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Restriction in computational power has proven to be a barrier in offering HDSL designs to potential customers. Off-the-shelf performing fast microprocessors and cellular-telephone handsets could create the potential for communication between high-speed in-house or local networks with remote consumers serviced by such components. A new set of protocols and routing tables that would over-lay these local communication infrastructures has recently emerged. The protocol stack is designed to work at the concrete physical level and could be used in each case where the IP network is linked to a network that can deliver HDSL data. The routing tables can then be used to route the HDSL traffic appropriately.
To make HDSL designs available to potential customers it is necessary to implement virtual HDSL components and define their functional properties and associated parameters. We use design science to produce this compendium of virtual components. These components are implemented and tested within the constraints of the traffic model and typical subscriber loop environment. The tight integration of these components into the traffic model results in a pliant framework that allows the system designer to objective-seek variant HDSL solutions. These variants are then tested in an appropriate subscriber loop environment.
The present work is useful if the subscriber loop environment is presumed to remain constant during the design process. In this environment, the loops are made adaptable to subscriber loop environments with continuously variable loop conditions; in addition to compensating for loop loss and performance degradation, the bridge design can also aggressively recover from lost frames and emulate the effects of line-coupling on the loop-group. Other anticipated applications include minimizing the number of subscriber loops that must be connected to the bridge network.
The most difficult problems, and those with the greatest potential in terms of design optimization, happen to be those for which the traditional search-based and heuristic-based optimization methods are inefficient. Models of these problems tend to be highly nonlinear, the resulting problem to be solved is either a complex multiple objectives problem or a “black box” such as the ADSL problem. A new population-based evolutionary algorithm Searching for Optimality (SEOS) is used to solve the model problem. d2c66b5586