Society's newfound dependence on wireless transmission systems has driven demand for access to the electromagnetic (EM) spectrum to an all-time high. In particular, wireless applications related to the fifth generation (5G) of cellular technology along with statically allocated radar systems have contributed to the increasing scarcity of the sub 6 GHz frequency bands. As a result, development of Dynamic Spectrum Access (DSA) techniques for sharing these frequencies has become a critical research area for the greater wireless community. Since among incumbent systems, radars are the largest consumers of spectrum in the sub 6 GHz regime, and are being used increasingly for civilian applications such as traffic control, adaptive cruise control, and collision avoidance, the need for radars which can adaptively tune specific transmission parameters in an intelligent manner to promote coexistence with other systems has arisen. Thus, fully-aware, dynamic, cognitive radar has been proposed as target for radars to evolve towards. In this thesis, we extend current research thrusts towards cognitive radar to utilize Reinforcement Learning (RL) techniques which allow a radar system to learn desired behavior using information obtained from past transmissions. Since radar systems inherently interact with their electromagnetic environment, it is natural to view the use of reinforcement learning techniques as a straightforward extension to previous adaptive techniques. However, in designing learning algorithms for radar systems, we must carefully define goal-driven rewards, formalize the learning process, and consider an appropriate amount of environmental information. In this thesis, we apply well-established and emerging reinforcement learning approaches to meet the demands of modern radar coexistence problems. In particular, function estimation using deep neural networks is examined, as Deep RL presents a scalable learning framework which allows many environmental states to be considered in the decision-making process. We then show how these techniques can be used to improve traditional radar performance metrics, such as interference avoidance, spectral efficiency, and target detectibility with simulated and experimental results. We also compare the learning techniques to each other and naive approaches, such as fixed bandwidth radar and avoiding interference reactively. Finally, online learning strategies are considered which aim to balance the fundamental learning trade-off between exploration and exploitation. We show that online learning techniques can be used to select individual waveforms or applied as a high-level controller in a hierarchical learning scheme based on the biologically inspired concept of metacognition. The general use of RL techniques provides a robust framework for decision making under uncertainty that is more flexible than previously proposed cognitive radar strategies. Further, the wide array of RL models and algorithms allow the underlying structure to be applied to both small and large-scale radar scenarios. / Master of Science / Society's newfound dependence on wireless transmission systems has driven demand for control of the electromagnetic (EM) spectrum to an all-time high. In particular, federal spectrum auctions and the fifth generation of wireless technologies have contributed to the scarcity of frequency bands below 6GHz. These frequencies are widely used by both radar and communications systems due to favorable propagation characteristics. However, current radar systems typically occupy a fixed bandwidth and are tend to be poorly equipped to share their allocated spectrum with other users, which has become a necessity given the growth of wireless traffic. In this thesis, we study learning algorithms which enable a radar to optimize its electromagnetic pulses based on feedback from received signals. In particular, we are interested in reinforcement learning algorithms which allow a radar to learn optimal behavior based on rewards defined by a human. Using these algorithms, radar system designers can choose which metrics may be most important for a given radar application which can then be optimized for the given setting. However, scaling reinforcement learning to real-world problems such as radar optimization is often difficult due to the massive scope of the problem. Here we attempt to identify potential issues with implementation of each algorithm and narrow in on algorithms that are well-suited for real-time radar operation.

The United States Federal Communications Commission (FCC) in its recent report and order has prescribed the creation of Citizens Broadband Radio Service (CRBS) in the 3.5 GHz band to enable sharing between wireless broadband devices and incumbent radar systems. This sharing will be enabled by use of geolocation database with supporting infrastructure termed as Spectrum Access System (SAS). Although using SAS for spectrum sharing has many pragmatic advantages, it also raises potentially serious operational security (OPSEC) issues. In this thesis, we explore OPSEC, location privacy in particular, of incumbent radars in the 3.5 GHz band. First, we show that adversarial secondary users can easily infer the locations of incumbent radars by making seemingly innocuous queries to the database. Then, we propose several obfuscation techniques that can be implemented by the SAS for countering such inference attacks. We also investigate obfuscation techniques' efficacy in minimizing spectral efficiency loss while preserving incumbent privacy. Recently, the 3GPP Rel.13 has specified a new standard to provide wide-area connectivity for IoT, termed as Narrowband IoT (NB-IoT). NB-IoT achieves excellent coexistence with legacy mobile standards, and can be deployed in any of the 2G/3G/4G spectrum (450 MHz to 3.5 GHz). Recent industry efforts show deployment of IoT networks in unlicensed spectrum, including shared bands (e.g., 3.5 GHz band). However, operating NB-IoT systems in the 3.5 GHz band can result in significant BLER and coverage loss. In this thesis, we analyse results from extensive experimental studies on the coexistence of NB-IoT and radar systems, and demonstrate the coverage loss of NB-IoT in shared spectrum. / Master of Science / Spectrum sharing has been viewed by spectrum regulators and industry stakeholders as the most viable solution to overcome the spectrum congestion and to enable next generation wireless networks. Towards this end, the Federal Communications Commission in the United States has prescribed rules to enable sharing between incumbent radars and broadband wireless networks in the 3.5 GHz band. This sharing however will be enabled geolocation databases and supporting infrastructure known as Spectrum Access System, which are prone to privacy attacks by malicious secondary users. Preserving privacy of incumbent systems is vital as they are mostly military radars. In this thesis, we demonstrate such attacks and later propose efficient techniques to preserve the privacy of the incumbent systems while enabling better spectrum utilization. The phenomenal growth in smarter end-user devices and machine-to-machine (M2M) connections is a clear indicator of the growth of Internet of Things (IoT), and growing importance of wide area IoT networks. Recently, the telecommunications standard development body, 3GPP, has defined Narrowband IoT (NB-IoT) optimized for IoT. Also, NB-IoT has many features common to LTE, and it is likely that NB-IoT will also be deployed in bands where LTE will be deployed, including shared bands (e.g., 3.5 GHz band). However, NB-IoT systems that operate in the 3.5 can be prone to harmful radar interference and directly impact coverage of the NB-IoT basestation. In this thesis, we analyse results from extensive experimental studies on the coexistence of NB-IoT and radar systems. We believe this study can be leveraged by future studies to mititage the impact of radar on IoT networks.

RF front-ends are nonlinear systems that have nonlinear frequency response and, hence, can impair receiver performance by harmful adjacent channel interference in non-intuitive ways. Next generation wireless networks will see unprecedented diversity across receiver and radio technologies accessing the same band of spectrum in spatio-temporal proximity. Ensuring adjacent channel co-existence is of prime importance for successful deployment and operations of next generation wireless networks. Vulnerabilities of receiver front-end can have a severe detrimental effect on network performance and spectrum co-existence. This dissertation addresses the technological challenges in understanding and accounting for receiver sensitivities in the design of next generation wireless networks. The dissertation has four major contributions. In the first contribution, we seek to understand how receiver nonlinearity impacts performance. We propose a computationally efficient framework to evaluate the adjacent channel interference in a given radio/spectrum environment. We develop novel tractable representation of receiver front-end nonlinearity to specify the adjacent channel signals that contribute to the interference at the desired channel and the total adjacent channel interference power at a given desired channel. In the second contribution, we seek to understand how the impact of receiver nonlinearity performance can be quantified. We quantify receiver performance in the presence of adjacent channel interference using information theoretic metrics. We evaluate the limits on achievable rate accounting for RF front-end nonlinearity and provide a framework to compare disparate receivers by forming generalized metrics. In the third contribution, we seek to understand how the impact of receiver nonlinearity can be managed at the network level. We develop novel and comprehensive wireless network management frameworks that account for the RF nonlinearity, impairments, and diversity of heterogeneous wireless devices. We further develop computationally efficient algorithms to optimize the proposed framework and examine network level performance. We demonstrate through extensive network simulations that the proposed receiver-centric frameworks provide substantially high spectrum efficiency gains over receiver-agnostic spectrum access in dense and diverse next generation wireless networks. In the fourth contribution, we seek to understand how scalable interference networks are with receiver nonlinearity. We propose practical achievable schemes for interference avoidance and assess the scalability of the next generation wireless networks with interference due to receiver nonlinearity. Further, we develop an algorithmic scheme to evaluate the upper bound on scalability of nonlinear interference networks. This provides valuable insights on scalability and schemes for nonlinear adjacent channel interference avoidance in next generation shared spectrum networks. / Ph. D. / There has been a dramatic increase in the demand for mobile data, since the introduction of smartphones. Over the air transmission of data utilizes a natural resource called radio frequency spectrum. The efficient utilization of the radio frequency spectrum and clever wireless network management is key for satisfying this demand. Besides improving the quality of wireless services, efficient spectrum utilization will also have profound economic benefits and spur growth. It has been shown that spectrum is most efficiently used when shared among various services rather than licensed to specific users and communication systems. This implies that next generation wireless networks will comprise of diverse types of wireless devices. Thus, network design and regulation should ensure their harmonious co-existence. However, the practicality of spectrum sharing technology and regulation is still in its infancy. In particular, the effect of radio receiver performance and vulnerabilities from signals in the spectral neighborhood on spectrum regulation and management is not well understood. A detailed study and analysis of this is of paramount importance spectrum sharing and regulation in next generation wireless networks. In this dissertation we develop the fundamentals, limitations, and management strategies on the impact of receiver performance on efficient spectrum access and co-existence. In addition, this key insights to maximize network efficiency in next generation wireless systems are presented. The outcome of this dissertation will aid in developing frameworks to increase social awareness about low-quality wireless devices and their implications on capacity. In summary, this dissertation provides a the necessary foundations to understand, design, and optimize the next generation wireless networks, which will have far reaching economic and social benefits.

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.

‘Game Theory’ is the formal study of conflict and cooperation. The theory is based on a set of tools that have been developed in order to assist with the modelling and analysis of individual, independent decision makers. These actions potentially affect any decisions, which are made by other competitors. Therefore, it is well suited and capable of addressing the various issues linked to wireless communications. This work presents a Green Game-Based Hybrid Vertical Handover Model. The model is used for heterogeneous wireless networks, which combines both dynamic (Received Signal Strength and Node Mobility) and static (Cost, Power Consumption and Bandwidth) factors. These factors control the handover decision process; whereby the mechanism successfully eliminates any unnecessary handovers, reduces delay and overall number of handovers to 50% less and 70% less dropped packets and saves 50% more energy in comparison to other mechanisms. A novel Game-Based Multi-Interface Fast-Handover MIPv6 protocol is introduced in this thesis as an extension to the Multi-Interface Fast-handover MIPv6 protocol. The protocol works when the mobile node has more than one wireless interface. The protocol controls the handover decision process by deciding whether a handover is necessary and helps the node to choose the right access point at the right time. In addition, the protocol switches the mobile nodes interfaces ‘ON’ and ‘OFF’ when needed to control the mobile node’s energy consumption and eliminate power lost of adding another interface. The protocol successfully reduces the number of handovers to 70%, 90% less dropped packets, 40% more received packets and acknowledgments and 85% less end-to-end delay in comparison to other Protocols. Furthermore, the thesis adapts a novel combination of both game and auction theory in dynamic resource allocation and price-power-based routing in wireless Ad-Hoc networks. Under auction schemes, destinations nodes bid the information data to access to the data stored in the server node. The server will allocate the data to the winner who values it most. Once the data has been allocated to the winner, another mechanism for dynamic routing is adopted. The routing mechanism is based on the source-destination cooperation, power consumption and source-compensation to the intermediate nodes. The mechanism dramatically increases the seller’s revenue to 50% more when compared to random allocation scheme and briefly evaluates the reliability of predefined route with respect to data prices, source and destination cooperation for different network settings. Last but not least, this thesis adjusts an adaptive competitive second-price pay-to-bid sealed auction game and a reputation-based game. This solves the fairness problems associated with spectrum sharing amongst one primary user and a large number of secondary users in a cognitive radio environment. The proposed games create a competition between the bidders and offers better revenue to the players in terms of fairness to more than 60% in certain scenarios. The proposed game could reach the maximum total profit for both primary and secondary users with better fairness; this is illustrated through numerical results.

In an effort to make more spectrum available, recent initiatives by the FCC let mobile providers offer spot service of their licensed spectrum to secondary users, hence paving the way to dynamic secondary spectrum markets. This dissertation investigates secondary spectrum markets under different regulatory regimes by identifying profitability conditions and possible competitive outcomes in an oligopoly model. We consider pricing in a market where multiple providers compete for secondary demand. First, we analyze the market outcomes when providers adopt a coordinated access policy, where, besides pricing, a provider can elect to apply admission control on secondary users based on the state of its network. We next consider a competition when providers implement an uncoordinated access policy (i.e., no admission control). Through our analysis, we identify profitability conditions and fundamental price thresholds, including break-even and market sharing prices. We prove that regardless of the specific form of the secondary demand function, competition under coordinated access always leads to a price war outcome. In contrast, under uncoordinated access, market sharing becomes a viable market outcome if the intervals of prices for which the providers are willing to share the market overlap. We then turn our attention to how a network provider use carrier (spectrum) aggregation in order to lower its break-even price and gain an edge over its competition. To this end, we determine the optimal (minimum) level of carrier aggregation that a smaller provider needs. Under a quality-driven (QD) regime, we establish an efficient way of numerically calculating the optimal carrier aggregation and derive scaling laws. We extend the results to delay-related metrics and show their applications to profitable pricing in secondary spectrum markets. Finally, we consider the problem of profitability over a spatial topology, where identifying system behavior suffers from the curse of dimensionality. Hence, we propose an approximation model that captures system behavior to the first-order and provide an expression to calculate the break-even price at each network location and provide simulation results for accuracy comparison. All of our results hold for general forms of demand, thus avoid restricting assumptions of customer preferences and the valuation of the spectrum.

The rising popularity of wireless services resulting in spectrum shortage has motivated dynamic spectrum sharing to facilitate e cient usage of the underutilized spectrum. Wideband spectrum sensing is a critical functionality to enable dynamic spectrum access by enhancing the opportunities of exploring spectral holes, but entails a major implemen- tation challenge in compact commodity radios that have limited energy and computation capabilities. The sampling rates speci ed by the Shannon-Nyquist theorem impose great challenges both on the acquisition hardware and the subsequent storage and digital sig- nal processors. Sub-Nyquist sampling was thus motivated to sample wideband signals at rates far lower than the Nyquist rate, while still retaining the essential information in the underlying signals. This thesis proposes several algorithms for invoking sub-Nyquist sampling in wideband spectrum sensing. Speci cally, a sub-Nyquist wideband spectrum sensing algorithm is proposed that achieves wideband sensing independent of signal sparsity without sampling at full bandwidth by using the low-speed analog-to-digital converters based on sparse Fast Fourier Transform. To lower signal spectrum sparsity while maintaining the channel state information, the received signal is pre-processed through a proposed permutation and ltering algorithm. Additionally, a low-complexity sub-Nyquist wideband spectrum sensing scheme is proposed that locates occupied channels blindly by recovering the sig- nal support, based on the jointly sparse nature of multiband signals. Exploiting the common signal support shared among multiple secondary users, an e cient coopera- tive spectrum sensing scheme is developed, in which the energy consumption on signal acquisition, processing, and transmission is reduced with the detection performance guar- antee. To further reduce the computation complexity of wideband spectrum sensing, a hybrid framework of sub-Nyquist wideband spectrum sensing with geolocation database is explored. Prior channel information from geolocation database is utilized in the sens- ing process to reduce the processing requirements on the sensor nodes. The models of the proposed algorithms are derived and veri ed by numerical analyses and tested on both real-world and simulated TV white space signals.

Abstract The commercial long-term evolution (LTE) networks of today offer fast and regionally wide access to the Internet and to the commercial applications and services at a reasonable price. At the same time, public safety (PS) users are still communicating with old-fashioned, second-generation voice and data services. Recently, the commercial LTE networks have been standardized to offer capabilities to mission-critical users. However, the commercial networks do not yet fully support the coverage requirements of the PS users. Moreover, the commercial infrastructure might be out of order in critical scenarios where PS actors are needed. Thus, the PS users require, for example, rapidly deployed LTE networks to support their own communication. This thesis studies the PS use of commercial operators' LTE networks and rapidly deployed closed LTE networks. The key tasks are to find out how to connect users seamlessly together between the different networks as well as finding out how the frequency planning is implemented. This thesis provides practical design solutions to guarantee network interoperability by connecting the networks as well as radio spectrum utilization solutions by licensed shared access (LSA). While the concept of LSA has been well developed, it has not been thoroughly investigated from the point of view of the PS actors, who have special requirements and should benefit from the concept. Herein, the alternatives for spectrum sharing between PS and commercial systems are discussed. Moreover, the thesis develops a specific LSA spectrum sharing system for the PS actors deploying their own network in scenarios where the commercial networks are insufficient. The solution is a robust LSA-based spectrum sharing mechanism. Note that PS actors also need to be able to utilize the spectrum when the LSA system is not available and when the commercial system has failed. Thus, this thesis proceeds on developing sensing methods for complementing LSA, where the sensing methods guarantee spectrum information for a rapidly deployed PS network. It is shown how PS actors can utilize available spectrum with a secondary spectrum licence. This is a good alternative to reserving the spectrum completely. The work assembles missing pieces of existing methods to ensure the functionality of the commercial and of the supporting rapidly deployed networks, both in terms of spectrum usage and application services. / Tiivistelmä Kaupalliset long-term evolution (LTE) -verkot tarjoavat nopean, edullisen ja alueellisesti kattavan pääsyn Internettiin sekä laajaan valikoimaan sovelluksia. Samaan aikaan turvallisuustoimijat (public safety (PS) -toimijat) käyttävät vanhanaikaisia äänen sekä vaatimattoman datayhteyden tarjoavia verkkoja. LTE-verkot ovat kuitenkin äskettäin standardoitu tarjoamaan valmiudet myös toimintokriittiseen kommunikointiin. Toisaalta, kaupalliset LTE-verkot eivät vielä tarjoa esimerkiksi tarvittavaa alueellista kattavuutta PS-käyttäjille. Lisäksi, kaupalliset verkot saattavat olla epäkunnossa kriittisissä tilanteissa. Tämän vuoksi PS-toimijat tarvitsevat omia nopeasti pystytettäviä LTE-verkkoja tukemaan nykyaikaista viestintäänsä. Opinnäytetyössä tutkitaan näiden nopeasti pystytettävien LTE-verkkojen käyttöä kaupallisten LTE-verkkojen kanssa. Keskeiset tehtävät ovat eri verkkojen PS-toimijoiden saumaton yhdistäminen sekä verkkojen taajuusjaon toteuttaminen. Tämä opinnäytetyö tarjoaa käytännön ratkaisuja verkkojen yhteentoimivuuden takaamiseksi ja radiotaajuuksien jakoratkaisuja lisensoidun jaetun käyttöoikeuden licensed shared access (LSA) -metodin avulla. Vaikka LSA:n käsite on jo pitkälle kehitetty, sitä ei ole tutkittu perusteellisesti PS-toimijoiden näkökulmasta ottaen huomioon heidän erityisvaatimuksensa. Tässä työssä syvennytään näiltä osin LSA järjestelmään yhtenä vaihtoehtona taajuuksien saamiseksi nopeasti pystytettäville verkoille. Lisäksi työssä kehitetään robusti LSA-pohjainen taajuuksien jakamisjärjestelmä nopeasti pystytettäville verkoille tilanteissa, joissa kaupalliset verkot ovat riittämättömät. Huomaa, että PS-toimijoiden on pystyttävä hyödyntämään taajuuksia myös silloin, kun LSA-järjestelmän kaikki osat eivät ole käytettävissä ja kun kaupallinen LTE järjestelmä on alhaalla. Tätä varten opinnäytetyössä kehitetään LSA:ta täydentävä havainnointimenetelmä, jolla taataan taajuustiedot vapaista taajuuksista nopeasti pystytettäville verkoille, sekä näytetään, miten PS-toimijat voivat hyödyntää LSA:ta toissijaisen taajuuslisenssin avulla. Tämä on hyvä vaihtoehto radiospektrin varaamiseksi kokonaan. Työ kokoaa puuttuvia osia olemassa oleviin menetelmiin, jotta voidaan varmistaa kaupallisten verkkojen toimivuus PS-käyttäjille yhdessä niitä tukevien nopeasti pystytettävien verkkojen kanssa taajuuksien käytön ja sovelluspalvelujen osalta.

Future wireless communications expect to experience a spectrum shortage problem. One practical solution is spectrum sharing. This thesis studies precoding strategies to allocate communication resources for spectrum sharing in multi-user wireless systems from a game-theoretic perspective. The approaches for the precoding games are developed under different constraints. It is shown that the precoding game with spectral mask constraints can be formulated as a convex optimization problem and a dual decomposition based algorithm can be exploited to solve it. However, the problem is non-convex if users also have total power constraints. This study shows that an efficient sub-optimal solution can be derived by allocating the bottleneck resource in the system. The sub-optimal solution is proved to be efficient and can even achieve an identical performance to that of the optimal solution in certain cases, but with significantly reduced complexity. / Communications

We consider a hierarchical spectrum sharing network consisting of a primary and a cognitive secondary transmitter-receiver pair, with non-backlogged traffic. The secondary transmitter may utilize cooperative transmission techniques to relay primary traffic while superimposing its own information, or transmit opportunistically when the primary user is idle. The secondary user meets a dilemma in this scenario. Choosing cooperation it can transmit a packet immediately even if the primary queue is not empty, but it has to bear the additional cost of relaying, since the primary performance needs to be guaranteed. To solve this dilemma we propose dynamic cooperative secondary access control that takes the state of the spectrum sharing network into account. We formulate the problem as a Markov Decision Process (MDP) and prove the existence of a stationary policy that is average cost optimal. Then we consider the scenario when the traffic and link statistics are not known at the secondary user, and propose to find the optimal transmission strategy using reinforcement learning. With extensive numerical evaluation, we demonstrate that dynamic cooperation with state aware sequential decision is very efficient in spectrum sharing systems with stochastic traffic, and show that dynamic cooperation is necessary for the secondary system to be able to adapt to changing load conditions or to changing available energy resource. Our results show, that learning based access control, with or without known primary buffer state, has close to optimal performance. / <p>QS 2013</p>