11 research outputs found
A semi-blind channel estimation method for multiuser multiantenna OFDM systems
A subspace-based blind method is proposed for estimating the channel responses of a multiuser and multiantenna orthogonal frequency division multiplexing (OFDM) uplink system. It gives estimations to all channel responses subject to a scalar matrix ambiguity and does not need precise channel order information (only upper bound for the orders is required). Furthermore, the scalar ambiguity matrix can be easily resolved by using only one pilot OFDM block, given that the number of users is smaller than the number of symbols in the pilot symbol block. Equalization methods are discussed based on the estimated channels. By using partial knowledge of the channels, a multipath subspace method is proposed that reduces the computational complexity. Simulations show that the methods are effective and robust.published_or_final_versio
Bridging the gap : an optimization-based framework for fast, simultaneous circuit & system design space exploration
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 107-110).Design of modern mixed signal integrated circuits is becoming increasingly difficult. Continued MOSFET scaling is approaching the global power dissipation limits while increasing transistor variability, thus requiring careful allocation of power and area resources to achieve increasingly more aggressive performance specifications. In this tightly constrained environment traditional iterative system-to-circuit redesign loop, is becoming inefficient. With complex system architectures and circuit specifications approaching technological limits of the process employed, the designers have less room to margin for the overhead of strict system and circuit design interdependencies. Severely constrained modern mixed IC design can take many iterations to converge in such a design flow. This is an expensive and time consuming process. The situation is particularly acute in high-speed links. As an important building block of many systems (high speed I/O, on-chip communication, ...) power efficiency and area footprint are of utmost importance. Design of these systems is challenging in both system and circuit domain. On one hand system architectures are becoming increasingly complex to provide necessary performance increase. On the other, circuit implementation of these increasingly complicated systems is difficult to achieve under tight power and area budget. To bridge this gap between system and circuit design, we formulate a circuit-to-system optimization-driven framework. It is an equation-based description, powered by a human designer. Provided with equation-based model we use fast optimization tools to quickly scout the available design space. Presence of a designer in the flow is invaluable resource enabling significant saving by simplifying the models to capture only the relevant information and constraining the search space to areas where meaningful solutions might be expected to be found.(cont) Thus, the computational effort overhead that plagues the simulation-based design space exploration and design optimization is greatly reduced. The flow is powered by a signomial optimization engine. The key challenge is to bring, from the modeling point of view, very different problems such as circuit design and system design into the realm of an optimization engine that can solve them jointly, thus breaking the re-design loop or at least cutting it shorter. Relying on signomial programming is necessary in order to accurately model all the necessary phenomenons that arise in electrical circuits and at system level. For example, defining regions of operation of transistors under polarization conditions can not be modeled accurately with simpler type of equations. Similarly, calculating the effect of filtering to a signal also requires possibility to handle signomial equations. Thus, signomial programming is necessary yet not fully explored and finding suitable formulation might take some experimenting as we will see in this thesis. Signomial programming, as a general non-convex optimization problem, is still an active research area. Most of the solutions proposed so far involve local convexification of the problem in addition to branch & bound type of search. Furthermore, most of the non-convex problems are solved for one particular system of equations, and general methodology that is reliable and efficient is not known. Thus, a big part the work to be presented in this thesis is detailing how to construct a system formulation that the optimization engine can solve efficiently and reliably. We tested different formulations and their performance measured in terms of parsing and solving speed and accuracy. From these tests we motivate and explain how a series of transformations we introduce improve our formulation and arrive to a well-behaved and reliable form. We show how to apply our design flow in high-speed link design.(cont) By restructuring the traditional design flow we derive system and circuit abstractions. These sub-problems are interfaced through a set of well defined interface variables, which enables code level separation of problem descriptions, thus building a modular and easy to read and maintain system and circuit model. Finally we develop a set of scripts to automate formulating parametrized system level description. We explain how our transformations influence the speed of this process as well as the size of the model produced.by Ranko Sredojević.S.M
Reverberation: models, estimation and application
The use of reverberation models is required in many applications such as acoustic measurements,
speech dereverberation and robust automatic speech recognition. The aim of this thesis is to
investigate different models and propose a perceptually-relevant reverberation model with suitable
parameter estimation techniques for different applications.
Reverberation can be modelled in both the time and frequency domain. The model parameters
give direct information of both physical and perceptual characteristics. These characteristics
create a multidimensional parameter space of reverberation, which can be to a large extent captured
by a time-frequency domain model. In this thesis, the relationship between physical and perceptual
model parameters will be discussed. In the first application, an intrusive technique is proposed to
measure the reverberation or reverberance, perception of reverberation and the colouration. The
room decay rate parameter is of particular interest.
In practical applications, a blind estimate of the decay rate of acoustic energy in a room
is required. A statistical model for the distribution of the decay rate of the reverberant signal
named the eagleMax distribution is proposed. The eagleMax distribution describes the reverberant
speech decay rates as a random variable that is the maximum of the room decay rates and anechoic
speech decay rates. Three methods were developed to estimate the mean room decay rate from
the eagleMax distributions alone. The estimated room decay rates form a reverberation model that
will be discussed in the context of room acoustic measurements, speech dereverberation and robust
automatic speech recognition individually
System Identification with Applications in Speech Enhancement
As the increasing popularity of integrating hands-free telephony on mobile portable devices
and the rapid development of voice over internet protocol, identification of acoustic
systems has become desirable for compensating distortions introduced to speech signals
during transmission, and hence enhancing the speech quality. The objective of this research
is to develop system identification algorithms for speech enhancement applications
including network echo cancellation and speech dereverberation.
A supervised adaptive algorithm for sparse system identification is developed for
network echo cancellation. Based on the framework of selective-tap updating scheme
on the normalized least mean squares algorithm, the MMax and sparse partial update
tap-selection strategies are exploited in the frequency domain to achieve fast convergence
performance with low computational complexity. Through demonstrating how
the sparseness of the network impulse response varies in the transformed domain, the
multidelay filtering structure is incorporated to reduce the algorithmic delay.
Blind identification of SIMO acoustic systems for speech dereverberation in the
presence of common zeros is then investigated. First, the problem of common zeros is
defined and extended to include the presence of near-common zeros. Two clustering algorithms
are developed to quantify the number of these zeros so as to facilitate the study
of their effect on blind system identification and speech dereverberation. To mitigate such
effect, two algorithms are developed where the two-stage algorithm based on channel
decomposition identifies common and non-common zeros sequentially; and the forced
spectral diversity approach combines spectral shaping filters and channel undermodelling
for deriving a modified system that leads to an improved dereverberation performance.
Additionally, a solution to the scale factor ambiguity problem in subband-based blind system identification is developed, which motivates further research on subbandbased
dereverberation techniques. Comprehensive simulations and discussions demonstrate
the effectiveness of the aforementioned algorithms. A discussion on possible directions
of prospective research on system identification techniques concludes this thesis