Adaptive Pilot Allocation Algorithm for Pilot Contamination Mitigation in TDD Massive MIMO Systems

Alkhaled, Makram; Alsusa, Emad; Hamdi, Khairi Ashour

doi:10.1109/wcnc.2017.7925885

Cited by 14 publications

(3 citation statements)

References 13 publications

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“…To show the effectiveness of our solutions to mitigate the effect of PC, we compare them with the traditional approach, where the pilots are randomly allocated to users, and to the Adaptive Pilot Allocation (APA) scheme [18] where unique pilots are assigned to the users with the smallest large-scale fading so that they will not interfere with other users in the system.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Then, the proposed SPA method assigns the pilot sequence with the smallest inter-cell interference to the user having the worst channel quality in a sequential way to improve its performance. The authors in [18] proposed an Adaptive Pilot Allocation (APA) scheme to mitigate the effect of PC. The objective is to improve the total achievable throughput in the network.…”

Section: Related Workmentioning

confidence: 99%

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Pilot Contamination Mitigation in Massive MIMO Cloud Radio Access Networks

et al. 2022

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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.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Pilot Contamination Mitigation in Massive MIMO Cloud Radio Access Networks

et al. 2022

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“…Specifically, the reciprocity characteristic of TDD can derivate the downlink CSIs based on the estimated uplink CSI. Accordingly, based on TDD, BSs are susceptible to use a small numbercompared to FDD-of the pilot sequences (PSs), which is explained by the reciprocity characteristic of the TDD in which the BSs are exempt from the estimation of the downlink (DL) CSIs; accordingly, the TDD is desired for mMIMO due to its susceptibility to reduce the cost of obtaining the CSI at the BSs [6] [7].…”

Section: Introductionmentioning

confidence: 99%

Ant Colony-based Optimization algorithm to overcome the pilot contamination issue within multi-cell Massive MIMO systems

Belhabib

Amadid²,

Khabba³

et al. 2022

E3S Web Conf.

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the pilot contamination (PC) issue still faces and limits the promising massive MIMO (mMIMO) technology, to unleash the high benefits of mMIMO, we provide here a new decontaminating strategy, which exploits the large-scale fading coefficients' characteristics to construct a search space for the Ant colony-based optimization (ACO) algorithm. This algorithm is employed to find the best pattern in which each UE is linked to its most concurrent UEs of the adjacent cells; specifically, each UEs has an enemy UEs that if they are allocated with the same pilot, the severity of the PC upon the two UEs become subversive for the quality-of-service of the two UEs. Hence, the ACO algorithm finds for each UE its enemy UE, which leads to construct a Hamiltonian graph. This graph is exploited during the assignment of the pilot sequences to the overall UEs; specifically, the linked UEs are successively allocated with the available OPSs, which leads to address the PC problem within multi-cell mMIMO systems.

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Greedy Matching-Based Pilot Allocation in Massive MIMO Systems

Wen

Gao

et al. 2021

Lecture Notes in Electrical Engineering

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Adaptive Pilot Allocation Algorithm for Pilot Contamination Mitigation in TDD Massive MIMO Systems

Cited by 14 publications

References 13 publications

Pilot Contamination Mitigation in Massive MIMO Cloud Radio Access Networks

Pilot Contamination Mitigation in Massive MIMO Cloud Radio Access Networks

Ant Colony-based Optimization algorithm to overcome the pilot contamination issue within multi-cell Massive MIMO systems

Greedy Matching-Based Pilot Allocation in Massive MIMO Systems

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