The growing demand for ubiquitous broadband network connectivity and continuously falling prices in hardware operating on the unlicensed bands have put Wi-Fi technology in a position to lead the way in rapid innovation towards high performance wireless for the future. The success story of Wi-Fi contributed to the development of widespread variety of options for unlicensed access (e.g., Bluetooth, Zigbee) and has even sparked regulatory bodies in several countries to permit access to unlicensed devices in portions of the spectrum initially licensed to TV services. In this thesis we present novel spectrum management algorithms for networks employing 802.11 and TV white spaces broadly aimed at efficient use of spectrum under consideration, lower contention (interference) and high performance. One of the target scenarios of this thesis is neighbourhood or citywide wireless access. For this, we propose the use of IEEE 802.11-based multi-radio wireless mesh network using omnidirectional antennae. We develop a novel scalable protocol termed LCAP for efficient and adaptive distributed multi-radio channel allocation. In LCAP, nodes autonomously learn their channel allocation based on neighbourhood and channel usage information. This information is obtained via a novel neighbour discovery protocol, which is effective even when nodes do not share a common channel. Extensive simulation-based evaluation of LCAP relative to the state-of-the-art Asynchronous Distributed Colouring (ADC) protocol demonstrates that LCAP is able to achieve its stated objectives. These objectives include efficient channel utilisation across diverse traffic patterns, protocol scalability and adaptivity to factors such as external interference. Motivated by the non-stationary nature of the network scenario and the resulting difficulty of establishing convergence of LCAP, we consider a deterministic alternative. This approach employs a novel distributed priority-based mechanism where nodes decide on their channel allocations based on only local information. Key enabler of this approach is our neighbour discovery mechanism. We show via simulations that this mechanism exhibits similar performance to LCAP. Another application scenario considered in this thesis is broadband access to rural areas. For such scenarios, we consider the use of long-distance 802.11 mesh networks and present a novel mechanism to address the channel allocation problem in a traffic-aware manner. The proposed approach employs a multi-radio architecture using directional antennae. Under this architecture, we exploit the capability of the 802.11 hardware to use different channel widths and assign widths to links based on their relative traffic volume such that side-lobe interference is mitigated. We show that this problem is NP-complete and propose a polynomial time, greedy channel allocation algorithm that guarantees valid channel allocations for each node. Evaluation of the proposed algorithm via simulations of real network topologies shows that it consistently outperforms fixed width allocation due to its ability to adapt to spatio-temporal variations in traffic demands. Finally, we consider the use of TV-white-spaces to increase throughput for in-home wireless networking and relieve the already congested unlicensed bands. To the best of our knowledge, our work is the first to develop a scalable micro auctioning mechanism for sharing of TV white space spectrum through a geolocation database. The goal of our approach is to minimise contention among secondary users, while not interfering with primary users of TV white space spectrum (TV receivers and microphone users). It enables interference-free and dynamic sharing of TVWS among home networks with heterogeneous spectrum demands, while resulting in revenue generation for database and broadband providers. Using white space availability maps from the UK, we validate our approach in real rural, urban and dense-urban residential scenarios. Our results show that our mechanism is able to achieve its stated objectives of attractiveness to both the database provider and spectrum requesters, scalability and efficiency for dynamic spectrum distribution in an interference-free manner.

Cognitive radios have been proposed as a new technology to counteract the spectrum scarcity issue and increase the spectral efficiency. In cognitive radios, the sparse assigned frequency bands are opened to secondary users, provided that interference induced on the primary licensees is negligible. Cognitive radios are established in two steps: the radios firstly sense the available frequency bands by detecting the presence of primary users and secondly communicate using the bands that have been identified as not in use by the primary users.<p><p>In this thesis we investigate how to improve the efficiency of cognitive radio networks when multiple cognitive radios cooperate to sense the spectrum or control their interferences. A major challenge in the design of cooperating devices lays in the need for exchange of information between these devices. Therefore, in this thesis we identify three specific types of control information exchange whose efficiency can be improved. Specifically, we first study how cognitive radios can efficiently exchange sensing information with a coordinator node when the reporting channels are noisy. Then, we propose distributed learning algorithms allowing to allocate the primary network sensing times and the secondary transmission powers within the secondary network. Both distributed allocation algorithms minimize the need for information exchange compared to centralized allocation algorithms. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

Cognitive radio (CR) networks and heterogeneous cellular networks are promising approaches to satisfy the demand for higher data rates and better connectivity. A CR network increases the utilization of the radio spectrum by opportunistically using it. Heterogeneous networks provide high data rates and improved connectivity by spatially reusing the spectrum and by bringing the network closer to the user. Interference presents a critical challenge for reliable communication in these networks. Accurately modeling it is essential in ensuring a successful design and deployment of these networks. We first propose modeling the aggregate interference power at a primary receiver (PU-Rx) caused from transmissions by randomly located cognitive users (CUs) in a CR network as a shifted lognormal random process. Its parameters are determined using a moment matching method. Extensive benchmarking shows that the proposed model is more accurate than the lognormal and Gaussian process models considered in the literature, even for a relatively dense deployment of CUs. It also compares favorably with the asymptotically exact stable and symmetric truncated stable distribution models, except at high CU densities. Our model accounts for the effect of imperfect spectrum sensing, interweave and underlay modes of CR operation, and path-loss, time-correlated shad-owing and fading of the various links in the network. It leads to new expressions for the probability distribution function, level crossing rate (LCR), and average exceedance duration (AED). The impact of cooperative spectrum sensing is also characterized. We also apply and validate the proposed model by using it to redesign the primary exclusive zone to account for the time-varying nature of interference. Next we model the uplink inter-cell aggregate interference power in homogeneous and heterogeneous cellular systems as a simpler lognormal random variable. We develop a new moment generating function (MGF) matching method to determine the lognormal’s parameters. Our model accounts for the transmit power control, peak transmit power constraint, small scale fading and large scale shadowing, and randomness in the number of interfering mobile stations and their locations. In heterogeneous net-works, the random nature of the number and locations of low power base stations is also accounted for. The accuracy of the proposed model is verified for both small and large values of interference. While not perfect, it is more accurate than the conventional Gaussian and moment-matching-based lognormal and Gamma distribution models. It is also performs better than the symmetric-truncated stable and stable distribution models, except at higher user density.

One statement that we can make with absolute certainty in our current time is that wireless communication is now the standard and the de-facto type of communication. Cognitive radios are able to interpret the frequency spectrum and adapt. The aim of this work is to be able to predict whether a frequency channel is going to be busy or free in a specific time located in the future. To do this, the problem is modeled as a time series problem where each usage of a channel is treated as a sequence of busy and free slots in a fixed time frame. For this time series problem, the method being implemented is one of the latest, state-of-the-art, technique in machine learning for time series and sequence prediction: long short-term memory neural networks, or LSTMs.

Cognitive radios require spectral occupancy information in a given location, to avoid any interference with the existing licensed users. This is achieved by spectrum sensing. Existing narrowband, serial spectrum sensors are spectrally inefficient and power hungry. Wideband spectrum sensing increases the number of probable fre-quency candidates for cognitive radio. Wideband RF systems cannot use analog to digital converters (ADCs) for spectrum sensing without increasing the sampling rate and power consumption. The use of ADCs is limited because of the dynamic range of the signals that need to be sampled and the frequency of operation. In this work, we have presented a CMOS based area efficient, dedicated and scalable wideband parallel/serial spectrum sensor for cognitive radio. The key contributions of the thesis are: 1. An injection locked oscillator cascade (ILOC) for parallel LO synthesis. An area-efficient, wideband RF frequency synthesizer, which simultaneously gen-erates multiple local oscillator (LO) signals, is designed. It is suitable for parallel wideband RF spectrum sensing in cognitive radios. The frequency synthesizer consists of an injection locked oscillator cascade where all the LO signals are derived from a single reference oscillator. The ILOC is implemented in a 130-nm technology with an active area of 0.017 mm2. It generates 4 uni-formly spaced LO carrier frequencies from 500 MHz to 2 GHz. 2. A wideband, parallel RF spectrum sensor for cognitive radios has been de-signed. This spectrum sensor is designed to detect RF occupancy from 250 MHz to 5.25 GHz by using an array of CMOS receivers with envelope detec-tors. A parallel LO synthesizer is implemented as an ILOC. The simulated sensitivity is around -25 dBm for 250 MHz wide bandwidth. 3. A mitigation technique for harmonic downconversion in wideband spectrum sensors. The downconversion of radio frequency (RF) components around the harmonics of the local oscillator (LO), and its impact on the accuracy of white space detection using integrated spectrum sensors, is (are) studied. We propose an algorithm to mitigate the impact of harmonic Down conversion by utilizing multiple parallel downconverters in the system architecture. The proposed algorithm is validated on a test-board using commercially avail-able integrated circuits (IC) and a test-chip implemented in a 130-nm CMOS technology. The measured data shows that the impact of the harmonic down-conversion is closely related to the LO characteristics, and that much of it can be mitigated by the proposed technique. 4. A wideband spectrum sensor for narrowband energy detection. A wideband spectrum sensing system for cognitive radio is designed and implemented in a 130-nm RF mixed-mode CMOS technology. The system employs an I-Q downconverter, a pair of complex filters and a pair of envelope detectors for energy detection. The spectrum sensor works from 250 MHz to 3.25 GHz. The design makes use of the band pass nature of the complex filter to achieve two objectives : i) Separation of upper sideband (USB) and lower sideband (LSB) around the local oscillator (LO) signal and ii) Resolution of smaller bands within a large detection bandwidth. The measured sensitivity is close to -45 dBm for a single tone test over a bandwidth of 40 MHz. The measured Image reject ratio (IRR) is close to 30 dB. The overall sensing bandwidth is 3.5 GHz and the overall wideband detection bandwidth is 250 MHz which is partitioned into 40 MHz narrowband chunks with 8 such overlapping chunks.

Software Defined Radios (SDRs) and Cognitive Radios (CRs) pave the way for next-generation radio technology. They promise versatility, flexibility and cognition which can revolutionize communications systems. However they present greater challenges to the design of radio frequency (RF) front-ends. RF front-ends for the radios in use today are narrow-band in their frequency response and are optimized and tuned to the carrier frequency of interest. SDRs and CRs demand front-ends which are versatile, configurable, tunable and be capable of transmitting and receiving signals with different bandwidths and modulation schemes. Integrating power amplifiers (PAs) with transmitters in CMOS has many advantages and challenges. This thesis deals with the design of an RF transmitter front-end for SDRs and CRs in CMOS. The thesis begins with an introduction to SDRs and the requirements they place on transmitters and the challenges involved in designing them in CMOS. After a brief overview of the existing techniques, the proposed architecture is presented and explained. A digitally intensive transmitter solution is proposed. The transmitter covers a wide frequency range of 750 MHz to 2.5 GHz. The inputs to the proposed transmitter are in-phase and quadrature (I & Q) data bit streams. Multiple stages of up-sampling and filtering are used to remove all spurs in the spectrum such that only the harmonics of the carrier remain. Differential rail-to-rail quadrature clocks are generated from a continuous wave signal at twice the carrier frequency. The clocks are corrected for their duty cycle and quadrature impairments. The heart of the transmitter is an integrated reconfigurable CMOS power amplifier (PA). A methodology to design reconfigurable Class E PAs with a series fixed inductor has been presented. A CMOS power amplifier that can span a wide frequency range with sufficient output power and efficiency, supporting varying envelope complex modulation signals, with good linearity has been designed. Digital pre-distortion (DPD) is used to linearize the PA. The full transmitter and the clock correction blocks have been designed and fabricated in a commercial 130-nm CMOS process and experimentally characterized. The PA delivers a maximum power of 13 dBm with an efficiency of 27% at 1 GHz. While transmitting a 16-QAM signal at 1 GHz, the measured EVM is 4%. It delivers a maximum power of around 11-13 dBm from 750 MHz to 1.5 GHz and up to 6.5 dBm of power till 2.5 GHz. Comparing the proposed system with recently published literature, it can be seen that the proposed design is one of the very few transmitters which has an integrated matching network, tunable across the frequency range. The proposed PA produces the highest output power and with largest efficiency for systems with on-chip output networks.

Abstract Cognitive radio (CR) is becoming a promising tool for solving the problem of the scarce radio resource and spectrum inefficiency. Spectrum sensing (signal detection) enables real-time detection of spectrum holes by unlicensed secondary users (SUs) in cognitive radio networks (CRNs). In this thesis, performance analysis of CRNs and radio resource allocation are considered. A continuous time Markov chain (CTMC) based analytical model taking into account all relevant elements as well as addressing the issue of the false alarm rate (FAR) associated with the continuous sensing is developed. In some cases, the PU can be modeled as time-slotted with constant state (transmitting or not) in each slot. In this case, assuming SU can synchronize to the slots, its intuitive to use beginning of a slot for sensing and rest (possibly) for communication. For this model, M/D/1 priority queueing scheme has been applied in this thesis to find waiting time and queue length for PU and SU. Multiple access among SUs in a time-slotted channel is considered next. A conventional method is e.g. using a channel access probability ψ in each slot similar to the slotted ALOHA. A radically new idea is introduced in this thesis: why not increase the false alarm probability PFA of each SU and use it as a multiple access method? A game theoretic approach to radio resource allocation for the downlink capacity providing fair resource sharing among mobile nodes located along a multihop link is presented. Furthermore, the problem of resource allocations in heterogeneous wireless networks is also studied. Finally, device-to-device (D2D) communication - with localized distribution, where users tend to gather around some areas (clusters/hot-spots) within the cell such as buildings is studied. Theoretical analysis with two dimensional clustering is presented including cases with correlated clusters. Correlation in cluster selection is shown to significantly improve performance. / Tiivistelmä Kognitiivinen radio (CR) on nousemassa lupaavaksi työkaluksi niukkojen radioresurssien ja spektrin käytön tehottomuuden ratkaisemisessa. Spektrin nuuskiminen (signaalin ilmaisu) mahdollistaa spektriaukkojen reaaliaikaisen tunnistamisen toissijaisten käyttäjien (SU) toimesta kognitiivisissa radioverkoissa (CRN). Tässä väitöskirjassa painotus on CRN verkkojen suorituskykyanalyysissa ja radioresurssien hallinnassa. Työssä kehitetään jatkuva-aikaiseen Markov ketjuun (CTMC) perustuva analyyttinen malli joka ottaa huomioon kaikki olennaiset asiat mukaan lukien jatkuva-aikaiseen spektrin nuuskimiseen liittyvän väärien hälytysten tiheyden (FAR). Joissakin tapauksissa PU:ta voidaan mallintaa aikajaoteltuna siten että PU:n tila on vakio kussakin aikavälissä. Olettaen että SU voi synkronoitua aikaväleihin, on intuitiivista käyttää aikavälin alkua nuuskimiselle ja loppuosaa (mahdollisesti) viestintää varten. M/D/1:n ensisijaisuus-jonotus-suunnitelmaa soveltamalla tässä väitöskirjassa saadaan tuloksia odotusajalle ja jonon pituudelle sekä SU:lle että PU:lle. Seuraavaksi käsitellään monikäyttöä SU:den joukossa aikajaotellussa kanavassa. Tavanomainen menetelmä käyttää esimerkiksi kanavapääsytodennäköisyyttä ψ kussakin aikavälissä vastaten aikajaoteltua ALOHA protokollaa. Tässä väitöskirjassa esitetään radikaali uusi idea: miksei lisätä väärän hälytyksen todennäköisyyttä kussakin SU:ssa ja käytetä sitä moniliittymämenetelmänä? Työssä esitetään peliteoreettinen lähestymistapa radioresurssien allokointiin siten että resurssit jaetaan oikeudenmukaisesti monen yhteysvälin linkeissä. Lisäksi tutkitaan myös resursoinnin ongelmaa heterogeenisissa langattomissa verkoissa. Lopuksi tutkitaan laitteiden välistä suoraa viestintää (D2D) paikallisen jakauman kanssa, jossa käyttäjillä on tapana kasaantua solun sisällä esim. rakennuksiin. Esitetään teoreettinen analyysi kaksiulotteisella klusteroinnilla myös korreloitujen ryhmien kanssa. Osoitetaan että korrelaatio ryhmän valinnassa parantavaa merkittävästi suorituskykyä.

Consider a large population containing a small number of defective items. A commonly encountered goal is to identify the defective items, for example, to isolate them. In the classical non-adaptive group testing (NAGT) approach, one groups the items into subsets, or pools, and runs tests for the presence of a defective itemon each pool. Using the outcomes the tests, a fundamental goal of group testing is to reliably identify the complete set of defective items with as few tests as possible. In contrast, this thesis studies a non-defective subset identification problem, where the primary goal is to identify a “subset” of “non-defective” items given the test outcomes. The main contributions of this thesis are: We derive upper and lower bounds on the number of nonadaptive group tests required to identify a given number of non-defective items with arbitrarily small probability of incorrect identification as the population size goes to infinity. We show that an impressive reduction in the number of tests is achievable compared to the approach of first identifying all the defective items and then picking the required number of non-defective items from the complement set. For example, in the asymptotic regime with the population size N → ∞, to identify L nondefective items out of a population containing K defective items, when the tests are reliable, our results show that O _ K logK L N _ measurements are sufficient when L ≪ N − K and K is fixed. In contrast, the necessary number of tests using the conventional approach grows with N as O _ K logK log N K_ measurements. Our results are derived using a general sparse signal model, by virtue of which, they are also applicable to other important sparse signal based applications such as compressive sensing. We present a bouquet of computationally efficient and analytically tractable nondefective subset recovery algorithms. By analyzing the probability of error of the algorithms, we obtain bounds on the number of tests required for non-defective subset recovery with arbitrarily small probability of error. By comparing with the information theoretic lower bounds, we show that the upper bounds bounds on the number of tests are order-wise tight up to a log(K) factor, where K is the number of defective items. Our analysis accounts for the impact of both the additive noise (false positives) and dilution noise (false negatives). We also provide extensive simulation results that compare the relative performance of the different algorithms and provide further insights into their practical utility. The proposed algorithms significantly outperform the straightforward approaches of testing items one-by-one, and of first identifying the defective set and then choosing the non-defective items from the complement set, in terms of the number of measurements required to ensure a given success rate. We investigate the use of adaptive group testing in the application of finding a spectrum hole of a specified bandwidth in a given wideband of interest. We propose a group testing based spectrum hole search algorithm that exploits sparsity in the primary spectral occupancy by testing a group of adjacent sub-bands in a single test. This is enabled by a simple and easily implementable sub-Nyquist sampling scheme for signal acquisition by the cognitive radios. Energy-based hypothesis tests are used to provide an occupancy decision over the group of sub-bands, and this forms the basis of the proposed algorithm to find contiguous spectrum holes of a specified bandwidth. We extend this framework to a multistage sensing algorithm that can be employed in a variety of spectrum sensing scenarios, including non-contiguous spectrum hole search. Our analysis allows one to identify the sparsity and SNR regimes where group testing can lead to significantly lower detection delays compared to a conventional bin-by-bin energy detection scheme. We illustrate the performance of the proposed algorithms via Monte Carlo simulations.

Cognitive Radio (CR) has received tremendous research attention over the past decade, both in the academia and industry, as it is envisioned as a promising solution to the problem of spectrum scarcity. ACR is a device that senses the spectrum for occupancy by licensed users(also called as primary users), and transmits its data only when the spectrum is sensed to be available. For the efficient utilization of the spectrum while also guaranteeing adequate protection to the licensed user from harmful interference, the CR should be able to sense the spectrum for primary occupancy quickly as well as accurately. This makes Spectrum Sensing(SS) one of the where the goal is to test whether the primary user is inactive(the null or noise-only hypothesis), or not (the alternate or signal-present hypothesis). Computational simplicity, robustness to uncertainties in the knowledge of various noise, signal, and fading parameters, and ability to handle interference or other source of non-Gaussian noise are some of the desirable features of a SS unit in a CR. In many practical applications, CR devices can exploit known structure in the primary signal. IntheIEEE802.22CR standard, the primary signal is a wideband signal, but with a strong narrowband pilot component. In other applications, such as military communications, and blue tooth, the primary signal uses a Frequency Hopping (FH)transmission. These applications can significantly benefit from detection schemes that are tailored for detecting the corresponding primary signals. This thesis develops novel detection schemes and rigorous performance analysis of these primary signals in the presence of fading. For example, in the case of wideband primary signals with a strong narrowband pilot, this thesis answers the further question of whether to use the entire wideband for signal detection, or whether to filter out the pilot signal and use narrowband signal detection. The question is interesting because the fading characteristics of wideband and narrowband signals are fundamentally different. Due to this, it is not obvious which detection scheme will perform better in practical fading environments. At another end of the gamut of SS algorithms, when the CR has no knowledge of the structure or statistics of the primary signal, and when the noise variance is known, Energy Detection (ED) is known to be optimal for SS. However, the performance of the ED is not robust to uncertainties in the noise statistics or under different possible primary signal models. In this case, a natural way to pose the SS problem is as a Goodness-of-Fit Test (GoFT), where the idea is to either accept or reject the noise-only hypothesis. This thesis designs and studies the performance of GoFTs when the noise statistics can even be non-Gaussian, and with heavy tails. Also, the techniques are extended to the cooperative SS scenario where multiple CR nodes record observations using multiple antennas and perform decentralized detection. In this thesis, we study all the issues listed above by considering both single and multiple CR nodes, and evaluating their performance in terms of(a)probability of detection error,(b) sensing-throughput trade off, and(c)probability of rejecting the null-hypothesis. We propose various SS strategies, compare their performance against existing techniques, and discuss their relative advantages and performance tradeoffs. The main contributions of this thesis are as follows: The question of whether to use pilot-based narrowband sensing or wideband sensing is answered using a novel, analytically tractable metric proposed in this thesis called the error exponent with a confidence level. Under a Bayesian framework, obtaining closed form expressions for the optimal detection threshold is difficult. Near-optimal detection thresholds are obtained for most of the commonly encountered fading models. Foran FH primary, using the Fast Fourier Transform (FFT) Averaging Ratio(FAR) algorithm, the sensing-through put trade off are derived in closed form. A GoFT technique based on the statistics of the number of zero-crossings in the observations is proposed, which is robust to uncertainties in the noise statistics, and outperforms existing GoFT-based SS techniques. A multi-dimensional GoFT based on stochastic distances is studied, which pro¬vides better performance compared to some of the existing techniques. A special case, i.e., a test based on the Kullback-Leibler distance is shown to be robust to some uncertainties in the noise process. All of the theoretical results are validated using Monte Carlo simulations. In the case of FH-SS, an implementation of SS using the FAR algorithm on a commercially off-the ¬shelf platform is presented, and the performance recorded using the hardware is shown to corroborate well with the theoretical and simulation-based results. The results in this thesis thus provide a bouquet of SS algorithms that could be useful under different CRSS scenarios.

This thesis proposes and analyzes data fusion algorithms that operate on the physical layer of a wireless sensor network, in the context of three applications of cognitive radios: 1. Cooperative spectrum sensing via binary consensus; 2. Multiple transmitter localization and communication footprint identification; 3.Target self-localization using beacon nodes. For the first application, a co-phasing based data combining scheme is studied under imperfect channel knowledge. The evolution of network consensus state is modeled as a Markov chain, and the average transition probability matrix is derived. Using this, the average hitting time and average consensus duration are obtained, which are used to determine and optimize the performance of the consensus procedure. Second, using the fact that a typical communication footprint map admits a sparse representation, two novel compressed sensing based schemes are proposed to construct the map using 1-bit decisions from sensors deployed in a geographical area. The number of transmitters is determined using the K-means algorithm and a circular fitting technique, and a design procedure is proposed to determine the power thresholds for signal detection at sensors. Third, an algorithm is proposed for self-localization of a target node using power measurements from beacon nodes transmitting from known locations. The geographical area is overlaid with a virtual grid, and the problem is treated as one of testing overlapping subsets of grid cells for the presence of the target node. The column matching algorithm from group testing literature is considered for devising the target localization algorithm. The average probability of localizing the target within a grid cell is derived using the tools from Poisson point processes and order statistics. This quantity is used to determine the minimum required node density to localize the target within a grid cell with high probability. The performance of all the proposed algorithms is illustrated through Monte Carlo simulations.