Channel Estimation for OFDM Systems Using MMSE and LS Algorithms

Srividhya, R; Jayachandran, T; M, Anto Bennet; Rajmohan, V

doi:10.1109/icoei53556.2022.9777139

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…) By preparing several matrices L P using the Conjugate Gradient Method (CGM), a predictive power delay profile f h (hml) is constructed through estimation models to update R. Through multiple iterations of the CGM, sufficient performance can be achieved, effectively suppressing computational complexity while ensuring performance [26,25]. This approach aligns well with the characteristics of high-speed rail communication channels, enhancing algorithm efficiency and performance.…”

Section: Solution For High Dynamics and Doppler Effect Issuesmentioning

confidence: 97%

“…To further optimize communication systems in high-speed mobility scenarios, spectrum estimation and signal processing techniques are utilized to mitigate the Doppler effect. By combining existing MMSE algorithms [24,25] with channel prediction and periodic information described in Equ. (1), parameter estimation and channel matrix update mechanisms are enhanced by incorporating predictable coefficients.…”

Section: Solution For High Dynamics and Doppler Effect Issuesmentioning

confidence: 99%

See 1 more Smart Citation

Periodic Prediction Based Integrated Solutions for Wireless Communication and Edge Computing in Smart Railway Systems

Ren,

Song,

Long

et al. 2024

Preprint

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This paper addresses the challenges of wireless communication and intelligence in smart railway system under high-speed mobile environments, particularly the issues of Doppler effect and dynamic variability. Solutions that integrate wireless communication and edge computing is proposed. The system model based on the operational characteristics of high-speed trains is established. By analyzing historical data and employing statistical methods, the probability distribution of high-speed train operation times is studied, revealing the operational patterns across different intervals. On this foundation, an intelligent task allocation model is proposed, which optimizes the deployment of edge servers and task allocation strategies to minimize task processing costs while ensuring the stability of communication links. To address the Doppler effect and selective channel enhancement issues in high-speed mobile environments, channel prediction with periodic information is used to reduce interference and ensure the reliability of data transmission. Furthermore, intelligent scheduling system based on edge computing with predictive models is established, supporting advanced scheduling and optimizes train timetables. The algorithm is based on established scheduling rules, effectively managing the high-speed railway system and improving system efficiency. Compared with existing scheduling algorithms, the proposed method demonstrates significant advantages in reducing signal distortion, enhancing signal reliability, and optimizing resource utilization and costs.

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Section: Solution For High Dynamics and Doppler Effect Issuesmentioning

confidence: 97%

Section: Solution For High Dynamics and Doppler Effect Issuesmentioning

confidence: 99%

Periodic Prediction Based Integrated Solutions for Wireless Communication and Edge Computing in Smart Railway Systems

Ren,

Song,

Long

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…MMSE channel estimation algorithm is based on LS estimation algorithm and mitigates the impact of noise. 14 The estimated signal by the MMSE is given by:…”

Section: Mmse Channel Estimationmentioning

confidence: 99%

Performance of mode division multiplexing system based on generalized polarization transformation with channel estimation

Shen,

Zhang,

Qing

et al. 2023

Fourteenth International Conference on Information Optics and Photonics (CIOP 2023)

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“…Alternatively, maximum likelihood estimation (ML) allows the receiver to perform not only channel estimation but also data detection [20]. Channel estimation at pilot frequencies can be performed not only using LS but also using minimum mean square error (MMSE) or least mean square (LMS), where MMSE has been shown to work much better than LS [21,22]. Another method that is widely used in OFDM channel estimation is linear MMSE (LMMSE) [23,24], since it is optimal for minimizing the root-mean-square error of channel estimates in the presence of Rayleigh.…”

Section: Introductionmentioning

confidence: 99%

Subspace Blind Channel Estimation Methods in OFDM Systems under Multi-path Channels

Al-Qadhi,

Abdul-Rahaim,

Sahib

et al. 2024

Preprint

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In this paper, we compare two methods of subspace blind channel estimation in orthogonal frequency division multiplexing (OFDM) systems using different guard intervals under a multi-path channel with an exponentially decaying power profile. In both methods, we propose the same solution to solve the scalar ambiguity problem, which is a common problem in subspace blind estimation methods, by using a single pilot signal. The bit error rate (BER) and the normalized mean square error (NMSE) of the estimated channel vector were compared under various signal-to-noise ratio (SNR) and different numbers of transmitted OFDM symbols. The impact of using different positions of the pilot signal is also demonstrated. The results of channel estimation, data detection, and system complexity show that the zero padding (ZP) model is superior to the model with a cyclic prefix (CP).

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Channel Estimation for OFDM Systems Using MMSE and LS Algorithms

Cited by 6 publications

References 10 publications

Periodic Prediction Based Integrated Solutions for Wireless Communication and Edge Computing in Smart Railway Systems

Periodic Prediction Based Integrated Solutions for Wireless Communication and Edge Computing in Smart Railway Systems

Performance of mode division multiplexing system based on generalized polarization transformation with channel estimation

Subspace Blind Channel Estimation Methods in OFDM Systems under Multi-path Channels

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