Block Distributed Compressive Sensing-Based Doubly Selective Channel Estimation and Pilot Design for Large-Scale MIMO Systems

Gong, Bo; Gui, Lin; Qin, Qibo; Ren, Xiang

doi:10.1109/tvt.2017.2715345

Cited by 27 publications

(28 citation statements)

References 30 publications

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“…In LS-MIMO, because of huge quantity of channel coefficients to be estimated during doubly selective (DS) channel estimation which was a challenging task which leads to pilot overhead in the system. To overcome these challenges, Gong, B. et al [11] had developed a DS channel estimation structure which created on block distributed compressive sensing (BDCS) and innovative pilot design procedure, block discrete stochastic optimization (BDSO). The basis expansion model (BEM) coefficients' common sparsity has to be analyzed on every BEM orders and every transmitting receiving antenna pair within delay area.…”

Section: Related Workmentioning

confidence: 99%

GWO Based Optimal Channel Estimation Technique for Large Scale Mimo in LTE Network

Patil*¹,

Kavipriya²,

Patil³

2019

IJITEE

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The Wireless Systems Are Employed With More Number Of Antennas For Fulfilling The Demand For High Data Rates. In Telecommunication, Lte-A (Long Term EvolutionAdvanced) Is A Well-Known Technology Intended For Wireless Broadband Communication Aimed At Data Terminals And Mobile Devices. Multiple Input Multiple Output (Mimo) Technology Is Used By Lte Which Is Also Known As Fourth Generation Mobile Networks To Attain Very High Data Rates In Downlink And Uplink Channels. Though The Mimo Systems In Massive Mimo Are Provided By Multiple Antennas, The Design Of The Appropriate Non-Erroneous Detection Algorithm Is A Major Challenge. Also, With The Increase In Quantity Of Antennas, The System's Spectral Efficiency Begins To Degrade. So As To Deal With This Issue, A Proper Algorithm Has To Be Utilized For Channel Estimation. The Bio Inspired Algorithms Have Shown Potential In Handling These Issues In Communication And Signal Processing. So, Grey Wolf Optimization (Gwo) Algorithm Is Used In This Approach To Estimate The Most Optimal Communication Channel. Also, The Spectral Efficiency And Data Capacity Are Enhanced Using The Presented Approach. The Proposed Approach’s Performance Is Compared With The Existing Approaches. The Simulation Result Exposes That The Presented Channel Estimation Approach Is Far Better Than Existing Channel Estimation Approaches In Performance Metrics Such As Bit Error Rate, Minimum Delay, Papr, Spectral Efficiency, Uplink Throughput, Downlink Throughput And Mean-Squared-Error

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Section: Related Workmentioning

confidence: 99%

GWO Based Optimal Channel Estimation Technique for Large Scale Mimo in LTE Network

Patil*¹,

Kavipriya²,

Patil³

2019

IJITEE

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“…Furthermore, observing the identical AOA/AOD between the uplink and downlink transmission, a joint uplink and downlink dictionary learning and compressed channel estimation algorithm is proposed to perform downlink channel estimation utilizing information from the simpler uplink training, which further improves downlink channel estimation. In [31] a channel estimator based on block distributed compressive sensing (BDCS) is proposed for the large-scale MIMO systems. BDCS exploits structured sparsity to reduce the pilot overhead.…”

Section: Introductionmentioning

confidence: 99%

Compressive Sensing Based Channel Estimation for Massive MIMO Communication Systems

Waseem

Naveed

Ali³

et al. 2019

Wireless Communications and Mobile Computing

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Massive multiple-input multiple-output (MIMO) is believed to be a key technology to get 1000x data rates in wireless communication systems. Massive MIMO occupies a large number of antennas at the base station (BS) to serve multiple users at the same time. It has appeared as a promising technique to realize high-throughput green wireless communications. Massive MIMO exploits the higher degree of spatial freedom, to extensively improve the capacity and energy efficiency of the system. Thus, massive MIMO systems have been broadly accepted as an important enabling technology for 5th Generation (5G) systems. In massive MIMO systems, a precise acquisition of the channel state information (CSI) is needed for beamforming, signal detection, resource allocation, etc. Yet, having large antennas at the BS, users have to estimate channels linked with hundreds of transmit antennas. Consequently, pilot overhead gets prohibitively high. Hence, realizing the correct channel estimation with the reasonable pilot overhead has become a challenging issue, particularly for frequency division duplex (FDD) in massive MIMO systems. In this paper, by taking advantage of spatial and temporal common sparsity of massive MIMO channels in delay domain, nonorthogonal pilot design and channel estimation schemes are proposed under the frame work of structured compressive sensing (SCS) theory that considerably reduces the pilot overheads for massive MIMO FDD systems. The proposed pilot design is fundamentally different from conventional orthogonal pilot designs based on Nyquist sampling theorem. Finally, simulations have been performed to verify the performance of the proposed schemes. Compared to its conventional counterparts with fewer pilots overhead, the proposed schemes improve the performance of the system.

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“…More recently, CS theory has been employed for DS channel estimation in multiple‐input–multiple‐output OFDM (MIMO‐OFDM) systems . The channel estimation in the MIMO case is more challenging than that in the SISO systems due to using multiple antennas at both the transmit and receive sides.…”

Section: Introductionmentioning

confidence: 99%

“…The work of Ren et al is based upon their earlier work, but it presents a low‐complexity noniterative position‐based ICI elimination method to get ICI‐free pilots for single‐input–multiple‐output OFDM (SIMO‐OFDM) channel estimation. A block DCS‐based channel estimator for large‐scale MIMO systems is developed by Gong et al Ma et al use a pseudorandom noise training in the time domain to obtain the partial common support of the channel. Then, they optimize the pilot locations by a genetic algorithm and employ the structured CS to recover the channel.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the CE‐BEM results in a strictly banded frequency‐domain channel matrix for DS channel. This property is exploited by some existing CS‐based DS channel estimation approaches to decouple the linear equations of different BEM orders or to get the ICI‐free pilots . However, these approaches cannot simply be extended for accurate BEMs without that property.…”

Section: Introductionmentioning

confidence: 99%

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Beyond‐sparsity–based doubly selective channel estimation for high‐mobility multiuser MIMO‐OFDM systems

Karani

Beheshti

Fazel

2019

Trans Emerging Tel Tech

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Wireless communications in high-mobility situations face with doubly selective (DS) fading. In addition, many practical wireless channels exhibit a sparse multipath structure. This paper investigates the DS multiuser multiple-input-multiple-output (MIMO) channel estimation via compressed sensing. The DS channel is characterized by a general sparse basis expansion model (BEM). This model reduces the channel estimation complexity as the channel estimation problem is reduced to estimating the BEM coefficients instead of numerous channel parameters. In addition, a pilot pattern is proposed for multiuser MIMO orthogonal frequency division multiplexing systems, which includes guard and silent pilots to reduce intercarrier interference and interuser interference, respectively. Then, two beyond-sparsity features (ie, block and joint sparsity) of the BEM coefficients are exploited to propose a beyond-sparsity-based channel estimation method. Moreover, a pilot design algorithm is proposed to optimize the pilot positions and enhance the estimator performance. Simulation results demonstrate that the proposed method significantly improves the estimation accuracy over the existing approaches, with a much smaller pilot overhead.Trans Emerging Tel Tech. 2019;30:e3636.wileyonlinelibrary.com/journal/ett

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Block Distributed Compressive Sensing-Based Doubly Selective Channel Estimation and Pilot Design for Large-Scale MIMO Systems

Cited by 27 publications

References 30 publications

GWO Based Optimal Channel Estimation Technique for Large Scale Mimo in LTE Network

GWO Based Optimal Channel Estimation Technique for Large Scale Mimo in LTE Network

Compressive Sensing Based Channel Estimation for Massive MIMO Communication Systems

Beyond‐sparsity–based doubly selective channel estimation for high‐mobility multiuser MIMO‐OFDM systems

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