Laplace Functional Ordering of Point Processes in Large-scale Wireless Networks

Stochastic orders on point processes are partial orders which capture notions like being larger or more variable. Laplace functional ordering of point processes is a useful stochastic order for comparing spatial deployments of wireless networks. It is shown that the ordering of point processes is preserved under independent operations such as marking, thinning, clustering, superposition, and random translation. Laplace functional ordering can be used to establish comparisons of several performance metrics such as coverage probability, achievable rate, and resource allocation even when closed form expressions of such metrics are unavailable. Applications in several network scenarios are also provided where tradeoffs between coverage and interference as well as fairness and peakyness are studied. Monte-Carlo simulations are used to supplement our analytical results.Comment: 30 pages, 5 figures, Submitted to Hindawi Wireless Communications and Mobile Computin

Power-Optimal Feedback-Based Random Spectrum Access for an Energy Harvesting Cognitive User

In this paper, we study and analyze cognitive radio networks in which secondary users (SUs) are equipped with Energy Harvesting (EH) capability. We design a random spectrum sensing and access protocol for the SU that exploits the primary link's feedback and requires less average sensing time. Unlike previous works proposed earlier in literature, we do not assume perfect feedback. Instead, we take into account the more practical possibilities of overhearing unreliable feedback signals and accommodate spectrum sensing errors. Moreover, we assume an interference-based channel model where the receivers are equipped with multi-packet reception (MPR) capability. Furthermore, we perform power allocation at the SU with the objective of maximizing the secondary throughput under constraints that maintain certain quality-of-service (QoS) measures for the primary user (PU)

Fine-grained performance analysis of massive MTC networks with scheduling and data aggregation

Abstract. The Internet of Things (IoT) represents a substantial shift within wireless communication and constitutes a relevant topic of social, economic, and overall technical impact. It refers to resource-constrained devices communicating without or with low human intervention. However, communication among machines imposes several challenges compared to traditional human type communication (HTC). Moreover, as the number of devices increases exponentially, different network management techniques and technologies are needed. Data aggregation is an efficient approach to handle the congestion introduced by a massive number of machine type devices (MTDs). The aggregators not only collect data but also implement scheduling mechanisms to cope with scarce network resources. This thesis provides an overview of the most common IoT applications and the network technologies to support them. We describe the most important challenges in machine type communication (MTC). We use a stochastic geometry (SG) tool known as the meta distribution (MD) of the signal-to-interference ratio (SIR), which is the distribution of the conditional SIR distribution given the wireless nodes’ locations, to provide a fine-grained description of the per-link reliability. Specifically, we analyze the performance of two scheduling methods for data aggregation of MTC: random resource scheduling (RRS) and channel-aware resource scheduling (CRS). The results show the fraction of users in the network that achieves a target reliability, which is an important aspect to consider when designing wireless systems with stringent service requirements. Finally, the impact on the fraction of MTDs that communicate with a target reliability when increasing the aggregators density is investigated

Interference Mitigation in Frequency Hopping Ad Hoc Networks

Radio systems today exhibit a degree of flexibility that was unheard of only a few years ago. Software-defined radio architectures have emerged that are able to service large swathes of spectrum, covering up to several GHz in the UHF bands. This dissertation investigates interference mitigation techniques in frequency hopping ad hoc networks that are capable of exploiting the frequency agility of software-defined radio platforms

Securing Large-Scale D2D Networks Using Covert Communication and Friendly Jamming

We exploit both covert communication and friendly jamming to propose a friendly jamming-assisted covert communication and use it to doubly secure a large-scale device-to-device (D2D) network against eavesdroppers (i.e., wardens). The D2D transmitters defend against the wardens by: 1) hiding their transmissions with enhanced covert communication, and 2) leveraging friendly jamming to ensure information secrecy even if the D2D transmissions are detected. We model the combat between the wardens and the D2D network (the transmitters and the friendly jammers) as a two-stage Stackelberg game. Therein, the wardens are the followers at the lower stage aiming to minimize their detection errors, and the D2D network is the leader at the upper stage aiming to maximize its utility (in terms of link reliability and communication security) subject to the constraint on communication covertness. We apply stochastic geometry to model the network spatial configuration so as to conduct a system-level study. We develop a bi-level optimization algorithm to search for the equilibrium of the proposed Stackelberg game based on the successive convex approximation (SCA) method and Rosenbrock method. Numerical results reveal interesting insights. We observe that without the assistance from the jammers, it is difficult to achieve covert communication on D2D transmission. Moreover, we illustrate the advantages of the proposed friendly jamming-assisted covert communication by comparing it with the information-theoretical secrecy approach in terms of the secure communication probability and network utility

Extending Wireless Powered Communication Networks for Future Internet of Things

Energy limitation has always been a major concern for long-term operation of wireless networks. With today's exponential growth of wireless technologies and the rapid movement towards the so-called Internet of Things (IoT), the need for a reliable energy supply is more tangible than ever. Recently, energy harvesting has gained considerable attention in research communities as a sustainable solution for prolonging the lifetime of wireless networks. Beside conventional energy harvesting sources such as solar, wind, vibration, etc. harvesting energy from radio frequency (RF) signals has drawn significant research interest in recent years as a promising way to overcome the energy bottleneck. Lately, the integration of RF energy transfer with wireless communication networks has led to the emergence of an interesting research area, namely, wireless powered communication network (WPCN), where network users are powered by a hybrid access point (HAP) which transfers wireless energy to the users in addition to serving the functionalities of a conventional access point. The primary aim of this thesis is to extend the baseline model of WPCN to a dual-hop WPCN (DH-WPCN) in which a number of energy-limited relays are in charge of assisting the information exchange between energy-stable users and the HAP. Unlike most of the existing research in this area which has merely focused on designing methods and protocols for uplink communication, we study both uplink and downlink information transmission in the DH-WPCN. We investigate sum-throughput maximization problems in both directions and propose algorithms for optimizing the values of the related parameters. We also tackle the doubly near-far problem which occurs due to unequal distance of the relays from the HAP by proposing a fairness enhancement algorithm which guarantees throughput fairness among all users