Joint CFO and channel estimation in OFDM-based massive MIMO systems

Hojatian, Hamed; Omidi, Mohammad Javad; Saeedi-Sourck, Hamid; Farhang, Arman

doi:10.1109/istel.2016.7881837

Cited by 14 publications

(15 citation statements)

References 16 publications

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“…The simulation results show that with only a few iterations the MSE of the channel estimation converges to that of the case where we have no CFOs. The results also show that this technique has better performance and lower complexity than [49]. The complexity of this technique increases linearly with the number of BS antennas.…”

Section: Hardware Impairmentsmentioning

confidence: 74%

“…Using inexpensive local oscillators increases the probability of oscillator instability and causes carrier frequency offset, which are due to the mismatch between the oscillator of the transmitter and the receiver. The effect of CFO is modeled as a phase rotation in the received time domain signal or a frequency shift in the spectrum of the received frequency domain signal [41,42,43,44,45,46,47,48,49,50,51]. In massive MIMO systems, since we have multiple users we also have multiple CFOs to estimate and compensate.…”

Section: Hardware Impairmentsmentioning

confidence: 99%

“…However, the complexity of this technique is high being a cubic function of the number of BS antennas. A joint CFO and channel estimation technique for OFDM massive MIMO system was proposed in [49] to keep the spectral efficiency high. Here, the pilots sent for channel estimation are used for both CFO and channel estimation.…”

Section: Hardware Impairmentsmentioning

confidence: 99%

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A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments

Mokhtari

Sabbaghian

Dinis

2019

Sensors

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Massive multiple input multiple output (MIMO) technology is one of the promising technologies for fifth generation (5G) cellular communications. In this technology, each cell has a base station (BS) with a large number of antennas, allowing the simultaneous use of the same resources (e.g., frequency and/or time slots) by multiple users of a cell. Therefore, massive MIMO systems can bring very high spectral and power efficiencies. However, this technology faces some important issues that need to be addressed. One of these issues is the performance degradation due to hardware impairments, since low-cost RF chains need to be employed. Another issue is the channel estimation and channel aging effects, especially in fast mobility environments. In this paper we will perform a comprehensive study on these two issues considering two of the most promising candidate waveforms for massive MIMO systems: Orthogonal frequency division multiplexing (OFDM) and single-carrier frequency domain processing (SC-FDP). The studies and the results show that hardware impairments and inaccurate channel knowledge can degrade the performance of massive MIMO systems extensively. However, using suitable low complex estimation and compensation techniques and also selecting a suitable waveform can reduce these effects.

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Section: Hardware Impairmentsmentioning

confidence: 74%

Section: Hardware Impairmentsmentioning

confidence: 99%

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A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments

Mokhtari

Sabbaghian

Dinis

2019

Sensors

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“…where H ∈ C N T ×N R denotes the channel matrix discussed in the next subsection and n ∼ CN 0, σ 2 I N s denotes the symmetric complex Gaussian distributed additive noise vector at the receiver. In a practical communication system, the non-ideal characteristics of components, including nonlinear power amplifier [14], inphase/quadrature-phase imbalance [15], phase noise [16] and carrier frequency offset [17], will greatly affect the estimation performance. Especially in mmWave band, the use of cheap equipment is encouraged, which leads to unignorable hardware impairments.…”

Section: System Modelmentioning

confidence: 99%

Comparison of CS-Based Channel Estimation for Millimeter Wave Massive MIMO Systems

Yang

Cai

et al. 2019

Applied Sciences

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Compressed sensing (CS) has great potential in channel estimation for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. To solve such a CS-based channel estimation problem, three categories of algorithms, namely convex relaxation algorithms, greedy iteration algorithms and Bayesian inference algorithms, are widely used. In this paper, with a unified massive MIMO framework, comprehensive comparisons among three categories of algorithms are presented in the perspective of the estimated accuracy, which is affected by the received signal-to-noise ratio (SNR), the number of resolvable paths, angular quantization error, the number of pilot symbols and hardware impairments. Specifically, it shows that convex relation algorithms achieve the best estimation accuracy at the high SNR range and it is mainly affected by the received SNR and transmitter’s hardware impairments. At the low SNR range, greedy iteration algorithms outperform others and the estimated accuracy is then limited by the angle quantization error. Furthermore, a tradeoff between the estimated error and complexity is achieved in Bayesian inference algorithms, a;though its estimated error is sensitive to the number of available pilot symbols.

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“…This algorithm is very complex as it requires a multidimensional grid search. Later, the authors in [14], converted the ML CFO estimator into a set of line search problems. However, this algorithm requires per antenna CFO estimation for all the users.…”

Section: Introductionmentioning

confidence: 99%

CFO Estimation for OFDM-Based Massive MIMO Systems in Asymptotic Regime

Sabeti

Farhang

Marchetti

et al. 2018

2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM)

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Massive multiple input multiple output (MIMO) plays a pivotal role in the fifth generation (5G) wireless networks. However, the carrier frequency offset (CFO) estimation is a challenging issue in the uplink of multiuser massive MIMO systems. In fact, frequency synchronization can impose a considerable amount of computational complexity to the base station (BS) due to a large number of BS antennas. In this paper, thanks to the properties of massive MIMO in the asymptotic regime, we develop a simple synchronization technique and derive a closed form equation for CFO estimation. We show that the phase information of the covariance matrix of the received signals is solely dependent on the users' CFOs. Hence, if a real-valued pilot is chosen, the CFO values can be straightforwardly calculated from this matrix. Hence, there is no need to deal with a complex optimization problem like the other existing CFO estimation techniques in the literature. Our simulation results testify the efficacy of our proposed CFO estimation technique. As we have shown, the performance of our method does not deteriorate as the number of users increases.

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Joint CFO and channel estimation in OFDM-based massive MIMO systems

Cited by 14 publications

References 16 publications

A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments

A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments

Comparison of CS-Based Channel Estimation for Millimeter Wave Massive MIMO Systems

CFO Estimation for OFDM-Based Massive MIMO Systems in Asymptotic Regime

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