Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks

Cognitive radio has been widely considered as one of the prominent solutions to tackle the spectrum scarcity. While the majority of existing research has focused on single-band cognitive radio, multiband cognitive radio represents great promises towards implementing efficient cognitive networks compared to single-based networks. Multiband cognitive radio networks (MB-CRNs) are expected to significantly enhance the network's throughput and provide better channel maintenance by reducing handoff frequency. Nevertheless, the wideband front-end and the multiband spectrum access impose a number of challenges yet to overcome. This paper provides an in-depth analysis on the recent advancements in multiband spectrum sensing techniques, their limitations, and possible future directions to improve them. We study cooperative communications for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also investigate several limits and tradeoffs of various design parameters for MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE Journal, Special Issue on Future Radio Spectrum Access, March 201

Statistical Properties and Applications of Empirical Mode Decomposition

Signal analysis is key to extracting information buried in noise. The decomposition of signal is a data analysis tool for determining the underlying physical components of a processed data set. However, conventional signal decomposition approaches such as wavelet analysis, Wagner-Ville, and various short-time Fourier spectrograms are inadequate to process real world signals. Moreover, most of the given techniques require \emph{a prior} knowledge of the processed signal, to select the proper decomposition basis, which makes them improper for a wide range of practical applications. Empirical Mode Decomposition (EMD) is a non-parametric and adaptive basis driver that is capable of breaking-down non-linear, non-stationary signals into an intrinsic and finite components called Intrinsic Mode Functions (IMF). In addition, EMD approximates a dyadic filter that isolates high frequency components, e.g. noise, in higher index IMFs. Despite of being widely used in different applications, EMD is an ad hoc solution. The adaptive performance of EMD comes at the expense of formulating a theoretical base. Therefore, numerical analysis is usually adopted in literature to interpret the behavior. This dissertation involves investigating statistical properties of EMD and utilizing the outcome to enhance the performance of signal de-noising and spectrum sensing systems. The novel contributions can be broadly summarized in three categories: a statistical analysis of the probability distributions of the IMFs and a suggestion of Generalized Gaussian distribution (GGD) as a best fit distribution; a de-noising scheme based on a null-hypothesis of IMFs utilizing the unique filter behavior of EMD; and a novel noise estimation approach that is used to shift semi-blind spectrum sensing techniques into fully-blind ones based on the first IMF. These contributions are justified statistically and analytically and include comparison with other state of art techniques

Belief Refinement Approaches to Communication and Inference Problems

This dissertation considers a problem where a single agent or a group of agents aim to estimate/learn unknown (possibly time-varying) parameters of interest despite making noisy observations. The agents take a Bayesian-like approach by maintaining a posterior probability distribution or “belief" over a parameter space conditioned on past observations. The agents aim to iteratively refine their belief over the parameter space as new information is acquired from their private observations or through collaboration with other agents. In particular, the agents aim to ensure that sufficient belief is assigned in neighborhoods centered around the true parameter with high probability or “reliability". In the context of communication problems considered in this dissertation, the agents may be active, i.e., agents may additionally take actions which provide new observations. Furthermore, agents may employ an adaptive strategy, i.e., using their past actions and the resulting observations, agents can adaptively choose actions to control the concentration of the belief. When the agents are active, we propose and analyze adaptive belief refinement approaches to obtain belief concentration on the unknown parameter with high reliability. In a different context, namely that of decentralized inference, we consider passive agents. Here, agents face an additional challenge due to the statistical insufficiency of their private observations to learn the unknown parameter. While individual agents’ observations are not informative enough, we assume that the agents’ observations are collectively informative to learn the unknown parameter. Here, we propose and analyze decentralized belief refining strategies to collaboratively obtain belief concentration on the unknown parameter. In the first part of this dissertation, we consider active strategies that are extensions of the posterior matching strategy (PM) introduced by Horstein, which is a generalization of the well-known binary search algorithm. We propose and analyze PM based strategies in the context of modern communication systems, namely the problem of establishing initial access in mm-Wave communication and spectrum sensing for Cognitive Radio. We propose and analyze channel coding strategies for real-time streaming and control applications. The second part of the dissertation investigates the belief refinement approaches for decentralized learning. In particular, it focusing on developing and analyzing a decentralized learning rule for statistical hypothesis testing and its application to decentralized machine learning