Adaptive Discrete Particle Swarm Optimization for Cognitive Radios

Mahdi, Ali H.; Mohanan, Jerome; Kalil, Mohamed A.; Mitschele‐Thiel, Andreas

doi:10.1109/icc.2012.6364817

Cited by 19 publications

(13 citation statements)

References 11 publications

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“…The ADPSO algorithm [8] is a modified version of the DPSO algorithm [7]. It uses the DPSO algorithm for com-978-1-4673-0009-4/13/$26.00 ©2013 IEEE puting the velocity of each particle, in order to find the global best.…”

Section: A Adpsomentioning

confidence: 99%

“…This function is based on the amount of P t sent by the transmitter as shown in equation (8) [11]. (8) where P ti is the transmit power in channel i and P t max is the maximum transmit power. The total fitness function used in our algorithm is shown in equation (9): (9) where the weights (w 1 , w 2 , w 3 , w 4 ) are defined as a policy of the link state.…”

Section: Clomentioning

confidence: 99%

“…However, they used fixed number of sub-carriers (32 sub-carrier) and the problem of local optimum was left as open issue. In our previous work [8], we developed an Adaptive DPSO (ADPSO) to overcome the local optimum problem in [7]. In [8] we showed that ADPSO outperforms DPSO and GA in terms of convergence value and convergence time.…”

Section: Introductionmentioning

confidence: 99%

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Cross layer optimization for efficient spectrum utilization in cognitive radios

Mahdi

Kalil

Thiel

2013

2013 International Conference on Computing, Networking and Communications (ICNC)

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Cognitive Radio (CR) systems are smart systems capable of sensing the surrounding radio environment and adapting their operating parameters for efficient utilization of the available radio spectrum. To reach this goal, different operating parameters across the OSI layers of a device should be optimized such as transmit power, modulation scheme and packet length etc. In this paper, we propose an efficient algorithm for optimizing and adapting CR operating parameters from physical, MAC and network layers for better utilization of radio spectrum. We include Automatic Repeat reQuest (ARQ) inside our proposed algorithm to check the validity of best parameters at the receiver side under specific link state. The proposed algorithm is called ARQ-Adaptive Discrete Particle Swarm Optimization (ARQ-ADPSO). The simulation results show an improvement by ARQ-ADPSO compared to the well-known genetic algorithm.

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Section: A Adpsomentioning

confidence: 99%

Section: Clomentioning

confidence: 99%

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Cross layer optimization for efficient spectrum utilization in cognitive radios

Mahdi

Kalil

Thiel

2013

2013 International Conference on Computing, Networking and Communications (ICNC)

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“…If the feedback is NACK then the CR learns that the solution approximated by the CBR is not optimized and it calculates the optimized solution through ADPSO [9].…”

Section: Fig 3: Case Based Decision Making With Adaptive Discrete Pamentioning

confidence: 99%

“…In our case based approach for designing a reasoning agent we use ADPSO (Adaptive Discrete Particle Swarm Optimization) [9]. The main aim of the work is to use case base theory to approximate the environmental variation completely and use the existing cases in making decision for new environmental changes, whereas the optimization algorithm runs for some instances but after sufficient training, enough experience is gained regarding the behavior of the environment.…”

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confidence: 99%

Design of Reasoning Agent for Cognitive Radio Networks

Singh¹

2017

IJCA

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The main task for a cognitive radio system is to adapt to the transmission parameters in a dynamically changing environment. The nature of wireless channels and the number of transmission parameters to be optimized in various layers add more complexity to the adaptation process. In these situations Case Based Reasoning (CBR) agent along with optimization algorithm provides a robust solution. CBR Agent develops cases as the changes occur in the environment and acquires an understanding of the system. As new situations occur, experience from the previously developed cases is taken into consideration and a solution to the system is provided. In real time with training, the Cognitive Engine (CE) learns the variations occurring in the environment and utilizes the previously existing cases to provide new solution. As well as if abruptly some unexpected situation is encountered then the optimization algorithm is initiated and a solution is designed as per the need of the environment and this case is also added to the case list for future reference. Hence variations in the environment are mapped extensively and the optimization process becomes more efficient. In the present work case based reasoning along with particle swarm optimization is implemented.

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Artificial intelligence based cognitive routing for cognitive radio networks

Qadir

2015

Artif Intell Rev

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Abstract-Cognitive radio networks (CRNs) are networks of nodes equipped with cognitive radios that can optimize performance by adapting to network conditions. While cognitive radio networks (CRN) are envisioned as intelligent networks, relatively little research has focused on the network level functionality of CRNs. Although various routing protocols, incorporating varying degrees of adaptiveness, have been proposed for CRNs, it is imperative for the long term success of CRNs that the design of cognitive routing protocols be pursued by the research community. Cognitive routing protocols are envisioned as routing protocols that fully and seamless incorporate AI-based techniques into their design. In this paper, we provide a self-contained tutorial on various AI and machine-learning techniques that have been, or can be, used for developing cognitive routing protocols. We also survey the application of various classes of AI techniques to CRNs in general, and to the problem of routing in particular. We discuss various decision making techniques and learning techniques from AI and document their current and potential applications to the problem of routing in CRNs. We also highlight the various inference, reasoning, modeling, and learning sub tasks that a cognitive routing protocol must solve. Finally, open research issues and future directions of work are identified.

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Adaptive Discrete Particle Swarm Optimization for Cognitive Radios

Cited by 19 publications

References 11 publications

Cross layer optimization for efficient spectrum utilization in cognitive radios

Cross layer optimization for efficient spectrum utilization in cognitive radios

Design of Reasoning Agent for Cognitive Radio Networks

Artificial intelligence based cognitive routing for cognitive radio networks

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