Biologically inspired cognitive radio engine model utilizing distributed genetic algorithms for secure and robust wireless communications and networking
Cognitive engine implementation for wireless multicarrier transceivers
Wireless Communications & Mobile Computing - Cognitive Radio, Software Defined Radio And Adaptive Wireless Systems
A cautionary perspective on cross-layer design
IEEE Wireless Communications
Teamwork and Collaboration in Cognitive Wireless Networks
IEEE Wireless Communications
Cross-layer optimization for OFDM wireless networks-part I: theoretical framework
IEEE Transactions on Wireless Communications
Protocol design and throughput analysis of frequency-agile multi-channel medium access control
IEEE Transactions on Wireless Communications
Cross-Layer Adaptive Resource Management for Wireless Packet Networks With OFDM Signaling
IEEE Transactions on Wireless Communications
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Cognitive networks: adaptation and learning to achieve end-to-end performance objectives
IEEE Communications Magazine
Mathematical decomposition techniques for distributed cross-layer optimization of data networks
IEEE Journal on Selected Areas in Communications
Adaptive transmission in cognitive radio networks
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
A Cognitive QoS Method Based on Parameter Sensitivity
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Full length article: Minority game for cognitive radios: Cooperating without cooperation
Physical Communication
Computer Networks: The International Journal of Computer and Telecommunications Networking
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The primary feature of cognitive radios for wireless communication systems is the capability to optimize the relevant communication parameters given a dynamic wireless channel environment. Recently, several research groups have presented promising preliminary results on the benefit of extending the cognitive process at the system level, capable of perceiving current network conditions and then acting according to end-to-end goals. System optimization however implies some challenging tasks: (1) Current network state information has to be known at all transmitters. This dramatically increases the amount of overhead as the number of parameters becomes large; (2) System optimization is often a non-linear problem with inter-parameter dependencies; (3) The optimization process should also support a dynamic quality of service (QoS) management scheme depending on the available network resources. In this paper, we invoke genetic algorithms (GAs) for iteratively finding the optimum parameters based on the acknowledgment (ACK) signal only. Neither network state information nor channel estimation is required. The set of accurate objective functions that we derive in our GA implementation control the optimization process at the system level toward any QoS. Simulation results show that our implementation achieves comparable performance to an exhaustive search over the whole set of parameters for which perfect network state information at the transmitter is assumed. It also outperforms the conventional scheme for which parameters are optimized at each layer separately.