E-Government, M-Government, L-Government. Exploring future ICT applications in public administration

The development of information and communication technologies (ICT) brings about considerable changes in the ways public administration provides information and delivers services to citizens, businesses, and other public administrations. This chapter reviews the application of ICT in the provision of public administration services. E-government tools have been implemented in various countries in the world and enabled the strengthening of existing public administration services and the activation of innovative ones. M-government tools, which are related to the emergence and diffusion of Internet mobile technology and devices, allow to both overcome infrastructure deficits and provide innovative services which are particularly sensitive to users' context conditions. Finally, l-government tools – i.e., ubiquitous, seamless, user-centric, and automated application of Internet technology to public administration services – have the potential to further redefine the terms of access of users to public administration services and to enhance the ties between citizens and businesses and the government

Machine learning in Radio resource scheduling

In access networks, the radio resource management is designed to deal with the system capacity maximization while the quality of service (QoS) requirements need be satisfied for different types of applications. In particular, the radio resource scheduling aims to allocate users' data packets in frequency domain at each predefined transmission time intervals (TTIs), time windows used to trigger the user requests and to respond them accordingly. At each TTI, the scheduling procedure is conducted based on a scheduling rule that aims to focus only on particular scheduling objective such as fairness, delay, packet loss, or throughput requirements. The purpose of this chapter is to formulate and solve an aggregate optimization problem that selects at each TTI the most convenient scheduling rule in order to maximize the satisfaction of all scheduling objectives concomitantly TTI-by-TTI. The use of reinforcement learning is proposed to solve such complex multi-objective optimization problem and to ease the decision making on which scheduling rule should be applied at each TTI