Subspace-Based Blind Channel Estimation for OFDM by Exploiting Cyclic Prefix

Tseng, Chi-Hsiang; Cheng, Yu-Chieh; Chung, Char-Dir

doi:10.1109/wcl.2013.100913.130660

Cited by 18 publications

(7 citation statements)

References 7 publications

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“…Several works in the literature adopt the blind CE techniques for different wireless communication technologies. A subspace based blind CE (SBCE) algorithm that is not affected by null subspace CE errors caused by noise and fading algorithm is proposed for CP-OFDM (ICBCE) schemes in [95]. The performance of this algorithm is compared with two conventional SBCE CE algorithms: RPBCE scheme and RMBCE scheme.…”

Section: Overview Of Blind Channel Estimationmentioning

confidence: 99%

Review of Channel Estimation for Candidate Waveforms of Next Generation Networks

et al. 2019

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The advancement in wireless communication applications encourages the use of effective and efficient channel estimation (CE) techniques because of the varying behaviour of the Rayleigh fading channel. In most cases, the emphasis of most proposed CE schemes is to improve the CE performance and complexity for ensuring quality signal reception and improved system throughput. Candidate waveforms whose designs are based on filter bank multi-carrier (FBMC) modulation techniques such as filter bank orthogonal frequency division multiplexing based on offset quadrature amplitude modulation (OFDM-OQAM), universal filtered multicarrier (UFMC) and generalised frequency division multiplexing based on offset quadrature amplitude modulation (GFDM-OQAM) are no exception to the use of these proposed CE techniques in the literature. These schemes are considered as potential waveform candidates for the physical/media access control layer of the emerging fifth generation (5G) networks. Therefore, pinpoint CE techniques represent an important requirement for these waveforms to attain their full potentials. In this regard, this paper reviews the concept of CE as applicable to these waveforms as well as other waveform candidates under consideration in the emerging 5G networks. Since the design of the majority of the waveform candidates is filter based, a review of the general filter design considerations is presented in this paper. Secondly, we review general CE techniques for candidate waveforms of next generation networks and classify some of the studied CE techniques. In particular, we classify the CE schemes used in filter bank OFDM-OQAM and GFDM-OQAM based transceivers and present a performance comparison of some of these CE schemes. Besides, the paper reviews the performances of two linear CE schemes and three adaptive based CE schemes for two FBMC based waveform candidates assuming near perfect reconstruction (NPR) and non-perfect reconstruction (Non-PR) filter designs over slow and fast frequency selective Rayleigh fading channels. The results obtained are documented through computer simulations, where the performances of the studied CE schemes in terms of the normalised mean square error (NMSE) are analysed. Lastly, we summarise the findings of this work and suggest possible research directions in order to improve the potentials of the studied candidate waveforms over Rayleigh fading channels.

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Section: Overview Of Blind Channel Estimationmentioning

confidence: 99%

Review of Channel Estimation for Candidate Waveforms of Next Generation Networks

et al. 2019

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“…If the reinforcement signal is equal to 1, the action of the k th pair of the automata is finished and the next pair of the automata starts its action. Otherwise, searching continues I iter times in dimension k based on Equations (1) to (6). After each action, the best response and the cell values are updated based on Equations (5) and (5).…”

Section: Population-based Intelligent Channel Estimatorsmentioning

confidence: 99%

“…In addition, the performance of data detection algorithms mainly depends on crucial information accuracy. Various techniques including blind and semi-blind methods [3][4][5][6][7][8] and pilot-based methods [9][10][11][12][13][14][15][16][17][18][19] have been represented for channel estimation. Most of the blind methods intrinsically have low convergence rate and weak performance.…”

Section: Introductionmentioning

confidence: 99%

Channel estimation based on learning automata for OFDM systems

Salehi

Majidi

Neda

2018

Int J Communication

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Summary The abundant benefits of Orthogonal Frequency‐Division Multiplexing and its high flexibility have resulted in its widespread applications in many telecommunication standards. One important parameter for improving wireless system's efficiency is the accurate estimation of channel state information. In the literatures, many techniques have been studied in order to estimate the channel state information. Nowadays, the techniques based on intelligent algorithms such as genetic algorithm and particle swarm optimization (PSO) have attracted attention of researchers. With a very low pilot overhead, these techniques are able to estimate the channel frequency response properly only using the received signals. Unfortunately, each of these techniques suffers a common weakness: they have a slow convergence rate. In this paper, a new intelligent and different method has been presented for channel estimation using learning automata, entitled LA estimator, where the learning automata are search agents, and each pair is responsible for searching 1 complex coefficient of the channel frequency response. This method can achieve an accurate channel estimation with a moderate computational complexity in comparison with GA and PSO estimators. Furthermore, with higher convergence rate, our proposed method is capable of providing the same performance as GA and PSO. For a 2‐path fast fading channel, simulation results demonstrate the robustness of our proposed scheme according to the bit error rate and the mean square error.

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“…Inserting pilot sequences into the transmitted data would reduce the spectral efficiency. To solve this problem, the blind channel estimation method is proposed for OFDM systems [13,14]. The blind channel estimation method does not require inserting pilots at the transmitter.…”

Section: Introductionmentioning

confidence: 99%

Singular Value Decomposition Channel Estimation in STBC MIMO-OFDM System

2019

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The multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technology is the combination of the OFDM and MIMO technologies, which could improve the system capacity and make efficient utilization of the frequency spectrum. This paper utilizes space-time block coding (STBC) to achieve diversity gains and combat the channel fading. However, channel estimation is an essential block for space-time block decoding (STBD). Many channel estimation methods are utilized for the single antenna OFDM system, but they cannot be directly applied to the multiple antennas system due to the interference from other antennas. In this paper, orthogonal pilot sequences are designed to suppress the interference of pilot symbols from other transmit antennas. This paper also derives a minimum mean square error (MMSE) channel estimation method in MIMO-OFDM systems. The MMSE method involves the inverse operation of the channel autocorrelation matrix, which has a large calculation complexity. To further reduce the complexity of the MMSE method, the singular value decomposition (SVD) is used to decompose the channel autocorrelation matrix, which avoids the inverse operation. Simulation results verify that the SVD channel estimation method with comb-type pilots and STBC can be effectively adapted to multipath propagation conditions. Keywords: channel estimation; multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM); space-time block coding (STBC); singular value decomposition (SVD); bit error rate (BER)

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Subspace-Based Blind Channel Estimation for OFDM by Exploiting Cyclic Prefix

Cited by 18 publications

References 7 publications

Review of Channel Estimation for Candidate Waveforms of Next Generation Networks

Review of Channel Estimation for Candidate Waveforms of Next Generation Networks

Channel estimation based on learning automata for OFDM systems

Singular Value Decomposition Channel Estimation in STBC MIMO-OFDM System

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