Sparse Channel Estimation Techniques in OFDM Systems
Summary
Sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems leverages the fact that multipath radio channels often exhibit only a few significant taps amid many negligible ones. Traditional pilot‐assisted methods require dense pilot insertion, which increases overhead and reduces spectral efficiency. In response, compressive sensing (CS) has emerged as a powerful framework to reconstruct sparse channel impulse responses from a reduced set of pilot measurements. Greedy algorithms such as orthogonal matching pursuit and CoSaMP, alongside iterative thresholding methods, enable accurate recovery by exploiting sparsity priors. Deterministic and randomised pilot‐allocation schemes have been devised to minimise mutual coherence of the sensing matrix, thereby improving estimation fidelity. Recent advances also address non‐sample-spaced channel taps through refined delay‐tracking grids and novel residual‐norm formulations. Complementary signal‐processing tools, such as wavelet denoising, have been integrated to suppress measurement noise prior to sparse recovery. Collectively, these techniques aim to reduce pilot overhead, lower computational complexity and enhance resilience to channel dynamics in evolving wireless standards such as 5G and beyond.
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Sparse Channel Estimation Techniques in OFDM Systems publication trend
The graph below shows the total number of articles in sparse channel estimation techniques in ofdm systems across all publications each year (not limited to Nature Index journals).
Technical terms
Orthogonal Frequency Division Multiplexing (OFDM): A multi-carrier modulation scheme dividing the channel into orthogonal subcarriers to combat frequency-selective fading.
Sparse Channel Estimation: A technique exploiting the fact that only a few channel impulse components carry significant energy, enabling reduced-pilot recovery.
Compressive Sensing (CS): A signal-processing framework that reconstructs sparse signals from fewer measurements than conventional sampling requires.
Pilot Symbol: A predefined reference signal inserted into specific subcarriers to facilitate channel estimation at the receiver.
Mutual Coherence: A metric quantifying the largest correlation between columns of a sensing matrix; lower coherence improves CS recovery.
Wavelet Denoising: A method using wavelet transforms to separate noise from signal features by thresholding wavelet coefficients.
Non-sample-spaced Channel: A propagation model in which multipath delays do not coincide with the discrete sampling grid, complicating standard estimation.
References
- Deterministic pilot pattern allocation optimization for sparse channel estimation based on CS theory in OFDM system. EURASIP Journal on Wireless Communications and Networking (2019).
- Efficient Compressed Sensing Based Non-Sample Spaced Sparse Channel Estimation in OFDM System. IEEE Access (2019).
- Compressed Sensing Channel Estimation Algorithm Combined with Wavelet Denoising. Journal of Physics Conference Series (2023).
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