New Strategy based on Large Scale Fading Coefficients to Mitigate the Pilot Contamination Problem in Massive MIMO Systems

Accurate channel estimation is of utmost importance for massive MIMO systems to provide significant improvements in spectral and energy efficiency. In this work, we present a study on the distribution of a simple but yet effective and practical channel estimator for multi-cell massive MIMO systems suffering from pilot-contamination. The proposed channel estimator performs well under moderate to aggressive pilot contamination scenarios without previous knowledge of the inter-cell large-scale channel coefficients and noise power, asymptotically approximating the performance of the linear MMSE estimator as the number of antennas increases. We prove that the distribution of the proposed channel estimator can be accurately approximated by the circularly-symmetric complex normal distribution, when the number of antennas, M, deployed at the base station is greater than 10.

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Performance Analysis of Large-Scale MU-MIMO With a Simple and Effective Chanel Estimator

10.20944/preprints201908.0173.v1 ◽

2019 ◽

Author(s):

Felipe Augusto Pereira de Figueiredo ◽

Claudio Ferreira Dias ◽

Eduardo Rodrigues de Lima ◽

Gustavo Fraidenraich

Keyword(s):

Mean Square Error ◽

Spectral Efficiency ◽

Massive Mimo ◽

Large Scale ◽

Mimo Systems ◽

Minimum Mean Square Error ◽

Noise Power ◽

Mean Square ◽

Pilot Contamination ◽

Channel Estimator

Accurate channel estimation is of utmost importance for massive MIMO systems that allow providing significant improvements in spectral and energy efficiency. In this work, we investigate the spectral efficiency performance and present a channel estimator for multi-cell massive MIMO systems subjected to pilot-contamination. The proposed channel estimator performs well under moderate to aggressive pilot contamination scenarios without prior knowledge of the inter-cell large-scale channel coefficients and noise power. The estimator approximates the performance of a linear Minimum Mean Square Error (MMSE) as the number of antennas increases. Following, we derive a lower bound closed-form spectral efficiency of the Maximum Ratio Combining (MRC) detector in the proposed channel estimator. The simulation results highlight that the proposed estimator performance approaches the linear minimum mean square error (LMMSE) channel estimator asymptotically.

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User grouping and optimization-based pilot scheduling for mitigating the pilot contamination in massive multi cell MIMO systems

International Journal of Modeling Simulation and Scientific Computing ◽

10.1142/s1793962320500579 ◽

2020 ◽

pp. 2050057

Author(s):

Ambala Pradeep Kumar ◽

Tadisetty Srinivasulu

Keyword(s):

Massive Mimo ◽

Communication Systems ◽

Large Scale ◽

Mimo Systems ◽

Spider Monkey ◽

Achievable Rate ◽

Pilot Contamination ◽

User Grouping ◽

Spider Monkey Optimization ◽

The Impact

Massive multiple-input multiple-output (massive MIMO) is a promising approach in wireless communication systems for providing improved link reliability and spectral efficiency and it helps several users. The main aim is to solve pilot contamination issue in massive MIMO systems; this research paper utilizes two approaches for reducing the contamination. This paper presents the user grouping approach based on sparse fuzzy C-means clustering (sparse FCM), which groups user parameters based on parameters such as large-scale fading factor, SINR, and user distance. Here, same pilot sequences are assigned to center users in which the impact of pilot contamination is limited, while the algorithm assigns orthogonal pilot sequences to the edge users that suffer severely from pilot contamination. Therefore, the proposed user grouping keeps away from the inappropriate grouping of users, enabling effective grouping even under the worst situations of the channel. Secondly, pilot scheduling is done based on elephant spider monkey optimization (ESMO), which is designed by integrating elephant herding optimization (EHO) into spider monkey optimization (SMO). The performance of pilot scheduling based on grouping-based ESMO is evaluated based on achievable rate and SINR. The proposed method achieves maximal achievable rate of 41.29[Formula: see text]bps/Hz and maximal SINR of 124.31[Formula: see text]dB.

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Smart Switching Strategy-Based Supervision Rule to Mitigate the Problem of Pilot Contamination in Massive MIMO Systems

Wireless Communications and Mobile Computing ◽

10.1155/2020/8868597 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Abdelfettah Belhabib ◽

Mohamed Boulouird ◽

Moha M’Rabet Hassani

Keyword(s):

Graph Coloring ◽

Massive Mimo ◽

Mimo Systems ◽

Weighted Graph ◽

Achievable Rate ◽

Pilot Contamination ◽

Block Diagonalization ◽

Switching Strategy ◽

Simulation Results ◽

Contamination Problem

Despite the large benefits that can be fulfilled through the exploitation of Massive Multi-input Multioutput (M-MIMO), this technology still constrained by a well-known constraint, called as pilot contamination problem (PCP), which is the main consequence of, simultaneously, reusing the same set of orthogonal pilot sequences (OPSs) for the users of several cells. Due to the scarcity of the OPS resources, the reuse of the same set of the OPSs for the users of different cells is unavoidable. Hence, this work proposes a novel decontaminating strategy, which is aimed at guaranteeing a trade-off between the use of the OPSs and the mitigation of the PCP. Specifically, to make the use of the available OPSs better, we propose the consolidation of two powerful decontaminating strategies. Under a derived supervision rule (SR), these strategies are the soft pilot reuse-based multicell block diagonalization precoding (SPR-MBDP) and the weighted graph coloring-based pilot assignment (WGC-PA). The SR enables the switching mechanism between the two strategies, which leads to address the PCP with a fewer number of the OPSs compared to the SPR-MBDP, therefore boosting the per-cell achievable rate. Simulation results prove the effectiveness of our proposed strategy.

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Partial Pilot Allocation Scheme in Multi-Cell Massive MIMO Systems for Pilot Contamination Reduction

Energies ◽

10.3390/en13123163 ◽

2020 ◽

Vol 13 (12) ◽

pp. 3163

Author(s):

Ahmed S. Al-hubaishi ◽

Nor Kamariah Noordin ◽

Aduwati Sali ◽

Shamala Subramaniam ◽

Ali Mohammed Mansoor ◽

...

Keyword(s):

Massive Mimo ◽

Large Scale ◽

Mimo Systems ◽

Cellular Systems ◽

Cumulative Distribution ◽

Pilot Contamination ◽

Allocation Scheme ◽

Pilot Allocation ◽

Allocation Process ◽

Simulation Results

Inter-cell interference has been identified as one of the major challenges of multiple-input–multiple-output (MIMO)-enabled cellular systems. This problem occurs when the same pilot sets are reused across adjacent cells to save bandwidth for data transmission. As a result, so-called pilot contamination occurs, which cannot be mitigated with an increased number of serving antennas. In this work, we proposed a partial pilot allocation scheme (PPA) to tackle the pilot contamination problem and consequently improve the uplink throughput of users in multi-cell massive MIMO systems. This was achieved by using the large-scale characteristics of the fading channel to keep users with a weak channel condition out of the effect of severe interference during the pilot allocation process. Simulation results showed that the proposed scheme outperformed both smart pilot allocation (SPA) and conventional schemes. In particular, PPA improved the uplink rate by 30% compared to the SPA—a recently proposed schema. Furthermore, our simulation results clearly showed that PPA improved the cumulative distribution function (CDF) of the signal-to-interference-plus-noise ratio (SINR) and uplink throughput.

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