Vehicular networks allow vehicles to share information and are expected to be an integral part of future intelligent transportation systems (ITS). To guide and validate the design process, analytical expressions of key performance metrics such as packet reception probabilities and throughput are necessary, in particular for accident-prone scenarios such as intersections. In this paper, we present a procedure to analytically determine the packet reception probability and throughput of a selected link, taking into account the relative increase in the number of vehicles (i.e., possible interferers) close to an intersection. We consider both slotted Aloha and CSMA/CA MAC protocols, and show how the procedure can be used to model different propagation environments of practical relevance. The procedure is validated for a selected set of case studies at low traffic densities.

The traffic in current wireless networks exhibits large variations in uplink (UL) and downlink (DL), which brings huge challenges to network operators in efficiently allocating radio resources. Dynamic time-division duplex (TDD) is considered as a promising scheme to flexibly adjust resource allocation based on UL and DL traffic demands, known as traffic adaptation. In this work, we study how traffic adaptation decreases cell service time and improves energy efficiency (EE) in small cell networks operating dynamic TDD. According to different UL and DL traffic parameters, we classify small cells into K ≥ 1 types, and accommodate the UL/DL configuration for each type of small cells with the objective to minimize the cell service time and maximize the network EE. In comparison with semi-static TDD scheme, dynamic TDD is shown to achieve larger service time gain as the traffic asymmetry between small cells increases. In summary, the proposed analytical framework allows us to elucidate the benefit of traffic adaptation to service time and EE in future dense networks with dynamic TDD.

The traffic in current wireless networks exhibits large variations in uplink (UL) and downlink (DL), which brings huge challenges to network operators in efficiently allocating radio resources. Dynamic time-division duplex (TDD) is considered a promising scheme that is able to adjust the resource allocation to the instantaneous UL and DL traffic conditions, also known as traffic adaptation. In this paper, we study how traffic adaptation and energy harvesting can improve the energy efficiency (EE) in multi-antenna small cell networks operating dynamic TDD. Given the queue length distribution of small cell access points (SAPs) and mobile users (MUs), we derive the optimal UL/DL configuration to minimize the service time of a typical small cell, and show that the UL/DL configuration that minimizes the service time also results in an optimal network EE, but does not necessarily achieve the optimal EE for SAP or MU individually. To further enhance the network EE, we provide SAPs with energy harvesting capabilities, and model the status of harvested energy at each SAP using a Markov chain. We derive the availability of the rechargeable battery under several battery utilization strategies, and observe that energy harvesting can significantly improve the network EE in the low traffic load regime. In summary, the proposed analytical framework allows us to elucidate the relationship between traffic adaptation and network EE in future dense networks with dynamic TDD. With this work, we quantify the potential benefits of traffic adaptation and energy harvesting in terms of service time and EE.

In order to meet the growing mobile data demand, future networks will be equipped with a multitude of access points (APs) and require the development of decentralized and sustainable radio resource management techniques. It is of critical importance to understand how the distribution of network management and signal processing operations affects the energy efficiency. In this paper, we provide a cross-layer framework to study the energy efficiency of wireless networks under different levels of distribution of the network management: (i) hybrid, where the network management operations are shared between nodes and APs, (ii) centralized, where network management is entirely implemented at the APs, and (iii) fully distributed, where all operations are performed by the nodes. We find that in practical scenarios, hybrid network management outperforms a fully distributed approach and exhibits an energy efficiency gain of 40% and above over a centralized approach.

Over the last decade, the growing amount of uplink (UL) and downlink (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 device-to-device (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 for evaluating the network performance in terms of load-aware coverage probability and network throughput. The proposed framework allows quantification of 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.

Over the last decade, mobile data traffic has risen dramatically and the amount of UL and DL transmissions 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 network (HetNet) where the macro tier and small cell tier both operate dynamic TDD and use orthogonal frequency bands. We propose a policy that leads to different associations in UL and DL, and derive the load-aware coverage probability. We evaluate how the association policy affects the system performance and derive the UL/DL configuration, base station density, and bias factor that maximize the per tier or network-wide coverage probability.

The densification of the network infrastructure is a possible solution to meet the explosive growth of mobile data demand. In the resulting interference-limited networks, interference management techniques are of interest to increase the spectral efficiency. Successive interference cancellation (SIC) provides modest gains when users are connected to the access point (AP) which provides the maximum average received signal power. In this paper, we focus on alternative association policies where SIC gives rise to a substantial performance gain. Specifically, we present a probabilistic framework to evaluate the performance of heterogeneous networks with SIC capabilities considering the minimum load association policy and range expansion. Numerical results show the effectiveness of SIC for these association policies.

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 consisting of a macrocell tier and a small cell tier are considered an attractive solution to cope with the fierce increase of mobile traffic demand. Nevertheless, a massive deployment of small cell access points (SAPs) leads also to a considerable increase in energy consumption. Motivated by growing environmental awareness and the high price of energy, the design of energy efficient wireless systems for both macrocells and small cells becomes crucial. In this work, we analyze the trade-off between traffic offloading from the macrocell and the energy consumption of the small cell. Using tools from stochastic geometry, we define the user detection performance of the SAP and derive the small cell capacity accounting for the uncertainties associated with the random position of the user, the propagation channel, activity of the users, and the aggregate network interference. The proposed framework yields design guidelines for energy efficient small cells.

Heterogeneous networks consisting of a macrocell tier and a small cell tier are foreseen as one of the solutions to meet the ever increasing mobile data demand. Since a massive deployment of small cell access points (SAPs) leads also to a considerable increase in energy consumption, the energy efficient design of those SAPs is crucial. Sleep mode techniques are a promising strategy to reduce the energy consumption, yet they require cognitive capabilities to detect the presence of a macrocell user. In this work, we define a fundamental limit on the interference density that allows robust user detection. Beyond this limit, which we call the interference wall, an energy efficient SAP design is impossible. In addition, we elucidate the relation between energy efficiency and sensing time using large deviations theory.

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.