adapt their configuration to the new conditions encountered in specific service areas and time. Reconfigurability may comprise dynamic spectrum allocation: a technique that varies spectrum allocation of different systems in order to meet changing demands. In this context of multiple access techniques and changing spectrum allocation, when a mobile is switched on, it has no information about the available systems in its area nor on the current spectrum allocation to these systems. In order to avoid the scanning of all the spectrum range and to facilitate the initial connection to the network, this paper proposes that the mobile listens first to a broadcast radio channel containing the necessary information to initiate its connection. The paper defines the content of this broadcast channel, denoted Common Pilot Channel, and proposes a technical implementation.

Coverage optimization is an important process for the operator as it is a crucial prerequisite towards offering a satisfactory quality of service to the end-users. The first step of this process is coverage prediction, which can be performed by interpolating geo-located measurements reported to the network by mobile users equipments. In previous works, we proposed a low complexity coverage prediction algorithm based on the adaptation of the Geo-statistics Fixed Rank Kriging (FRK) algorithm. We supposed that the geo-location information reported with the radio measurements was perfect, which is not the case in reality. In this paper, we study the impact of location uncertainty on the coverage prediction accuracy and we extend the previously proposed algorithm to include geo-location error in the prediction model. We validate the proposed algorithm using both simulated and real field measurements.The FRK extended to take into account the location uncertainty proves to enhance the prediction accuracy while keeping a reasonable computational complexity.

Abstract-Coverage planning and optimization is one of the most crucial tasks for a radio network operator. Efficient coverage optimization requires accurate coverage estimation. This estimation relies on geo-located field measurements which are gathered today during highly expensive drive tests (DT); and will be reported in the near future by users' mobile devices thanks to the 3GPP Minimizing Drive Tests (MDT) feature [1]. This feature consists in an automatic reporting of the radio measurements associated with the geographic location of the user's mobile device. Such a solution is still costly in terms of battery consumption and signaling overhead. Therefore, predicting the coverage on a location where no measurements are available remains a key and challenging task. This paper describes a powerful tool that gives an accurate coverage prediction on the whole area of interest: it builds a coverage map by spatially interpolating geolocated measurements using the Kriging technique. The paper focuses on the reduction of the computational complexity of the Kriging algorithm by applying Fixed Rank Kriging (FRK). The performance evaluation of the FRK algorithm both on simulated measurements and real field measurements shows a good tradeoff between prediction efficiency and computational complexity. In order to go a step further towards the operational application of the proposed algorithm, a multicellular use-case is studied. Simulation results show a good performance in terms of coverage prediction and detection of the best serving cell.