Optimal Pilot Design for Pilot Contamination Elimination/Reduction in Large-Scale Multiple-Antenna Aided OFDM Systems

Guo, Xinying; Chen, Sheng; Zhang, Jiankang; Mu, Xiaomin; Hanzo, Lajos

doi:10.1109/twc.2016.2599171

Cited by 59 publications

(29 citation statements)

References 18 publications

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“…with the covariance matrix Ψ H (r/u) r given by 1 We assume that the pilot contamination imposed by the UDs served by the adjacent RRUs has been eliminated by optimal pilot design [40], [41].…”

Section: B Signal Model Of Ud-to-rrumentioning

confidence: 99%

Boosting Fronthaul Capacity: Global Optimization of Power Sharing for Centralized Radio Access Network

Zhang

Chen

Guo

et al. 2019

IEEE Trans. Veh. Technol.

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The limited fronthaul capacity imposes a challenge on the uplink of centralized radio access network (C-RAN). We propose to boost the fronthaul capacity of massive multipleinput multiple-output (MIMO) aided C-RAN by globally optimizing the power sharing between channel estimation and data transmission both for the user devices (UDs) and the remote radio units (RRUs). Intuitively, allocating more power to the channel estimation will result in more accurate channel estimates, which increases the achievable throughput. However, increasing the power allocated to the pilot training will reduce the power assigned to data transmission, which reduces the achievable throughput. In order to optimize the powers allocated to the pilot training and to the data transmission of both the UDs and the RRUs, we assign an individual power sharing factor to each of them and derive an asymptotic closed-form expression of the signal-to-interference-plus-noise for the massive MIMO aided C-RAN consisting of both the UD-to-RRU links and the RRU-to-baseband unit (BBU) links. We then exploit the C-RAN architecture's central computing and control capability for jointly optimizing the UDs' power sharing factors and the RRUs' power sharing factors aiming for maximizing the fronthaul capacity. Our simulation results show that the fronthaul capacity is significantly boosted by the proposed global optimization of the power allocation between channel estimation and data transmission both for the UDs and for their host RRUs. As a specific example of 32 receive antennas (RAs) deployed by RRU and 128 RAs deployed by BBU, the sum-rate of 10 UDs achieved with the optimal power sharing factors improves 33% compared with the one attained without optimizing power sharing factors.

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Section: B Signal Model Of Ud-to-rrumentioning

confidence: 99%

Boosting Fronthaul Capacity: Global Optimization of Power Sharing for Centralized Radio Access Network

Zhang

Chen

Guo

et al. 2019

IEEE Trans. Veh. Technol.

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“…Each cell comprises of M antennas at the BS and N single antenna users. To improve the spectral efficiency, orthogonal frequency division multiplexing (OFDM) is adopted [17,18].…”

Section: Massive Mimo System Modelmentioning

confidence: 99%

Efficient Bayesian compressed sensing-based channel estimation techniques for massive MIMO-OFDM systems

Al-Salihi

Nakhai

2017

J Wireless Com Network

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Efficient and highly accurate channel state information (CSI) at the base station (BS) is essential to achieve the potential benefits of massive multiple input multiple output (MIMO) systems. However, the achievable accuracy that is attainable is limited in practice due to the problem of pilot contamination. It has recently been shown that compressed sensing (CS) techniques can address the pilot contamination problem. However, CS-based channel estimation requires prior knowledge of channel sparsity to achieve optimum performance, also the conventional CS techniques show poor recovery performance for low signal to noise ratio (SNR). To overcome these shortages, in this paper, an efficient channel estimation approach is proposed for massive MIMO systems using Bayesian compressed sensing (BCS) based on prior knowledge of statistical information regarding channel sparsity. Furthermore, by utilizing the common sparsity feature inherent in the massive MIMO system channel, we extend the proposed Bayesian algorithm to a multi-task (MT) version, so the developed MT-BCS can obtain better performance results than the single task version. Several computer simulation based experiments are performed to confirm that the proposed methods can reconstruct the original channel coefficient more effectively when compared to the conventional channel estimator in terms of estimation accuracy.

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“…However, in the realistic multi-cell scenario, owing to the inevitable reuse of the same pilot sequences among neighboring cells, pilot contamination (PC) may occur, which may result in the BS being unable to reliably estimate the true channels between its serving users and itself, even when an infinite number of antennas are deployed. PC constitutes a serious impairment that limits the system's achievable performance, and hence considerable research efforts have been focused on mitigating or eliminating PC [8]- [17]. Among various PC mitigating/eliminating schemes [8]- [17], the scheme of [17] has been shown to be most effective and efficient.…”

Section: Introductionmentioning

confidence: 99%

“…PC constitutes a serious impairment that limits the system's achievable performance, and hence considerable research efforts have been focused on mitigating or eliminating PC [8]- [17]. Among various PC mitigating/eliminating schemes [8]- [17], the scheme of [17] has been shown to be most effective and efficient. This is because the PC elimination schemes [8]- [16] require an excessively long channel coherence time to work, and they can no longer be used if the channel coherent time is insufficiently large 1 .…”

Section: Introductionmentioning

confidence: 99%

Optimal Uplink Pilot-Data Power Allocation for Large-Scale Antenna Array-Aided OFDM Systems

Guo

Zhang

Chen

et al. 2020

IEEE Trans. Veh. Technol.

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The effective aggregate rate (AR) performance of large-scale multiple-input multiple-output aided orthogonal frequency division multiplexing systems based on time division duplexing protocol critically depends on uplink (UL) pilot-data power allocations for all users. We derive an asymptotic closedform expression of the system's achievable AR for typical channel estimation schemes, which is a function of individual users' UL power allocation factors. We prove that the effective AR is a convex function of each UL power allocation factor, when all the other factors are given. The globally optimal UL pilot-data power allocation that maximizes the system's effective AR can easily be obtained using an iterative procedure, by solving an univariate convex optimization with the golden section method for individual UL power allocation factors one by one in each iteration. Owing to the piecewise convexity of the effective AR, this iterative algorithm guarantees to find the unique globally optimal solution with only one iteration. The simulation results confirm that the achievable effective AR is significantly enhanced through the proposed optimization of UL pilot-data power allocation.

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Optimal Pilot Design for Pilot Contamination Elimination/Reduction in Large-Scale Multiple-Antenna Aided OFDM Systems

Cited by 59 publications

References 18 publications

Boosting Fronthaul Capacity: Global Optimization of Power Sharing for Centralized Radio Access Network

Boosting Fronthaul Capacity: Global Optimization of Power Sharing for Centralized Radio Access Network

Efficient Bayesian compressed sensing-based channel estimation techniques for massive MIMO-OFDM systems

Optimal Uplink Pilot-Data Power Allocation for Large-Scale Antenna Array-Aided OFDM Systems

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