At present, operators address the explosive growth of mobile data demand by densification of the cellular network so as to reduce the transmitter-receiver distance and to achieve higher spectral efficiency. Due to such network densification and the intense proliferation of wireless devices, modern wireless networks are interference-limited, which motivates the use of interference mitigation and coordination techniques. In this work, we develop a statistical framework to evaluate the performance of multi-tier heterogeneous networks with successive interference cancellation (SIC) capabilities, accounting for the computational complexity of the cancellation scheme and relevant network related parameters such as random location of the access points (APs) and mobile users, and the characteristics of the wireless propagation channel. We explicitly model the consecutive events of canceling interferers and we derive the success probability to cancel the n-th strongest signal and to decode the signal of interest after n cancellations. When users are connected to the AP which provides the maximum average received signal power, the analysis indicates that the performance gains of SIC diminish quickly with n and the benefits are modest for realistic values of the signal-to-interference ration (SIR). We extend the statistical model to include several association policies where distinct gains of SIC are expected: (i) maximum instantaneous SIR association, (ii) minimum load association, and (iii) range expansion. Numerical results show the effectiveness of SIC for the considered association policies. This work deepens the understanding of SIC by defining the achievable gains for different association policies in multi-tier heterogeneous networks.

Heterogeneous networks using a mix of macrocells and small cells are foreseen as one of the solutions to meet the ever increasing mobile traffic demand. Nevertheless, a massive deployment of small cell access points (SAPs) leads also to a considerable increase in energy consumption. Spurred by growing environmental awareness and the high price of energy, it is crucial to design energy efficient wireless systems for both macrocells and small cells. In this work, we evaluate a distributed sleep-mode strategy for cognitive SAPs and we analyze the trade-off between traffic offloading from the macrocell and the energy consumption of the small cells. Using tools from stochastic geometry, we define the user discovery performance of the SAP and derive the uplink capacity of the small cells located in the Voronoi cell of a macrocell base station, accounting for the uncertainties associated with random position, density, user activity, propagation channel, network interference generated by uncoordinated activity, and the sensing scheme. In addition, we define a fundamental limit on the interference density that allows robust detection and we elucidate the relation between energy efficiency and sensing time using large deviations theory. Through the formulation of several optimization problems, we propose a framework that yields design guidelines for energy efficient small cell networks.

Abstract-Over the last decade, the growing amount of UL and DL mobile data traffic has been characterized by substantial asymmetry and time variations. Dynamic time-division duplex (TDD) has the capability to accommodate to the traffic asymmetry by adapting the UL/DL configuration to the current traffic demands. In this work, we study a two-tier heterogeneous cellular network (HCN) where the macro tier and small cell tier operate according to a dynamic TDD scheme on orthogonal frequency bands. To offload the network infrastructure, mobile users in proximity can engage in D2D communications, whose activity is determined by a carrier sensing multiple access (CSMA) scheme to protect the ongoing infrastructure-based and D2D transmissions. We present an analytical framework to evaluate the network performance in terms of load-aware coverage probability and network throughput. The proposed framework allows to quantify the effect on the coverage probability of the most important TDD system parameters, such as the UL/DL configuration, the base station density, and the bias factor. In addition, we evaluate how the bandwidth partition and the D2D network access scheme affect the total network throughput. Through the study of the tradeoff between coverage probability and D2D user activity, we provide guidelines for the optimal design of D2D network access.

Many traffic-related applications require the nodes in a vehicular ad-hoc network (VANET) to periodically broadcast their state information. As measurements campaigns or simulations to evaluate the reliability of packet transmission are slow and scenario-specific, we present an analytic performance assessment tool that accounts for the spatial statistics of the nodes on a road, in a scenario of crossing roads and fast fading. Based on stochastic geometry, our tool is able to capture a static twodimensional road geometry and the effect of interference due to node clustering in the vicinity of an intersection. Numerical results reveal how packet transmission is affected as the receiver gets closer to the intersection.

Abstract-In order to meet the growing mobile data demand, future wireless networks will be equipped with a multitude of access points (APs). Besides the important implications for the energy consumption, the trend towards densification requires the development of decentralized and sustainable radio resource management techniques. It is critically important to understand how the distribution of signal processing operations affects the energy efficiency of wireless networks. In this paper, we provide a cross-layer framework to evaluate and compare the energy efficiency of wireless networks under different levels of distribution of the signal processing load: (i) hybrid, where the signal processing operations are shared between nodes and APs, (ii) centralized, where signal processing is entirely implemented at the APs, and (iii) fully distributed, where all operations are performed by the nodes. We find that in practical wireless networks, hybrid signal processing exhibits a significant energy efficiency gain over both centralized and fully distributed approaches.