In this thesis, we investigate the impact of channels that have a bursty nature in a cognitive radio network scenario. Our goal is to design a general channel usage model that can handle bursty primary user channel usage. The proposed model describes idle periods with a discrete platoon arrival process and describes busy periods with a discrete phase type distribution. The performance of the proposed model is compared with two more traditionally encountered channel usage models in three different secondary user access schemes. First, we design a reactive access scheme to show the poor performance results an in- vestigator can potentially obtain when ignoring bursty data traffic. We have also analyzed two proactive secondary network access schemes. Numerical results show that the achiev- able utilization and interference probability of the network are affected when traditional channel models are used in a bursty PU channel.

In this thesis, we investigate the impact of channels that have a bursty nature in a cognitive radio network scenario. Our goal is to design a general channel usage model that can handle bursty primary user channel usage. The proposed model describes idle periods with a discrete platoon arrival process and describes busy periods with a discrete phase type distribution. The performance of the proposed model is compared with two more traditionally encountered channel usage models in three different secondary user access schemes. First, we design a reactive access scheme to show the poor performance results an in- vestigator can potentially obtain when ignoring bursty data traffic. We have also analyzed two proactive secondary network access schemes. Numerical results show that the achiev- able utilization and interference probability of the network are affected when traditional channel models are used in a bursty PU channel.

In the expanding spectrum marketplace, there has been a long term evolution towards more market€“oriented mechanisms, such as Opportunistic Spectrum Access (OSA), enabled through Cognitive Radio (CR) technology. However, the potential of CR technologies to revolutionize wireless communications, also introduces challenges based upon the potentially non€“deterministic CR behaviour in the Electrospace. While establishing and enforcing compliance to spectrum etiquette rules are essential to realization of successful OSA networks in the future, there has only been recent increased research activity into enforcement. This dissertation presents novel work on the spectrum monitoring aspect, which is crucial to effective enforcement of OSA. An overview of the challenges faced by current compliance monitoring methods is first presented. A framework is then proposed for the use of random spectral sampling techniques to reduce data collection complexity in wideband sensing scenarios. This approach is recommended as an alternative to Compressed Sensing (CS) techniques for wideband spectral occupancy estimation, which may be difficult to utilize in many practical congested scenarios where compliance monitoring is required. Next, a low€“cost computational approach to online randomized temporal sensing deployment is presented for characterization of temporal spectrum occupancy in cognitive radio scenarios. The random sensing approach is demonstrated and its performance is compared to CS€“based approach for occupancy estimation. A novel frame€“based sampling inversion technique is then presented for cases when it is necessary to track the temporal behaviour of individual CRs or CR networks. Parameters from randomly sampled Physical Layer Convergence Protocol (PLCP) data frames are used to reconstruct occupancy statistics, taking account of missed frames due to sampling design, sensor limitations and frame errors. Finally, investigations into the use of distributed and mobile spectrum sensing to collect spatial diversity to improve the above techniques are presented, for several common monitoring tasks in spectrum enforcement. Specifically, focus is upon techniques for achieving consensus in dynamic topologies such as in mobile sensing scenarios.

DARPA's neXt Generation (XG) communication program proposes the use of Dynamic Spectrum Access (DSA) wherein intelligent radios can realize opportunistic usage of frequency bands by identifying the under-utilized spectrum and reasoning about it. Implementing such a flexible scheme requires changes in the current static spectrum management approach. As a result, declarative spectrum management through policy-based dynamic spectrum access has garnered significant attention recently. Policy-based dynamic spectrum access decouples the Spectrum Access Policies and Policy Processing Components from the Radio Platform. The Policies define conditions under which the radios are allowed to transmit in terms of frequencies used, geographic locations, time etc. The Policy Processing Components include a reasoning engine called the Policy Reasoner, which is responsible for enforcing these policies. This thesis describes the design and implementation of a novel policy reasoner called Bi- nary Decision Diagram based Reasoner for processing Spectrum Access Policies (BRESAP). BRESAP processes spectrum policies efficiently by reframing the policy reasoning problem as a graph based Boolean function manipulation problem. BRESAP uses Binary Decision Diagrams (BDDs) to represent, analyze and process the policies. It uses a set of efficient graph-theoretic algorithms to merge these policies into a single meta-policy and compute opportunity constraints. Our policy reasoner has the capability to respond to invalid and under-specified transmission requests sent by the System Strategy Reasoner (SSR). In case of invalid or under-specified transmission requests, BRESAP returns a set of opportunity constraints which inform the SSR of the changes needed to the transmission parameters in order to make them conform to the policies. We also propose three algorithms for computing the opportunity constraints. The complexity of the first algorithm is proportional to the number of variables in the metapolicy BDD, while the complexities of the second and third algorithms are proportional to sum of number of variables and the size (i.e., number of nodes) of the meta-policy BDD. / Master of Science

Traditionally, networking technology has been limited because of the networks inability to adapt resulting in sub-optimal performance. Limited in state, scope and response mechanisms, network elements consisting of nodes, protocol layers and policies have been unable to make intelligent decisions. Modern networks often operate in environments where network resources (e.g. node energy, link quality, bandwidth, etc.), application data (e.g. location of user) and user behaviors (e.g. user mobility and user request pattern) experience changes over time. These changes degrade the network performance and cause service interruption. In recent years, the words "cognitive" and "smart" have become the buzzwords and have been applied to many different networking and communication systems. Cognitive networks are being touted as the next generation network services which will perceive the current network conditions and dynamically adjust their parameters to achieve better productivity. Cognitive radios will provide the end-user intelligence needed for cognitive networks and provide dynamic spectrum access for better spectrum efficiency. We are interested in assessing the practical impact of Cognitive Networks on the Wireless Communication industry. Our goal is to propose a formal business model that will help assess the implications of this new technology in the real world and the practical feasibility of its implementation. We use the layered business model proposed by Ballon [8] which follows a multi-parameter approach by defining four levels on which business models operate and by identifying three critical design parameters on each layer. The Value Network layer identifies the important entities which come into the picture in the light of the new technology. The Functional layer addresses the issue of different architectural implementations of the Cognitive Networks. At the Financial layer, we propose a NPV model which highlights the cost/revenue implications of the technology in the real world and contrasts the different Dynamic Spectrum Access (DSA) schemes from a financial perspective. Finally, the Value Proposition layer seeks to explain the end-user flexibility and efficient spectrum management provided by the use of Cognitive radios and Cognitive networks. / Master of Science

ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NV / Spectrum sharing between Federal and commercial users is a technique proposed by the FCC and NTIA to open up the 3.5 GHz band for wireless broadband use and small cell technology is one of the candidates for its' realization. The traffic on small cells is temporal and their chances of interfering with other services in shared spectrum are limited. DoD has a documented requirement of 865 MHz by 2025 to support telemetry but only 445 MHz is presently available. DoD is conducting researches to realize test and evaluation spectrum efficient technology with the aim to develop, demonstrate, and evaluate technology components required to enable flight and ground test telemetry operations. This paper will provide an overview on spectrum sharing using small cell technology for LTE-Advanced and dynamic spectrum access would be briefly described. Research challenges for protocols and algorithms would be addressed for future studies.

This report describes my M.A.Sc. thesis research work. The emerging 4th generation (4G) mobile systems and networks (so called 4G HetNets) are designed as multilayered cellular topology with a number of asymmetrically located, asymmetrically powered, self-organizing, and user-operated indoor small cell (e.g., pico/femto cells and WLANs) with a variety of cell architectures that are overlaid by a large cell (macro cell) with some or all interfering wireless links. These designs of 4G HetNets bring new challenges such as increased dynamics of user mobility and data traffic trespassing over the multi-layered cell boundaries. Traditional approaches of radio resource allocation and inter-cell (cochannel) interference management that are mostly centralized and static in the network core and are carried out pre-hand by the operator in 3G and lower cellular technologies, are liable to increased signaling overhead, latencies, complexities, and scalability issues and, thus, are not viable in case of 4G HetNets. In this thesis a comprehensive research study is carried out on improving the radio resource sharing and inter-cell interference management in 4G HetNets. The solution strategy exploits dynamic and adaptive channel allocation approaches such as dynamic and opportunistic spectrum access (DSA, OSA) techniques, through exploiting the spatiotemporal diversities among transmissions in orthogonal frequency division multiple access (OFDMA) based medium access in 4G HetNets. In this regards, a novel framework named as Hybrid Radio Resource Sharing (HRRS) is introduced. HRRS comprises of these two functional modules: Cognitive Radio Resource Sharing (CRRS) and Proactive Link Adaptation (PLA) scheme. A dynamic switching algorithm enables CRRS and PLA modules to adaptively invoke according to whether orthogonal channelization is to be carried out exploiting the interweave channel allocation (ICA) approach or non-orthogonal channelization is to be carried out exploiting the underlay channel allocation (UCA) approach respectively when relevant conditions regarding the traffic demand and radio resource availability are met. Benefits of CRRS scheme are identified through simulative analysis in comparison to the legacy cochannel and dedicated channel deployments of femto cells respectively. The case study and numerical analysis for PLA scheme is carried out to understand the dynamics of threshold interference ranges as function of transmit powers of MBS and FBS, relative ranges of radio entities, and QoS requirement of services with the value realization of PLA scheme.

The radio frequency spectrum is not being efficiently utilized partly due to the current policy of allocating the frequency bands to specific services and users. In opportunistic spectrum access (OSA), the ``white spaces'' that are not occupied by primary users (a.k.a. incumbent users) can be opportunistically utilized by secondary users. To achieve this, we need to solve two problems: (i) primary-secondary incumbent protection, i.e., prevention of harmful interference from secondary users to primary users; (ii) secondary-secondary network coexistence, i.e., mitigation of mutual interference among secondary users. The first problem has been addressed by spectrum sensing techniques in cognitive radio (CR) networks and geolocation database services in database-driven spectrum sharing. The second problem is the main focus of this dissertation. To obtain a clear picture of coexistence issues, we propose a taxonomy of heterogeneous coexistence mechanisms for shared spectrum access. Based on the taxonomy, we choose to focus on four typical coexistence scenarios in this dissertation. Firstly, we study sensing-based OSA, when secondary users are capable of employing the channel aggregation technique. However, channel aggregation is not always beneficial due to dynamic spectrum availability and limited radio capability. We propose a channel usage model to analyze the impact of both primary and secondary user behaviors on the efficiency of channel aggregation. Our simulation results show that user demands in both the frequency and time domains should be carefully chosen to minimize expected cumulative delay. Secondly, we study the coexistence of homogeneous CR networks, termed as self-coexistence, when co-channel networks do not rely on inter-network coordination. We propose an uplink soft frequency reuse technique to enable globally power-efficient and locally fair spectrum sharing. We frame the self-coexistence problem as a non-cooperative game, and design a local heuristic algorithm that achieves the Nash equilibrium in a distributed manner. Our simulation results show that the proposed technique is mostly near-optimal and improves self-coexistence in spectrum utilization, power consumption, and intra-cell fairness. Thirdly, we study the coexistence of heterogeneous CR networks, when co-channel networks use different air interface standards. We propose a credit-token-based spectrum etiquette framework that enables spectrum sharing via inter-network coordination. Specifically, we propose a game-auction coexistence framework, and prove that the framework is stable. Our simulation results show that the proposed framework always converges to a near-optimal distributed solution and improves coexistence fairness and spectrum utilization. Fourthly, we study database-driven OSA, when secondary users are mobile. The use of geolocation databases is inadequate in supporting location-aided spectrum sharing if the users are mobile. We propose a probabilistic coexistence framework that supports mobile users by locally adapting their location uncertainty levels in order to find an appropriate trade-off between interference mitigation effectiveness and location update cost. Our simulation results show that the proposed framework can determine and adapt the database query intervals of mobile users to achieve near-optimal interference mitigation with minimal location updates. / Ph. D.

With commercial deployment of the Citizens Band Radio Service commencing in the last quarter of 2018, efforts are in progress to improve the efficiency of the Spectrum Access System (SAS) functions. An area of concern as identified in recent field trials is the timebound evacuation of unlicensed secondary users from a frequency band by the SAS on the arrival of an incumbent user. In this thesis, we propose a way to optimize the evacuation process by reducing the number of secondary spectrum grant revocations to be performed. The proposed work leverages knowledge of incumbent user spectrum occupancy pattern obtained from historical spectrum usage data. Using an example model trained on 48 hours of an incumbent user transmission information, we demonstrate prediction of future incumbent user spectrum occupancy for the next 15 hours with 94.4% accuracy. The SAS uses this information to set the time validity of the secondary spectrum grants appropriately. In comparison to a case where spectrum grants are issued with no prior knowledge, the number of revocations declines by 87.5% with a 7.6% reduction in channel utilization. Further, the proposed technique provides a way for the SAS to plan ahead and prepare a backup channel to which secondary users can be redirected which can reduce the evacuation time significantly. / Master of Science / Studies on spectrum occupancy show that, in certain bands, licensed incumbent users use the spectrum only for some time or only within certain geographical limits. The dynamic spectrum access paradigm proposes to reclaim the underutilized spectrum by allowing unlicensed secondary users to access the spectrum opportunistically in the absence of the licensed users. In the United States, the Federal Communications Commission (FCC) has identified 150 MHz of spectrum space from 3550-3700 MHz to implement a dynamic spectrum sharing service called the Citizens Broadband Radio Service (CBRS). The guiding principle of this service is to maximize secondary user channel utilization while ensuring minimal incumbent user disruption. In this study, we propose that these conflicting requirements can be best balanced in the Spectrum Access System (SAS) by programming it to set the time validity of the secondary license grants by taking into consideration the incumbent spectrum occupancy pattern. In order to enable the SAS to learn incumbent spectrum occupancy in a privacy-preserving manner, we propose the use of a deep learning model, specifically the long-short term memory (LSTM). This model can be trained by federal agencies on historical incumbent spectrum occupancy information and then shared with the SAS in a secure manner to obtain prediction information about possible incumbent activity. Then, using the incumbent spectrum occupancy information from the LSTM model, the SAS could issue license grants that would expire before expected arrival time of incumbent user, thus minimizing the number of revocations on incumbent arrival. The scheme was validated using simulations that demonstrated the effectiveness of this approach in minimizing revocation complexity.

The exploding increase of wireless communications combined with the existing inefficient usage of the licensed spectrum gives a strong impetus to the development and standardization of cognitive radio networking and communications. In this dissertation, a framework for Dynamic Spectrum Access (DSA) is first presented, which is the enabling technology for increasing the spectral efficiency of wireless communications. Based on that, Cognitive Radio (CR) can be developed as an enabling technology for supporting the DSA, which means that the wireless users are provided with enhanced capability for sensing the operating radio environment and for exploiting the network side information obtained from this sensing. The DSA concept means that the users of a wireless system are divided into a multi-tiered hierarchy with the primary users (PUs) entitled to protection and with cognitive radio capable secondary users (SUs). The improved spectrum efficiency is obtained by means of a medium access control protocol with knowledge about the statistical properties or available local information of the channels already occupied by PUs as well as knowledge about the interference tolerance within which the interference to PUs is kept to a given level. Related to this, emphasis is laid on the protocol capability to determine the efficiency of the secondary sharing of spectrum. Based on the type of available local information, the capacity of opportunistic communication is investigated for three models. These are: with dynamic, distributed channels information; with dynamic, parallel channels information; and under a dynamic sub-channels allocation scheme. The results indicate that this capacity is robust with reference to the uncertainty associated with localized sensing of distributed dynamic channels and with timely sensing of parallel dynamic channels. The extension to dynamic parallel sub-channels enables resource allocation to be carried out in sub-channels. The analytical results on the performance of sub-channel allocation indicate a robust traffic capacity in terms of blocking probability, drop-out probability and delay performance as function of PUs traffic loads.