Smart antennas by using LMS and SMI algorithms reduces interfernce

Mubeen, Suraya; Prasad, A. Mallikarjuna; Rani, A. Jhansi

doi:10.1109/iceeot.2016.7755027

Cited by 5 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, in (Mubeen, Prasad, & Rani, 2012), once the autocorrelation matrix is known, the different steps leading to the detection of the angles of arrival are as follows:…”

Section:  Music Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Integration of Small Cells with Smart Antennas in Macrocells for Improving the Performance of Mobile Telephone Networks

Abana,

Bavoua Kenfack,

Tonye

et al. 2023

NCT

View full text Add to dashboard Cite

In macrocellular networks, there are areas where it is difficult to have satisfactory connectivity. In practice, the delays for sending or downloading multimedia applications, or even consulting web applications can become quite long. One solution is to deploy cells with smaller ranges called small cells inside the macrocell, closer to subscribers in order to boost network capacity. Only, with the instability of electrical energy, the small cells may not be powered properly. A way should therefore be found to further reduce the energy consumption of small cells in order to facilitate their deployment in Cameroon. To do this, we opted for the solution of smart antennas to be integrated into the small cells; antennas that transmit at the request of users and according to their needs. In this work, we design and build an application that illustrates the performance of small cells with three key innovations: Smart antennas make it possible to estimate users’ directions and deliver a maximum radiation pattern in those directions, while minimizing interference. The capacity of a heterogeneous network is boosted compared to that of a homogeneous network. The signal attenuation in free space in a micro cell is a function of the user-small cell distance. The application that we propose is therefore a relevant decision-making tool for the engineering and planning of mobile networks.

show abstract

“…Indeed, in (Mubeen, Prasad, & Rani, 2012), once the autocorrelation matrix is known, the different steps leading to the detection of the angles of arrival are as follows:…”

Section:  Music Algorithmmentioning

confidence: 99%

“…The particularity of this shaper, compared to the conventional shaper, is that it also allows (assuming that we have information on the interferents), to cancel the interference lobes. It then maximizes the SIR ratio (Mubeen, Prasad, & Rani, 2012): .…”

Section:  Lobe Canceling Conformatormentioning

confidence: 99%

Integration of Small Cells with Smart Antennas in Macrocells for Improving the Performance of Mobile Telephone Networks

Abana,

Bavoua Kenfack,

Tonye

et al. 2023

NCT

View full text Add to dashboard Cite

show abstract

“…Moreover, suppressing inter-user-interference (IUI) and additive white Gaussian noise is essential for array antenna. A well-known method for adaptive array interference suppression is sample matrix inversion (SMI) [15], [16]. SMI utilizes covariance matrix which is obtained from known pilots and the received signals of each antenna element to calculate desirable weights.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Zero-Padding with Impulsive Training Signal MMSE-SMI Adaptive Array Interference Suppression

Kojima

Maruta

et al. 2023

IEICE Trans. Fundamentals

View full text Add to dashboard Cite

In mobile communication systems, the channel state information (CSI) is severely affected by the noise effect of the receiver. The adaptive subcarrier grouping (ASG) for sample matrix inversion (SMI) based minimum mean square error (MMSE) adaptive array has been previously proposed. Although it can reduce the additive noise effect by increasing samples to derive the array weight for co-channel interference suppression, it needs to know the signal-to-noise ratio (SNR) in advance to set the threshold for subcarrier grouping. This paper newly proposes adaptive zero padding (AZP) in the time domain to improve the weight accuracy of the SMI matrix. This method does not need to estimate the SNR in advance, and even if the threshold is always constant, it can adaptively identify the position of zero-padding to eliminate the noise interference of the received signal. Simulation results reveal that the proposed method can achieve superior bit error rate (BER) performance under various Rician K factors.

show abstract

“…In general, inter-user-interference (IUI) and additive white Gaussian noise are essential for array antenna. Sample matrix inversion(SMI) [9], [10] is a well-known method for interference suppression adaptive array antennas. SMI utilizes a covariance matrix derived from the pilot symbols and the received signals of each antenna element to calculate desirable weights.…”

Section: Introductionmentioning

confidence: 99%

Data-aided Weight with Subcarrier Grouping for Adaptive Array Interference Suppression

Wang

Kojima

et al. 2022

JCOMSS

View full text Add to dashboard Cite

The effect of additive noise on the channel state information (CSI) quality is a crucial issue in mobile communication systems. The adaptive subcarrier grouping (ASG) for sample matrix inversion (SMI) based minimum mean square error (MMSE) adaptive array has been previously proposed. However, this method needs to know the signal-to-noise ratio (SNR) in advance to set the threshold, perform grouping, and take the average, causing an insufficient number of signal samples. As a result, the ability to eliminate noise is limited. In this paper, we propose a new method based on data-aided weight calculation and the least mean square (LMS) algorithm without SNR information, which increases the number of samples. The decision results and initial weight are obtained by the SMI method with subcarrier grouping, and then the LMS method with subcarrier grouping is applied to reduce the channel estimation error as well as the amount of computation. Simulation results demonstrate that the proposed scheme is an efficient approach to improve Bit Error Rate (BER) performance under various Rician K factors.

show abstract

Smart antennas by using LMS and SMI algorithms reduces interfernce

Cited by 5 publications

References 12 publications

Integration of Small Cells with Smart Antennas in Macrocells for Improving the Performance of Mobile Telephone Networks

Integration of Small Cells with Smart Antennas in Macrocells for Improving the Performance of Mobile Telephone Networks

Adaptive Zero-Padding with Impulsive Training Signal MMSE-SMI Adaptive Array Interference Suppression

Data-aided Weight with Subcarrier Grouping for Adaptive Array Interference Suppression

Contact Info

Product

Resources

About