Frequency-Domain Modeling of OFDM Transmission with Insufficient Cyclic Prefix Using Toeplitz Matrices

Cisek, Grzegorz; Zieliński, Tomasz P.

doi:10.1109/vtcfall.2018.8690835

Cited by 5 publications

(7 citation statements)

References 15 publications

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“…The presented results prove that nonidealities of frequency‐domain channel modeling can be reduced by utilization of the evaluated algorithms 26,27 . An empirical comparison between the BER and BLER characteristics of the OFDM system testbed utilizing time‐ and frequency‐domain channel models clearly shows that utilization of a trivial block‐fading model results in notably different performance metrics compared with the time‐domain TDL model.…”

Section: Resultsmentioning

confidence: 60%

“…It is shown that utilization of a trivial block‐fading model for frequency‐domain channel modeling leads to inaccurate results. However, the presented results prove that the interpolation of ICI proposed in Reference 26 and ISI reconstruction through Toeplitz approximation in Reference 27 improves BER/BLER prediction substantially compared to the trivial block‐fading model.…”

Section: Introductionmentioning

confidence: 64%

“…In order to emulate the impact of the ISI distortion, the model given by Equation (4) can be modified to include the mutual influence of the neighboring OFDM symbols: 27

r_{u} = \sum_{v = 1}^{N_{U E}} false(G_{u}^{(v)} s_{u}^{(v)} + Φ false(s_{u - 1}^{(v)} - 𝒲_{CP} s_{u}^{(v)} false) false) + η_{u} .

…”

Section: Simulation Of Wireless Channel In Time and Frequency Domainmentioning

confidence: 99%

“…Assuming B is approximated using the upper‐triangular Toeplitz matrix

\overset{bold-italic‾}{B}

, the elements of the matrix

Φ

may be calculated using the algorithm presented in Reference 27, consisting of the following steps: Define the vector

ρ

to contain elements of the first row of the matrix

\overset{bold-italic‾}{B}

. The elements are calculated based on the temporal channel impulse response (2):

ρ = {false[0, \dots, 0, h false(n_{u}, N_{DS} - 1 false), \dots, h false(n_{u}, N_{CP} + 1 false) false]}^{T},

where N DS is a delay spread span of the channel and n u is an optimal sampling point of CIR that minimizes the error between B and

\overset{bold-italic‾}{B}

for the OFDMA symbol u . Calculate the vectors

ξ_{0}

and

ξ_{1}

using the weighted inverse digital Fourier transform operation.…”

Section: Simulation Of Wireless Channel In Time and Frequency Domainmentioning

confidence: 99%

“…The frequency domain models are proven to be more computationally efficient in case of multiuser scenarios. Despite that some studies concerning testbeds for the C‐RAN architecture evaluation were reported, 19 the following article is the first to address the problem of efficient fading channel modeling in a process of commercial C‐RAN equipment validation using the fast frequency‐domain HIL‐based approach. Concluding the series of conference publications on the frequency‐domain channel simulation with improved accuracy 26‐28 . New details that were not published before are presented in this article.…”

Section: Introductionmentioning

confidence: 98%

See 4 more Smart Citations

Validation of cloud‐radio access network control unit with intra‐PHY architecture: Hardware‐in‐the‐loop framework based on frequency‐domain channel models

Cisek

Zieliński

2020

Trans Emerging Tel Tech

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In this article, a signal processing framework for hardware‐in‐the‐loop (HIL) validation of the cloud‐radio access network (C‐RAN) control unit (CU) with a crosshaul intra‐PHY architecture split is evaluated. The framework is based on the assumption that data exchanged over the crosshaul interface between the lower and higher parts of the physical layer have the form of frequency‐domain orthogonal frequency division multiplexing (OFDM) subcarriers. Therefore, it is proposed to simulate the effects of a wireless fading channel directly in the frequency domain, instead of the conventionally selected time domain. This approach leads to complexity reduction of the channel simulation model by minimizing the number of required FFT/IFFT operations, at the cost of simulation accuracy in high‐mobility and high‐delay spread scenarios. Analysis of bit error ratio (BER) and block error ratio (BLER) metrics obtained using both time‐ and frequency‐domain channel models is presented in order to validate the proposal and evaluate the accuracy loss caused by the usage of nonideal frequency‐domain models. The frequency‐domain methods are compared against the conventional wide sense stationary uncorrelated scattering (WSSUS) model implementation based on the tapped delay line (TDL). It is shown that the utilization of improved frequency‐domain models instead of the block‐fading model substantially decreases the BER/BLER prediction error in specific scenarios.

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Section: Resultsmentioning

confidence: 60%

Section: Introductionmentioning

confidence: 64%

“…In order to emulate the impact of the ISI distortion, the model given by Equation (4) can be modified to include the mutual influence of the neighboring OFDM symbols: 27

r_{u} = \sum_{v = 1}^{N_{U E}} false(G_{u}^{(v)} s_{u}^{(v)} + Φ false(s_{u - 1}^{(v)} - 𝒲_{CP} s_{u}^{(v)} false) false) + η_{u} .

…”

Section: Simulation Of Wireless Channel In Time and Frequency Domainmentioning

confidence: 99%

“…Assuming B is approximated using the upper‐triangular Toeplitz matrix

\overset{bold-italic‾}{B}

, the elements of the matrix

Φ

may be calculated using the algorithm presented in Reference 27, consisting of the following steps: Define the vector

ρ

to contain elements of the first row of the matrix

\overset{bold-italic‾}{B}

. The elements are calculated based on the temporal channel impulse response (2):

ρ = {false[0, \dots, 0, h false(n_{u}, N_{DS} - 1 false), \dots, h false(n_{u}, N_{CP} + 1 false) false]}^{T},

where N DS is a delay spread span of the channel and n u is an optimal sampling point of CIR that minimizes the error between B and

\overset{bold-italic‾}{B}

for the OFDMA symbol u . Calculate the vectors

ξ_{0}

and

ξ_{1}

using the weighted inverse digital Fourier transform operation.…”

Section: Simulation Of Wireless Channel In Time and Frequency Domainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

See 3 more Smart Citations

Validation of cloud‐radio access network control unit with intra‐PHY architecture: Hardware‐in‐the‐loop framework based on frequency‐domain channel models

Cisek

Zieliński

2020

Trans Emerging Tel Tech

Self Cite

View full text Add to dashboard Cite

show abstract

DNN Based Iterative Detection for High Order QAM OFDM Systems With Insufficient Cyclic Prefix

Cai

Zhao

Shi

2020

2020 IEEE/CIC International Conference on Communications in China (ICCC)

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Deep Learning Based Equalizer for MIMO-OFDM Systems with Insufficient Cyclic Prefix

Sun

Wang

Cai

et al. 2020

2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall)

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In this paper, we study the equalization design for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with insufficient cyclic prefix (CP). In particular, the signal detection performance is severely impaired by inter-carrier interference (ICI) and inter-symbol interference (ISI) when the multipath delay spread exceeding the length of CP. To tackle this problem, a deep learning-based equalizer is proposed for approximating the maximum likelihood detection. Inspired by the dependency between the adjacent subcarriers, a computationally efficient joint detection scheme is developed. Employing the proposed equalizer, an iterative receiver is also constructed and the detection performance is evaluated through simulations over measured multipath channels. Our results reveal that the proposed receiver can achieve significant performance improvement compared to two traditional baseline schemes.

show abstract

Frequency-Domain Modeling of OFDM Transmission with Insufficient Cyclic Prefix Using Toeplitz Matrices

Cited by 5 publications

References 15 publications

Validation of cloud‐radio access network control unit with intra‐PHY architecture: Hardware‐in‐the‐loop framework based on frequency‐domain channel models

Validation of cloud‐radio access network control unit with intra‐PHY architecture: Hardware‐in‐the‐loop framework based on frequency‐domain channel models

DNN Based Iterative Detection for High Order QAM OFDM Systems With Insufficient Cyclic Prefix

Deep Learning Based Equalizer for MIMO-OFDM Systems with Insufficient Cyclic Prefix

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