In multi-rate IEEE 802.11 WLANs, the traditional user association based on the strongest received signal and the well known anomaly of the MAC protocol can lead to overloaded Access Points (APs), and poor or heterogeneous performance. Our goal is to propose an alternative game-theoretic approach for association. We model the joint resource allocation and user association as a matching game with complementarities and peer effects consisting of selfish players solely interested in their individual throughputs. Using recent game-theoretic results we first show that various resource sharing protocols actually fall in the scope of the set of stability-inducing resource allocation schemes. The game makes an extensive use of the Nash bargaining and some of its related properties that allow to control the incentives of the players. We show that the proposed mechanism can greatly improve the efficiency of 802.11 with heterogeneous nodes and reduce the negative impact of peer effects such as its MAC anomaly. The mechanism can be implemented as a virtual connectivity management layer to achieve efficient APs-user associations without modification of the MAC layer.

The evaluation of performance and quality of service needs the modeling of wireless networks. Among the different models usually considered, the hexagonal network is the most popular. However, it requires extensive numerical computations. The Poisson network model, for which the base stations (BS) locations form a stochastic spatial Poisson process, allows to consider a non constant distance between base stations. Therefore, it characterizes more realistically operational networks. The Fluid network model, for which the interfering BS are replaced by a continuum of infinitesimal interferers, allows to establish closed-form formula for the SINR (Signal on Interference plus Noise Ratio). This model was validated by comparison with an hexagonal network. The two models establish very close results. In this paper, we show that the Fluid network model can also be used to analyze Poisson networks. Therefore, the evaluation of quality of service and performance becomes very easy, whatever the type of model, by using the analytical expression of the SINR established by considering the fluid model.

Inter-cell interference is a major issue in OFDMA networks, due to the increasing density of Low Power Nodes (LPN) used to offload the macro base stations. Coordination between these nodes also called Coordination MultiPoint (CoMP), is identified as a promising solution to improve the signal quality and the achievable throughput while ensuring spectral efficiency over the network. Joint Transmission (JT) mode of CoMP consists to jointly transmit the useful signal from more than one BS, typically the best serving one, and one or several other base stations. In dense networks, the performance evaluation and the analysis of JT CoMP approach become a hard task which needs lot of time and huge resources to conduct simulations. In this paper, we present a new mathematical framework based on spatial fluid modeling which reduces the analysis complexity and provides a macroscopic evaluation of the performance, quite faithful to those obtained using Monte Carlo simulations. The key idea is to consider a continuum of nodes rather than a fixed finite number, and to derive the mean impact of a density of nodes in a certain region of the network. The closed-form formulas of the downlink interference factor are defined for three scenarios depending on the number of coordinated nodes. Then, they are used to evaluate the signal quality improvement, particularly at the cell edge.

Superposition coding (SC) has been known to be capacity-achieving for the Gaussian memoryless broadcast channel for more than 30 years. However, SC regained interest in the context of non-orthogonal multiple access (NOMA) in 5G. From an information theory point of view, SC is capacity-achieving in the broadcast Gaussian channel, even when the number of users tends to infinity. However, using SC has two drawbacks: the decoder complexity increases drastically with the number of simultaneous receivers, and the latency is unbounded since SC is optimal only in the asymptotic regime. To evaluate these effects quantitatively in terms of fundamental limits, we introduce a finite time transmission constraint imposed at the base station, and we evaluate fundamental trade-offs between the maximal number of superposed users, the coding block-length and the block error probability. The energy efficiency loss due to these constraints is evaluated analytically and by simulation. Orthogonal sharing appears to outperform SC for hard delay constraints (equivalent to short block-length) and in low spectral efficiency regime (below one bit per channel use). These results are obtained by the association of stochastic geometry and finite block-length information theory.

We consider the problem of optimizing the trajectory of an Unmanned Aerial Vehicle (UAV). Assuming a traffic intensity map of users to be served, the UAV must travel from a given initial location to a final position within a given duration and serves the traffic on its way. The problem consists in finding the optimal trajectory that minimizes a certain cost depending on the velocity and on the amount of served traffic. We formulate the problem using the framework of Lagrangian mechanics. We derive closed-form formulas for the optimal trajectory when the traffic intensity is quadratic (single-phase) using Hamilton-Jacobi equations. When the traffic intensity is bi-phase, i.e. made of two quadratics, we provide necessary conditions of optimality that allow us to propose a gradient-based algorithm and a new algorithm based on the linear control properties of the quadratic model. These two solutions are of very low complexity because they rely on fast convergence numerical schemes and closed form formulas. These two approaches return a trajectory satisfying the necessary conditions of optimality. At last, we propose a data processing procedure based on a modified K-means algorithm to derive a bi-phase model and an optimal trajectory simulation from real traffic data.