QoS-Guaranteed User Scheduling and Pilot Assignment for Large-Scale MIMO-OFDM Systems

Xiong, Xin; Jiang, Bin; Gao, Xiqi; You, Xiaohu

doi:10.1109/tvt.2015.2477683

Cited by 12 publications

(10 citation statements)

References 34 publications

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“…At present, massive MIMO pilot decontamination is mainly carried out from three aspects: channel estimation method [24][25][26][27][28], matrix precoding method case [29,30], and pilot allocation strategy [21]. The authors in [25] utilize a diagonal Jacket matrix for pilot reduction which has low complexity, excellent eigenvector and a constant diagonal treatment and an energy harvest.…”

Section: State-of-the-art Pilot Decontamination Algorithmsmentioning

confidence: 99%

“…In [29], the authors propose a zero forcing (ZF) time-shifted pilot scheme, which was known to mitigate the pilot contamination effectively using conjugate beamforming. The authors in [30] proposed interreference-cancellation (IC) precoding scheme for pilot contamination mitigation in massive MIMO systems. They investigated the quality-of-service (QoS) guaranteed user-scheduling which is improved by deploying their proposed scheme.…”

Section: State-of-the-art Pilot Decontamination Algorithmsmentioning

confidence: 99%

“…The Linear Time-Varying (LTV) channel is estimated and the optimal pilot symbols are derived following the minimum mean square error (MMSE) criterion and it is shown that the optimal pilot strategy is to group consecutive pilot tones together as a pilot cluster and to distribute uniformly all pilot clusters in frequency-domain. Depending on the pilot allocation strategy, the research found that: In [30], under the principle of maximizing Signal Leakage Noise Ratio (SLNR) precoding, the same guidance is adopted for users with small ICI. Users with high frequency and mutual interference user orthogonal pilots to improve the overall performance of the system in the case of pilot contamination.…”

Section: State-of-the-art Pilot Decontamination Algorithmsmentioning

confidence: 99%

“…BER comparison against the number of cell users for the proposed pseudo-random pilot design and other cases. Figure 19 compares the NMSE of the proposed IPLUG algorithm with the conventional state-ofthe-art algorithms [29,30,32] wherein the IPLUG algorithm corresponds to a decision parameter 0.9. As can be seen from the figure that the proposed IPLUG algorithm shows better NMSE performance than the conventional algorithms for increasing number of base station antennas.…”

Section: Analysis Of Pseudo-random Pilot Schemementioning

confidence: 99%

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Efficient Pilot Decontamination Schemes in 5G Massive MIMO Systems

et al. 2019

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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.

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Section: State-of-the-art Pilot Decontamination Algorithmsmentioning

confidence: 99%

Section: State-of-the-art Pilot Decontamination Algorithmsmentioning

confidence: 99%

Section: State-of-the-art Pilot Decontamination Algorithmsmentioning

confidence: 99%

Section: Analysis Of Pseudo-random Pilot Schemementioning

confidence: 99%

See 2 more Smart Citations

Efficient Pilot Decontamination Schemes in 5G Massive MIMO Systems

et al. 2019

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show abstract

“…MIMO system is delay tolerant and renders communication with high data rate with better quality of service (QoS) [1], which is attained through multiplexing, and diversity with the exploitation of the multi-antenna spatial dimensions. The large-scale MIMO received interest with greater spectral and energy efficiency [2] and enhanced the capacity and reliability of the channel with multiple antenna pairs in case of the parallel data transmission. OFDM is a multiplexing method for frequency selective fading channels, which is decomposed to a set of frequency flat fading channels.…”

Section: Introductionmentioning

confidence: 99%

Hybrid optimisation for priority‐based scheduling in multi‐user STBC‐MIMO‐OFDM

Raut¹,

Jalnekar²

2019

IET Communications

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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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QoS-Guaranteed User Scheduling and Pilot Assignment for Large-Scale MIMO-OFDM Systems

Cited by 12 publications

References 34 publications

Efficient Pilot Decontamination Schemes in 5G Massive MIMO Systems

Efficient Pilot Decontamination Schemes in 5G Massive MIMO Systems

Hybrid optimisation for priority‐based scheduling in multi‐user STBC‐MIMO‐OFDM

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

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