A Comparative Study of Adaptive Beamforming Techniques in Smart Antenna Using LMS Algorithm and Its Variants

Senapati, Anupama; Ghatak, Kaustabh; Roy, Jibendu Sekhar

doi:10.1109/cine.2015.21

Cited by 21 publications

(12 citation statements)

References 13 publications

(19 reference statements)

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“…It is a mechanism of using the sensor nodes in the right fashion to deliver the data to the right resource and send the jamming signal towards the wrong resource. The generation of maximum radiation towards the actual user and side lobe towards the interference user is called Beam forming [32]- [38]. There are many techniques which are used to achieve beam forming like autocorrelation and cross-correlation computation using Sample Matrix Inverse, reducing the mean square error by making use of Least Mean Square The antenna arrays which are distributed in nature will send selective beams towards the receiver which will increase the transmission range the amount of transmission power can be decreased by nodes due to energy dissipation being shared among the transmitting devices.…”

Section: Beam Formingmentioning

confidence: 99%

Reckoning Network Lifetime Ratio for Wireless Sensor Network

Saqhib¹,

S²

2019

IJITEE

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Advanced Technologies such as Internet of Things, Machine Networking give rise to the deployment of autonomous Wireless Sensor Nodes. They are used for various domains namely battlefield monitoring, enemy detection and monitoring the environment change. These Wireless Sensor Nodes have the properties of low cost and high battery life. NL (Network Lifetime) is an important phase of Wireless Sensor Network (WSNs), in which the nodes can maintain sensing for a more amount of time. NL can be improved by use of multiple techniques namely Opportunistic Transmission, Scheduling of Timed Data Packets, Clustering of Nodes, Energy Harvesting and Connectivity. This paper provides the energy consumption computation, life time ratio definition and the overview of NL improvement techniques. The paper also presents brief review of the Destination based and Source based routing algorithm

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Section: Beam Formingmentioning

confidence: 99%

Reckoning Network Lifetime Ratio for Wireless Sensor Network

Saqhib¹,

S²

2019

IJITEE

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“…In figure 7 and figure 11, VSLMS algorithm does not work properly for step-size value of 0.04 as it is found that LMS algorithm does not work for higher values of step-size parameter [13] and so the variable step-size LMS algorithm. The performances of VSLMS and VSNLMS algorithms for main beam direction, null depth at desired null direction and maximum side lobe level (SLLmax) are compared in Table 2.…”

Section: Asian Journal Of Applied Sciences (Issn: 2321 -0893) Volume mentioning

confidence: 99%

Adaptive Beam Formation in Smart Antenna Using VSLMS and VSNLMS Algorithms for Side Lobe Level Reduction

Bairagya

2017

AJAS

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In mobile communication using smart antenna, the capacity of a cellular network can be enhanced significantly. Adaptive beamforming is one of the critical issues in smart antenna. A comparative study of beamforming algorithms by using their variable step size parameter like, variable step size least mean square algorithm (VSLMS) and variable step size normalized least mean square algorithm (VSNLMS) are presented in this paper. Smart antennas of 8 elements and 10 elements linear array are considered here. It is found that using VSNLMS algorithm a lower side lobe level is achieved compared to VSLMS algorithm for various angles of desired user and undesired interferer.

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“…In the SISO case, either the transmitter or the receiver uses a single antenna for the communication process; while in the MIMO, an antenna array is employed. In literature, it has been demonstrated how the use of directional antennas and the most recent smart antenna systems (SAS) technology is capable of significantly allowing high quality of service (QoS) requirements in spite of the omnidirectional systems that foresee limited functionalities [6,7]. However, these solutions are unlikely to satisfy the requirements for 5G wireless communication systems technology.…”

Section: Introductionmentioning

confidence: 99%

Smart Antenna Systems Model Simulation Design for 5G Wireless Network Systems

Inzillo¹,

Rango²,

Zampogna³

et al. 2019

Array Pattern Optimization

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The most recent antenna array technologies such as smart antenna systems (SAS) and massive multiple input multiple output (MIMO) systems are giving a strong increasing impact relative to 5G wireless communication systems due to benefits that they could introduce in terms of performance improvements with respect to omnidirectional antennas. Although a considerable number of theoretical proposals already exist in this field, the most common used network simulators do not implement the latest wireless network standards and, consequently, they do not offer the possibility to emulate scenarios in which SAS or massive MIMO systems are employed. This aspect heavily affects the quality of the network performance analysis with regard to the next generation wireless communication systems. To overcome this issue, it is possible, for example, to extend the default features offered by one of the most used network simulators such as Omnet++ which provides a very complete suite of network protocols and patterns that can be adapted in order to support the latest antenna array systems. The main goal of the present chapter is to illustrate the improvements accomplished in this field allowing to enhance the basic functionalities of the Omnet++ simulator by implementing the most modern antenna array technologies.

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A Comparative Study of Adaptive Beamforming Techniques in Smart Antenna Using LMS Algorithm and Its Variants

Cited by 21 publications

References 13 publications

Reckoning Network Lifetime Ratio for Wireless Sensor Network

Reckoning Network Lifetime Ratio for Wireless Sensor Network

Adaptive Beam Formation in Smart Antenna Using VSLMS and VSNLMS Algorithms for Side Lobe Level Reduction

Smart Antenna Systems Model Simulation Design for 5G Wireless Network Systems

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