218 research outputs found
Throughput and Delay Scaling in Supportive Two-Tier Networks
Consider a wireless network that has two tiers with different priorities: a
primary tier vs. a secondary tier, which is an emerging network scenario with
the advancement of cognitive radio technologies. The primary tier consists of
randomly distributed legacy nodes of density , which have an absolute
priority to access the spectrum. The secondary tier consists of randomly
distributed cognitive nodes of density with , which
can only access the spectrum opportunistically to limit the interference to the
primary tier. Based on the assumption that the secondary tier is allowed to
route the packets for the primary tier, we investigate the throughput and delay
scaling laws of the two tiers in the following two scenarios: i) the primary
and secondary nodes are all static; ii) the primary nodes are static while the
secondary nodes are mobile. With the proposed protocols for the two tiers, we
show that the primary tier can achieve a per-node throughput scaling of
in the above two scenarios. In the associated
delay analysis for the first scenario, we show that the primary tier can
achieve a delay scaling of
with . In the second scenario, with two mobility
models considered for the secondary nodes: an i.i.d. mobility model and a
random walk model, we show that the primary tier can achieve delay scaling laws
of and , respectively, where is the random walk
step size. The throughput and delay scaling laws for the secondary tier are
also established, which are the same as those for a stand-alone network.Comment: 13 pages, double-column, 6 figures, accepted for publication in JSAC
201
Throughput and Delay Analysis in Cognitive Overlaid Networks
Consider a cognitive overlaid network (CON) that has two tiers with different
priorities: a primary tier vs. a secondary tier, which is an emerging network scenario
with the advancement of cognitive radio (CR) technologies. The primary tier consists
of randomly distributed primary radios (PRs) of density n, which have an absolute
priority to access the spectrum. The secondary tier consists of randomly distributed
CRs of density m = n^y with y greater than or equal to 1, which can only access the spectrum opportunistically to limit the interference to PRs. In this dissertation, the fundamental limits
of such a network are investigated in terms of the asymptotic throughput and packet
delay performance when m and n approaches infinity. The following two types of
CONs are considered: 1) selfish CONs, in which neither the primary tier nor the
secondary tier is willing to route the packets for the other, and 2) supportive CONs,
in which the secondary tier is willing to route the packets for the primary tier while
the primary tier does not. It is shown that in selfish CONs, both tiers can achieve
the same throughput and delay scaling laws as a stand-alone network. In supportive
CONs, the throughput and delay scaling laws of the primary tier could be significantly
improved with the aid of the secondary tier, while the secondary tier can still achieve
the same throughput and delay scaling laws as a stand-alone network. Finally, the
throughput and packet delay of a CON with a small number of nodes are investigated.
Specifically, we investigate the power and rate control schemes for multiple CR links in the same neighborhood, which operate over multiple channels (frequency bands)
in the presence of PRs with a delay constraint imposed on data transmission. By
further considering practical limitations in spectrum sensing, an efficient algorithm is
proposed to maximize the average sum-rate of the CR links over a finite time horizon
under the constraints on the CR-to-PR interference and the average transmit power
for each CR link. In the proposed algorithm, the PR occupancy of each channel is
modeled as a discrete-time Markov chain (DTMC). Based on such a model, a novel
power and rate control strategy based on dynamic programming (DP) is derived,
which is a function of the spectrum sensing output, the instantaneous channel gains
for the CR links, and the remaining power budget for the CR transmitter. Simulation results show that the proposed algorithm leads to a significant performance
improvement over heuristic algorithms
User Association in 5G Networks: A Survey and an Outlook
26 pages; accepted to appear in IEEE Communications Surveys and Tutorial
Towards Real-time Wireless Sensor Networks
Wireless sensor networks are poised to change the way computer systems interact with the physical world. We plan on entrusting sensor systems to collect medical data from patients, monitor the safety of our infrastructure, and control manufacturing processes in our factories. To date, the focus of the sensor network community has been on developing best-effort services. This approach is insufficient for many applications since it does not enable developers to determine if a system\u27s requirements in terms of communication latency, bandwidth utilization, reliability, or energy consumption are met. The focus of this thesis is to develop real-time network support for such critical applications. The first part of the thesis focuses on developing a power management solution for the radio subsystem which addresses both the problem of idle-listening and power control. In contrast to traditional power management solutions which focus solely on reducing energy consumption, the distinguishing feature of our approach is that it achieves both energy efficiency and real-time communication. A solution to the idle-listening problem is proposed in Energy Efficient Sleep Scheduling based on Application Semantics: ESSAT). The novelty of ESSAT lies in that it takes advantage of the common features of data collection applications to determine when to turn on and off a node\u27s radio without affecting real-time performance. A solution to the power control problem is proposed in Real-time Power Aware-Routing: RPAR). RPAR tunes the transmission power for each packet based on its deadline such that energy is saved without missing packet deadlines. The main theoretical contribution of this thesis is the development of novel transmission scheduling techniques optimized for data collection applications. This work bridges the gap between wireless sensor networks and real-time scheduling theory, which have traditionally been applied to processor scheduling. The proposed approach has significant advantages over existing design methodologies:: 1) it provides predictable performance allowing for the performance of a system to be estimated upon its deployment,: 2) it is possible to detect and handle overload conditions through simple rate control mechanisms, and: 3) it easily accommodates workload changes. I developed this framework under a realistic interference model by coordinating the activities at the MAC, link, and routing layers. The last component of this thesis focuses on the development of a real-time patient monitoring system for general hospital units. The system is designed to facilitate the detection of clinical deterioration, which is a key factor in saving lives and reducing healthcare costs. Since patients in general hospital wards are often ambulatory, a key challenge is to achieve high reliability even in the presence of mobility. To support patient mobility, I developed the Dynamic Relay Association Protocol -- a simple and effective mechanism for dynamically discovering the right relays for forwarding patient data -- and a Radio Mapping Tool -- a practical tool for ensuring network coverage in 802.15.4 networks. We show that it is feasible to use low-power and low-cost wireless sensor networks for clinical monitoring through an in-depth clinical study. The study was performed in a step-down cardiac care unit at Barnes-Jewish Hospital. This is the first long-term study of such a patient monitoring system
Models and Methods for Network Selection and Balancing in Heterogeneous Scenarios
The outbreak of 5G technologies for wireless communications can be considered a response to the need for widespread coverage, in terms of connectivity and bandwidth, to guarantee broadband services, such as streaming or on-demand programs offered by the main television networks or new generation services based on augmented and virtual reality (AR / VR).
The purpose of the study conducted for this thesis aims to solve two of the main problems that will occur with the outbreak of 5G, that is, the search for the best possible connectivity, in order to offer users the resources necessary to take advantage of the new generation services, and multicast as required by the eMBMS.
The aim of the thesis is the search for innovative algorithms that will allow to obtain the best connectivity to offer users the resources necessary to use the 5G services in a heterogeneous scenario. Study UF that allows you to improve the search for the best candidate network and to achieve a balance that allows you to avoid congestion of the chosen networks. To achieve these two important focuses, I conducted a study on the main mathematical methods that made it possible to select the network based on QoS parameters based on the type of traffic made by users. A further goal was to improve the computational computation performance they present.
Furthermore, I carried out a study in order to obtain an innovative algorithm that would allow the management of multicast. The algorithm that has been implemented responds to the needs present in the eMBMS, in realistic scenarios
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