Novel (Channel Modeling, Feedback and Cognitive) Approaches in Wireless Communications

PI: Bhaskar Rao

Sponsor: National Science Foundation

Period: 8/1/2011 – 7/30/2014

Abstract: This research project examines modeling and algorithmic issues that address fundamental problems at the physical layer that arise in wireless communications. The research plan includes 1) Development of novel channel modeling methods that decompose the channel into a specular component and a diffuse component. A key consideration in this work is developing a rigorous framework for channel prediction and utilizing the insight to develop robust feedback based MIMO systems that degrade gracefully, 2) the development and analysis of feedback based multi-user MIMO-OFDM systems. This includes novel channel estimation and representations schemes, novel schemes for encoding sparsity and performance analysis of reduced feedback MIMOOFDM systems, 3) the development of advanced cognition at the physical layer. This includes waveform design in cognitive radios based on timing consideration and more comprehensive models for channels to provide awareness based on location, learning and memory.

A Multi-User Communication and Information Theoretic Approach to the Sparse Signal Recovery Problem

PI: Bhaskar Rao

Co-PI: Young-Han Kim

Sponsor: National Science Foundation

Period: 10/1/2011-9/30/2013

Abstract: This research project examines theoretical, algorithmic, and computational issues that arise in compressed sensing (CS) and signal processing problems where there is a need to compute sparse solutions. In addition to the exciting compressed sensing area, this research will benefit the numerous signal processing applications where sparsity constraint on the solution vector naturally arises. Brain imaging techniques such as MEG and EEG, sparse communication channels with large delay spread, high resolution spectral analysis, direction of arrival estimation are a few examples. An effective solution to this problem will have significant impact, providing new and valuable tools to the practicing signal processing engineer. In addition, the tools will be of interest to researchers in cognitive science, neuroscience, and machine learning where sparsity naturally arises, such as sparse coding of signals in the brain or learning from data which is often assumed to lie on a low dimensional manifold.

This project will provide a comprehensive and tighter integration of the compressed sensing field and multi-user information theory making it possible to provide significantly more leveraging of the rich results available in network information theory and the practical communication systems they foster. The theoretical tools necessary to enable this integration will be developed by the investigators. The bridges developed will enable significant advances in the CS field, both theory and practice. The information theoretic insights will be leveraged to provide insights into the limits of performance and guidance on practical CS based system design. The implementation experience from communication systems will be translated to practical algorithm development and efficient CS based system design.

Next Generation of Cognitive Networks: From Agile Radios to Smart Phones

PI: Tara Javidi

Co-PI: Massimo Franceschetti, Alon Orlitsky, Bhaskar Rao and Ramesh Rao

Sponsors: Huawei, Qualcomm, Viasat, L-3, Center for Wireless Communications

Duration: 10/1/2011-9/30/2013

As the demand for mobile/wireless services as well as the complexity and the diversity of networks and devices continue to grow, there is a need for developing affordable and scalable means for effectively utilizing the available resources to deliver the complex applications and services of future. It this context, cognitive networking has come to the forefront of wireless networking research. While, cognitive radio technology was first envisioned around the notion of spectrum agility, with the ever increasing popularity of the “smart” devices -not only equipped with multiple wireless interface cards but also with significant storage and computational capabilities- the once dream-like notions of sensing and network computations, adaptability, and learning are now generalized across the network and protocol stack.

There is an ever-increasing need for network cognition, i.e. estimation and control protocols that can rapidly evaluate, track and manage dynamics both in the type of information content exchanged on networks and in the environmental factors such as location, time, and spectrum availability. This project leverages our existing cognitive networking testbed (UCSD-CogNet testbed) to propose a comprehensive plan enabling learning-based and decision theoretic approach to network design. The proposal consists of the following three components:

First we consider the problem of cognitive protocol design. This set of research activities are focused on how context, model, and situation awareness can be integrated into the protocol stack design.

Secondly, we consider the issues regarding estimation and learning in networks. This set of research activities are focused on how the context, model, and situations are learned and/or estimated in a realistic network where network resources are constrained in the number of observations, time to learn, and/or statistical correlation structure.

The last element of our proposed research integrates and validates our theoretical research on an experimental testbed as validation and proof of concept. To develop new knowledge on cognitive networking, we built a 36 node cognitive networking testbed (UCSD-CogNet testbed). We are planning to use the experimental testbed to verify and compare our theoretical tools and approaches.

D2D and Relay Assisted Cellular Architecture

PI: Pam Cosman

Co-PI’s: Zhongren Cao, Tara Javidi, Young-Han Kim, Larry Milstein, Bhaskar Rao

Sponsors: Ericsson, Huawei, Center for Wireless Communications

Period: 10/1/2011-9/30/2013

To address the challenge of explosively growing mobile Internet traffic on the capacity of radio access networks, we will study in-band device-to-device (D2D) underlay communications within future cellular-based systems. We will emphasize video applications, a key driver of traffic growth. In current 3G systems, mobile devices can only transmit to and receive from a base station. To increase capacity, without huge infrastructure costs, we propose to shift the existing architecture paradigm by adding D2D and relay links. In D2D underlay communications, closely located devices can directly communicate with each other whenever needed, reusing the same channel spectrum resources from the base station. With closely located devices, D2D communication can achieve higher throughput and/or lower power, thus reducing the interference to other users. Also, the base station can reuse the same resources to communicate with another user in the same cell. Furthermore, multiple D2D communication pairs can co-exist in the same cell using the same radio resources as long as the interference is under control. This proposal will explore the feasibility and benefits of in-band D2D and relay communications enhancing
a cellular architecture.

A first component will be algorithm development. We will develop and evaluate run-time strategies for a base station for initiating and terminating direct D2D links in a cellular system, and for allocating resources, including frequency bands and relays. We will design and test algorithms for D2D session management, including peer discovery, session initiation, monitoring and termination. We will take a formal optimization approach, where the objective function to be optimized is throughput, or peak-signal-to-noise ratio for video applications. We will also study relay-enhanced communications for layered video in which relays may be dedicated to a video base layer or an enhancement layer to optimize received quality.

A second component is aimed at theoretical understanding. Recent results in networking and information theory will be extended to understand the fundamental theoretical benefits of D2D communications. In particular, we will study the common-message broadcast problem, and the related problem, for layered video, of the two-layer broadcast network with auxiliary D2D communication. We will determine the optimal transmission strategy and coding as well as a characterization of
the capacity region.

The last component is a hardware testbed. An existing experimental testbed will be used to prototype the D2D underlay system, perform experiments and collect data. The testbed nodes emulate a base station and multiple mobile nodes, allowing one to change distances and interference levels, and to evaluate the D2D session management and resource allocation schemes in a lab-friendly but practice-relevant testbed for insight on real system deployment.

Cognitive Wireless Systems

PI: Bhaskar Rao

Sponsor: Broadcom Foundation Grant

Period: 10/1/2011-

Single and Multi-Microphone Based Source Separation and Speech Enhancement

PI: Bhaskar Rao

Sponsor: Qualcomm

Period: 9/1/2010-

This proposal develops single and multiple microphone based speech enhancement and auditory stream segregation based methods. Harmonic modeling has played an important role in speech coding but their application to the abovementioned problems has been limited and will be a topic of research. In particular, for the single microphone based methods, we will examine subset selection (sparsity promoting) methods for spectral analysis as a front-end for the audio segregation task. We will develop methods that combine harmonic modeling with auditory cues such as common offsets, harmonic concordance etc. The resulting methods are expected to have superior ability to deal with transient interferences that are usually hard to suppress using conventional methods. Multiple microphone based enhancement provide an opportunity to combine speech structure with novel spatial processing. Robust adaptive beamforming methods as well as methods based on a synergistic combination of ideas from MIMO blind equalization and ICA (Independent Component Analysis) will be developed. In particular, the evaluation, analysis and extension of the Independent Vector Analysis (IVA) based methods will be given special attention.

Theory and Algorithms for Exploiting Sparsity in Signal Processing Applications

PI: Bhaskar Rao

Co-PI: Kenneth Kreutz-Delgado

Sponsor: National Science Foundation

Period: 9/15/20008 – 9/1/2012

Abstract: This research project examines theoretical, algorithmic, and computational issues that arise in signal processing problems where there is a need to compute sparse solutions. The research plan includes 1) Extensions and generalizations of the sparse source recovery problem to greatly broaden the application domain. A key consideration in this work is developing a rigorous framework to deal with dependency in the sparsity framework. Motivated by applications with sparse but local structure, we will consider intra-vector dependency in the single measurement case, as well as intra-vector dependency as required in the multiple measurement context, among others, 2) the development of connections between multiuser communication theory and the sparse signal recovery problem to shed light on the stability with which sparse signal recovery is possible and to develop an understanding of the limits of suboptimal source recovery methods, 3) the development of on-line adaptive algorithms that exploit the inherent sparse structure of the application, and 4) Evaluation of the resulting algorithms in several important application domains.

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