Handover (HO) management is one of the most crucial tasks in dense cellular networks with mobile users. A problem in the HO management is to deal with increasing HOs due to network densification in the 5G evolution and various HO skipping techniques have so far been studied in the literature to suppress excessive HOs. In this paper, we propose yet another HO skipping scheme, called periodic HO skipping. The proposed scheme prohibits the HOs of a mobile user equipment (UE) for a certain period of time, referred to as skipping period, thereby enabling flexible operation of the HO skipping by adjusting the length of the skipping period. We investigate the performance of the proposed scheme on the basis of stochastic geometry. Specifically, we derive analytical expressions of two performance metrics—the HO rate and the expected downlink data rate—when a UE adopts the periodic HO skipping. Numerical results based on the analysis demonstrate that the periodic HO skipping scenario can outperform the scenario without any HO skipping in terms of a certain utility metric representing the trade-off between the HO rate and the expected downlink data rate, in particular when the UE moves fast. Furthermore, we numerically show that there can exist an optimal length of the skipping period, which locally maximizes the utility metric, and approximately provide the optimal skipping period in a simple form. Numerical comparison with some other HO skipping techniques is also conducted.

Disordered complex networks are of fundamental interest in statistical physics, and they have attracted recent interest as stochastic models for information transmission over wireless networks. While mathematically tractable, a network based on the regulation Poisson point process model offers challenges vis-a-vis network efficiency. Strongly correlated alternatives, such as networks based on random matrix spectra (the Ginibre network), on the other hand offer formidable challenges in terms of tractability and robustness issues. In this work, we demonstrate that network models based on random perturbations of Euclidean latticesinterpolatebetween Poisson and rigidly structured networks, and allow us to achieve thebest of both worlds: significantly improve upon the Poisson model in terms of network efficacy measured by theSignal to Interference plus Noise Ratio(abbrv. SINR) and the related concept ofcoverage probabilities, at the same time retaining a considerable measure of mathematical and computational simplicity and robustness to erasure and noise. We investigate the optimal choice of the base lattice in this model, connecting it to the celebrated problem optimality of Euclidean lattices with respect to the Epstein Zeta function, which is in turn related to notions of lattice energy. This leads us to the choice of the triangular lattice in 2D and face centered cubic lattice in 3D, whose Gaussian perturbations we consider. We provide theoretical analysis and empirical investigations to demonstrate that the coverage probability decreases with increasing strength of perturbation, eventually converging to that of the Poisson network. In the regime of low disorder, our studies suggest an approximate statistical behaviour of the coverage function near a base station as a log-normal distribution with parameters depending on the Epstein Zeta function of the lattice, and related approximate dependencies for a power-law constant that governs the network coverage probability at large thresholds. In 2D, we determine the disorder strength at which the perturbed triangular lattice (abbrv. PTL) and the Ginibre networks are theclosestmeasured by comparing their network topologies via a comparison of theirPersistence Diagramsin the total variation as well as the symmetrized nearest neighbour distances. We demonstrate that, at this very same disorder, the PTL and the Ginibre networks exhibit very similar coverage probability distributions, with the PTL performing at least as well as the Ginibre. Thus, the PTL network at this disorder strength can be taken to be an effective substitute for the Ginibre network model, while at the same time offering the advantages of greater tractability both from theoretical and empirical perspectives.

Age of information (AoI) has been widely accepted as a measure quantifying freshness of status information in real-time status update systems. In many of such systems, multiple sources share a limited network resource and therefore the AoIs defined for the individual sources should be correlated with each other. However, there are not found any results in the literature studying the correlation of two or more AoIs in a status update system with multiple sources. In this work, we consider a multi-source system sharing a common service facility and provide a framework to investigate joint performance of the multiple AoIs. We then apply our framework to a simple pushout server with multiple sources and derive a closed-form expression for the joint Laplace transform of the AoIs in the case with independent M/G inputs. We further show some properties of the correlation coefficient of AoIs in the two-source system.

Owing to its flexibility in modeling real-world spatial configurations of users and base stations (BSs), the Poisson cluster process (PCP) has recently emerged as an appealing way to model and analyze heterogeneous cellular networks (HetNets). Despite its undisputed relevance to HetNets-corroborated by the models used in the industry-the PCP's use in performance analysis has been limited. This is primarily because of the lack of analytical tools to characterize the performance metrics, such as the coverage probability of a user connected to the strongest BS. In this paper, we develop an analytical framework for the evaluation of the coverage probability, or equivalently the complementary cumulative density function (CCDF) of signal-tointerference-and-noise ratio (SINR), of a typical user in a K-tier HetNet under a max power-based association strategy, where the BS locations of each tier follow either a Poisson point process (PPP) or a PCP. The key enabling step involves conditioning on the parent PPPs of all the PCPs, which allows us to express the coverage probability as a product of sum-product and probability generating functionals (PGFLs) of the parent PPPs. In addition to several useful insights, our analysis provides a rigorous way to study the impact of the cluster size on the SINR distribution, which was not possible using the existing PPP-based models.

The expected data rate and the handoff rate are important performance metrics in mobile wireless communications. In this paper, we consider a single-tier homogeneous cellular network and provide a stochastic geometric framework for analysis of the expected downlink data rate and the handoff rate for a moving user equipment (UE). To investigate a tradeoff between frequent and infrequent handoffs, we consider two scenarios, in one of which a UE experiences a handoff whenever it crosses a boundary between two coverage areas of base stations (BSs), and in the other a UE does not experience any handoffs during a fixed period of time. In each scenario, we derive analytical expressions for the two performance metrics for a moving UE. From the analysis, we find that, when a UE experiences a handoff whenever it crosses a boundary between BS coverage areas, the expected downlink data rate per unit of time is invariant from that for a static UE. On the other hand, when a UE does not do during a fixed period of time, both the handoff rate and the expected data rate depend on the distribution of the moving speed even when its average is preserved. We expect that our study can make a step forward in understanding data transmission in the situation where UEs are moving with variable speed.

The multi-tier cellular networks (heterogeneous networks, HetNets) have been attracting attention because of the increasing variety and popularity of mobile applications. Furthermore, the multi-antenna transmission techniques which can virtually increase the bandwidth of data communication are also effective solution to the problem of network traffic increase. The studies incorporating these techniques have been made a lot, e.g., Poisson multi-input multi-output (MIMO) HetNet models. However, it is not possible to consider the correlation of the configuration of the base stations (BSs) in Poisson MIMO HetNet model since the configuration of BSs follows the Poisson point processes (PPPs). We should consider the configuration of BSs depending on the BS class since the strength of the received signal and the interference depends on the distance between nodes. For example, the configuration of the macrocells tends to be more regular than that of the femtocells. In this study, we analyze the coverage probability for K-tier MIMO HetNet model, which is composed of the a-Ginibre point processes (a-GPPs) and PPPs. In the proposed model, we can smoothly adjust the degree of repulsion of the configuration of BSs between the PPP and GPP depending on the BS class. For this model, we investigate the impact of the repulsion parameters in a-GPP and MIMO parameters on the coverage probability from the results of some numerical experiments.

We consider the spatial stochastic model of single-tier downlink cellular networks, where the wireless base stations are deployed according to a general stationary point process on the Euclidean plane with general i.i.d. propagation effects. Recently, Ganti & Haenggi (2016) consider the same general cellular network model and, as one of many significant results, derive the tail asymptotics of the signal-to-interference ratio (SIR) distribution. However, they do not mention any conditions under which the result holds. In this paper, we compensate their result for the lack of the condition and expose a sufficient condition for the asymptotic result to be valid. We further illustrate some examples satisfying such a sufficient condition and indicate the corresponding asymptotic results for the example models. We give also a simple counterexample violating the sufficient condition.

Recently, spatial stochastic models based on determinantal point processes (DPP) are studied as promising models for analysis of cellular wireless networks. Indeed, the DPPs can express the repulsive nature of the macro base station (BS) configuration observed in a real cellular network and have many desirable mathematical properties to analyze the network performance. However, almost all the prior works on the DPP based models assume the Rayleigh fading while the spatial models based on Poisson point processes have been developed to allow arbitrary distributions of fading/shadowing propagation effects. In order for the DPP based model to be more promising, it is essential to extend it to allow non-Rayleigh propagation effects. In the present paper, we propose the downlink cellular network model where the BSs are deployed according to the Ginibre point process, which is one of the main examples of the DPPs, over Nakagami-m fading. For the proposed model, we derive a numerically computable form of the coverage probability and reveal some properties of it numerically and theoretically.

Coverage probability is one of the most important metrics for evaluating the performance of wireless networks. However, the spatial stochastic models for which a computable expression of the coverage probability is available are restricted (such as the Poisson based or α-Ginibre based models). Furthermore, even if it is available, the practical numerical computation may be time-consuming (in the case of α-Ginibre based model). In this paper, we propose the application of Padé approximation to the coverage probability in the wireless network models based on general spatial stationary point processes. The required Maclaurin coefficients are expressed in terms of the moment measures of the point process, so that the approximants are expected to be available for a broader class of point processes. Through some numerical experiments for the cellular network model, we demonstrate that the Padé approximation is effectively applicable for evaluating the coverage probability.

One problem with active measurement is that, while it is suitable for measuring time-average network performance, it is difficult to measure per-flow quality of service (QoS), which is defined as the average over packets in the flow. To achieve such per-flow QoS measurement, the authors proposed a new technique, called the change- of- measure-based passive/active monitoring (CoMPACT Monitor), which is based on the change-of-measure framework in probability/measure theory and transforms actively obtained information by using passively monitored data. This technique enables us to concurrently measure one-way delay information about individual users, applications, and organizations in detail in a lightweight manner. This paper presents the mathematical formulation for the CoMPACT Monitor and verifies that it works well under some weak conditions. In addition, we investigate its characteristics regarding several implementation issues through simulation and actual network experiments. The results reveal that our technique provides highly qualified estimates involving only a limited amount of extra traffic from active probes.

To make a scalable and lightweight QoS monitoring system, we have proposed a new QoS monitoring technique, change-of-measure based passive/active monitoring (CoMPACT monitor), which is based on change-of-measure framework and is an active measurement transformed by using passively monitored data. This technique enables us to measure detailed QoS information for individual users, applications, and organizations, in a scalable and lightweight manner. In this paper, we present the mathematical foundation of CoMPACT monitor. In addition, we show its characteristics through simulations in terms of typical implementation issues for inferring the delay distributions. The results show that CoMPACT monitor gives accurate QoS estimations with only a small amount of extra traffic for active measurement.

In ATM networks, each node has its own buffer to store some cells. The buffer size represents a trade-off between cell loss and transmission delay, and therefore, it is of significant concern to evaluate the performance with respect to the buffer size. In this paper, we investigate the sensitivity of the loss probability with respect to the buffer size by considering a gradual input queue with a finite buffer. Gradual inputs are considered as those representing a bursty cell traffic. Applying the perturbation analysis technique to this model, we derive the strongly consistent sensitivity estimates, and combining with the likelihood ratio method, we confirm that our estimates lead to reasonable simulation results.