Matching Items (7)
Description
Recently, the location of the nodes in wireless networks has been modeled as point processes. In this dissertation, various scenarios of wireless communications in large-scale networks modeled as point processes are considered. The first part of the dissertation considers signal reception and detection problems with symmetric alpha stable noise which is from an interfering network modeled as a Poisson point process. For the signal reception problem, the performance of space-time coding (STC) over fading channels with alpha stable noise is studied. We derive pairwise error probability (PEP) of orthogonal STCs. For general STCs, we propose a maximum-likelihood (ML) receiver, and its approximation. The resulting asymptotically optimal receiver (AOR) does not depend on noise parameters and is computationally simple, and close to the ML performance. Then, signal detection in coexisting wireless sensor networks (WSNs) is considered. We define a binary hypothesis testing problem for the signal detection in coexisting WSNs. For the problem, we introduce the ML detector and simpler alternatives. The proposed mixed-fractional lower order moment (FLOM) detector is computationally simple and close to the ML performance. Stochastic orders are binary relations defined on probability. The second part of the dissertation introduces stochastic ordering of interferences in large-scale networks modeled as point processes. Since closed-form results for the interference distributions for such networks are only available in limited cases, it is of interest to compare network interferences using stochastic. In this dissertation, conditions on the fading distribution and path-loss model are given to establish stochastic ordering between interferences. Moreover, Laplace functional (LF) ordering is defined between point processes and applied for comparing interference. Then, the LF orderings of general classes of point processes are introduced. It is also shown that the LF ordering is preserved when independent operations such as marking, thinning, random translation, and superposition are applied. The LF ordering of point processes is a useful tool for comparing spatial deployments of wireless networks and can be used to establish comparisons of several performance metrics such as coverage probability, achievable rate, and resource allocation even when closed form expressions for such metrics are unavailable.
ContributorsLee, Junghoon (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Committee member) / Reisslein, Martin (Committee member) / Kosut, Oliver (Committee member) / Arizona State University (Publisher)
Created2014
Description
Traditional wireless communication systems operate in duplexed modes i.e. using time division duplexing or frequency division duplexing. These methods can respectively emulate full duplex mode operation or realize full duplex mode operation with decreased spectral efficiency. This thesis presents a novel method of achieving full duplex operation by actively cancelling out the transmitted signal in pseudo-real time. With appropriate hardware, the algorithms and techniques used in this work can be implemented in real time without any knowledge of the channel or any training sequence. Convergence times of down to 1 ms can be achieved which is adequate for the coherence bandwidths associated with an indoor environment. By utilizing adaptive cancellation, additional overhead for re-calibrating the system in other open-loop methods is not needed.
ContributorsAvasarala, Sanjay (Author) / Kiaei, Sayfe (Thesis advisor) / Kitchen, Jennifer (Committee member) / Bakkaloglu, Bertan (Committee member) / Arizona State University (Publisher)
Created2016
Description
To establish reliable wireless communication links it is critical to devise schemes to mitigate the effects of the fading channel. In this regard, this dissertation analyzes two types of systems: point-to-point, and multiuser systems. For point-to-point systems with multiple antennas, switch and stay diversity combining offers a substantial complexity reduction for a modest loss in performance as compared to systems that implement selection diversity. For the first time, the design and performance of space-time coded multiple antenna systems that employ switch and stay combining at the receiver is considered. Novel switching algorithms are proposed and upper bounds on the pairwise error probability are derived for different assumptions on channel availability at the receiver. It is proved that full spatial diversity is achieved when the optimal switching threshold is used. Power distribution between training and data codewords is optimized to minimize the loss suffered due to channel estimation error. Further, code design criteria are developed for differential systems. Also, for the special case of two transmit antennas, new codes are designed for the differential scheme. These proposed codes are shown to perform significantly better than existing codes. For multiuser systems, unlike the models analyzed in literature, multiuser diversity is studied when the number of users in the system is random. The error rate is proved to be a completely monotone function of the number of users, while the throughput is shown to have a completely monotone derivative. Using this it is shown that randomization of the number of users always leads to deterioration of performance. Further, using Laplace transform ordering of random variables, a method for comparison of system performance for different user distributions is provided. For Poisson users, the error rates of the fixed and random number of users are shown to asymptotically approach each other for large average number of users. In contrast, for a finite average number of users and high SNR, it is found that randomization of the number of users deteriorates performance significantly.
ContributorsBangalore Narasimhamurthy, Adarsh (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Duman, Tolga M. (Committee member) / Spanias, Andreas S (Committee member) / Reisslein, Martin (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2010
Description
Radio communication has become the dominant form of correspondence in modern society. As the demand for high speed communication grows, the problems associated with an expanding consumer base and limited spectral access become more difficult to address. One communications system in which people commonly find themselves is the multiple access cellular network. Users operate within the same geographical area and bandwidth, so providing access to every user requires advanced processing techniques and careful subdivision of spectral access. This is known as the multiple access problem. This paper addresses this challenge in the context of airborne transceivers operating at high altitudes and long ranges. These operators communicate by transmitting a signal through a target scattering field on the ground without a direct line of sight to the receiver. The objective of this investigation is to develop a model for this communications channel, identify and quantify the relevant characteristics, and evaluate the feasibility of using it to effectively communicate.
ContributorsHerschfelt, Andrew William (Author) / Bliss, Daniel (Thesis director) / Cochran, Douglas (Committee member) / Aberle, James (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
A distributed framework is proposed for addressing resource sharing problems in communications, micro-economics, and various other network systems. The approach uses a hierarchical multi-layer decomposition for network utility maximization. This methodology uses central management and distributed computations to allocate resources, and in dynamic environments, it aims to efficiently respond to network changes. The main contributions include a comprehensive description of an exemplary unifying optimization framework to share resources across different operators and platforms, and a detailed analysis of the generalized methods under the assumption that the network changes are on the same time-scale as the convergence time of the algorithms employed for local computations.Assuming strong concavity and smoothness of the objective functions, and under some stability conditions for each layer, convergence rates and optimality bounds are presented. The effectiveness of the framework is demonstrated through numerical examples. Furthermore, a novel Federated Edge Network Utility Maximization (FEdg-NUM) architecture is proposed for solving large-scale distributed network utility maximization problems in a fully decentralized way. In FEdg-NUM, clients with private utilities communicate with a peer-to-peer network of edge servers. Convergence properties are examined both through analysis and numerical simulations, and potential applications are highlighted. Finally, problems in a complex stochastic dynamic environment, specifically motivated by resource sharing during disasters occurring in multiple areas, are studied. In a hierarchical management scenario, a method of applying a primal-dual algorithm in higher-layer along with deep reinforcement learning algorithms in localities is presented. Analytical details as well as case studies such as pandemic and wildfire response are provided.
ContributorsKarakoc, Nurullah (Author) / Scaglione, Anna (Thesis advisor) / Reisslein, Martin (Thesis advisor) / Nedich, Angelia (Committee member) / Michelusi, Nicolò (Committee member) / Arizona State University (Publisher)
Created2023
Description
Recent years have seen machine learning makes growing presence in several areas inwireless communications, and specifically in large-scale Multiple-Input Multiple-Output
(MIMO) systems. This comes as a result of its ability to offer innovative solutions to some
of the most daunting problems that haunt current and future large-scale MIMO systems,
such as downlink channel-training and sensitivity to line-of-sight (LOS) blockages to name
two examples. Machine learning, in general, provides wireless systems with data-driven
capabilities, with which they could realize much needed agility for decision-making and
adaptability to their surroundings. Bearing the potential of machine learning in mind, this
dissertation takes a close look at what deep learning can bring to the table of large-scale
MIMO systems. It proposes three novel frameworks based on deep learning that tackle
challenges rooted in the need to acquire channel state information. Framework 1, namely
deterministic channel prediction, recognizes that some channels are easier to acquire than
others (e.g., uplink are easier to acquire than downlink), and, as such, it learns a function
that predicts some channels (target channels) from others (observed channels). Framework
2, namely statistical channel prediction, aims to do the same thing as Framework 1, but it
takes a more statistical approach; it learns a large-scale statistic for target channels (i.e.,
per-user channel covariance) from observed channels. Differently from frameworks 1 and
2, framework 3, namely vision-aided wireless communications, presents an unorthodox
perspective on dealing with large-scale MIMO challenges specific to high-frequency communications.
It relies on the fact that high-frequency communications are reliant on LOS
much like computer vision. Therefore, it recognizes that parallel and utilizes multimodal
deep learning to address LOS-related challenges, such as downlink beam training and LOSlink
blockages. All three frameworks are studied and discussed using datasets representing
various large-scale MIMO settings. Overall, they show promising results that cement the
value of machine learning, especially deep learning, to large-scale MIMO systems.
ContributorsAlrabeiah, Muhammad (Author) / Alkhateeb, Ahmed A (Thesis advisor) / Turaga, Pavan P (Committee member) / Dasarathy, Gautam G (Committee member) / Tepedelenlioglu, Cihan C (Committee member) / Arizona State University (Publisher)
Created2021
Description
In wireless communication systems, the process of data transmission includes the estimation of channels. Implementing machine learning in this process can reduce the amount of time it takes to estimate channels, thus, resulting in an increase of the system’s transmission throughput. This maximizes the performance of applications relating to device-to-device communications and 5G systems. However, applying machine learning algorithms to multi-base-station systems is not well understood in literature, which is the focus of this thesis.
ContributorsCosio, Karla (Author) / Ewaisha, Ahmed (Thesis director) / Spanias, Andreas (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2022-05