Performance analysis of least mean square sample matrix inversion algorithm for smart antenna system

Ali, Weal A. E.; Mohamed, Darwish A. E.; Hassan, Adel H. G.

doi:10.1109/lapc.2013.6711976

Cited by 19 publications

(6 citation statements)

References 4 publications

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“…One of the disadvantages of LMS is that the algorithm needs more iteration for convergence to be satisfactory. To overcome this, sample matrix inversion (SMI) is used [31][32][33]. The sample matrix, which uses K-time samples, is a time-average approximation of the array correlation matrix.…”

Section: Adaptive Signal Processing Algorithmsmentioning

confidence: 99%

Thinned Smart Antenna of a Semi-circular Dipole Array for Massive MIMO Systems

Khan,

Roy

2023

AEM

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Massive MIMO (multiple-input multiple-output) is a multi-user MIMO technology that can provide high-speed multimedia services in 5G wireless networks using sub-6 GHz and millimeter wave bands. The massive MIMO (MMIMO) installs array antennas in the base stations, using hundreds of transceivers with other RF modules. One of the drawbacks of the MMIMO system is its huge power consumption, and the beamforming network with RF modules for a large number of antennas is the main contributor to the power consumption. In this paper, a novel beamforming method is proposed for the low power consumption of an MMIMO system. The proposed thinned smart antenna (TSA) of a semi-circular array produces a secure beam toward the user’s terminal with reduced interference. By thinning the antenna array, some of the antenna elements are kept off, resulting in less power consumption, while the array pattern remains the same as a fully populated array with a reduced side lobe level (SLL). The sub-6 GHz band of 5 GHz is used for the design of thinned array antennas. The genetic algorithm (GA) is used to determine the array sequence in thinning, and the adaptive signal processing algorithms least mean square (LMS), recursive least square (RLS), and sample matrix inversion (SMI) are used for the beamforming of the TSA, and the corresponding algorithms are GA-LMS, GA-RLS, and GA-SMI. The power saving of 40% to 55% is achieved using TSA. The maximum SLL reductions of 13 dB, 12 dB, and 14 dB are achieved for TSA using GA-LMS, GA-RLS, and GA-SMI algorithms, respectively.

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Section: Adaptive Signal Processing Algorithmsmentioning

confidence: 99%

Thinned Smart Antenna of a Semi-circular Dipole Array for Massive MIMO Systems

Khan,

Roy

2023

AEM

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“…The DMI method is selected to suppress the strong clutter considering its advantages of fast convergence. The recovered signal of the SE algorithm is utilised as the input of the DMI adaptive filter [16, 17]. The iteration process of the DMI filter is of the following form:

G = {bold-italicR}_{v v}^{- 1} P .

The variables in (13) are defined as follows:…”

Section: Processing Approaches In the Surveillance Channelmentioning

confidence: 99%

Data block processing structure for airship‐borne passive bistatic radar

Liu

et al. 2019

J. eng.

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“…Sample matrix is a time average estimate of the array correlation matrix using K-time samples. The sample matrix is defined as the time average estimate of the array correlation, which uses N samples, and if the random process is ergodic in correlation, then time average estimate is equal to the real correlation matrix [9,10]:…”

Section: Fig 1: Sample Matrix Inversion Adaptive Beamforming Networkmentioning

confidence: 99%