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

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.

scholarly journals Efficient Pilot Decontamination Schemes in 5G Massive MIMO Systems

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 55 ◽  
Author(s):  
Omar A. Saraereh ◽  
Imran Khan ◽  
Byung Moo Lee ◽  
Ashraf Tahat
Keyword(s):  
Massive Mimo ◽  
Communication Systems ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Path Loss ◽  
Achievable Rate ◽  
Pilot Contamination ◽  
User Grouping ◽  
Channel Conditions ◽  
Input Multiple Output

Massive Multiple-input Multiple-output (MIMO) is an emerging technology for the 5G wireless communication systems which has the potential to provide high spectral efficient and improved link reliability and accommodate large number of users. Aiming at the problem of pilot contamination in massive MIMO systems, this paper proposes two algorithms to mitigate it. The first algorithm is depending on the idea of Path Loss to perform User Grouping (PLUG) which divide the users into the center and edge user groups depending on different levels of pilot contamination. It assigns the same pilot sequences to the center users which slightly suffer from pilot contamination and assign orthogonal pilot sequences to the edge users which severely suffer from pilot contamination. It is assumed that the number of users at the edge of each cell is the same. Therefore, to overcome such limitations of PLUG algorithm, we propose an improved PLUG (IPLUG) algorithm which provides the decision parameters for user grouping and selects the number of central and edge users in each cell in a dynamic manner. Thus, the algorithm prevents the wrong division of users in good channel conditions being considered as an edge user which causes large pilot overhead, and also identifies the users with worst channel conditions and prevents the wrong division of such users from the center user group. The second algorithm for pilot decontamination utilizes the idea of pseudo-random codes in which orthogonal pilot are assigned to different cells. Such codes are deployed to get a transmission pilot by scrambling the user pilot in the cell. Since the pilot contamination is generated because different cells multiplex the same set of orthogonal pilots and the pseudo-random sequences have good cross-correlation characteristics, this paper uses this feature to improve the orthogonality of pilots between different cells. Simulation results show that the proposed algorithms can effectively improve channel estimation performance and achievable rate as compared with other schemes.

Hybrid optimization-based pilot scheduling for reducing pilot contamination in massive MIMO systems

2020 ◽  
Vol 18 (04) ◽  
pp. 2050028
Author(s):  
Ambala Pradeep Kumar ◽  
Tadisetty Srinivasulu
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Fitness Function ◽  
Scheduling Algorithm ◽  
Multiple Input Multiple Output ◽  
Spider Monkey ◽  
Achievable Rate ◽  
Pilot Contamination ◽  
Spider Monkey Optimization

Massive multiple-input multiple-output (MIMO) is an emerging technology used in next-generation cellular networks. The major challenge in the massive MIMO system is the pilot contamination. The contamination of the pilot sequences causes inaccurate channel estimation leading to huge errors in the transmissions. This paper proposes an approach for pilot contamination reduction in massive MIMO systems. In order to reduce the pilot contamination, a pilot scheduling algorithm is devised by proposing an optimization algorithm named Elephant-based Spider Monkey Optimization (ESMO) for scheduling the pilots. The proposed ESMO is designed by combining Elephant Herding Optimization (EHO) into Spider Monkey Optimization (SMO). The pilot scheduling approach employs proposed ESMO and user degradation for scheduling the pilots. Moreover, the optimal pilot scheduling is carried out using the newly devised fitness function that considers achievable rate using various user grouping parameters, such as utility function, and grouping parameter. Thus, the proposed ESMO-based pilot scheduling and fitness function are responsible for initiating optimal pilot scheduling. The performance of the proposed method is compared with the existing methods, and the proposed ESMO outperformed the existing methods with maximal achievable rate value of 39.257[Formula: see text]bps/Hz, and maximal SINR with value 118.75[Formula: see text]dB, respectively.

scholarly journals Time-shifted Pilot-based Scheduling with Adaptive Optimization for Pilot Contamination Reduction in Massive MIMO

2020 ◽  
Vol 4 ◽  
pp. 10-21
Author(s):  
Ambala Pradeep Kumar ◽  
Tadisetty Srinivasulu
Keyword(s):  
Massive Mimo ◽  
Communication Systems ◽  
Large Scale ◽  
Mimo System ◽  
Base Station ◽  
Superior Performance ◽  
Optimal Time ◽  
Pilot Contamination ◽  
Adaptive Optimization ◽  
User Grouping

Massive multiple-input multiple-output (MIMO) is considered to be an emerging technique in wireless communication systems, as it offers the ability to boost channel capacity and spectral efficiency. However, a massive MIMO system requires huge base station (BS) antennas to handle users and suffers from inter-cell interference that leads to pilot contamination. To cope with this, time-shifted pilots are devised for avoiding interference between cells, by rearranging the order of transmitting pilots in different cells. In this paper, an adaptive-elephant-based spider monkey optimization (adaptive ESMO) mechanism is employed for time-shifted optimal pilot scheduling in a massive MIMO system. Here, user grouping is performed with the sparse fuzzy c-means (Sparse FCM) algorithm, grouping users based on such parameters as large-scale fading factor, SINR, and user distance. Here, the user grouping approach prevents inappropriate grouping of users, thus enabling effective grouping, even under the worst conditions in which the channel operates. Finally, optimal time-shifted scheduling of the pilot is performed using the proposed adaptive ESMO concept designed by incorporating adaptive tuning parameters. The efficiency of the adaptive ESMO approach is evaluated and reveals superior performance with the highest achievable uplink rate of 43.084 bps/Hz, the highest SINR of 132.9 dB, and maximum throughput of 2.633 Mbps

scholarly journals On the Distribution of an Effective Channel Estimator for Multi-Cell Massive MIMO

2019 ◽  
Author(s):  
Felipe Augusto Pereira de Figueiredo ◽  
Claudio Ferreira Dias ◽  
Fabbryccio A. C. M. Cardoso ◽  
Gustavo Fraidenraich
Keyword(s):  
Massive Mimo ◽  
Large Scale ◽  
Mimo Systems ◽  
Base Station ◽  
Noise Power ◽  
Pilot Contamination ◽  
Channel Estimator ◽  
Symmetric Complex ◽  
Mmse Estimator ◽  
Effective Channel

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.

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

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.

scholarly journals On Matrix Completion-Based Channel Estimators for Massive MIMO Systems

Symmetry ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1377
Author(s):  
Mingjun Ding ◽  
Xiaodong Yang ◽  
Rui Hu ◽  
Zhitao Xiao ◽  
Jun Tong ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Massive Mimo ◽  
Communication Systems ◽  
Large Scale ◽  
Hybrid Methods ◽  
Mimo Systems ◽  
Low Cost ◽  
Matrix Completion ◽  
Spectrum Efficiency ◽  
Robust Solution

Large-scale symmetric arrays such as uniform linear arrays (ULA) have been widely used in wireless communications for improving spectrum efficiency and reliability. Channel state information (CSI) is critical for optimizing massive multiple-input multiple-output(MIMO)-based wireless communication systems. The acquisition of CSI for massive MIMO faces challenges such as training shortage and high computational complexity. For millimeter wave MIMO systems, the low-rankness of the channel can be utilized to address the challenge of training shortage. In this paper, we compared several channel estimation schemes based on matrix completion (MC) for symmetrical arrays. Performance and computational complexity are discussed and compared. By comparing the performance in different scenarios, we concluded that the generalized conditional gradient with alternating minimization (GCG-Alt) estimator provided a low-cost, robust solution, while the alternating direction method of multipliers (ADMM)-based hybrid methods achieved the best performance when the array response was perfectly known.

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

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.

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

Energies ◽  
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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