Wireless Power Transfer in Massive MIMO Aided HetNets with User Association

This paper explores the potential of wireless power transfer (WPT) in massive multiple input multiple output (MIMO) aided heterogeneous networks (HetNets), where massive MIMO is applied in the macrocells, and users aim to harvest as much energy as possible and reduce the uplink path loss for enhancing their information transfer. By addressing the impact of massive MIMO on the user association, we compare and analyze two user association schemes. We adopt the linear maximal ratio transmission beam-forming for massive MIMO power transfer to recharge users. By deriving new statistical properties, we obtain the exact and asymptotic expressions for the average harvested energy. Then we derive the average uplink achievable rate under the harvested energy constraint.Comment: 36 pages, 11 figures, to appear in IEEE Transactions on Communication

Final Specification of Cooperative Functionalities

This deliverable presents the specification of the final version of the Cooperative AP Functionalities that have been designed in the context of Work Package (WP) 4 of the Wi-5 project. In detail, we present a general cooperative framework that includes functionalities for a Radio Resource Management (RRM) algorithm, which provides channel assignment and transmit power adjustment strategies, an AP selection policy, which also provides horizontal handover, and a Radio Access Technology (RAT) selection solution for vertical handover. The RRM algorithm achieves an important improvement for network performance in terms of several parameters through the channel assignment approach and the transmit power adjustment. The AP selection solution extends the approach presented in deliverables D4.1 and D4.2 and is based on a centralised potential game, which optimises the distribution of the so-called Fittingness Factor (FF) parameter among the Wi-Fi users. Such a parameter efficiently matches the suitability of the available spectrum resource to the users’ application requirements. Moreover, the RAT selection solution extends the AP selection algorithm towards vertical handover functionality including 3G/4G networks. The assessment of the newest algorithms developed in the context of WP4 is illustrated in this deliverable through the analysis of several performance results in a simulated environment against other strategies found in the literature. Finally, the set of smart AP functionalities developed in the context of WP3, implemented on the Wi5 APs and on the Wi-5 controller, and their use in the proposed algorithms are illustrated. Specifically, this deliverable describes how these functionalities can enable the correct deployment of the proposed cooperative AP solutions in realistic scenarios. Therefore, the main novel contributions of this deliverable are i) the strengthening of the AP selection algorithm, ii) the design and assessment of a new algorithm for vertical handover and iii) the presentation of the finalised integration of the cooperative AP functionalities of the Wi-5 system

Dynamic capacity provision for wireless sensors connectivity: A profit optimization approach

[EN] We model a wireless sensors' connectivity scenario mathematically and analyze it using capacity provision mechanisms, with the objective of maximizing the profits of a network operator. The scenario has several sensors' clusters with each one having one sink node, which uploads the sensing data gathered in the cluster through the wireless connectivity of a network operator. The scenario is analyzed both as a static game and as a dynamic game, each one with two stages, using game theory. The sinks' behavior is characterized with a utility function related to the mean service time and the price paid to the operator for the service. The objective of the operator is to maximize its profits by optimizing the network capacity. In the static game, the sinks' subscription decision is modeled using a population game. In the dynamic game, the sinks' behavior is modeled using an evolutionary game and the replicator dynamic, while the operator optimal capacity is obtained solving an optimal control problem. The scenario is shown feasible from an economic point of view. In addition, the dynamic capacity provision optimization is shown as a valid mechanism for maximizing the operator profits, as well as a useful tool to analyze evolving scenarios. Finally, the dynamic analysis opens the possibility to study more complex scenarios using the differential game extension.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Spanish Ministry of Economy and Competitiveness through project TIN2013-47272-C2-1-R; AEI/FEDER, UE through project TEC2017-85830-C2-1-P; and co-supported by the European Social Fund BES-2014-068998.Sanchis-Cano, Á.; Guijarro, L.; Condoluci, M. (2018). 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Smart antenna system management utilising multi-agent systems

Abstract : Cellular communication networks are large and distributed systems that provide billions of people around the world with means of communication. Antennas as used currently in cellular communication networks do not provide efficient resource management given the growth in the current communication network scenario. Most of the problems are related to the number of devices that can connect to an antenna, the coverage map of an antenna, and frequency management. A smart antenna grid can cover the same area as traditional cellular system towers with some enhancements. Smart antenna grids can include a device in an area that requires connectivity rather than covering of the entire area. Frequencies are handled per antenna base, with more focus on providing stable communication. The objective of the dissertation is to improve resource management of smart antenna grids by making use of a multi-agent system. The dissertation uses a simulation environment that illustrates a smart antenna grid that operates with a multi-agent system that is responsible for resource management. The simulation environment is used to execute ten scenarios that intends to place large amounts of strain on the resources of the smart antenna grid to determine the effectiveness of using a multi-agent system. The ten scenarios show that when resources deplete, the multi-agent system intervenes, and that when there are too many devices connected to one smart antenna, the devices are managed. At the same time, when there are antennas that have frequency problems, the frequencies are reassigned. One of the scenarios simulated the shutdown of antennas forcing devices to disconnect from the antenna and connect to a different antenna. The multi-agent system shows that the different agents can manage the resources in a smart grid that is related to frequencies, antennas and devices.M.Sc. (Computer Science