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Register a new userEnhancing network transmission capacity by efficiently allocating node capability
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Added by
Guoqiang Zhang
Publication date
2009
fields
Informatics Engineering
and research's language is
English
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A networks transmission capacity is the maximal rate of traffic inflow that the network can handle without causing congestion. Here we study how to enhance this quantity by redistributing the capability of individual nodes while preserving the total sum of node capability. We propose a practical and effective node-capability allocation scheme which allocates a nodes capability based on the local knowledge of the nodes connectivity. We show the scheme enhances the transmission capacity by two orders of magnitude for networks with heterogenous structures.
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Establishing robust connectivity in heterogeneous networks (HetNets) is an important yet challenging problem. For a HetNet accommodating a large number of nodes, establishing perturbation-invulnerable connectivity is of utmost importance. This paper provides a robust advantaged node placement strategy best suited for sparse network graphs. In order to offer connectivity robustness, this paper models the communication range of an advantaged node with a hexagon embedded within a circle representing the physical range of a node. Consequently, the proposed node placement method of this paper is based on a so-called hexagonal coordinate system (HCS) in which we develop an extended algebra. We formulate a class of geometric distance optimization problems aiming at establishing robust connectivity of a graph of multiple clusters of nodes. After showing that our formulated problem is NP-hard, we utilize HCS to efficiently solve an approximation of the problem. First, we show that our solution closely approximates an exhaustive search solution approach for the originally formulated NP-hard problem. Then, we illustrate its advantages in comparison with other alternatives through experimental results capturing advantaged node cost, runtime, and robustness characteristics. The results show that our algorithm is most effective in sparse networks for which we derive classification thresholds.
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State-of-the-art performance for many emerging edge applications is achieved by deep neural networks (DNNs). Often, these DNNs are location and time sensitive, and the parameters of a specific DNN must be delivered from an edge server to the edge device rapidly and efficiently to carry out time-sensitive inference tasks. We introduce AirNet, a novel training and analog transmission method that allows efficient wireless delivery of DNNs. We first train the DNN with noise injection to counter the wireless channel noise. We also employ pruning to reduce the channel bandwidth necessary for transmission, and perform knowledge distillation from a larger model to achieve satisfactory performance, despite the channel perturbations. We show that AirNet achieves significantly higher test accuracy compared to digital alternatives under the same bandwidth and power constraints. It also exhibits graceful degradation with channel quality, which reduces the requirement for accurate channel estimation.
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We successfully demonstrate a transmission of a high layer split mobile interface for cell-less, high-speed train network applications using a commercially available XGS-PON. Operation is also demonstrated for a GbE interface.
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