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Three applications in wireless networks where model-free stochastic learning is applicable, are discussed. The learning based optimization problems are formulated and simulation results are presented. Some open issues are also discussed.
The next generation of wireless communication is a ubiquitous radio system concept, providing wireless access from short-range to wide-area, with one single reconfigurable and adaptive system for all envisaged radio environments. This paper presents the design approach of RCO (reconfigurable concurrent oscillator) that simultaneously generates two or more signals of different frequencies that eliminate...
Todaypsilas access network is migrating towards broadband IP, fixed/mobile convergence and service convergence. Thus, the next generation access network (NGAN) requires a converged optical/wireless access network infrastructure, other than the existing architecture that connect to the cellular, broadband wireless, and optical access networks separately, which complicate the network and increase the...
This paper addresses adaptive channel estimation for time-varying mobile wireless channels with nonstationary statistics. We presents a reduced complexity adaptive channel estimator based on a set membership filtering approach known as the Optimal Bounding Ellipsoid (OBE) algorithm. To exploit time and frequency domain correlation properties of the channel in an efficient low-complexity way, we allow...
Internet Relay Chat (IRC) is a critical application in military networks for maintaining situational awareness and conducting battle management at all echelons. But these networks are often subject to frequent disconnections, limited bandwidth, and long delays, especially when using satellite services and field radios. Such conditions are inhospitable to TCP, and thus the standard IRC protocol, which...
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