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Capacity Scaling of General Cognitive Networks

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2 Author(s)
Wentao Huang ; Dept. of Electron. Eng., Shanghai Jiao Tong Univ., Shanghai, China ; Xinbing Wang

There has been recent interest within the networking research community to understand how performance scales in cognitive networks with overlapping n primary nodes and m secondary nodes. Two important metrics, i.e., throughput and delay, are studied in this paper. We first propose a simple and extendable decision model, i.e., the hybrid protocol model, for the secondary nodes to exploit spatial gap among primary transmissions for frequency reuse. Then, a framework for general cognitive networks is established based on the hybrid protocol model to analyze the occurrence of transmission opportunities for secondary nodes. We show that if the primary network operates in a generalized TDMA fashion, or employs a routing scheme such that traffic flows choose relays independently, then the hybrid protocol model suffices to guide the secondary network to achieve the same throughput and delay scaling as a standalone network without harming the performance of the primary network, as long as the secondary transmission range is smaller than the primary range in order. Our approach is general in the sense that we only make a few weak assumptions on both networks, and therefore it obtains a wide variety of results. We show secondary networks can obtain the same order of throughput and delay as standalone networks when primary networks are classic static networks, networks with random walk mobility, hybrid networks, multicast networks, CSMA networks, networks with general mobility, or clustered networks. Our work presents a relatively complete picture of the performance scaling of cognitive networks and provides fundamental insight on the design of them.

Published in:

Networking, IEEE/ACM Transactions on  (Volume:20 ,  Issue: 5 )