A Joint Approach for Low-Complexity Channel Estimation in 5G Massive MIMO Systems

Bangash, Kifayatullah; Khan, Imran; Lloret, Jaime; León, Antônio Ponce de

doi:10.3390/electronics7100218

Cited by 8 publications

(18 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their result showed increase in accuracy. A low complexity channel estimation algorithm [5] is presented using singular value decomposition and iterative least square with projection for covariance matrix and obtained reduction in deviation, improved channel estimation accuracy, accurate CSI with low computational complexity. Further, researchers [6] put forward block low rank channel estimation algorithm with low computational complexity for matrix inversion.…”

Section: Related Workmentioning

confidence: 99%

Performance Optimization of LS/LMMSE Using Swarm Intelligence in 3D MIMO-OFDM Systems

Bhange¹,

Dethe²

2020

View full text Add to dashboard Cite

The capability of Least square (LS) and least minimum mean squared error (LMMSE) channel estimation techniques are limited due to one or two factors (inherent additive Gaussian noise and Inter Carrier Interference, higher computational complexity). These factors tend to be severe when the system grows in terms of numbers of transmitting and receiving antennas, channel parameters, noise etc. Accurate channel parameters estimation using these techniques is still not possible even with smaller Multi input multi output (MIMO) systems at higher signal to noise ratios (SNR) due to complex nature of channel parameters. Swarm Intelligence consisting of agents spread in search space having limited capabilities and random behaviour when interacts with each other and within their own locality are capable of finding solution for a complex problem. When the constructive behaviour of such particles in particle swarm optimization (PSO) within the search space limited to some constraint is applied to optimize the performance of 3D-Pilot Aided Channel Estimation (3D-PACE) of MIMO-OFDM system, results showed that the bit error rate (BER) is significantly decreased. The channel parameters at the receiver obtained using LS and LMMSE are further optimized using PSO with proper and careful setting of PSO initial parameters. Keywords:bit error rate, 3D-PACE, multi input multi output, orthogonal frequency division multiplexing, particle swarm optimization

show abstract

Section: Related Workmentioning

confidence: 99%

Performance Optimization of LS/LMMSE Using Swarm Intelligence in 3D MIMO-OFDM Systems

Bhange¹,

Dethe²

2020

View full text Add to dashboard Cite

show abstract

“…However, in practice, the above processing method faces two difficulties: First, as the number of channel parameters increases sharply with the number of BS antennas, the time required for downlink channel estimation will also increase sharply, and may even exceed the coherence time of the system [26], and, if pre-coded with outdated channel information, will undoubtedly greatly deteriorate the system performance. Second, as the estimated channel information needs to be fed back to the BS through limited feedback, when the channel parameters increase sharply, the system feedback overhead will also increase dramatically.…”

Section: Related Workmentioning

confidence: 99%

“…How to accurately determine the sparse basis of the channel vector is an urgent problem to be solved. When the noise is small, the classical compressed sensing technology can solve the problem of sparse signal reconstruction better; but when the noise is large, it is very difficult to determine the sparse basis and the corresponding coefficient [26][27][28][29][30][31][32]. However, the sparse signals' recovery in the shortest time is not considered in [27][28][29].…”

Section: Research Problemmentioning

confidence: 99%

A Low Rank Channel Estimation Scheme in Massive Multiple-Input Multiple-Output

et al. 2018

Self Cite

View full text Add to dashboard Cite

Aiming at the problem of computational complexity of channel estimation, this paper proposes a low-complexity block matching pursuit (BMP) algorithm based on antenna grouping and block sparsity for frequency division duplex (FDD) massive Multiple-input Multiple-output orthogonal frequency division multiplexing (OFDM) systems. The system coherence time may be exceeded as a result of time consumption when adopting an orthogonal pilot symbol in the time domain. To solve this problem, an antenna grouping transmission scheme is proposed to reduce the total channel estimation time by sacrificing the observed data length. The simulation results show that the proposed BMP algorithm has good anti-noise performance, and it can accurately determine the non-zero position of the sparse vector and adaptively determine the sparsity of the channel, which effectively translates to improved channel estimation performance and better overall system performance than the existing algorithms.

show abstract

“…In order to meet the demands of growing mobile service which contain high data rates, large capacity, high reliability, and low latency, fifth-generation (5G) communication technologies are being developed [1][2][3][4][5]. At present, a massive MIMO active antenna is being adopted in 5G base stations (BSs).…”

Section: Introductionmentioning

confidence: 99%

A High Channel Consistency Subarray of Plane-Wave Generators for 5G Base Station OTA Testing

2019

View full text Add to dashboard Cite

A high channel consistency subarray of plane-wave generators (PWG) is described for fifth-generation (5G) base station (BS) over-the-air (OTA) testing. Firstly, the variation of the near field radiation characteristics of the subarray based on the feed amplitude and phase errors of the traditional power divider network is analyzed. The recommended amplitude and phase errors between channels are given. After that, a novel subarray which combines four pyramidal horn antennas and a compact 1:4 waveguide power divider is designed. The optimized perfectly symmetrical zigzag waveguide transmission lines are used to realize consistent power allocation among antenna elements. No intermediate pins are employed, which avoids the significant deterioration of channel consistency caused by assembly errors. The size of the subarray is 4.89 λ0 × 4.97 λ0 × 1.23 λ0 (λ0 is the working wavelength corresponding to the subarray center frequency at 3.5 GHz). The VSWR < 1.5 impedance bandwidth covers 3.4 GHz to 3.6 GHz. The amplitude difference between the four elements of the subarray is less than 0.5 dB, and the phase difference is less than 3°. The simulated and measured results agree well in this design.

show abstract

A Joint Approach for Low-Complexity Channel Estimation in 5G Massive MIMO Systems

Cited by 8 publications

References 29 publications

Performance Optimization of LS/LMMSE Using Swarm Intelligence in 3D MIMO-OFDM Systems

Performance Optimization of LS/LMMSE Using Swarm Intelligence in 3D MIMO-OFDM Systems

A Low Rank Channel Estimation Scheme in Massive Multiple-Input Multiple-Output

A High Channel Consistency Subarray of Plane-Wave Generators for 5G Base Station OTA Testing

Contact Info

Product

Resources

About