The Experts below are selected from a list of 27 Experts worldwide ranked by ideXlab platform
P S Satyanarayana - One of the best experts on this subject based on the ideXlab platform.
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soft computing technique based call Admission Control Decision mechanism
International Conference on Computer Science and Information Technology, 2012Co-Authors: H Ramesh S Babu, G Mahesh, P S SatyanarayanaAbstract:The Decision Mechanism is the concluding phase of any Decision making process. This paper discusses on the different methodologies available for implementing the Decision mechanisms. The paper preambles with a brief description on set of conventional Multi criteria Decision Mechanisms (MCDM) like Analytical Hierarchy Process (AHP), Simple Additive weighting Method (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Grey Rational Analysis (GRA) along with benefits and limitations of each technique. The different intelligent/soft computing techniques that are widely used in Decision making processes like fuzzy logic, neural networks are discussed and finally confines the discussions to the different neural network (NN) based Decision support systems. The paper proposes a fuzzy neural network based architecture for call Admission Control Decision mechanism in a heterogeneous wireless network environment.
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an intelligent call Admission Control Decision mechanism for wireless networks
arXiv: Networking and Internet Architecture, 2010Co-Authors: H Ramesh S Babu, P S SatyanarayanaAbstract:The Call Admission Control (CAC) is one of the Radio Resource Management (RRM) techniques plays instrumental role in ensuring the desired Quality of Service (QoS) to the users working on different applications which have diversified nature of QoS requirements. This paper proposes a fuzzy neural approach for call Admission Control in a multi class traffic based Next Generation Wireless Networks (NGWN). The proposed Fuzzy Neural Call Admission Control (FNCAC) scheme is an integrated CAC module that combines the linguistic Control capabilities of the fuzzy logic Controller and the learning capabilities of the neural networks .The model is based on Recurrent Radial Basis Function Networks (RRBFN) which have better learning and adaptability that can be used to develop the intelligent system to handle the incoming traffic in the heterogeneous network environment. The proposed FNCAC can achieve reduced call blocking probability keeping the resource utilisation at an optimal level. In the proposed algorithm we have considered three classes of traffic having different QoS requirements. We have considered the heterogeneous network environment which can effectively handle this traffic. The traffic classes taken for the study are Conversational traffic, Interactive traffic and back ground traffic which are with varied QoS parameters. The paper also presents the analytical model for the CAC .The paper compares the call blocking probabilities for all the three types of traffic in both the models. The simulation results indicate that compared to Fuzzy logic based CAC, Conventional CAC, The simulation results are optimistic and indicates that the proposed FNCAC algorithm performs better where the call blocking probability is minimal when compared to other two methods.
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call Admission Control approaches in beyond 3g networks using multi criteria Decision making
Computational Intelligence Communication Systems and Networks, 2009Co-Authors: H Ramesh S Babu, Gowri Shankar, P S SatyanarayanaAbstract:The next generation wireless networks (NGWN) should cater the varied requirements of the user and applications. The Call Admission Control (CAC) is one of the radio resource management (RRM) technique used in the wireless networks. Unlike the CAC in homogeneous wireless networks the CAC in next generation wireless networks is very complex. In the next generation heterogeneous wireless networks, a user with a multi-interface terminal may have network access to different service providers using various technologies. This makes call Admission Control a challenging task where single criteria based CAC algorithms may not be able handle the Decision making process efficiently, to address the heterogeneous architectures which characterize next generation wireless network, it is apparent that the call Admission Control algorithms should be based on multiple criteria. Various approaches have been proposed to solve the call Admission Control Decision problem using multi criteria Decision making (MCDM). This article provides a comprehensive survey of the different approaches for Call Admission Control using multi criteria Decision making (MCDM) for incoming calls in heterogeneous wireless networks. The advantages and disadvantages of each approach are discussed.
H Ramesh S Babu - One of the best experts on this subject based on the ideXlab platform.
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soft computing technique based call Admission Control Decision mechanism
International Conference on Computer Science and Information Technology, 2012Co-Authors: H Ramesh S Babu, G Mahesh, P S SatyanarayanaAbstract:The Decision Mechanism is the concluding phase of any Decision making process. This paper discusses on the different methodologies available for implementing the Decision mechanisms. The paper preambles with a brief description on set of conventional Multi criteria Decision Mechanisms (MCDM) like Analytical Hierarchy Process (AHP), Simple Additive weighting Method (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Grey Rational Analysis (GRA) along with benefits and limitations of each technique. The different intelligent/soft computing techniques that are widely used in Decision making processes like fuzzy logic, neural networks are discussed and finally confines the discussions to the different neural network (NN) based Decision support systems. The paper proposes a fuzzy neural network based architecture for call Admission Control Decision mechanism in a heterogeneous wireless network environment.
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an intelligent call Admission Control Decision mechanism for wireless networks
arXiv: Networking and Internet Architecture, 2010Co-Authors: H Ramesh S Babu, P S SatyanarayanaAbstract:The Call Admission Control (CAC) is one of the Radio Resource Management (RRM) techniques plays instrumental role in ensuring the desired Quality of Service (QoS) to the users working on different applications which have diversified nature of QoS requirements. This paper proposes a fuzzy neural approach for call Admission Control in a multi class traffic based Next Generation Wireless Networks (NGWN). The proposed Fuzzy Neural Call Admission Control (FNCAC) scheme is an integrated CAC module that combines the linguistic Control capabilities of the fuzzy logic Controller and the learning capabilities of the neural networks .The model is based on Recurrent Radial Basis Function Networks (RRBFN) which have better learning and adaptability that can be used to develop the intelligent system to handle the incoming traffic in the heterogeneous network environment. The proposed FNCAC can achieve reduced call blocking probability keeping the resource utilisation at an optimal level. In the proposed algorithm we have considered three classes of traffic having different QoS requirements. We have considered the heterogeneous network environment which can effectively handle this traffic. The traffic classes taken for the study are Conversational traffic, Interactive traffic and back ground traffic which are with varied QoS parameters. The paper also presents the analytical model for the CAC .The paper compares the call blocking probabilities for all the three types of traffic in both the models. The simulation results indicate that compared to Fuzzy logic based CAC, Conventional CAC, The simulation results are optimistic and indicates that the proposed FNCAC algorithm performs better where the call blocking probability is minimal when compared to other two methods.
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call Admission Control approaches in beyond 3g networks using multi criteria Decision making
Computational Intelligence Communication Systems and Networks, 2009Co-Authors: H Ramesh S Babu, Gowri Shankar, P S SatyanarayanaAbstract:The next generation wireless networks (NGWN) should cater the varied requirements of the user and applications. The Call Admission Control (CAC) is one of the radio resource management (RRM) technique used in the wireless networks. Unlike the CAC in homogeneous wireless networks the CAC in next generation wireless networks is very complex. In the next generation heterogeneous wireless networks, a user with a multi-interface terminal may have network access to different service providers using various technologies. This makes call Admission Control a challenging task where single criteria based CAC algorithms may not be able handle the Decision making process efficiently, to address the heterogeneous architectures which characterize next generation wireless network, it is apparent that the call Admission Control algorithms should be based on multiple criteria. Various approaches have been proposed to solve the call Admission Control Decision problem using multi criteria Decision making (MCDM). This article provides a comprehensive survey of the different approaches for Call Admission Control using multi criteria Decision making (MCDM) for incoming calls in heterogeneous wireless networks. The advantages and disadvantages of each approach are discussed.
R Meenakshi - One of the best experts on this subject based on the ideXlab platform.
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intelligent call Admission Control Decision maker for cloud based smart city applications in the 5g environment
2021Co-Authors: R MeenakshiAbstract:Recent advancements in Artificial Intelligence (AI) have had a great influence on smart societies in areas such as marketing, security, defense, manufacturing, transportation, search, social networking, and advertisement. The development of smart city communities prompts basic moves identified with network security and communication models. Communication between gadgets is secured, and we are certain that the security and prosperity of clients, and society, by and large, is guaranteed. The Fifth Generation (5G) structures are expected to experience goliath traffic clog from cell phones. The 5G cell frameworks need to adjust to such launching traffic demands from these gadgets to lessen network clog in smart city applications. This chapter introduces an AI with a cloud-based Transistor Access Network (TAN) that assumes the significant job of expanding the information rate. In spite of the fact that this Call Admission Control (CAC) method improves the framework effectiveness, the limit of call blocking likelihood because of the traffic blockage is the primary test. In this way, an answer for this test is presented using the ideal leader in a fuzzy framework in the Cuttle Fish Optimization (CFO) proposed to locate the ideal principle sets for smart communication. In this methodology, during a clog, some deferred tolerant associations are redistributed from the private cloud to the open cloud with certain cost. Simulation outcomes demonstrate that the exhibition of the proposed ideal fuzzy-based TAN and CAC plan outflanks that of the Fuzzy-based CAC regarding call blocking probability, throughput, network traffic rate, Control traffic rate, and asset use.
N. Kumaratharan - One of the best experts on this subject based on the ideXlab platform.
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Call Admission Control Decision Maker Based on Optimized Fuzzy Inference System for 5G Cloud Radio Access Networks
Wireless Personal Communications, 2021Co-Authors: K. Suresh, N. KumaratharanAbstract:Fifth generation (5G) cell frameworks are relied upon to encounter colossal traffic congestion from mobile devices. To decrease network congestion, the 5G cell systems need to be changed to accommodate the soaring traffic demands from these devices. In a 5G network, a cloud-based radio access network (C-RAN) has a significant role to increase the data rate. With the support of C-RAN, traffic congestion in the 5G network is handled by presenting the Call Admission Control (CAC) technique. Although this CAC technique improves the system efficiency, maximum of call blocking probability due to the traffic congestion is the main challenge. So, the solution to this challenge is introducing the optimal call Admission Decision maker. In this paper, Artificial Fish Swarm Algorithm based Fuzzy Inference System (FIS-AFSA) is proposed as a Decision maker. Using AFSA algorithm, the fuzzy parameters are optimized in this strategy. In this approach, few delay tolerant connections are outsourced from private cloud to public cloud with certain price when congestion. The proposed FIS-AFSA based CAC scheme's performance outperforms that of the Fuzzy based CAC technique in the basis of call blocking probability, throughput and resource utilization as show in simulation results.
K. Suresh - One of the best experts on this subject based on the ideXlab platform.
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Call Admission Control Decision Maker Based on Optimized Fuzzy Inference System for 5G Cloud Radio Access Networks
Wireless Personal Communications, 2021Co-Authors: K. Suresh, N. KumaratharanAbstract:Fifth generation (5G) cell frameworks are relied upon to encounter colossal traffic congestion from mobile devices. To decrease network congestion, the 5G cell systems need to be changed to accommodate the soaring traffic demands from these devices. In a 5G network, a cloud-based radio access network (C-RAN) has a significant role to increase the data rate. With the support of C-RAN, traffic congestion in the 5G network is handled by presenting the Call Admission Control (CAC) technique. Although this CAC technique improves the system efficiency, maximum of call blocking probability due to the traffic congestion is the main challenge. So, the solution to this challenge is introducing the optimal call Admission Decision maker. In this paper, Artificial Fish Swarm Algorithm based Fuzzy Inference System (FIS-AFSA) is proposed as a Decision maker. Using AFSA algorithm, the fuzzy parameters are optimized in this strategy. In this approach, few delay tolerant connections are outsourced from private cloud to public cloud with certain price when congestion. The proposed FIS-AFSA based CAC scheme's performance outperforms that of the Fuzzy based CAC technique in the basis of call blocking probability, throughput and resource utilization as show in simulation results.