Pilot Contamination Problem Research Articles

A new pilot assignment scheme for mitigating pilot contamination in uplink massive multi-input–multi-output (MIMO) systems

Pilot contamination is a serious issue in massive multi–input–multi–output systems which significantly degrades system performance. In this paper, we investigate a new pilot assignment scheme by integrating two-dimensional genetic algorithm with Tabu-Search algorithm (TS) to mitigate the pilot contamination problem. Firstly, we design a two-dimensional genetic algorithm equipped with elitism strategy as a principal algorithm for solving the pilot assignment problem; then, aiming to enhance the convergence speed of the genetic algorithm to the ideal optimal solution, we integrate TS with the genetic algorithm. This integrated pilot assignment scheme, henceforth designated as GATS-PA, is found to be powerful in mitigating the pilot contamination problem. Numerical simulation results verify that the proposed pilot assignment scheme is very close to the ideal optimal solution with few numbers of iterations and outperforms existing methods in terms of enhancing the average uplink rate per user over a wide range of simulation parameters.

Subspace-Based Pilot Decontamination in User-Centric Scalable Cell-Free Wireless Networks

We consider a cell-free wireless system operated in Time Division Duplex (TDD) mode with user-centric clusters of remote radio units (RUs). Since the uplink pilot dimensions per channel coherence slot is limited, co-pilot users might incur mutual pilot contamination. In the current literature, it is assumed that the long-term statistical knowledge of all user channels is available. This enables Minimum Mean-Square Error channel estimation or simplified dominant subspace projection, which achieves significant pilot decontamination under certain assumptions on the channel covariance matrices. However, estimating the channel covariance matrix or even just its dominant subspace at all RUs forming a user cluster is not an easy task. In fact, if not properly designed, a piloting scheme for such long-term statistics estimation will also be subject to the contamination problem. In this paper, we propose a new channel subspace estimation scheme explicitly designed for cell-free wireless networks. Our scheme is based on 1) a sounding reference signal (SRS) using latin squares wideband frequency hopping, and 2) a subspace estimation method based on robust Principal Component Analysis (R-PCA). The SRS hopping scheme ensures that for any user and any RU participating in its cluster, only a few pilot measurements will contain strong co-pilot interference. These few heavily contaminated measurements are (implicitly) eliminated by R-PCA, which is designed to regularize the estimation and discount the “outlier” measurements. Our simulation results show that the proposed scheme achieves almost perfect subspace knowledge, which in turns yields system performance very close to that with ideal channel state information, thus essentially solving the problem of pilot contamination in cell-free user-centric TDD wireless networks.

Downlink channel analysis at multi-cell massive MIMO: novel adaptive channel allocation scheme

In large multiple-input multiple-output (MIMO) systems, we offer a new access strategy for crowd situations. Crowd situations include a high number users who have intermittent access patterns, making orthogonal scheduling impossible. In this paper, the work is driven by the massive MIMO problem at crowded environment. The pilot contamination problem is dependent on the protocol assumptions is observed in the system. We investigate a crowd situation in which the users number and their access behaviours make scheduling broadcasts impossible. The pilot assignment is time-slot randomized, but the data portion is repeated, allowing it to use successive-interference-cancellation across copies of comparable packets. In result analysis, the transmission of downlink to the user has shown with changing the number of users, fading effect, transmission power and the number of base station antennas.

Contemporary Approaches to Mitigate Pilot Contamination in Massive Mimo Systems

Massive Multiple Input Multiple Output (MIMO) is a promising technology for meeting the demand of high data capacity for mobile networks in the future. Obtaining channel state information (CSI) in MIMO systems provides energy and spectrum efficiency. Hence, the accuracy of the CSI is very important. Time Division Duplexing (TDD) is a good alternative method to obtain CSI in Massive MIMO systems. Pilot sequences are used to obtain CSI. In practice, it is not possible to assign a different pilot sequence to each user terminal. In particular, the use of the same pilot sequences in different cells in Massive MIMO systems causes the problem of pilot contamination. Many studies have been conducted in an attempt to mitigate this problem. In this study, the reasons for pilot contamination are discussed and the methods proposed in the literature for eliminating or mitigating pilot contamination are presented. In addition, the case of pilot contamination-related security in Massive MIMO and other related issues that may be important in future systems are highlighted.

Pilot Allocation and Data Power Optimization Based on Access Point Selection in Cell-Free Massive MIMO

Focusing on the pilot contamination problem of cell-free massive multi-input multi-output (MIMO), a pilot allocation algorithm based on access point (AP) selection is proposed. To improve the system performance further, data power optimization is carried out. First, the AP that serves each user is selected through the user-centered idea and the large-scale fading matrix. Use the same service AP numbers between users to measure the pilot contamination intensity and then assign orthogonal pilots to strong potential pilot contamination users. A spectrum efficiency (SE) scheme to maximize the minimum user is proposed for the fairness problem in data power optimization. It is proved that the objective function of data power optimization accords with linear programming, and a dichotomy is given to solve the problem. Simulation results show that the pilot allocation algorithm based on AP selection can significantly improve the total SE of the system and reduce the computational complexity of the system. At the same time, the data power optimization algorithm improves minimum SE for users in the system.

Downlink Processing of Massive MIMO-NOMA Networks Using Cell Sectored Approach for 5G Communication

This article shows the heterogeneous network of Massive Multiple-Input Multiple-Output (mMIMO) system and Non-Orthogonal Multiple Access (NOMA) scheme in Downlink (DL) scenario. The performance of mMIMO systems using Maximal Ratio Transmission (MRT) and Zero-Forcing (ZF) precoding techniques has been investigated and compared with mMIMO-NOMA systems. The problem of Pilot Contamination (PC) arises when the channel is estimated at the Base Station (BS) due to the reuse of the same pilot matrix in co-channel cells. To reduce the co-channel interference, it has been shown that the Cell Sectoring (CS) of 120 degree and 60 degree can be employed. Sum-Rate (SR) capacities have been derived for the mMIMO and the mMIMO-NOMA systems for un-sectored and sectored cells considering both Perfect Channel State Information (PCSI), and Imperfect Channel State Information (ICSI). In a multi-cell scenario, it has been shown that the mMIMO-NOMA system exploits the precoding advantage along with successive interference cancellation and outperforms the standalone mMIMO system. Further, the ZF precoded mMIMO-NOMA system with 60 degree CS has been observed to be the most appropriate candidate amongst all three systems viz. 120 degree CS, 60 degree CS, and un-sectored system in terms of reduced interference.

Large-Scale Fading Coefficients Classification-Aided Power Control Strategy for Mitigating the Pilot Contamination in Massive MIMO Systems

Deploying base stations with a massive number of antennas give birth to massive multi-input multi-output technology, which can boost the quality of the wireless communication; however, the limitations imposed by the length of the coherence interval has, directly, limited the number of the orthogonal pilot sequences (OPSs) that can be exploited for acquiring such accurate channel state information at each base station (BS). Hence, the available pilot resources are reused among several cells, which causes the problem of pilot contamination (PC). In this paper, the PC problem is analyzed, and by exploiting the large-scale fading coeffificients of the users’ equipment (UEs), we have classifified the users within each cell; therefore, the same OPS is, solely, reused by the far-distant UEs; hence, the inflfluence of the PC problem is made as small as possible. Since the UEs are subject to different channel conditions, we have consolidated our proposal by a power control algorithm, which boosts the performance of our proposal. Simulation results prove the effectiveness of our proposed decontaminating strategy.

Addressing Residual Interference in Massive MIMO Utilizing Intelligent Reflecting Surfaces

This paper proposes a novel method to address coherent interference (pilot contamination) in Massive MIMO by incorporating control over propagation environment through Intelligent Reflecting Surfaces. In earlier research it was established that each mobile device has unique spatial signature which can be used as identifier even if they are sharing same pilot sequence. However in certain circumstances when their signals are received at BS with same Angle of Arrival their spatial structure become similar thus their individual identity is lost. This situation adds error in acquisition of channel state information, the accuracy of which is the key to benefit from Massive MIMO. This paper present a method to resolve the mentioned accepted gap using mathematical basis and simulation methods. A mathematical model of Massive MIMO is presented which is widely accepted by the subject modeling community. The model is then used to generate the basis for the presented algorithm through further mathematical studies and simulations. The basic algorithm used for the resolution of pilot contamination problem is finally presented in this paper. The algorithm was simulated using MATLAB and simulation results are tabulated to identify the results of pilot de-contamination after incorporating signal propagation control.

Pilot Contamination Mitigation in Massive MIMO Cloud Radio Access Networks

Massive multiple-input multiple-output (MIMO) technology is expected to achieve significant gains in both signal-to-noise-plus-interference ratio (SINR) and throughput for 5G cellular wireless networks. Efficient and highly accurate channel state information (CSI) acquisition at the base stations (BS) is essential to achieve the potential benefits of massive MIMO systems. However, the inadequate number of orthogonal pilot sequences used for CSI estimation leads to erroneous channel estimation as it causes interference between pilot sequences. This phenomenon is coined pilot contamination and severely limits the system performance. Therefore, we address in this paper the pilot contamination problem in massive MIMO Cloud-Radio Access Networks systems. Leveraging on a real telecom operator data delivered by Call Detail Records (CDR), our objective is to maximize the average uplink achievable rate by reducing the effect of pilot contamination in massive MIMO Cloud-Radio Access Networks (C-RAN). As such a problem is a non-linear integer problem that has no solution in polynomial time, we develop a two-stage solution to solve it. First, a coalition game is proposed where Remote Radio Heads (RRHs) gather into clusters with random user-pilot allocation. Then, two greedy heuristics are applied to match each user in the cluster with a given pilot. The goal is to further improve the average uplink rate achieved in the first stage. Simulation results show that our heuristic solutions for pilot contamination mitigation outperform the traditional pilot allocation solution and a state-of-the-art pilot allocation scheme based on large-scale fading in terms of average uplink achievable rate and SINR.

Pilot Decontamination Processing in Cell-Free Massive MIMO

This letter focuses on the pilot contamination problem in the uplink and downlink of cell-free massive multiple-input multiple-output networks with different degrees of cooperation between access points. The optimum minimum mean square error processing can take advantage of large-scale fading coefficients for canceling the interference of pilot-sharing user-equipments and thus achieves asymptotically unbounded capacity. However, it is computationally demanding and can only be implemented in a fully centralized network. Here, sub-optimal schemes are derived that provide unbounded capacity with linear-growing complexity and using only local channel estimates but global channel statistics. This makes them suited for both centralized and distributed networks. In this latter case, the best performance is achieved with a generalized maximum ratio combiner that maximizes a capacity bound based on channel statistics only.

Ant colony‐based strategy to mitigate the pilot contamination problem in multicell massive multiple‐input multiple‐output systems

AbstractDue to its ability to serve a large number of the user equipments (UEs), massive multiple‐input multiple‐output (mMIMO) technology has been elected to meet the requirement of the upcoming new five‐generation systems. Despite its advantages, mMIMO is still obstructed by the pilot contamination (PC) problem, which is birth from, simultaneously, reusing the same orthogonal pilot sequences (OPSs) across several cells. Due to the scarcity of the OPSs, it is necessary to properly allocate the available OPSs to the UEs. Specifically, the available OPSs should be assigned to the UEs while the severity of the PC problem, between the UEs that employ the same pilot sequences, remains as small as possible. To reach that goal, we have employed the ant colony‐based optimization (ACO) algorithm to find the best pattern in which the severity of the PC problem is reduced among all the UEs. In a nutshell, a graph that sizes the strength of the PC among the UEs is constructed; therefore, the ACO algorithm is employed to connect every two UEs that causes to each other the highest influence of the PC, therefore, the UEs are, successively, assigned with the available OPSs where the severity of the PC is significantly reduced.

AoA-Based Pilot Assignment in Massive MIMO Systems Using Deep Reinforcement Learning

In this letter, the problem of pilot contamination in a multi-cell massive multiple input multiple output (M-MIMO) system is addressed using deep reinforcement learning (DRL). To this end, a pilot assignment strategy is designed that adapts to the channel variations while maintaining a tolerable pilot contamination effect. Using the angle of arrival (AoA) information of the users, a cost function, portraying the reward, is presented, defining the pilot contamination effects in the system. Numerical results illustrate that the DRL-based scheme is able to track the changes in the environment, learn the near-optimal pilot assignment, and achieve a close performance to that of the optimum pilot assignment performed by exhaustive search, while maintaining a low computational complexity.

Encryption Based Strategy to Overcome the Problem of Pilot Contamination Within Multi-cellular Massive MIMO Systems

The problem of inter-cell interferences keeps its site as the most challenging constraint that faces the massive multi-input multi-output (M-MIMO) technology. This constraint, known as the problem of pilot contamination (PC), is a direct result of reusing the same set of orthogonal pilot sequences (OPSs) across several cells, due to the scarcity of available pilot resources, compared to the number of the user equipments (UEs) that must be served, the reuse of the same OPSs within different cells is unavoidable. Accordingly, assigning the available OPSs to the UEs should be managed to address the PC problem. To reach that goal, a new decontaminating strategy is proposed herein, which is referred to as the ENCryption-based decontaminating strategy (ENC). The ENC strategy is based on injecting some recognition pilot symbols within the pilot sequence of the UEs; hence, the base stations become able to distinguish between the desired pilot signals from the undesired ones, therefore, the quality of servicecan be enhanced, furthermore, the proposed ENC strategy is of low computational complexity compared to the existing strategies.

Pilot Contamination Mitigation in Massive Multi-Input Multi-Output (MIMO) System

Massive MIMO is one of the key components in the technologies of the next generation wireless communication network; the fifth generation (5G). Although massive MIMO is considered a promising technology in 5G, its efficiency is limited by the challenge of the pilot contamination problem. Pilot contamination problem occurs when user terminals in different cells concurrently share the same control signal set (called pilot sets), which results in causing interference in the estimated channel. In this paper, a model to reduce the pilot contamination problem and to increase the system sum-rate is proposed. The proposed model is a combination of three schemes: 1) A time-shifting protocol where the pilots are transmitted by users without overlapping in time; 2) A directional pilot scheme where the users directionally transmit the pilot; 3) A pilot allocation scheme for channel estimation based on the greedy algorithm to optimize the system sum-rate and the signal to interference and noise ratio with low complexity, and to mitigate the pilot contamination. Simulation results show that significant improvements are achieved compared to existing directional, time shifting and pilot allocation schemes.

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

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.

A Unified Framework of Non-Orthogonal Pilot Design for Multi-Cell Massive MIMO Systems

In this work, we propose a novel non-orthogonal pilot design framework to tackle the pilot contamination problem in multi-cell massive multiple input multiple output (MIMO) systems. Assuming the minimum mean square error (MMSE) estimators are adopted at the base stations (BSs), we design pilot signals under power constraints by minimizing the total mean square errors (MSEs) of the MMSE estimators of all BSs. The pilot design problem is difficult to solve due to the non-convex objective function. A linearized alternating direction method of multipliers (L-ADMM) algorithm is introduced to solve the above non-convex optimization problem. The L-ADMM algorithm could approximate the objective function in a linear form, which makes the original problem solvable. In addition, a new non-orthogonal pilot design problem that maximizes the total spectral efficiency of all users in the cellular network is established. We show that the proposed L-ADMM-based pilot design method can be directly extended to solve it. Finally, numerical simulations validate that the proposed pilot design framework can achieve higher channel estimation accuracy and uplink achievable sum rate compared to the state-of-the-art approaches with less computational complexity.

IPAS: A deep Monte Carlo Tree Search-based intelligent pilot-power allocation scheme for massive MIMO system

Massive Multiple-Input-Multiple-Output (MIMO) is a promising technology to meet the demand for the connection of massive devices and high data capacity for mobile networks in the next generation communication system. However, due to the massive connectivity of mobile devices, the pilot contamination problem will severely degrade the communication quality and spectrum efficiency of the massive MIMO system. We propose a deep Monte Carlo Tree Search (MCTS)-based intelligent Pilot-power Allocation Scheme (iPAS) to address this issue. The core of iPAS is a multi-task deep reinforcement learning algorithm that can automatically learn the radio environment and make decisions on the pilot sequence and power allocation to maximize the spectrum efficiency with self-play training. To accelerate the searching convergence, we introduce a Deep Neural Network (DNN) to predict the pilot sequence and power allocation actions. The DNN is trained in a self-supervised learning manner, where the training data is generated from the searching process of the MCTS algorithm. Numerical results show that our proposed iPAS achieves a better Cumulative Distribution Function (CDF) of the ergodic spectral efficiency compared with the previous suboptimal algorithms.

Pilot Decontamination for Massive MIMO Network With UAVs

This letter studies the pilot contamination (PC) problem for massive multiple-input multiple-output (MIMO) networks with coexisting terrestrial users and unmanned aerial vehicles (UAVs). Due to the strong line-of-sight (LoS) air-to-ground channels between UAVs and base stations (BSs), UAVs usually cause a more severe PC issue as compared to the traditional terrestrial users. To mitigate the PC caused by UAVs, we propose a low-complexity distributed scheme by exploiting the full-dimensional beamforming of massive MIMO BSs and the angle-dependent LoS channels between them and high-altitude UAVs. Numerical results show the effectiveness of the proposed pilot decontamination scheme and the significant signal-to-interference-plus-noise ratio (SINR) gains in both the uplink and downlink after pilot decontamination.

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

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.

Low-complexity AoA-driven pilot assignment for multi-cell massive MIMO systems

In this paper, the problem of pilot contamination in a multi-cell massive multiple input multiple output (MIMO) system is addressed. We propose a pilot assignment scheme, in which compared to the conventional methods, the corresponding optimization problem is solved in a low-complexity manner. To this end, by utilization of multi-path model comprising angle of arrival (AoA) distributions of users for propagation channels, the pilot assignment pattern would be defined by an optimization problem with discrete variables, and a criterion is presented to realize the obtained problem via an alternative involving continuous optimization variables. In addition, the resultant problem is dealt with in an iterative manner, where the sequential convex programming (SCP) is exploited. The numerical analysis reveals that the proposed method converges by only limited iterations. Furthermore, the proposed scheme out-performs compared to the conventional methods from complexity point of view, for a negligible degradation in the uplink sum rate.