On the Interaction between TCP and the Wireless Channel in CDMA2000 Networks

In this work, we conducted extensive active measurements on a large nationwide CDMA2000 1xRTT network in order to characterize the impact of both the Radio Link Protocol and more importantly, the wireless scheduler, on TCP. Our measurements include standard TCP/UDP logs, as well as detailed RF layer statistics that allow observability into RF dynamics. With the help of a robust correlation measure, normalized mutual information, we were able to quantify the impact of these two RF factors on TCP performance metrics such as the round trip time, packet loss rate, instantaneous throughput etc. We show that the variable channel rate has the larger impact on TCP behavior when compared to the Radio Link Protocol. Furthermore, we expose and rank the factors that influence the assigned channel rate itself and in particular, demonstrate the sensitivity of the wireless scheduler to the data sending rate. Thus, TCP is adapting its rate to match the available network capacity, while the rate allocated by the wireless scheduler is influenced by the sender's behavior. Such a system is best described as a closed loop system with two feedback controllers, the TCP controller and the wireless scheduler, each one affecting the other's decisions. In this work, we take the first steps in characterizing such a system in a realistic environment.

Fusion Artmap: A Neural Network Architecture for Multi-Channel Data Fusion and Classification

Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Single-channel Fusion ARTMAP is functionally equivalent to Fuzzy ART during unsupervised learning and to Fuzzy ARTMAP during supervised learning. The network has a symmetric organization such that each channel can be dynamically configured to serve as either a data input or a teaching input to the system. An ART module forms a compressed recognition code within each channel. These codes, in turn, become inputs to a single ART system that organizes the global recognition code. When a predictive error occurs, a process called paraellel match tracking simultaneously raises vigilances in multiple ART modules until reset is triggered in one of them. Parallel match tracking hereby resets only that portion of the recognition code with the poorest match, or minimum predictive confidence. This internally controlled selective reset process is a type of credit assignment that creates a parsimoniously connected learned network. Fusion ARTMAP's multi-channel coding is illustrated by simulations of the Quadruped Mammal database.

Fusion ARTMAP: An Adaptive Fuzzy Network for Multi-Channel Classification

Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Fusion ARTMAP generalizes the fuzzy ARTMAP architecture in order to adaptively classify multi-channel data. The network has a symmetric organization such that each channel can be dynamically configured to serve as either a data input or a teaching input to the system. An ART module forms a compressed recognition code within each channel. These codes, in turn, beco1ne inputs to a single ART system that organizes the global recognition code. When a predictive error occurs, a process called parallel match tracking simultaneously raises vigilances in multiple ART modules until reset is triggered in one of thmn. Parallel match tracking hereby resets only that portion of the recognition code with the poorest match, or minimum predictive confidence. This internally controlled selective reset process is a type of credit assignment that creates a parsimoniously connected learned network.