Location-Aware Pilot Allocation in Multicell Multiuser Massive MIMO Networks

Akbar, Noman; Yan, Shihao; Yang, Nan; Yuan, Jinhong

doi:10.1109/tvt.2018.2831224

Cited by 31 publications

(28 citation statements)

References 13 publications

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“…6 depicts the achievable SINR of Bob 11 ,θ 11,800 , versusp in for different values of N . In this figure, we assume that Alice l is equipped with 800 antennas andp in must satisfy the constraint given by (19). We find that increasingp in reducesθ 11,800 .…”

Section: B Impact Of Pc Attack On Achievable Sinrsmentioning

confidence: 99%

“…Considering the fact that PC degrades the performance of the massive MIMO network, a number of solutions have been proposed to compensate for this degradation [9], [14]. Such solutions can be broadly grouped into five categories: 1) the protocol-based method which restricts the simultaneous transmission from the users having the same pilot sequence or wisely assigns pilot sequences among users to alleviate PC [15]- [17]; 2) the precoding-based method which relies on specifically designed precoders to reduce the interference caused by PC [10], [18]; 3) the angle-of-arrival (AoA)-based method which mitigates the interference from the users having the same pilot sequence and mutually non-overlapping AoAs [19], [20]; 4) the blind method which partitions the signal space into desired signal subspace and interference signal subspace and then develops algorithms to reduce the interference from the latter [21], [22]; and 5) the pilot sequence design methods which aim at designing pilot sequences such that the PC does not severely impacts the network performance [11]- [13]. It is worth mentioning that most conventional methods assumed orthogonal pilot sequences to perform PC analysis [10], [15], [19], [20].…”

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confidence: 99%

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On the Pilot Contamination Attack in Multi-Cell Multiuser Massive MIMO Networks

Akbar

Yan

Khattak

et al. 2020

IEEE Trans. Commun.

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In this paper, we analyze pilot contamination (PC) attacks on a multi-cell massive multiple-input multiple-output (MIMO) network with correlated pilots. We obtain correlated pilots using a user capacity-achieving pilot sequence design. This design relies on an algorithm which designs correlated pilot sequences based on signal-to-interference-plus-noise ratio (SINR) requirements for all the legitimate users. The pilot design is capable of achieving the SINR requirements for all users even in the presence of PC. However, this design has some intrinsic limitations and vulnerabilities, such as a known pilot sequence and the non-zero cross-correlation among different pilot sequences. We reveal that such vulnerabilities may be exploited by an active attacker to increase PC in the network. Motivated by this, we analyze the correlated pilot design for vulnerabilities that can be exploited by an active attacker. Based on this analysis, we develop an effective active attack strategy in the massive MIMO network with correlated pilot sequences. Our examinations reveal that the user capacity region of the network is significantly reduced in the presence of the active attack. Importantly, the SINR requirements for the worst-affected users may not be satisfied even with an infinite number of antennas at the base station.

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Section: B Impact Of Pc Attack On Achievable Sinrsmentioning

confidence: 99%

mentioning

confidence: 99%

On the Pilot Contamination Attack in Multi-Cell Multiuser Massive MIMO Networks

Akbar

Yan

Khattak

et al. 2020

IEEE Trans. Commun.

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“…The authors in [14,15] developed the location-based pilot assignment approaches for pilot decontamination. A new expression for line of sight (LOS) interference is derived in [14] which is considered as the criteria for pilot allocation. Although, there was an improvement in the sum spectral efficiency (SE), but the pilot assignment process takes a longer time to be implemented, especially in large networks.…”

Section: Related Workmentioning

confidence: 99%

“…Differing from the aforementioned works [11,20], we consider the source of inter-cell interference throughout pilot assignment, which is essentially the cause of the pilot contamination. In some other works [12][13][14][15], the availability of some factors (e.g., user location, AoA, or LOS interference) are needed for pilot assignment which are not always easy to estimate, while our approach requires only large-scale fading coefficients, which can be tracked easily as they do not frequently change during coherence interval. Besides, comparing to previous works [17][18][19]21], our algorithm is not computationally intensive, and therefore it can be applied for large-scale networks.…”

Section: Related Workmentioning

confidence: 99%

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An Efficient Pilot Assignment Scheme for Addressing Pilot Contamination in Multicell Massive MIMO Systems

et al. 2019

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The reuse of the same pilot group across cells to address bandwidth limitations in a network has resulted in pilot contamination. This causes severe inter-cell interference at the targeted cell. Pilot contamination is associated with multicell massive multiple-input multiple-output (MIMO) systems which degrades the system performance even when extra arrays of antennas are added to the network. In this paper, we propose an efficient pilot assignment (EPA) scheme to address this issue by maximizing the minimum uplink rate of the target cell’s users. To achieve this, we exploit the large-scale characteristics of the fading channel to minimize the amount of outgoing inter-cell interference at the target cell. Results from the simulation show that the EPA scheme outperforms both the conventional and the smart pilot assignment (SPA) schemes by reducing the effect of inter-cell interference. These results, show that the EPA scheme has significantly improved the system performance in terms of achievable uplink rate and cumulative distribution function (CDF) for both signal-to-interference-plus-noise ratio (SINR), and uplink rate.

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Pilot Allocation Scheme Based on Machine Learning Algorithm and Users’ Angle of Arrival in Massive MIMO System

Chen

2021

Communications and Networking

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Massive MIMO system has attracted attention due to it's significant improvement in system capacity and spectrum utilization. Pilot pollution greatly limited the performance of Massive MIMO system. To optimize the pilot pollution in Massive MIMO system. In this paper, a pilot allocation scheme based on machine learning algorithm and users' angle of arrival is proposed. The scheme firstly classified all users according to whether the users' angle of arrival overlaps with each other. It randomly assigned pilot sequences to users whose angle of arrival do not overlap with each other. Secondly, it used machine learning algorithm to classify users whose angle of arrival overlap with each other into interfering group and non-interfering groups based on users' location information. We assign orthogonal pilots to users in the interfering group and randomly assign pilot sequences to users in the non-interfering group. Simulation results show that when the number of antenna reached 300, the pilot efficiency can be increased by about 11.67%. The pilot allocation scheme proposed in this paper can effectively suppress the impact of pilot pollution on the performance of Massive MIMO system, improve pilot efficiency and reduce pilot overhead.

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Location-Aware Pilot Allocation in Multicell Multiuser Massive MIMO Networks

Cited by 31 publications

References 13 publications

On the Pilot Contamination Attack in Multi-Cell Multiuser Massive MIMO Networks

On the Pilot Contamination Attack in Multi-Cell Multiuser Massive MIMO Networks

An Efficient Pilot Assignment Scheme for Addressing Pilot Contamination in Multicell Massive MIMO Systems

Pilot Allocation Scheme Based on Machine Learning Algorithm and Users’ Angle of Arrival in Massive MIMO System

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