Extensive research in recent years has shown the benefits of cognitive radio technologies to improve the flexibility and efficiency of spectrum utilization. This new communication paradigm, however, requires a well-designed spectrum allocation mechanism. In this paper, we propose an auction framework for cognitive radio networks to allow unlicensed secondary users (SUs) to share the available spectrum of licensed primary users (PUs) fairly and efficiently, subject to the interference temperature constraint at each PU. To study the competition among SUs, we formulate a non-cooperative multiple-PU multiple-SU auction game and study the structure of the resulting equilibrium by solving a non-continuous two-dimensional optimization problem. A distributed algorithm is developped in which each SU updates its strategy based on local information to converge to the equilibrium. We then extend the proposed auction framework to the more challenging scenario with free spectrum bands. We develop an algorithm based on the no-regret learning to reach a correlated equilibrium of the auction game. The proposed algorithm, which can be implemented distributedly based on local observation, is especially suited in decentralized adaptive learning environments as cognitive radio networks. Finally, through numerical experiments, we demonstrate the effectiveness of the proposed auction framework in achieving high efficiency and fairness in spectrum allocation.

To address the challenge of more spectrum for the Internet-of-things (IoT) connectivity, this paper proposes a shared access (SA) framework with rotating radars. The proposed framework is based on the results of our measurement campaign in which we measured spectrum usage patterns and signal characteristics of three different ground-based fixed rotating radar systems near Oulu, Finland. In our work, we review different IoT protocols and their use of licensed or unlicensed spectrum. We make the case that IoT systems generate much data which cannot be accommodated with licensed/unlicensed spectrum, which already suffer from congestion. We identify the suitability of shared access between different rotating radars and IoT networks. We then present a zone-based SA framework in rotating radar spectrum for the operators providing IoT services, highlight its benefits, and also specify challenges in its implementation. To fully develop the considered zone-based SA method that ensures coexistence of IoT devices with no harmful interference to the rotating radars, we propose an Radio Environment Map (REM)-enabled architecture for the SA. The proposed architecture provides principles and rules for using the SA for the IoTs, and it does not require modifications in the incumbent radar systems.

We consider the problem of complementing the capacity of an existing network of macro base stations by dynamically placing a network of 5G small base stations in the form of Unnamed Aerial Vehicles UAV (better known as drones). Our goal is to maximize the capacity boost provided by the UAVs in each considered time frame and extend the battery life of the served mobile users. With this in mind, we propose two clustering algorithms that build on mobile users' spatiotemporal data excess demand (here intended as the portion of demand which is not satisfactory addressed by the existing macro base stations). For the numerical analysis, we use real Beijing downtown trajectory data. The obtained results show that our algorithms perform well and can be considered for enabling real time connection provisioning.

In this paper, we tackle the problem of opportunistic spectrum access in largescale cognitive radio networks, where the unlicensed Secondary Users (SU) access the frequency channels partially occupied by the licensed Primary Users (PU). Each channel is characterized by an availability probability unknown to the SUs. We apply population game theory to model the spectrum access problem and develop distributed spectrum access policies based on imitation, a behavior rule widely applied in human societies consisting of imitating successful behaviors. We develop two imitation-based spectrum access policies based on the basic Proportional Imitation (PI) rule and the more advanced Double Imitation (DI) rule given that a SU can only imitate the other SUs operating on the same channel. A systematic theoretical analysis is presented for both policies on the induced imitation dynamics and the convergence properties of the proposed policies to the Nash Equilibrium. Simple and natural, the proposed imitationbased spectrum access policies can be implemented distributedly based on solely local interactions and thus is especially suited in decentralized adaptive learning $ This work is supported by the project TEROPP (technologies for TERminal in OPPortunistic radio applications) funded by the French National Research Agency (ANR).