Performance Evaluation of LMS and CM Algorithms for Beamforming

Atzemourt, M.; Farchi, Abdelmajid; Chihab, Younes; Hachkar, Z.

doi:10.1155/2022/7744625

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…By adjusting the complex weights of antenna elements, an adaptive beamformer can automatically optimize the array pattern to maximize the output power in the direction of the desired signal while minimizing the output power in interfering signals' directions. This technique has proven to be effective in combating issues such as multipath fading, noise, and interference [5,15,16]. The result is an enhanced SINR, channel capacity, and maximum gain.…”

Section: Related Workmentioning

confidence: 99%

“…In the context of mMIMO systems, adaptive beamforming can provide a means to separate different co-channel users by exploiting the spatial dimension [5], [15]. As a result, the system can counteract interference towards the intended signal, leading to improved signal quality and increased capacity.…”

Section: Related Workmentioning

confidence: 99%

“…There are two different types of adaptive beamforming algorithms: the non-blind ABF algorithm and the blind ABF algorithm [5][6][7]. The non-blind adaptive beamforming algorithm requires statistical knowledge about the signal to converge the complex weights to optimal values.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid Adaptive Beamforming Algorithm for SINR Enhancement in Massive MIMO Systems

Manai¹,

Slama²,

Bouallègue³

2023

RADIOENGINEERING

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With the extreme density of devices and fast change of their directions in massive MIMO networks, a fast adaptive beamforming algorithm is required to provide high directivity and an enhanced signal-to-interference and noise ratio (SINR). Blind adaptive beamforming is suitable but less efficient, while non-blind adaptive beamforming is more efficient but requires significant training time. This study proposes a hybrid adaptive beamforming algorithm that addresses these issues. The algorithm integrates an improved direction-finding method to estimate the directions of arrival (DoAs) of incident signals at the antenna array, even in coherent signals cases, and a cascading combination of a blind and non-blind algorithms. The proposed algorithm generates an accurate main beam toward the desired direction and deep nulls in the direction of interfering signals, resulting in enhanced SINR. Compared to other algorithms, our approach achieves better performance without requiring additional antenna elements.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Adaptive Beamforming Algorithm for SINR Enhancement in Massive MIMO Systems

Manai¹,

Slama²,

Bouallègue³

2023

RADIOENGINEERING

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“…This method of beamforming exploits the properties of the smart antenna (SA) and makes use of the property of a thinned antenna array, which reduces power consumption by keeping some of the antennas in the array off. An SA is an antenna array of any type of antenna that by using a digital signal processor, produces a highly secure radiation beam towards the user and a null towards the interferer [18][19][20]. The smart antenna identifies the direction of arrival (DOA) of the signal from the mobile and produces a retro-directive beam towards the user.…”

Section: Introductionmentioning

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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“…The performance of the adaptive algorithm is the main factor in the performance of the entire SAS. There are several adaptive signal processing algorithms [4], [5], each of them having its specific pros and cons. The most common algorithms used for SAS-based beamforming include the least mean square (LMS) algorithm [6], [7], the recursive least square (RLS) algorithm [8] and the sample matrix inverse (SMI) algorithm [9].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Designing Smart Antennas Using Machine Learning Algorithms

Samantaray,

Das,

Roy

2023

JTIT

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Smart antenna technologies improve spectral efficiency, security, energy efficiency, and overall service quality in cellular networks by utilizing signal processing algorithms that provide radiation beams to users while producing nulls for interferers. In this paper, the performance of such ML solutions as the support vector machine (SVM) algorithm, the artificial neural network (ANN), the ensemble algorithm (EA), and the decision tree (DT) algorithm used for forming the beam of smart antennas are compared. A smart antenna array made up of 10 half-wave dipoles is considered. The ANN method is better than the remaining approaches when it comes to achieving beam and null directions, whereas EA offers better performance in terms of reducing the side lobe level (SLL). The maximum SLL is achieved using EA for all the user directions. The performance of the ANN algorithm in terms of forming the beam of a smart antenna is also compared with that of the variable-step size adaptive algorithm.

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Performance Evaluation of LMS and CM Algorithms for Beamforming

Cited by 5 publications

References 9 publications

A Hybrid Adaptive Beamforming Algorithm for SINR Enhancement in Massive MIMO Systems

A Hybrid Adaptive Beamforming Algorithm for SINR Enhancement in Massive MIMO Systems

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

Designing Smart Antennas Using Machine Learning Algorithms

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