Browsing by Subject "Cognitive radio"

Now showing 1 - 4 of 4
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    Circuit techniques for cognitive radio receiver front-ends
    (2012-06) Sadhu, Bodhisatwa
    This thesis discusses the design of the receiver front-end for software defined radio (SDR) based cognitive radio applications. Two aspects of SDRs for cognitive radios are distinguished: signaling and spectrum sensing. Narrowband wide tuning signaling architectures and instantaneous wideband spectrum sensing architectures are identified as candidates for feasible SDR implementations. Several architectures and circuit im- plementations are reviewed. Wide tuning range, low phase noise frequency synthesizers for signaling, and RF samplers and signal processors for spectrum sensing are identified as critical circuit design blocks.A number of voltage controlled oscillator (VCO) techniques for wide-tuning range, and low phase noise frequency synthesis techniques are developed. Wide-tuning range techniques based on switched inductors are proposed as a way to design inductor- capacitor (LC) VCOs with wide-tuning ranges that maintain a good phase noise and power dissipation performance over the entire tuning range. Switched inductor VCOs are analyzed in detail, and a design framework is developed. Optimized capacitor array design techniques for wide-tuning ranges are discussed. Based on these techniques, mea- surements from two prototype designs are presented, that achieve tuning ranges of 87% and 157% in measurement. They also maintain good phase noise, power consumption, and figure of merit (FOM) over the entire tuning range.In addition, a new family of VCOs that achieve superior phase noise is introduced. This set of novel topologies are based on linearized transconductance using capacitive feedback techniques. They achieve higher amplitudes of oscillation, and consequently, a superior phase noise performance. A wide tuning range is also maintained. The VCOs are analyzed, and detailed measurement results from a design prototype are presented. For spectrum sensing, the design of CRAFT (Charge Re-use Analog Fourier Trans- form): an RF front-end channelizer for software defined radios (SDR) based on a 16 point analog domain FFT is described. The design relies on charge re-use to achieve 47dB average output SNDR on a 5GS/s input, and consumes only 12.2pJ/conv. These numbers represent orders of magnitude improvement on the work reported previously in literature. The thesis also briefly discusses the modeling of circuit non-idealities in CRAFT, and outlines circuit techniques for mitigating these. These design principles enable this implementation to achieve a large dynamic range even at high speeds. Ad- ditionally, these techniques can be easily extended to improve the performance of other passive switched capacitor designs.
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    Data-driven Channel Learning for Next-generation Communication Systems
    (2019-10) Lee, Donghoon
    The turn of the decade has trademarked the `global society' as an information society, where the creation, distribution, integration, and manipulation of information have significant political, economic, technological, academic, and cultural implications. Its main drivers are digital information and communication technologies, which have resulted in a "data deluge", as the number of smart and Internet-capable devices increases rapidly. Unfortunately, establishing information infrastructure to collect data becomes more challenging particularly as communication networks for those devices become larger, denser, and more heterogeneous to meet the quality-of-service (QoS) for the users. Furthermore, scarcity in spectral resources due to an increased demand for mobile devices urges the development of a new methodology for wireless communications possibly facing unprecedented constraints both on hardware and software. At the same time, recent advances in machine learning tools enable statistical inference with efficiency as well as scalability in par with the volume and dimensionality of the data. These considerations justify the pressing need for machine learning tools that are amenable to new hardware and software constraints, and can scale with the size of networks, to facilitate the advanced operation of next-generation communication systems. The present thesis is centered on analytical and algorithmic foundations enabling statistical inference of critical information under practical hardware/software constraints to design and operate wireless communication networks. The vision is to establish a unified and comprehensive framework based on state-of-the-art data-driven learning and Bayesian inference tools to learn the channel-state information that is accurate yet efficient and non-demanding in terms of resources. The central goal is to theoretically, algorithmically, and experimentally demonstrate how valuable insights from data-driven learning can lead to solutions that markedly advance the state-of-the-art performance on inference of channel-state information. To this end, the present thesis investigates two main research thrusts: i) channel-gain cartography leveraging low-rank and sparsity; and ii) Bayesian approaches to channel-gain cartography for spatially heterogeneous environment. The aforementioned research thrusts introduce novel algorithms that aim to tackle the issues of next-generation communication networks. Potential of the proposed algorithms is showcased by rigorous theoretical results and extensive numerical tests.
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    Dynamic learning and resource management under uncertainties for smart grid and cognitive radio networks
    (2014-05) Yahyasoltani, Nasin
    The importance of timely applications and decisions in dynamic environments, has led to the integration of intelligent networks to increase efficiency and end-user satisfaction in various application domains including telecommunication and power grid networks. Contemporary intelligent networks require advanced statistical signal processing and optimization tools to learn, infer and control their operation. This integration poses new challenges and has witnessed the emergence of novel resource management and learning techniques to cope with dynamics. In addition, in order to have implementable resource management algorithms, it is crucial to model the underlying sources of uncertainty in the optimization framework. This thesis develops algorithms for resource allocation under channel uncertainty in cognitive radio (CR) communication networks and contributes to demand coordination under uncertainty in power networks.Demand coordination through real-time pricing is addressed first by capitalizing on the uncertainty involved in the consumption behavior of consumers. Prerequisite to the demand coordination task is learning the uncertainty present in power consumption data. The dependency of consumers' consumption behavior on the announced prices and their neighbors' behavior, is modeled through graphical models. In particular, the electric vehicle (EV) consumers are considered and the adopted model also captures dynamics of EV consumers' time-varying charging decisions. Leveraging the online convex optimization (OCO) framework, an online algorithm for tracking the model is devised. With minimal assumptions on the structure of the temporal dynamics, and while accounting for the possibly adversarial consumption behavior of consumers, the proposed online algorithm provides performance guarantees. The probability distributions obtained through the tracking algorithm are then deployed as input to stochastic economic profit maximization for real-time price setting.Learning in the presence of missing data is a pervasive problem in statistical data analysis. Next, attention is turned to tracking the dynamic charging behavior of EV consumers, when at each time slot some of the consumers' consumption decisions are possibly missing. The problem amounts to online classification with missing labels. An online algorithm is proposed to wed real-time estimation of the missing data with learning of complete data in the OCO framework.As regards CR networks, this thesis introduces novel resource allocation algorithms for orthogonal frequency-division multiple access (OFDMA) CR under channel uncertainty where the unique approaches can be fitted to a class of large-scale robust mixed-integer problems. Due to the lack of cooperation of the licensed system, CRs must resort to less efficient channel estimation techniques thus incurring an inevitable channel estimation error. It is shown that CR interference constraints under channel uncertainty can be cast as chance constraints. On the other hand, instead of just modeling the user rates by logarithmic functions of transmit-powers, justified under ideal Gaussian coding, practical finite-alphabet constellations are adopted which leads to an optimization objective of a weighted sum of mutual information. When multiple users are present, due to the combinatorial search for optimal subcarrier assignment, the problem is non-convex and hard to solve, as the optimization variables are coupled across all subcarriers. To circumvent the resulting computational hurdle, tight and conservative approximations of the chance constraint are introduced to break the coupling and enforce separability per subcarrier. The separableproblem across subcarriers opens the door to the dual decomposition approach, which leads to a near-optimal and computationally efficient solution.
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    Frugal sensing and estimation over wireless networks
    (2014-04) Mehanna, Omar
    Spectrum sensing and channel estimation are two important examples of background tasks needed for efficient wireless network operations. Channel and spectrum state communication overheads can become a serious burden, unless appropriate sensing and estimation strategies are designed that can do the job well with very limited, judicious feedback. This thesis considers two `frugal' sensing and estimation problems in this regime: crowdsourced power spectrum sensing using a network of low-end sensors broadcasting few bits; and channel estimation and tracking for transmit beamforming in frequency-division duplex (FDD) mode.In the case of spectrum sensing, each sensor is assumed to pass the received signal through a random wideband filter, measure the average power at the output of the filter, and send out a single bit to a fusion center (FC) depending on its measurement. Exploiting linearity with respect to the autocorrelation as well as important non negativity properties in a novel linear programming (LP) formulation, it is shown that adequate power spectrum sensing is possible from few bits, even for dense spectra. The formulation can be viewed as generalizing classical nonparametric spectrum estimation to the case where the data is in the form of inequalities, rather than equalities. Taking into account fading and insufficient sample averaging considerations, a different convex maximum likelihood (ML) formulation is developed, outperforming the LP formulation when the power estimates prior to thresholding are noisy. Assuming availability of a downlink channel that the FC can use to send threshold information, active sensing strategies are developed which quickly narrow down the power spectrum estimate.For the downlink channel tracking problem, the receiver is assumed to send back to the transmitter a coarsely quantized version of the received transmitter-beamformed pilot signal, instead of sending quantized channel information as in codebook-based beamforming. A novel channel tracking approach is proposed that exploits the quantization bits in a maximum a posteriori (MAP) estimation formulation, and closed-form expressions for the channel estimation mean-squared error and the corresponding signal-to-noise ratio are derived under certain conditions.