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Author

S. Nandakumar

Bio: S. Nandakumar is an academic researcher from VIT University. The author has contributed to research in topic(s): Handover & Cognitive radio. The author has an hindex of 6, co-authored 22 publication(s) receiving 92 citation(s).

Papers
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Proceedings Article
04 Jun 2009
Abstract: Computational complexity and error propagation phenomenon are important drawbacks of existing Decision Feedback Equalizers (DFE) for dispersive channels. A new Iterative Block DFE (IBDFE) is considered where the equalization is performed iteratively on blocks of received signal in the frequency domain i.e. both signal processing and filter design are in frequency domain. Thus computational complexity is reduced and error propagation is limited to one block. The feed forward and feedback filters of DFE are designed with the minimization of Mean Square Error (MSE) at detector input as the parameter for effective detection. Two design methods have been solved and simulated for a Rayleigh fading channel. Channel is assumed to be time in-variant during one block of data (128 symbols) transmission. In the first method, the hard detected data are used as the input to the feedback, and filters are designed according to the correlation between detected and transmitted data. In the second method, the feedback signal is directly designed from soft detection of the equalized signal at the previous iteration. Estimates of the parameters involved in the FF and FB filters are also solved and used to evaluate the filter coefficients. From simulation, it was found that the IBDFE as claimed in the research literature performs better than the time domain DFE.

13 citations

Journal ArticleDOI
TL;DR: The novel method of IWO algorithm for decision making during Vertical Handoff is brought out and the proposed VHO decision making algorithm is compared with the existing SSF and OPTG methods.
Abstract: Heterogeneous wireless networks are an integration of two different networks. For better performance, connections are to be exchanged among the different networks using seamless Vertical Handoff. The evolutionary algorithm of invasive weed optimization algorithm popularly known as the IWO has been used in this paper, to solve the Vertical Handoff (VHO) and Horizontal Handoff (HHO) problems. This integer coded algorithm is based on the colonizing behavior of weed plants and has been developed to optimize the system load and reduce the battery power consumption of the Mobile Node (MN). Constraints such as Receiver Signal Strength (RSS), battery lifetime, mobility, load and so on are taken into account. Individual as well as a combination of a number of factors are considered during decision process to make it more effective. This paper brings out the novel method of IWO algorithm for decision making during Vertical Handoff. Therefore the proposed VHO decision making algorithm is compared with the existing SSF and OPTG methods.

11 citations

Journal ArticleDOI
TL;DR: The concept of multiple attributes for decision making was implemented, under which, the simple additive weights method and the technique for order preference by similarity to ideal solution method were compared based on a performance involving the triple play of services.
Abstract: Cognitive radio networks improve spectrum efficiency by employing vigilant and accurate spectrum management techniques. This is done to enable an unlicensed user to use the underutilized spectrum. Spectrum sensing determines if a spectrum hole exists for the unlicensed user, this is accomplished using various techniques such as energy detection, minimum Eigen value detection, and matched filter technique. Conventional energy detection techniques do not achieve high values of detection; hence an improved adaptive energy detection technique has been proposed to improve the efficiency. The proposed methodology exhibits better numerical results than conventional techniques. Another important spectrum management procedure is spectrum handoff. The concept of multiple attributes for decision making was implemented, under which, the simple additive weights method and the technique for order preference by similarity to ideal solution method were compared based on a performance involving the triple play of services.

11 citations

Journal ArticleDOI
A Datta, S. Nandakumar1
01 Nov 2017
TL;DR: The author presents a brief survey on various bio inspired swarm intelligence based protocols used in routing data in wireless sensor networks while outlining their general principle and operation.
Abstract: Recent studies have shown that utilizing a mobile sink to harvest and carry data from a Wireless Sensor Network (WSN) can improve network operational efficiency as well as maintain uniform energy consumption by the sensor nodes in the network. Due to Sink mobility, the path between two sensor nodes continuously changes and this has a profound effect on the operational longevity of the network and a need arises for a protocol which utilizes minimal resources in maintaining routes between the mobile sink and the sensor nodes. Swarm Intelligence based techniques inspired by the foraging behavior of ants, termites and honey bees can be artificially simulated and utilized to solve real wireless network problems. The author presents a brief survey on various bio inspired swarm intelligence based protocols used in routing data in wireless sensor networks while outlining their general principle and operation.

10 citations

Journal ArticleDOI
TL;DR: Various resource allocation algorithms and methodologies have been seriously analysed and evaluated based on the degree of involvement of the Base Station to figure out the research gap and to provide a strong theoretical basis for the research problems related to resource allocation in D2D communication.
Abstract: Device to Device communication is an important aspect of the fifth-generation(5G) and beyond fifth-generation (B5G) wireless networks. 5G facilitates network connectivity among a large number of devices. This tremendous growth in the number of devices requires a large number of spectrum resources to support a variety of applications and also lays a huge burden on the Base Station. D2D skips the need to forward the data to the Base Station and helps the devices to take part in direct Peer-to-Peer (P2P) transmission. This enables high-speed data transmission, efficient information transmission with improved latency and most importantly is used to offload the traffic that is laid on the Base Station. D2D has many practical issues and challenges that are briefly explained in this paper, out of which resource allocation is the main area of focus as it plays an important role in the performance of the system. The optimal allocation of resources such as power, time and spectrum can improve the system performance. Therefore, in order to identify the open research issues in the field of resource allocation in D2D communication, a detailed survey is needed. In this paper, various resource allocation algorithms and methodologies have been seriously analysed and evaluated based on the degree of involvement of the Base Station to figure out the research gap and to provide a strong theoretical basis for the research problems related to resource allocation in D2D communication.

7 citations


Cited by
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01 Jan 2007
Abstract: This document provides updates to IEEE Std 802.16's MIB for the MAC, PHY and asso- ciated management procedures in order to accommodate recent extensions to the standard.

1,474 citations

Journal ArticleDOI
TL;DR: The experimental results show that the EIWO algorithm can find equal or better optimal solution compared with other algorithms, and the convergence ability, stability and robustness are verified.
Abstract: In this study, an improved invasive weed optimization (EIWO) algorithm is investigated to solve the optimal chiller loading (OCL) problem for minimization of the power consumption. In the proposed algorithm, several components are developed, such as decimal-based representation, reproduction approach, spatial dispersal method, and competitive selection mechanism. Then, the local search strategy for elite weed is proposed, which can improve the searching ability of the algorithm. To verify the efficiency and effectiveness of the proposed algorithm, three well-known instances based on the OCL problem in air-conditioning systems are tested with the comparison with other recently published algorithms. The experimental results show that the EIWO algorithm can find equal or better optimal solution compared with other algorithms. The convergence ability, stability and robustness are also verified after the detailed comparisons.

76 citations

Journal ArticleDOI
TL;DR: This paper reviews the application of several methodologies under the CI umbrella to the WSAN field and describes and categorizes existing works leaning on fuzzy systems, neural networks, evolutionary computation, swarm intelligence, learning systems, and their hybridizations to well-known or emerging WSAN problems along five major axes.
Abstract: Wireless sensor and actuator networks (WSANs) are heterogeneous networks composed of many different nodes that can cooperatively sense the environment, determine an appropriate action to take, then change the environment’s state after acting on it. As a natural extension of wireless sensor networks (WSNs), WSANs inherit from them a variety of research challenges and bring forth many new ones. These challenges are related to dealing with imprecise and vague information, solving complicated optimization problems or collecting and processing data from multiple sources. Computational intelligence (CI) is an overarching term denoting a conglomerate of biologically and linguistically inspired techniques that provide robust solutions to NP-hard problems, reason in imprecise terms and yield high-quality yet computationally tractable approximate solutions to real-world problems. Many researchers have consequently turned to CI in hope of finding answers to a plethora of WSAN-related challenges. This paper reviews the application of several methodologies under the CI umbrella to the WSAN field. We describe and categorize existing works leaning on fuzzy systems , neural networks , evolutionary computation , swarm intelligence , learning systems , and their hybridizations to well-known or emerging WSAN problems along five major axes: 1) actuation; 2) communication; 3) sink mobility; 4) topology control; and 5) localization. The survey offers informative discussions to help reason through all the studies under consideration. Finally, we point to future research avenues by: 1) suggesting suitable CI techniques to specific problems; 2) borrowing concepts from WSNs that have yet to be applied to WSANs; or 3) describing the shortcomings of current methods in order to spark interest on the development of more refined models.

35 citations

Journal ArticleDOI
TL;DR: Experimental results show that the proposed SaDIWO outperforms the existing state-of-the-art algorithms and improves the best known solutions for 132 out of 480 problem instances.
Abstract: This paper proposes a self-adaptive discrete invasive weed optimization (SaDIWO) to solve the blocking flow-shop scheduling problem (BFSP) with the objective of minimizing total tardiness which has important applications in a variety of industrial systems. In the proposed SaDIWO, an improved NEH-based heuristic is firstly presented to generate an initial solution with high quality. Then, to guide the global exploration and local exploitation, a self-adaptive insertion-based spatial dispersal is presented. A distance-based competitive exclusion is developed to strike a compromise between the quality and diversity of offspring population. A variable neighborhood search with a speed-up mechanism is embedded to further enhance exploitation in the promising region around the individuals. Afterward, the parameters setting and the effectiveness of each component of the proposed algorithm are investigated through numerical experiments. The performance of the proposed algorithm is evaluated by comparisons with the existing state-of-the-art algorithms in the literature. Experimental results show that the proposed algorithm outperforms the existing state-of-the-art algorithms. Furthermore, the proposed SaDIWO also improves the best known solutions for 132 out of 480 problem instances.

25 citations

Journal ArticleDOI
Yun Lin1, Haojun Zhao1, Xuefei Ma1, Ya Tu1, Meiyu Wang1 
TL;DR: The results indicate that the accuracy of the target model reduce significantly by adversarial attacks, when the perturbation factor is 0.001, and iterative methods show greater attack performances than that of one-step method.
Abstract: Deep learning (DL) models are vulnerable to adversarial attacks, by adding a subtle perturbation which is imperceptible to the human eye, a convolutional neural network (CNN) can lead to erroneous results, which greatly reduces the reliability and security of the DL tasks. Considering the wide application of modulation recognition in the communication field and the rapid development of DL, by adding a well-designed adversarial perturbation to the input signal, this article explores the performance of attack methods on modulation recognition, measures the effectiveness of adversarial attacks on signals, and provides the empirical evaluation of the reliabilities of CNNs. The results indicate that the accuracy of the target model reduce significantly by adversarial attacks, when the perturbation factor is 0.001, the accuracy of the model could drop by about 50 ${\%}$ on average. Among them, iterative methods show greater attack performances than that of one-step method. In addition, the consistency of the waveform before and after the perturbation is examined, to consider whether the added adversarial examples are small enough (i.e., hard to distinguish by human eyes). This article also aims at inspiring researchers to further promote the CNNs reliabilities against adversarial attacks.

17 citations