@conference {
	title = {Performance Analysis Using a Hierarchical Loss Network Model},
	author = {Liu, Mingyan., Baras, John S.},
	year = {2000},
	month = {11/2000},
	abstract = {In this paper we present a hierarchical loss network model for estimating the end-to-end blocking probabilities of large networks. As networks grow in size, nodes tend to form clusters geographically and hierarchical routing schemes are more commonly used. Loss network and reduced load models are often used to approximate end-to-end call blocking probabilities and hence throughput. However so far all work being done in this area is for flat networks with flat routing schemes. We aim at developing a more efficient approximation method for networks that have a natural hierarchy and/or when some form of hierarchical routing policy is used. We present two hierarchical models in detail for fixed hierarchical routing and dynamic hierarchical routing policies, respectively, via the notion of network abstraction, route segmentation, traffic segregation and aggregation. Computation is done separately within each cluster (local) and among clusters (global), and the fixed point is obtained by iteration between local and global computations. We present results from both numerical experiments and discrete event simulations.},
	address = {San Fransisco, CA},
	booktitle = {IEEE Globecom 2000,Comunications: The Global Bridge},
	pages = {pp. 1793-1797}
}
@conference {
	title = {Maximum Likelihood Slow Frequency-Selective Fading Channel Estimation Using Frequency Domain approach},
	author = {Jiang, Yimin., Sun, Feng-Wen., Baras, John S.},
	year = {2000},
	month = {11/2000},
	abstract = {This paper addresses the channel estimation problem for slow frequency-selective fading channels using training sequences and the maximum likelihood (ML) approach. In the literature people usually assume a symbol period spaced delay-tapped-line model and additive white Gaussian noise (AWGN). Due to the pre-filtering in the receiver front end, if the sampling rate is larger than one sample per symbol or sampling epoch is unknown (Le., the timing information is unavailable), the AWGN model is not valid anymore. A more general ML channel estimation method using the discrete Fourier transform (DFT) is derived for colored Gaussian noise and over-sampling. A similar idea can be adopted to derive the ML joint carrier phase and timing offsets estimation algorithm.},
	address = {San Francisco, CA},
	booktitle = {IEEE Globecom2000, Communications:The Global Bridge},
	pages = {pp. 955-959}
}
@conference {
	title = {Generalized TCP Congestion Avoidance and Its Effect on Bandwidth Sharing and Variability},
	author = {Misra, Archan., Baras, John S., Ott, Teunis.},
	year = {2000},
	month = {11/2000},
	abstract = {To model possible suggested changes in TCP window adaptation in response to randomized feedback, such as ECN, we formulate a generalized verslon of the TCP congestion avoidance algorithm. We first consider multiple such generalized TCP flows sharing a bottleneck buffer under the Assured Service model and use a fixed point technique to obtain the mean window sizes and throughputs for the TCP flows. To further study how changes in the adaptation algorithm affect the variability in the throughput, we use an analytical-cum-numerical technique to derive the window distribution (and related statistics) of a single generalized flow under state-dependent randomized congestion feedback.},
	address = {San Francisco, California},
	booktitle = {IEEE Global Telecommunications Conference (GLOBECOM), Communications: The Global Bridge},
	pages = {pp. 329-337.}
}
@conference {
	title = {Using Drop{\textendash}Biasing to stabilize the Occupancy of Random-Drop Queues with TCP Traffic},
	author = {Misra, Archan., Ott, Teunis., Baras, John S.},
	year = {2000},
	month = {11/2000},
	abstract = {The paper describes how the use of {\textquoteleft}drop-biasing{\textquoteright}, a technique to control the distribution of the gap between consecutive packet losses in random drop queues (such as RED) can be used to reduce the variability of the queue occupancy with TCP traffic. Reducing the variance of the queue occupancy reduces delay jitter for buffered packets, as well as decreases the likelihood of buffer underflow. We find that modifying the packet drop probabilities to ensure a minimum separation between consecutive packet drops serves to decrease the variability in the queue occupancy. This is really achieved as a result of the increased negative correlation among the congestion windows of the constituent TCP flows. Such negative correlation explains why the use of simple drop-biasing strategies can reduce the queue variability without increasing the likelihood of bursts of packet losses. The results of our investigations have relevance for the design and deployment of RED-like algorithms for congestion control in the Internet.},
	address = {Singapore},
	booktitle = {IEEE International Conference on Communications Systems(ICCS)},
	pages = {pp. 1-9}
}
