CREAM-MAC: Cognitive Radio-Enabled Multi-channel MAC for Wireless Networks

2011 ◽  
pp. 129-158
Author(s):  
Xi Zhang ◽  
Hang Su
Keyword(s):  
Wireless Networks ◽  
Cognitive Radio

A survey on network coding: From traditional wireless networks to emerging cognitive radio networks

2014 ◽  
Vol 46 ◽  
pp. 166-181 ◽  
Author(s):  
Muhammad Zubair Farooqi ◽  
Salma Malik Tabassum ◽  
Mubashir Husain Rehmani ◽  
Yasir Saleem
Keyword(s):  
Wireless Networks ◽  
Cognitive Radio ◽  
Network Coding ◽  
Cognitive Radio Networks ◽  
Radio Networks

scholarly journals Characterization of Primary Users in Cognitive Radio Wireless Networks using Support Vector Machine

2017 ◽  
Vol 10 (32) ◽  
pp. 1-12
Author(s):  
Danilo Lopez Sarmiento ◽  
Edwin Rivas Trujillo ◽  
Oscar Eduardo Gualdron ◽  
◽  
◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Wireless Networks ◽  
Cognitive Radio ◽  
Support Vector ◽  
Primary Users

Large Scale Cognitive Wireless Networks

2013 ◽  
pp. 26-52
Author(s):  
Liang Song ◽  
Petros Spachos ◽  
Dimitrios Hatzinakos
Keyword(s):  
Energy Efficiency ◽  
Wireless Networks ◽  
Cognitive Radio ◽  
Data Aggregation ◽  
Network Architecture ◽  
Large Scale ◽  
Vehicular Networks ◽  
Network Resource ◽  
Cognitive Wireless Networks ◽  
Definition Of

Cognitive radio has been proposed to have spectrum agility (or opportunistic spectrum access). In this chapter, the authors introduce the extended network architecture of cognitive radio network, which accesses not only spectrum resource but also wireless stations (networking nodes) and high-level application data opportunistically: the large-scale cognitive wireless networks. The developed network architecture is based upon a re-definition of wireless linkage: as functional abstraction of proximity communications among wireless stations. The operation spectrum and participating stations of such abstract wireless links are opportunistically decided based on their instantaneous availability. It is able to maximize wireless network resource utilization and achieve much higher performance in large-scale wireless networks, where the networking environment can change fast (usually in millisecond level) in terms of spectrum and wireless station availability. The authors further introduce opportunistic routing and opportunistic data aggregation under the developed network architecture, which results in an implementation of cognitive unicast and cognitive data-aggregation wireless-link modules. In both works, it is shown that network performance and energy efficiency can improve with network scale (such as including station density). The applications of large-scale cognitive wireless networks are further discussed in new (and smart) beyond-3G wireless infrastructures, including for example real-time wireless sensor networks, indoor/underground wireless tracking networks, broadband wireless networks, smart grid and utility networks, smart vehicular networks, and emergency networks. In all such applications, the cognitive wireless networks can provide the most cost-effective wireless bandwidth and the best energy efficiency.

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