Efficient Compressed Sensing Based Non-Sample Spaced Sparse Channel Estimation in OFDM System

Xie, Hui; Wang, Yide; Andrieux, Guillaume; Ren, Xinmin

doi:10.1109/access.2019.2941152

Cited by 7 publications

(6 citation statements)

References 23 publications

(61 reference statements)

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“…The DMRS length must be carefully considered to strike a balance between DMRS overhead and block error performance. The study conducted by authors in [16] indicates that the optimal channel estimation performance is achieved when the DMRS length adequately captures the predominant energy of the channel impulse response while estimating the channel.…”

Section: B Length Of Dmrsmentioning

confidence: 99%

“…This is the author's version which has not been fully edited and It can be implied from (18) that obtaining the initial n ′ samples of h i t (n ′ ) allows capturing its dominant channel energy. The findings from [16] recommend that the DMRS length should encompass the dominant energy of the channel impulse response. Furthermore, [16] proposes that for accurate estimation of an impulse response with a length of n ′ , the corresponding DMRS sequence should be of minimum length n ′ .…”

Section: B Length Of Dmrsmentioning

confidence: 99%

See 1 more Smart Citation

Pre-DFT Multiplexing of Reference Signals and Data in DFT-s-OFDM Systems

Gudimitla,

Khan,

Amuru

et al. 2024

IEEE Open J. Commun. Soc.

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The current 5 th Generation-New Radio (5G-NR) systems are designed to meet the demands of various applications, offering high data rates, low latency, and high reliability. In the current 5G-NR systems, the Discrete Fourier Transform-spread-Orthogonal Frequency Division Multiplexing (DFT-s-OFDM) waveform is the most commonly employed waveform to transmit user data in uplink transmissions, specifically in coveragelimited scenarios. To enable coherent data demodulation, pilot signals, commonly referred to as Demodulation Reference Signals (DMRS), are transmitted along with data-carrying DFT-s-OFDM symbols. However, in the current DFT-s-OFDM architecture, the DMRS and data are transmitted on distinct OFDM symbols. This time separation necessitates the demodulation process to commence only after receiving the initial DMRS symbol, resulting in significant processing delays. Furthermore, the existing architecture consumes more DMRS resources and necessitates complex time interpolation techniques to support users with high mobility. To address these challenges, we propose an improved DFT-s-OFDM architecture that enables instantaneous data demodulation, leading to a substantial reduction in processing delays. Furthermore, despite not utilizing any time interpolation techniques, the proposed method effectively caters to high-speed users, thereby conserving computational resources and hence contributing to minimizing the system complexity and latency. We thoroughly investigate the proposed architecture and evaluate its performance in different simulation settings. The results demonstrate that the proposed architecture significantly improves packet error performance, particularly in high Doppler scenarios, while using 16% lesser DMRS overhead. This reduction in DMRS overhead frees up additional resources for data transmission, ultimately enhancing data rates.

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Section: B Length Of Dmrsmentioning

confidence: 99%

Section: B Length Of Dmrsmentioning

confidence: 99%

Pre-DFT Multiplexing of Reference Signals and Data in DFT-s-OFDM Systems

Gudimitla,

Khan,

Amuru

et al. 2024

IEEE Open J. Commun. Soc.

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“…We draw on [31] to analyze the chaotic behavior of the NC-sine map. The expression (8) of the NC-sine can be transformed into (10).…”

Section: ) a Performance Analysis Of The Nc-sine Chaotic Mapmentioning

confidence: 99%

“…Compressive sensing (CS) [1], which breaks through the Nyquist sampling theorem, has outstanding advantages for reducing signal storage space, as well as improving signal transmission in real-time and high efficiency. As a result, CS is widely used in image processing [2]- [4], spectrum sensing [5], [6], channel estimation [7], [8], and other fields. The implementation of CS for signals involves three main components: sparse representation, compression, and reconstruction [9].…”

Section: Introductionmentioning

confidence: 99%

Design of a Dynamic Sparse Circulant Measurement Matrix Based on a New Compound Sine Chaotic Map

et al. 2022

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The performance of the measurement matrix is always the key to affecting the application of compressed sensing in engineering practice. The measurement matrix designed based on the chaotic map is easy to implement in physical circuits, but the weak chaotic behavior and small chaotic interval of the common one-dimensional chaotic map directly affects the signal reconstruction accuracy. To solve this problem, this paper uses the ratio form of the logistic chaotic map to the simple quadratic chaotic map to improve the sine chaotic map, and obtain a new type of compound sine (NC-sine) chaotic map. Its good chaotic behavior and chaotic interval expansion characteristics are verified by the bifurcation diagram, the Lyapunov exponent, and the complexity analysis. Based on the NC-sine chaotic map, a dynamic sparse circulant (DSC) measurement matrix with adaptive zero-setting elements is designed. The simulation results show that compared with the sine measurement matrix, the reconstruction success rate of the DSC measurement matrix is increased by 5% and 9.69% on average for a one-dimensional signal when the measurements and sparsity change, respectively. The peak signal-to-noise ratio of the reconstructed twodimensional signals under different compression rates is improved by more than 0.92 dB on average, and the reconstruction efficiency is higher. The average structural similarity of the reconstructed signals at different initial values is improved by more than 0.027 compared to the Gaussian measurement matrix. This can then be utilized for the promotion of signal transmission in rate and accuracy.

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“…The use of high-performance wireless communication systems is steadily growing [1]. As a multicarrier modulation technique, the orthogonal frequency division multiplexing (OFDM) technique can provide reliable high data rate transmission in different communication scenarios [2,3].…”

Section: Introductionmentioning

confidence: 99%

Channel Estimation and Carrier Frequency Offset Compensation in Orthogonal Frequency Division Multiplexing System Using Adaptive Filters in Wavelet Transform Domain

Fereydouni

Charmin

Vahdaty

et al. 2020

IJE

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In this paper, a combination of channel, receiver frequency-dependent IQ imbalance, and carrier frequency offset estimation under short cyclic prefix length is considered in orthogonal frequency division multiplexing system. An adaptive algorithm based on the set-membership filtering algorithm is used to compensate for these impairments. In short CP length, per-tone equalization structure is used to avoid inter-symbol interference. Due to CFO impairment and IQ imbalance in the receiver, we will expand the PTEQ structure to a two-branch structure. This structure has high computational complexity, so using the set-membership filtering idea with variable step size while reducing the average computation of the system can also increase the convergence speed of the estimates. On the other hand, applying wavelet transform on each branch of this structure before applying adaptive filters will increase the estimation speed. The proposed algorithm will be named SMF-WP-NLMS-PTEQ. The results of the simulations show better performance than the usual adaptive algorithms. Besides, estimation and compensation of channel effects, receiver IQ imbalance and carrier frequency offset under short CP can be easily accomplished by this algorithm.

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Efficient Compressed Sensing Based Non-Sample Spaced Sparse Channel Estimation in OFDM System

Cited by 7 publications

References 23 publications

Pre-DFT Multiplexing of Reference Signals and Data in DFT-s-OFDM Systems

Pre-DFT Multiplexing of Reference Signals and Data in DFT-s-OFDM Systems

Design of a Dynamic Sparse Circulant Measurement Matrix Based on a New Compound Sine Chaotic Map

Channel Estimation and Carrier Frequency Offset Compensation in Orthogonal Frequency Division Multiplexing System Using Adaptive Filters in Wavelet Transform Domain

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