3 research outputs found
Contributions to Resource Allocation in Cognitive Radio Networks
The continuous increase in the number of wireless devices and the huge demand for higher data rates have promoted the development of new wireless communications technologies with improved spectrum sharing features. Recently, the concept of cognitive radio (CR) has gained increased popularity for the efficient utilization of radio frequency (RF) spectrum. A CR is characterized as a communication system which is capable to learn the spectrum environment through sensing, and to adapt its signaling schemes for a better utilization of the radio frequency resources. Resource allocation, which involves scheduling of spectrum and power resources, represents a crucial problem for the performance of CR networks in terms of system throughput and bandwidth utilization.
In this dissertation, we investigate resource allocation problems in a CR network by exploring a variety of optimization techniques. Specifically, in the first part of the dissertation, our goal is to maximize the total throughput of secondary users (SUs) in an orthogonal frequency division multiple access (OFDMA) CR network. In addition, the power of SUs is controlled to keep the interference introduced to primary users (PUs) under certain limits, which gives rise to a non-convex mixed integer non-linear programming (MINLP) optimization problem. It is illustrated that the original non-convex MINLP formulation admits a special structure and the optimal solution can be achieved efficiently using any standard convex optimization method under a general and practical assumption.
In the second part of the dissertation, considering the imperfect sensing information, we study the joint spectrum sensing and resource allocation problem in a multi-channel-multi-user CR network. The average total throughput of SUs is maximized by jointly optimizing the sensing threshold and power allocation strategies. The problem is also formulated as a non-convex MINLP problem. By utilizing the continuous relaxation and convex optimization tools, the dimension of the non-convex MINLP problem is significantly reduced, which helps to reformulate the optimization problem without resorting to integer variables. A newly-developed optimization technique, referred to as the monotonic optimization, is then employed to obtain an optimal solution. Furthermore, a practical low-complexity spectrum sensing and resource allocation algorithm is proposed to reduce the computational cost
Reliable and Efficient Cognitive Radio Communications Using Directional Antennas
Cognitive Radio (CR) is a promising solution that enhances spectrum utilization by allowing an unlicensed or Secondary User (SU) to access licensed bands in a such way that its imposed interference on a license holder Primary User (PU) is limited, and hence fills the spectrum holes in time and/or frequency domains. Resource allocation, which involves scheduling of available time and transmit power, represents a crucial problem for the performance evaluation of CR systems. In this dissertation, we study the spectral efficiency maximization problem in an opportunistic CR system. Specifically, in the first part of the dissertation, we consider an opportunistic CR system where the SU transmitter (SUtx) is equipped to a Reconfigurable Antenna (RA). RA, with the capabilities of dynamically modifying their characteristics can improve the spectral efficiency, via beam steering and utilizing the spectrum white spaces in spatial (angular) domain. In our opportunistic CR system, SUtx relies on the beam steering capability of RA to detect the direction of PU\u27s activity and also to select the strongest beam for data transmission to SU receiver (SUrx). We study the combined effects of spectrum sensing error and channel training error as well as the beam detection error and beam selection error on the achievable rates of an opportunistic CR system with a RA at SUtx. We also find the best duration for spectrum sensing and channel training as well as the best transmit power at SUtx such that the throughput of our CR system is maximized subject to the Average Transmit Power Constraint (ATPC) and Average Interference Constraint (AIC). In the second part of the dissertation, we consider an opportunistic Energy Harvesting (EH)-enabled CR network, consisting of multiple SUs and an Access Point (AP), that can access a wideband spectrum licensed to a primary network. Assuming that each SU is equipped with a finite size rechargeable battery, we study how the achievable sum-rate of SUs is impacted by the combined effects of spectrum sensing error and imperfect Channel State Information (CSI) of SUs–AP links. We also design an energy management strategy that maximizes the achievable sum-rate of SUs, subject to a constraint on the average interference that SUs can impose on the PU
Channel Access and Reliability Performance in Cognitive Radio Networks:Modeling and Performance Analysis
Doktorgradsavhandling ved Institutt for Informasjons- og kommunikasjonsteknologi, Universitetet i AgderAccording to the facts and figures published by the international telecommunication
union (ITU) regarding information and communication technology (ICT)
industry, it is estimated that over 3.2 billion people have access to the Internet in
2015 [1]. Since 2000, this number has been octupled. Meanwhile, by the end of
2015, there were more than 7 billion mobile cellular subscriptions in the world, corresponding
to a penetration rate of 97%. As the most dynamic segment in ICT,
mobile communication is providing Internet services and consequently the mobile broadband penetration rate has reached 47% globally. Accordingly, capacity,
throughput, reliability, service quality and resource availability of wireless services
become essential factors for future mobile and wireless communications. Essentially,
all these wireless technologies, standards, services and allocation policies
rely on one common natural resource, i.e., radio spectrum.
Radio spectrum spans over the electromagnetic frequencies between 3 kHz and
300 GHz. Existing radio spectrum access techniques are based on the fixed allocation
of radio resources. These methods with fixed assigned bandwidth for exclusive
usage of licensed users are often not efficient since most of the spectrum
bands are under-utilized, either/both in the space domain or/and in the time domain.
In reality, it is observed that many spectrum bands are largely un-occupied
in many places [2], [3]. For instance, the spectrum bands which are exclusively allocated
for TV broadcasting services in USA remain un-occupied from midnight to
early morning according to the real-life measurement performed in [4]. In addition
to the wastage of radio resources, spectrum under-utilization constraints spectrum
availability for other intended users. Furthermore, legacy fixed spectrum allocation
techniques are not capable of adapting to the changes and interactions in the system,
leading to degraded network performance.
Unlike in the static spectrum allocation, a fraction of the radio spectrum is
allocated for open access as license-free bands, e.g., the industrial, scientific and
medical (ISM) bands (902-928, 2400-2483.5, 5725-5850 MHz). In 1985, the federal
communications commission (FCC) permitted to use the ISM bands for private
and unlicensed occupancy, however, under certain restrictions on transmission
power [5]. Consequently, standards like IEEE 802.11 for wireless local area networks
(WLANs) and IEEE 802.15 for wireless personal area networks (WPAN)
have grown rapidly with open access spectrum policies in the 2.4 GHz and 5 GHz
ISM bands. With the co-existence of both similar and dissimilar radio technologies,
802.11 networks face challenges for providing satisfactory quality of service (QoS).
This and the above mentioned spectrum under-utilization issues motivate the spectrum
regulatory bodies to rethink about more flexible spectrum access for licenseexempt
users or more efficient radio spectrum management. Cognitive radio (CR) is
probably the most promising technology for achieving efficient spectrum utilization
in future wireless networks