ECE Theses and Dissertations

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A Data and Platform-Aware Framework For Large-Scale Machine Learning
(2015-04-24) Mirhoseini, Azalia; Koushanfar, Farinaz; Aazhang, Behnaam; Baraniuk, Richard; Jermaine, Christopher
This thesis introduces a novel framework for execution of a broad class of iterative machine learning algorithms on massive and dense (non-sparse) datasets. Several classes of critical and fast-growing data, including image and video content, contain dense dependencies. Current pursuits are overwhelmed by the excessive computation, memory access, and inter-processor communication overhead incurred by processing dense data. On the one hand, solutions that employ data-aware processing techniques produce transformations that are oblivious to the overhead created on the underlying computing platform. On the other hand, solutions that leverage platform-aware approaches do not exploit the non-apparent data geometry. My work is the first to develop a comprehensive data- and platform-aware solution that provably optimizes the cost (in terms of runtime, energy, power, and memory usage) of iterative learning analysis on dense data. My solution is founded on a novel tunable data transformation methodology that can be customized with respect to the underlying computing resources and constraints. My key contributions include: (i) introducing a scalable and parametric data transformation methodology that leverages coarse-grained parallelism in the data to create versatile and tunable data representations, (ii) developing automated methods for quantifying platform-specific computing costs in distributed settings, (iii) devising optimally-bounded partitioning and distributed flow scheduling techniques for running iterative updates on dense correlation matrices, (iv) devising methods that enable transforming and learning on streaming dense data, and (v) providing user-friendly open-source APIs that facilitate adoption of my solution on multiple platforms including (multi-core and many-core) CPUs and FPGAs. Several learning algorithms such as regularized regression, cone optimization, and power iteration can be readily solved using my APIs. My solutions are evaluated on a number of learning applications including image classification, super-resolution, and denoising. I perform experiments on various real-world datasets with up to 5 billion non-zeros on a range of computing platforms including Intel i7 CPUs, Amazon EC2, IBM iDataPlex, and Xilinx Virtex-6 FPGAs. I demonstrate that my framework can achieve up to 2 orders of magnitude performance improvement in comparison with current state-of-the-art solutions.
A globally convergent algorithm for training multilayer perceptrons for data classification and interpolation
(1991) Madyastha, Raghavendra K.; Aazhang, Behnaam
This thesis addresses the issue of applying a "globally" convergent optimization scheme to the training of multi-layer perceptrons, a class of Artificial Neural Networks, for the detection and classification of signals in single- and multi-user communication systems. The research is motivated by the fact that a multi-layer perceptron is theoretically capable of approximating any nonlinear function to within any specified accuracy. The object function to which we apply the optimization algorithm is the error function of the multilayer perceptron, i.e., the average of the sum of the squares of the differences between the actual and the desired outputs to specified inputs. Until recently, the most widely used training algorithm has been the Backward Error Propagation algorithm, which is based on the algorithm for "steepest descent" and hence, is at best linearly convergent. The algorithm discussed here combines the merits of two well known "global" algorithms--the Conjugate Gradients and the Trust Region algorithms. A further technique known as preconditioning is used to speed up the convergence by clustering the eigenvalues of the "effective Hessian". The Preconditioned Conjugate Gradients--Trust Regions algorithm is found to be superlinearly convergent and hence, outperforms the standard backpropagation routine.
A hybrid relaying protocol for the parallel-relay network
(2010) Summerson, Samantha Rose; Aazhang, Behnaam
Cooperation among radios in wireless networks has been shown to improve communication in several aspects. We analyze a wireless network which employs multiple parallel relay transceivers to assist in communication between a single source-destination pair, demonstrating that gains are achieved when a random subset of relays is selected. We derive threshold values for the received signal-to-noise ratios (SNRs) at the relays based on outage probabilities; these thresholds essentially determine the active subset of relays in each time frame for our parallel relay network; due the random nature of wireless channels, this active subset is a random. Two established forwarding protocols for the relays, Amplify-and-Forward and Decode-and-Forward, are combined to create a hybrid relaying protocol which is analyzed in conjunction with both regenerative coding and distributed space-time coding at the relays. Finally, the allocation of power resources to minimize the end-to-end probability of outage is considered.
A Matter of Perspective: Reliable Communication and Coping with Interference with Only Local Views
(2012-09-05) Kao, David; Sabharwal, Ashutosh; Aazhang, Behnaam; Knightly, Edward W.; Tapia, Richard A.; Chiang, Mung
This dissertation studies interference in wireless networks. Interference results from multiple simultaneous attempts to communicate, often between unassociated sources and receivers, preventing extensive coordination. Moreover, in practical wireless networks, learning network state is inherently expensive, and nodes often have incomplete and mismatched views of the network. The fundamental communication limits of a network with such views is unknown. To address this, we present a local view model which captures asymmetries in node knowledge. Our local view model does not rely on accurate knowledge of an underlying probability distribution governing network state. Therefore, we can make robust statements about the fundamental limits of communication when the channel is quasi-static or the actual distribution of state is unknown: commonly faced scenarios in modern commercial networks. For each local view, channel state parameters are either perfectly known or completely unknown. While we propose no mechanism for network learning, a local view represents the result of some such mechanism. We apply the local view model to study the two-user Gaussian interference channel: the smallest building block of any interference network. All seven possible local views are studied, and we find that for five of the seven, there exists no policy or protocol that universally outperforms time-division multiplexing (TDM), justifying the orthogonalized approach of many deployed systems. For two of the seven views, TDM-beating performance is possible with use of opportunistic schemes where opportunities are revealed by the local view. We then study how message cooperation --- either at transmitters or receivers --- increases capacity in the local view two-user Gaussian interference channel. The cooperative setup is particularly appropriate for modeling next-generation cellular networks, where costs to share message data among base stations is low relative to costs to learn channel coefficients. For the cooperative setting, we find: (1) opportunistic approaches are still needed to outperform TDM, but (2) opportunities are more abundant and revealed by more local views. For all cases studied, we characterize the capacity region to within some known gap, enabling computation of the generalized degrees of freedom region, a visualization of spatial channel resource usage efficiency.
A Resource-Aware Streaming-based Framework for Big Data Analysis
(2015-12-02) Darvish Rouhani, Bita; Koushanfar, Farinaz; Aazhang, Behnaam; Baraniuk, Richard
The ever growing body of digital data is challenging conventional analytical techniques in machine learning, computer vision, and signal processing. Traditional analytical methods have been mainly developed based on the assumption that designers can work with data within the confines of their own computing environment. The growth of big data, however, is changing that paradigm especially in scenarios where severe memory and computational resource constraints exist. This thesis aims at addressing major challenges in big data learning problem by devising a new customizable computing framework that holistically takes into account the data structure and underlying platform constraints. It targets a widely used class of analytical algorithms that model the data dependencies by iteratively updating a set of matrix parameters, including but not limited to most regression methods, expectation maximization, and stochastic optimizations, as well as the emerging deep learning techniques. The key to our approach is a customizable, streaming-based data projection methodology that adaptively transforms data into a new lower-dimensional embedding by simultaneously considering both data and hardware characteristics. It enables scalable data analysis and rapid prototyping of an arbitrary matrix-based learning task using a sparse-approximation of the collection that is constantly updated inline with the data arrival. Our work is supported by a set of user-friendly Application Programming Interfaces (APIs) that ensure automated adaptation of the proposed framework to various datasets and System on Chip (SoC) platforms including CPUs, GPUs, and FPGAs. Proof of concept evaluations using a variety of large contemporary datasets corroborate the practicability and scalability of our approach in resource-limited settings. For instance, our results demonstrate 50-fold improvement over the best known prior-art in terms of memory, energy, power, and runtime for training and execution of deep learning models in deployment of different sensing applications including indoor localization and speech recognition on constrained embedded platforms used in today's IoT enabled devices such as autonomous vehicles, robots, and smartphone.
A sample realization approach for optimization of code division multiple access systems
(1994) Mandayam, Narayan B.T.; Aazhang, Behnaam
Efforts in performance analysis of Code Division Multiple Access (CDMA) systems have concentrated on obtaining asymptotic approximations and bounds for system error probabilities. As such, these cannot capture the sensitivities of the system performance to any class of parameters, and the optimization of such systems (with respect to any class of parameters) presents itself to no analytical solutions. A discrete event dynamic systems (DEDS) formulation is developed for CDMA systems whereby the sensitivity of the average probability of error can be evaluated with respect to a wide class of system parameters via sample path based gradient estimation techniques like infinitesimal perturbation analysis (IPA) and the likelihood ratio (LR) method. Appropriate choice of the sample path and the corresponding sample performance function leads to analyzing the sensitivity of the average probability of error to near-far effects, power control, and code parameters. Further, these sensitivity analysis methods are incorporated in gradient algorithms for optimizing system performance in terms of the minimum probability of detection error. Specifically, for direct-sequence CDMA systems, IPA based stochastic gradient algorithms are used to develop a class of adaptive linear detectors that are optimum in that they minimize the average probability of bit-error. These detectors outperform both the matched filter and MMSE detectors, and also alleviate the disadvantage of multiuser detection schemes that require implicit information on the multiple access interference. For CDMA systems in the optical domain, IPA based stochastic algorithms are used to develop a class of adaptive threshold detectors that minimize the average probability of bit-error. These detectors outperform the correlation detector and also preclude the need for assumptions on the interference statistics required by existing optimum one-shot detectors. All adaptive detection schemes developed here are easily implementable owing to the simple recursive structures that arise out of our sample realization based approach. The sequential versions of the adaptive detectors developed in here require no preamble, which makes them a viable choice for CDMA channels subject to temporal variations due to dispersion effects and variable number of users in the channel.
Advanced techniques for next-generation wireless systems
(1999) Sendonaris, Andrew; Aazhang, Behnaam
In order to meet the demands of next-generation wireless systems, which will be required to support multirate multimedia at high data rates, it is necessary to employ advanced algorithms and techniques that enable the system to guarantee the quality of service desired by the various media classes. In this work, we present a few novel methods for improving wireless system performance and achieving next-generation goals. Our proposed methods include finding signal sets that are designed for fading channels and support multirate, exploiting knowledge of the fading statistics during the data detection process, exploiting the existence of Doppler in the received signal, and allowing mobile users to cooperate in order to send their information to the base station. We evaluate the performance of our proposed ideas and show that they provide gains with respect to conventional systems. The benefits include multirate support, higher data rates, and more stable data rates. It should be mentioned that, while we focus mainly on a CDMA framework for analyzing our ideas, many of these ideas may also be applied to other wireless system environments.
Antenna arrays for wireless CDMA communication systems
(1997) Madyastha, Raghavendra K.; Aazhang, Behnaam
The estimation of code delays along with amplitudes and phases of different users constitutes the first stage in the demodulation process in a CDMA communication system. The delay estimation stage is termed the acquisition stage and forms the bottleneck for the detection of users' bitstreams; accurate detection necessitates accurate acquisition. Most existing schemes incorporate a single sensor at the receiver, which leads to an inherent limit in the acquisition based capacity, which is the number of users that can be simultaneously acquired. In this thesis we combine the benefits of spatial processing in the form of an antenna array at the receiver along with code diversity to gain an increase in the capacity of the system. An additional parameter to be estimated now is the direction of arrival (DOA) of each user. We demonstrate the gains in parameter estimation with the incorporation of spatial diversity. We propose two classes of delay-DOA estimation algorithms--a maximum likelihood algorithm and a subspace based algorithm (MUSIC). With reasonable assumptions on the system we are able to derive computationally efficient estimation algorithms and demonstrate the gains achieved in exploiting multiple sensors at the receiver. In addition, we also investigate the benefits of spatial diversity in linear multiuser detection. We consider two linear multiuser detectors, the decorrelating detector and the linear MMSE detector (chosen for their near-far properties) and characterize the performance increase in the multisensor case. We observe that in many cases, the gain can be directly captured in terms of the number of sensors in the array.
Beyond Interference Avoidance: Distributed Sun-network Scheduling in Wireless Networks with Local Views
(2013-09-16) Santacruz, Pedro; Sabharwal, Ashutosh; Aazhang, Behnaam; Knightly, Edward W.; Hicks, Illya V.
In most wireless networks, nodes have only limited local information about the state of the network, which includes connectivity and channel state information. With limited local information about the network, each node’s knowledge is mismatched; therefore, they must make distributed decisions. In this thesis, we pose the following question - if every node has network state information only about a small neighborhood, how and when should nodes choose to transmit? While link scheduling answers the above question for point-to-point physical layers which are designed for an interference-avoidance paradigm, we look for answers in cases when interference can be embraced by advanced code design, as suggested by results in network information theory. To make progress on this challenging problem, we propose two constructive distributed algorithms, one conservative and one aggressive, which achieve rates higher than link scheduling based on interference avoidance, especially if each node knows more than one hop of network state information. Both algorithms schedule sub-networks such that each sub-network can employ advanced interference-embracing coding schemes to achieve higher rates. Our innovation is in the identification, selection and scheduling of sub-networks, especially when sub-networks are larger than a single link. Using normalized sum-rate as the metric of network performance, we prove that the proposed conservative sub-network scheduling algorithm is guaranteed to have performance greater than or equal to pure coloring-based link scheduling. In addition, the proposed aggressive sub-network scheduling algorithm is shown, through simulations, to achieve better normalized sum-rate than the conservative algorithm for several network classes. Our results highlight the advantages of extending the design space of possible scheduling strategies to include those that leverage local network information.
Capacity of low power multiuser systems with antenna arrays
(2005) Muharemovic, Tarik; Aazhang, Behnaam; Sabharwal, Ashutosh
In this thesis, we study wireless multiuser communication systems in the regime of low spectral efficiencies, where users and the multiple access point are equipped with antenna arrays. Our first contribution is to develop a generic mathematical framework which captures tradeoffs between fundamental parameters of a low power multiuser system: spectral efficiency and energy per information bit, of each user. Using the framework that we developed we next consider variable data rate multiple access problem, in low power systems, where we remove the usual assumption of tight user coordination, and we allow users to select their own data rates and trans mit powers, without coordinating, and without negotiating with the access point. Here, every user has a set of low power codebooks, that we name the policy, which accommodates a range of small spectral efficiencies, but particular data rates of other users are assumed to be an unknown---compound parameter---at each mobile. In antenna-array transmission and reception, we demonstrate an elegant interpretation of users policies, where each policy is represented by partitioning spatial dimensions into blocks, and each block is dedicated to a different user. Finally, we address the paradigm of statistically correlated antenna arrays, where we derive the effective number of uncorrelated receive spatial dimensions, which we partition to represent users policies. As more correlated antennas are packed into a limited area we show that effective receive dimensionality converges to a finite limit which we evaluate for some simple geometries.
Channel estimation for code division multiple access communication systems
(1994) Bensley, Stephen Edward; Aazhang, Behnaam
We consider the estimation of channel parameters for code division multiple access (CDMA) communication systems operating over channels with either single or multiple propagation paths. We present two approaches for decomposing this multiuser channel estimation problem into a series of single user problems. In the first method, the interfering users are treated as colored, non-Gaussian noise, and the maximum likelihood estimate is formed from the sample mean and sample covariance matrix of the received signal. In the second method, we exploit the eigenstructure of the sample correlation matrix to partition the observation space into a signal subspace and a noise subspace. The channel estimate is formed by projecting a given user's spreading waveform into the estimated noise subspace and then either maximizing the likelihood or minimizing the Euclidean norm of this projection. Both of these approaches yield algorithms which are near-far resistant and are capable of tracking slowly varying channels.
Code design and multiuser detection for code division multiple access systems with continuous phase modulation
(1996) Papasakellariou, Aristides; Aazhang, Behnaam
The proliferation of wireless communications services combined with the limited spectrum availability have placed the bandwidth utilization as a major performance measure. Consequently, the bandwidth allocation technique to multiple signals and the bandwidth occupied by each signal are issues of paramount importance. For voice and bursty data communications, code-division multiple-access provides excellent bandwidth management. The objective to produce constant-envelope signals with compact spectral characteristics is most effectively accomplished using continuous phase modulation. The purpose of this study is to examine detection issues for signals that combine the above techniques. For a synchronous system, the reliable operation of a single-user receiver without power control requires spreading codes that exhibit minimal mutual interference. Signal memory is essential for good performance and precludes the existence of orthogonal codes. Code design is examined for two signal formats that offer different spectral and error rate characteristics. A recursive algorithm that provides the structure and maximum number of codes is presented for both signal formats. Moreover, the code performance is evaluated for an asynchronous system with power control. To avoid the performance limitations of the single-user receiver in the presence of interference and the disadvantages of power control, multiuser detectors are considered for both synchronous and asynchronous systems. The optimum coherent multiuser detector is briefly analyzed and its computational complexity is shown to be prohibitively large for practical applications. For this reason, the emphasis is placed on suboptimum detectors with linear complexity and near-optimum performance. The choice of an appropriate set of decision statistics is crucial for this objective and conventional detectors, if applicable, perform poorly. Two linear complexity detection methods that can be applied to both signal formats are proposed for each system. The individual code design to optimize the error rate for a specific receiver complexity is determined and substantial gains are achieved over antipodal signaling. Moreover, the spectral and error rate performance are largely independent and impressive capacity improvements are obtained over conventional systems for a modest increase in the complexity of the receiver.
Code design for multiple-antenna systems
(2001) Memarzadeh, Mahsa; Aazhang, Behnaam
We propose a systematic method for the design of space-time codes for AWGN slowly and fast Rayleigh fading channels. This can be accomplished by adopting a concatenated space-time code structure, where an orthogonal transmit diversity system constitutes the inner encoder. We will show that this will cause in decoupling of the problems of spatial and temporal diversity gains maximization, involved in the design of space-time codes. This decoupling significantly simplifies the code design procedure and presents a systematic code construction technique. In the case of slowly fading channels, where no temporal diversity gain is available, the concatenated structure of the space-time code, will help to decouple the problems of spatial diversity and coding gains maximization. However in a fast fading channel, the proposed system will decouple the problems of spatial and temporal diversity gains maximization. At the end, some issues involved in designing codes for downlink broadcast channels will be discussed.
Code design for the relay channel
(2007) Chakrabarti, Arnab; Aazhang, Behnaam
The design of wireless communication networks is based on the premise that networks are collections of reliable point-to-point links between communicating nodes. Such a communication model, although simple, is inefficient because wireless propagation is not point-to-point in nature. It is more efficient for nodes to share their resources by cooperating at the symbol level to forward each other's physical layer packets collectively to the destination. The improved paradigm is known as cooperative communication. My work develops practical coding techniques that approach the theoretical limits of cooperation predicted by information theory. Cooperative strategies, often called relay protocols, describe the processing performed by the information forwarding (relaying) nodes. My research proposes implementable schemes for two relay protocols - decode-and-forward and estimate-and-forward. In each case, the starting point is an optimal but non-constructive information theoretic random coding scheme, which motivates a practical code construction. Novel code design princi ples and some surprising insights emerge from this work of research. The performance of the each scheme developed here is found to approach theoretical limits. For decode-and-forward relaying, we propose dual-rate low-density parity-check (LDPC) codes. In our designs, the source transmission is decoded with the help of side information in the form of additional parity bits from the relay. The key challenge is to discover codes that simultaneously perform well for the source-relay and the source-destination links. The asymptotic noise thresholds of the discovered relay code profiles are a fraction of a decibel away from the achievable lower bound for decode-and-forward relaying. With random component LDPC codes, the overall relay coding scheme performs within 1.2 dB of the theoretical limit. In estimate-and-forward relaying, the key challenge is to form a quantized estimate of the source transmission from the received signal at the relay. We illustrate with an example that the existing approach of distortion minimization at the relay is suboptimal. We derive an improved quantizer design criterion based on rate-constrained mutual information maximization between the source transmission and the quantizer output, using which, we obtain performance less than 0.9 dB from the achievable rate at a BER of 10-4.
Coding for Phase Change Memory Performance Optimization
(2012-09-05) Mirhoseini, Azalia; Koushanfar, Farinaz; Baraniuk, Richard G.; Aazhang, Behnaam
Over the past several decades, memory technologies have exploited continual scaling of CMOS to drastically improve performance and cost. Unfortunately, charge-based memories become unreliable beyond 20 nm feature sizes. A promising alternative is Phase-Change-Memory (PCM) which leverages scalable resistive thermal mechanisms. To realize PCM's potential, a number of challenges, including the limited wear-endurance and costly writes, need to be addressed. This thesis introduces novel methodologies for encoding data on PCM which exploit asymmetries in read/write performance to minimize memory's wear/energy consumption. First, we map the problem to a distance-based graph clustering problem and prove it is NP-hard. Next, we propose two different approaches: an optimal solution based on Integer-Linear-Programming, and an approximately-optimal solution based on Dynamic-Programming. Our methods target both single-level and multi-level cell PCM and provide further optimizations for stochastically-distributed data. We devise a low overhead hardware architecture for the encoder. Evaluations demonstrate significant performance gains of our framework.
Coding-spreading tradeoff for lattice codes
(2001) Khoshnevis, Ahmad; Aazhang, Behnaam
A fixed bandwidth expansion can be achieved either by coding or spreading, while each have different effect on the resultant signal space. Coding increases both Shannon and Fourier bandwidth whereas spreading only increases the Fourier bandwidth. In this document we are looking for the optimum combination of coding and spreading, in a code division multiple access (CDMA) system, that minimizes the average frame error rate under fading channel with multiple antenna at transmitter and receiver. Using the theory of lattice code, we show that in a system with K users, the optimum spreading factor N equals K. Simulation results support the analysis. In simulations we used Minimum Mean Square Error (MMSE), and Matched Filter (MF) as multi-user detector. We also assumed that receiver knows the channel state information (CSI). In case of multiple antennas Alamouti scheme [Ala98] at transmitter and maximum ratio combining (MRC) at the receiver are applied.
Cooperative communication over two-way channels
(2008) Steger, Christopher B.; Aazhang, Behnaam; Sabharwal, Ashutosh
In this thesis, we define and analyze communication scenarios in which the source and destination cooperate across noncoherent two-way fading channels. As in practical communication systems, we constrain the bandwidth and power resources available at both nodes. By constraining resources, we automatically produce situations in which neither the source nor the destination knows the fading state perfectly, and the nodes are unable to perfectly share knowledge with one another. Thus, not only is the channel state information imperfect, but the ability to cooperate in a coordinated fashion is also impaired. As a result, performance predictions based on perfect channel state information and perfect coordination can prove highly unrealistic. However, we have discovered that even in the presence of imperfect channel state information and imprecise coordination, cooperative communication over two-way channels offers significant performance gains over comparable one-way systems, and we have developed novel schemes to leverage cooperative gains in the presence of uncertainty. In the first part of this work, we analyze the reliability of a system that uses channel inversion power control to combat the effects of fading. We demonstrate that a simplistic approach to training leaves the system crippled by channel estimation errors. As an alternative, we propose a novel two-way training scheme that limits the impact of estimation error and provides the best known performance of any system subjected to full resource accounting. We then show that the benefits of two-way training extend to systems with multiple transmit antennas and two-way systems in which the data flow is bidirectional. In the second part, we employ a more general system model and study the precise amount of mutual information lost to uncertain channel state information at the destination. Our results illuminate the relationship between the loss of mutual information and the entropy of the channel. We also capture the effect of coherence time and the degree to which data-aided estimation can mitigate channel estimation errors. We then apply our results to quantify the performance of a number of practical systems and demonstrate the effectiveness of two-way cooperation in realistic scenarios.
Cooperative Strategies for Near-Optimal Computation in Wireless Networks
(2013-07-24) Nokleby, Matthew; Aazhang, Behnaam; Sabharwal, Ashutosh; Knightly, Edward W.; Damjanovic, Danijela
Computation problems, such as network coding and averaging consen- sus, have become increasingly central to the study of wireless networks. Network coding, in which intermediate terminals compute and forward functions of others’ messages, is instrumental in establishing the capacity of multicast networks. Averaging consensus, in which terminals compute the mean of others’ measurements, is a canonical building block of dis- tributed estimation over sensor networks. Both problems, however, are typically studied over graphical networks, which abstract away the broad- cast and superposition properties fundamental to wireless propagation. The performance of computation in realistic wireless environments, there- fore, remains unclear. In this thesis, I seek after near-optimal computation strategies under realistic wireless models. For both network coding and averaging con- sensus, cooperative communications plays a key role. For network cod- ing, I consider two topologies: a single-layer network in which users may signal cooperatively, and a two-transmitter, two-receiver network aided by a dedicated relay. In the former topology, I develop a decode-and- forward scheme based on a linear decomposition of nested lattice codes. For a network having two transmitters and a single receiver, the proposed scheme is optimal in the diversity-multiplexing tradeo↵; otherwise it pro- vides significant rate gains over existing non-cooperative approaches. In the latter topology, I show that an amplify-and-forward relay strategy is optimal almost everywhere in the degrees-of-freedom. Furthermore, for symmetric channels, amplify-and-forward achieves rates near capacity for a non-trivial set of channel gains. For averaging consensus, I consider large networks of randomly-placed nodes. Under a path-loss wireless model, I characterize the resource de- mands of consensus with respect to three metrics: energy expended, time elapsed, and time-bandwidth product consumed. I show that existing con- sensus strategies, such as gossip algorithms, are nearly order optimal in the energy expended but strictly suboptimal in the other metrics. I propose a new consensus strategy, tailored to the wireless medium and cooperative in nature, termed hierarchical averaging. Hierarchical averaging is nearly order optimal in all three metrics for a wide range of path-loss exponents. Finally, I examine consensus under a simple quantization model, show- ing that hierarchical averaging achieves a nearly order-optimal tradeo↵ between resource consumption and estimation accuracy.
Design and analysis of direct-sequence multiuser receivers
(1988) Stanners, Steven Paul; Aazhang, Behnaam
Matched filter receivers are commonly used for Direct-Sequence Spread-Spectrum Multiple-Access communication systems. These receivers are easy to implement and analyze, and are optimal for single user Gaussian noise channels. However, for applications in spread-spectrum networks, lower average bit-error probability can be achieved by a receiver which takes into account the effect of the interfering users. Two such receivers which form Maximum-Likelihood decisions given an observation vector consisting of chip correlator outputs are shown to perform better than the matched filter. The performance of these receivers is analyzed via Monte Carlo simulations using Importance Sampling. The level of improvement over the matched filter is dependent upon the relative levels of Gaussian noise and multiple-access interference. These receivers demonstrate less deterioration of performance in near-far situations than the matched filter, and find application in wide-band radio networks.
Design of prototyping platforms for multiple antenna wireless communications
(2005) Murphy, Patrick O.; Aazhang, Behnaam
As the demand for higher performance wireless communications continues to grow, novel algorithms have been developed which provide increased performance and efficiency. One such class of algorithms involves the use of multiple antennas on either end of a wireless link. Many of these multiple input multiple output (MIMO) algorithms offer impressive performance gains over their single antenna counterparts. The practicality of implementing such algorithms in a real system, however, has received far less attention. A primary reason for this is the scarcity of hardware platforms suitable for implementing and evaluating complex wireless communications algorithms. We present in this thesis two such platforms designed specifically to fill this void. The first platform is constructed from commercial off the shelf hardware, including equipment for baseband processing, RF up/downconversion and wireless channel emulation. The second, more ambitious platform, is built from custom hardware designed specifically for flexible MIMO prototyping.

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