Doctor of Philosophy / Department of Electrical and Computer Engineering / Balasubramaniam Natarajan / With the explosive growth of wireless systems and services, bandwidth has become a treasured commodity. Traditionally, licensed frequency bands were exclusively reserved for use by the primary license holders (primary users), whereas, unlicensed frequency bands
allow spectrum sharing. Recent spectrum measurements indicate that
many licensed bands remain relatively unused for most of the time.
Therefore, allowing secondary users (users without a license to
operate in the band) to operate with minimal or no interference to primary users is one way of sharing spectrum to increase
efficiency. Recently, Federal Communications Commission (FCC) has
opened up licensed bands for opportunistic use by secondary users.
A cognitive radio (CR) is one enabling technology for systems
supporting opportunistic use. A cognitive radio adapts to the
environment it operates in by sensing the spectrum and quickly
decides on appropriate frequency bands and transmission parameters
to use in order to achieve certain performance goals. A cognitive
radio network (CRN) refers to a network of cognitive
radios/secondary users.
In this dissertation, we consider a competitive CRN with multiple
channels available for opportunistic use by multiple secondary
users. We also assume that multiple secondary users may coexist in a
channel and each secondary user (SU) can use multiple channels to
satisfy their rate requirements. In this context, firstly, we
introduce an integrated modeling and forecasting tool that provides
an upper bound estimate of the number of secondary users that may be
demanding access to each of the channels at the next instant.
Assuming a continuous time Markov chain model for both primary and
secondary users activities, we propose a Kalman filter based
approach for estimating the number of primary and secondary users.
These estimates are in turn used to predict the number of primary
and secondary users in a future time instant. We extend the modeling
and forecasting framework to the case when SU traffic is governed by
Erlangian process. Secondly, assuming that scheduling is complete
and SUs have identified the channels to use, we propose two quality
of service (QoS) constrained resource allocation frameworks. Our
measures for QoS include signal to interference plus noise ratio
(SINR) /bit error rate (BER) and total rate requirement. In the
first framework, we determine the minimum transmit power that SUs
should employ in order to maintain a certain SINR and use that
result to calculate the optimal rate allocation strategy across
channels. The rate allocation problem is formulated as a maximum
flow problem in graph theory. We also propose a simple heuristic to
determine the rate allocation. In the second framework, both
transmit power and rate per channel are simultaneously optimized
with the help of a bi-objective optimization problem formulation.
Unlike prior efforts, we transform the BER requirement constraint
into a convex constraint in order to guarantee optimality of
resulting solutions. Thirdly, we borrow ideas from social behavioral
models such as Homo Egualis (HE), Homo Parochius (HP) and Homo
Reciprocan (HR) models and apply it to the resource management
solutions to maintain fairness among SUs in a competitive CRN
setting. Finally, we develop distributed user-based approaches
based on ``Dual Decomposition Theory" and ``Game Theory" to solve
the proposed resource allocation frameworks. In summary, our body of
work represents significant ground breaking advances in the analysis
of competitive CRNs.
| Identifer | oai:union.ndltd.org:KSU/oai:krex.k-state.edu:2097/3892 |
| Date | January 1900 |
| Creators | Akter, Lutfa |
| Publisher | Kansas State University |
| Source Sets | K-State Research Exchange |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
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