Pilot Contamination Elimination for Channel Estimation with Complete Knowledge of Large-Scale Fading in Downlink Massive MIMO Systems

Abdullah, Qazwan; Abdullah, Norsaliza; Salh, Adeeb; Audah, Lukman; Farah, Nabil; Ugurenver, Abbas; Saif, Abdu

doi:10.48550/arxiv.2106.13507

Cited by 2 publications

(4 citation statements)

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“…In [3], the authors used orthogonal pilot sequence and gained knowledge about the large scale fading factor to reduce PC. The impressive data rate is obtained by the technique due to the estimation of better channels and the reduced performance loss.…”

Section: Related Workmentioning

confidence: 99%

“…Massive MIMO increases the SE of cellular networks through spatial multiplexing of a high number of users (UEs) for all cells [1]. The utility of massive MIMO technology has the capability to enhance the spectral efficiency (SE) and providing better coverage with improved robustness and are highly reliable [3][4][5]. As a result, MIMO achieves one of the key aims of the future technology by minimizing service delay, which includes both propagation and processing delays [6].…”

Section: Introductionmentioning

confidence: 99%

“…Figure . 1 Effect of pilot contamination on massive MIMO system this contamination. However, the channel estimation uncertainty is often affected by the pilot reuse sequence sent among the users of surrounding cells [3,9]. In addition, the PC in the massive MIMO is reduced using the channel estimation techniques [10,11] that use adaptive methods for the training of pilot sequences.…”

Section: Introductionmentioning

confidence: 99%

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Enhancement of Spectral Efficiency in Massive MIMO System Availing Eagle-Crow Optimization-Based Scheduling Algorithm

2023

IJIES

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The core idea of massive multiple-input multiple-output (MIMO) systems is to raise the throughput and spectral efficiency (SE) of the cell system by placing a huge number of antennas at the base station (BS) to serve multiple users. Since the use of pilots is mandatory for channel estimation, this article discusses the problem of pilot contamination (PC) that affects the SE of the massive MIMO system. Due to the short coherence time in communication systems, the length of the corresponding pilot sequence is limited, and the number of orthogonal pilots that can be assigned to each cell is limited, which will inevitably lead to the repeated use of the pilot sequence in nearby cells which leads to appearance the PC problem. The PC in the networks could be eliminated through the proper scheduling of the pilot sequences and will be achieved by the proper pilot scheduling algorithm. Accordingly, this paper proposes a scheduling algorithm named eagle-crow optimization, which eliminates the contamination and promotes the SE of the massive MIMO system, under different practical constraints and requirements such as the number of antennas and users. The proposed algorithm is a combination of two optimization algorithms that are the crow search (CSA) and bald eagle search (BES) algorithms. Many studies have been performed using crow search (CSA) and bald eagle search (BES) algorithms. However, most of them did not consider PC problems in MIMO systems. The motivation behind the use of these two techniques is that the proposed eagle-crow optimization inherits the hunting characteristics of the eagle and the memory features of the crow in order to prevent the repetition of pilot assignments. The achievements of the proposed scheduling are assessed based on the performance of SE, when the number of antennas in the BS increases to 1000, the SE of the proposed method is 44.91 bps/Hz, and the percentage improvement of the proposed method is high when compared to the existing methods, Random pilot allocation, WGC-PD, and SPRS+WGC-PD.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancement of Spectral Efficiency in Massive MIMO System Availing Eagle-Crow Optimization-Based Scheduling Algorithm

2023

IJIES

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“…As the number of antennas grows, the receiver must evaluate more channel coefficients, raising pilot overhead and processing complexity and lowering the system's total throughput. It is a difficult problem that has been addressed in [8]. According to the literature [9], the massive MIMO channel exhibits sparse properties for computationally efficient channel estimation.…”

Section: Introductionmentioning

confidence: 99%

Enhancing 5G Massive MIMO Systems Using a Compressive Sensing-Based Approach

Kanaparthi,

Yarrabothu,

Sundar

2023

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The capacity of massive multiple-input multiple-output (MIMO) systems is significantly enhanced through the use of abundant antennas at the base station (BS), supporting a multitude of users. However, in Time Division Duplex (TDD) mode, the multiplexing of pilots inevitably leads to Pilot Contamination (PC). In this study, a novel approach to interference alignment and pilot purification in a large MIMO system was proposed, utilizing the principles of compressive sensing and deep learning. The primary aim of the Compressive Sensing-Based Location Scheduling System (CSLSS) is to mitigate PC issues while optimizing the pilot sequence for the user. By introducing an additional sequence, the pilot set is enhanced using our location-based decontamination technique. Furthermore, a Feed-Forward Convolutional Neural Network (FFCNN) is employed for interference alignment in the MIMO system. Experimental results indicate significant improvements in the parameters under consideration. The sum rate showed an increase of 74.6%, the Bit Error Rate (BER) improved by 57.8%, the Signal-to-Interference-plus-Noise Ratio (SINR) rose by 79.8%, and the spectral efficiency of the channel improved by 98% in terms of interference alignment. In addition, a compressive sensing-based parameter showed an enhancement of 98%. The Mean Square Error (MSE) for CSLSS was reduced by 64.5% for the Signal-to-Noise Ratio (SNR), and the BER for the proposed compressive sensing method improved by 41.2% for SNR. The throughput also improved, with an increase of 97.5% for SNR and 97.8% for the number of antennas used in the BS.

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Pilot Contamination Elimination for Channel Estimation with Complete Knowledge of Large-Scale Fading in Downlink Massive MIMO Systems

Cited by 2 publications

References 0 publications

Enhancement of Spectral Efficiency in Massive MIMO System Availing Eagle-Crow Optimization-Based Scheduling Algorithm

Enhancement of Spectral Efficiency in Massive MIMO System Availing Eagle-Crow Optimization-Based Scheduling Algorithm

Enhancing 5G Massive MIMO Systems Using a Compressive Sensing-Based Approach

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