Orthogonal Frequency Division Multiplexing Channel Estimation Research Articles

Channel estimation and PAPR reduction in OFDM based on dual layers-superimposed training

Superimposed Training (ST) is one of the most appealing channel estimation techniques for Orthogonal Frequency Division Multiplexing (OFDM), to be possibly exploited in 6G. The data and pilot symbols are sharing the same time and frequency resources, and hence, the overhead is significantly reduced. Moreover, the superimposed pilots can be also used for the reduction of the Peak-to-Average Power Ratio (PAPR). However, a joint channel estimation and PAPR reduction procedure has not been addressed yet. In this work, a novel scheme denoted as Dual Layers-Superimposed Training (DL-ST) is proposed for this joint purpose. The Training Sequence (TS) of the first layer is targeted to perform channel estimation, while the TS of a second layer is designed for PAPR reduction and it is made transparent to the first one. Both layers can be independently processed, which implies a reduced complexity. To verify the performance of the proposed technique, the analytical expression of the channel estimation Mean Squared Error (MSE) is derived. Finally, several numerical results further illustrate the performance of the proposal, showing how the MSE and achievable rate are improved while significant PAPR reductions are attained with negligible complexity.

Channel Estimation and Turbo Equalization for Coded OTFS and OFDM: A Comparison

In this letter, we study joint channel estimation and turbo equalization for coded orthogonal time frequency space (OTFS) and orthogonal frequency division multiplexing (OFDM). To the best of our knowledge, this is the first study that compares performance of OTFS and OFDM coded systems using the state-of-the-art message passing (MP) and soft minimum mean square error (MMSE) equalizers, under imperfect channel estimation. We show that the commonly used threshold-based channel estimator incurs noticeable performance loss for OTFS. Hence, we propose a basis expansion model (BEM) channel estimation technique for OTFS, which improves performance of the threshold-based channel estimator and yields a superior performance to OFDM when using small modulations of BPSK and QPSK. Our work also reveals that OTFS can perform inferior to OFDM as the modulation order increases. This is due to the increased 2D detection complexity of OTFS.

Deep Learning-Assisted OFDM Channel Estimation and Signal Detection Technology

The orthogonal frequency division multiplexing (OFDM) technique has received wide attention because of its high spectrum utilization. However, the drawback of inter-subcarrier interference in OFDM systems makes the channel estimation and signal detection performance of OFDM systems with few pilots and short cyclic prefixes (CP) poor. In this paper, we use deep learning to assist OFDM in recovering nonlinearly distorted transmission data. Specifically, we use a self-normalizing network (SNN) for channel estimation, combined with a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU) for signal detection, thus proposing a novel SNN-CNN-BiGRU network structure (SCBiGNet). The simulation results show that the SCBiGNet model outperforms the existing techniques for the different numbers of pilots and lengths of CPs. The BER performance is improved by 0.2~9 dB.

Device-Free Sensing in OFDM Cellular Network

This paper considers device-free sensing in an orthogonal frequency division multiplexing (OFDM) cellular network to enable integrated sensing and communication (ISAC). A novel two-phase sensing framework is proposed to localize the passive targets that cannot transmit/receive reference signals to/from the base stations (BSs), where the ranges of the targets are estimated based on their reflected OFDM signals to the BSs in Phase I, and the location of each target is estimated based on its ranges to different BSs in Phase II. Specifically, in Phase I, we design a model-free range estimation approach by leveraging the OFDM channel estimation technique for determining the delay values of all the two-way BS-target-BS paths, which does not rely on any BS-target channel model. In Phase II, we reveal that ghost targets may be falsely detected in some cases as all the targets reflect the same signals to the BSs, which thus do not know how to match each estimated range with the right target. Interestingly, we show that the above data association issue is not a fundamental limitation for device-free sensing: under the ideal case of perfect range estimation in Phase I, the probability for ghost targets to exist is proved to be negligible when the targets are randomly located. Moreover, under the practical case of imperfect range estimation in Phase I, we propose an efficient algorithm for joint data association and target localization in Phase II. Numerical results show that our proposed two-phase framework can achieve very high accuracy in the localization of passive targets, which increases with the system bandwidth.

A Proposed Channel Estimation Based on Enhanced Sub-carrier Index Modulation and Packet-Discrete Wavelet Transform to Minimize Bit Error Rate

For Orthogonal Frequency Division Multiplexing (OFDM) and other communication systems, many estimating approaches have been developed to estimate the channel state information and lower the Bit Error Rate (BER). These estimating methods, however, are still subject to the influence of large peak powers compared to average powers. Reduced computational complexity is one of the most significant factors to consider while developing a new estimate algorithm. This study aims to provide a novel design of the Packet-Discrete Wavelet Transform (P-DWT) algorithm for channel estimation in wireless OFDM instead of the fast Fourier transform (FFT). It is presented to retrieve the code of a spread spectrum signal and transmitted data bits, and it is compared to particle swarm optimization PSO and least mean square (LMS) optimization. The suggested approach reduces the computing cost of DWT by recognizing the Packet Wavelet Transform (PWT) coefficients and local points, findings utilizing P-DWT channels generated from both models and measurements show that the proposed technique outperforms pilot-based channel estimation in terms of bit error rate under sparseness conditions BER. Moreover, as compared to typical semi-blind approaches, the estimation accuracy is enhanced while computing cost is reduced.

Channel estimation of OFDM in C-band communication systems under different distribution conditions

<p>Orthogonal frequency division multiplexing (OFDM) is a transmission system that uses multiple orthogonal carriers that are sent out at the same time. OFDM is a technique for mobile and wireless communication that has high-efficient frequency utilization, high data-rate transmission, simple and efficient implementation using the fast Fourier transform (FFT) and the inverse fast Fourier transform (IFFT), and reduces inter symbol interference (ISI) by inserting cyclic prefix (CP). One of the most important approaches in an OFDM system is channel estimation. In this paper, the orthogonal frequency division multiplexing system with the Rayleigh channel module is analyzed for different areas. The proposed approach used large numbers of subcarriers to transmit the signals over 64-QAM modulation with pilot add channel estimation. The accuracy of the OFDM system is shown in the measuring of the relationships of peak power to the noise ratio and bit error rate.</p>

Joint MAP channel estimation and data detection for OFDM in presence of phase noise from free running and phase locked loop oscillator

This paper addresses a computationally compact and statistically optimal joint Maximum a Posteriori (MAP) algorithm for channel estimation and data detection in the presence of Phase Noise (PHN) in iterative Orthogonal Frequency Division Multiplexing (OFDM) receivers used for high speed and high spectral efficient wireless communication systems. The MAP cost function for joint estimation and detection is derived and optimized further with the proposed cyclic gradient descent optimization algorithm. The proposed joint estimation and detection algorithm relaxes the restriction of small PHN assumptions and utilizes the prior statistical knowledge of PHN spectral components to produce a statistically optimal solution. The frequency-domain estimation of Channel Transfer Function (CTF) in frequency selective fading makes the method simpler, compared with the estimation of Channel Impulse Response (CIR) in the time domain. Two different time-varying PHN models, produced by Free Running Oscillator (FRO) and Phase-Locked Loop (PLL) oscillator, are presented and compared for performance difference with proposed OFDM receiver. Simulation results for joint MAP channel estimation are compared with Cramer-Rao Lower Bound (CRLB), and the simulation results for joint MAP data detection are compared with “NO PHN” performance to demonstrate that the proposed joint MAP estimation and detection algorithm achieve near-optimum performance even under multipath channel fading.

Efficient interpolation based OMP for sparse channel estimation in underwater acoustic OFDM

To achieve higher accuracy of path delay estimation with lower computational complexity, the orthogonal matching pursuit (OMP) method with interpolation algorithm has been adopted for the sparse channel estimation, such as underwater acoustic (UWA) communication channels. For an orthogonal frequency division multiplexing (OFDM) system with uniform pilots, estimating the path delay by OMP in each its iteration is equivalent to the frequency estimation of single-tone signals with discrete Fourier transform (DFT). In this paper, based on the existing frequency estimation algorithms, we propose two novel interpolation based OMP methods for baseband sampling grid and over-sampling grid respectively, aiming at improving path delay estimation efficiency. Moreover, the closed-form of Cramer-Rao lower bound (CRLB) for single path delay estimation is derived to serve as a benchmark for simulations. The performances of the two proposed methods are verified by both simulations and UWA communication experiment in Yellow Sea, China, which show a higher estimation accuracy than that of the traditional OMP method, and the similar estimation performance but lower computational complexity than that of the existing high-accuracy interpolation based OMP method.

Incorporation of prior knowledge into sparse time dispersive OFDM channel estimation via weighted atomic norm minimisation

A new estimator for sparse time dispersive channels in pilot aided orthogonal frequency division multiplexing (OFDM) systems is developed by considering prior knowledge on channel time dispersions. The authors propose a weighted atomic norm minimisation (WANM) in order to incorporate the prior information into the estimator. The dual of the WANM is then converted to a tractable semidefinite programming using positive trigonometric polynomial theory. After solving the dual problem, the channel response is identified by solving a least squares approach. In this work, they assume that time dispersions associated delays can take any value with a mild minimum separation condition on the normalised interval [ 0 , 1 ) . The performance of the new estimator is compared with conventional approaches. With respect to the pilot number and signal to noise ratio (SNR), simulation results reveal that the proposed estimator performs superior to that of traditional methods. It is shown that both a lower SNR and number of pilots are required to achieve the same mean square error reported in previous works.

Performance evaluation of high mobility OFDM channel estimation techniques

In wireless communication, Orthogonal Frequency Division Multiplexing (OFDM) has been adopted due to its robustness to multipath fading and high data rate transmissions. At the other hand, the performance of OFDM systems severely degraded due to multi-path fading and Doppler frequency shifts in mobile systems, which causes inter-carrier-interference (ICI). Thus, Estimation of channel parameters is required at the receiver using a pre designed estimator where pilot tones are inserted in each OFDM symbol. In this paper, a random pilot data are generated and inserted in each OFDM symbol at equally spaced locations. The performance test of Least Square (LS) and Linear Minimum Mean Square (LMMSE) estimation methods are proposed with Discrete Fourier Transform (DFT) based on both LS and LMMSE, where different ITU channel models are considered in order to compare their performance for data transmission in high mobile systems with different Doppler frequencies exceeds 200 Hz and minimal number of pilots.

Enhanced DFT-Based Channel Estimator for Leakage Effect Mitigation in OFDM Systems

The leakage effect occurs in a DFT-based channel estimator (CE) due to the presence of guard bands, where the energy of the channel impulse response (CIR) is dispersed to other estimated taps. Furthermore, some of the CIR energy leaks outside of the estimation range and fails to be captured in the channel estimation. This letter presents an enhanced DFT-based CE for orthogonal frequency-division multiplexing (OFDM) systems. The proposed method is composed of two techniques ( spectral extrapolation and circular shift ) to mitigate the leakage effect, followed by a denoising post-processing to enhance the estimation accuracy. Through performance analysis, it is demonstrated that the spectral extrapolation eliminates the leakage effect corresponding to the first CIR tap, and the circular shift improves estimation accuracy by incorporating the leaked CIR energy in the channel estimation. The simulation results show that the proposed method has superior performance compared with conventional CEs. In addition, the proposed method is feasible from a computational complexity perspective.

Underwater Acoustic OFDM Channel Estimation with Unknown Sparsity

Underwater acoustic channels estimation accuracy seriously affects the demodulation performance of the receiver in underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) communications. Many channel estimation algorithms based on compressed sensing (CS) have been proposed. However, these algorithms usually needs the information of sparsity, while the information of sparsity is unavailable in many UWA OFDM communications. In this paper, a novel channel estimation algorithm based on fractional Fourier transform (FRFT) and orthogonal matching pursuit (OMP) is proposed, FRFT is introduced to process the synchronization signal, so sparsity is estimated. OMP is adopted to reconstruct channel impulse response (CIR). The most innovative feature of the proposed algorithm is the estimation of sparsity, which makes OMP algorithm more practical. Simulation results show that the proposed algorithm outperforms the least square (LS) and in UWA OFDM communication.

Joint Mutual Coherence and Total Coherence Pilot Design for OFDM Channel Estimation

Existing works design the pilot based on the mutual coherence criterion or total coherence criterion alone. However, using the mutual coherence criterion or total coherence criterion alone to design the pilot will yield the sensing matrix which corresponds to a certain pilot with a large total coherence or mutual coherence. However, the sensing matrix with a large mutual coherence or total coherence will deteriorate the sparse recovery performance and is not optimal from the perspective of the sparse recovery. Therefore, in this paper, we jointly design the pilot based on both the mutual coherence criterion and the total coherence criterion for sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems. We first prove that the optimal pilot pattern with respect to the total coherence criterion is the cyclic difference set and given a pilot pattern, the pilot power allocation can be cast as a second order cone programming problem with respect to the total coherence criterion. A joint mutual coherence and total coherence pilot design algorithm is proposed to design the pilot jointly while cyclic difference set does not exist for practical OFDM systems. The proposed algorithm first optimizes the pilot pattern with the mutual coherence criterion assuming that the pilot power is equal. After obtaining the pilot pattern, the pilot power is allocated with the total coherence criterion. Simulation results have shown that the proposed pilot design algorithm can achieve the best performance in terms of normalized mean square error and bit error rate in comparison with existing pilot design methods.

Channel Estimation Based on Compressed Sensing Technique for Low-Voltage Power Line Communication

Broadband low-voltage power line communication (PLC) has many advantages including less investment cost, construction speed, and convenient access. Since the orthogonal frequency division multiplexing (OFDM) technology has strong anti-jamming and anti-frequency selective fading characteristics naturally it becomes a better low voltage power line communication solution. We proposed an OFDM channel estimation method based on compressed sensing (CS) technique according to the channel characteristics of low-voltage power lines. CS algorithm in OFDM system was discussed and an orthogonal matching pursuit (OMP) algorithm was applied to reconstruct the PLC channel information. The simulation results showed that the communication channel estimation method based on CS technique was feasible in PLC system, and the validity of information transmission in OFDM systems can be enhanced.

Channel Estimation Based on Statistical Frames and Confidence Level in OFDM Systems

Channel estimation is an important module for improving the performance of the orthogonal frequency division multiplexing (OFDM) system. The pilot-based least square (LS) algorithm can improve the channel estimation accuracy and the symbol error rate (SER) performance of the communication system. In pilot-based channel estimation, a certain number of pilots are inserted at fixed intervals between OFDM symbols to estimate the initial channel information, and channel estimation results can be obtained by one-dimensional linear interpolation. The minimum mean square error (MMSE) and linear minimum mean square error (LMMSE) algorithms involve the inverse operation of the channel matrix. If the number of subcarriers increases, the dimension of the matrix becomes large. Therefore, the inverse operation is more complex. To overcome the disadvantages of the conventional channel estimation methods, this paper proposes a novel OFDM channel estimation method based on statistical frames and the confidence level. The noise variance in the estimated channel impulse response (CIR) can be largely reduced under statistical frames and the confidence level; therefore, it reduces the computational complexity and improves the accuracy of channel estimation. Simulation results verify the effectiveness of the proposed channel estimation method based on the confidence level in time-varying dynamic wireless channels.

Model-based compressed sensing algorithms for MIMO- OFDM channel estimation

High data rates on the wireless channel can be achieved by combining orthogonal frequency division multiplexing (OFDM) with multiple input multiple output (MIMO) communication modulation scheme. MIMO-OFDM system impulse response of the channel is approximately sparse. Sparse channelestimation can be done using Compressive Sensing (CS) techniques. In this paper, a low complexity model based CoSaMp Compressive Sensing (CS) algorithm with conventional tools namely Least Square (LS) and Least Mean Square (LMS) are used for MIMO-OFDM channel estimation. Simulation results show amodel based CoSaMP for MIMO-OFDM channel estimation with LMS tool the Normalized Mean Square Error(NMSE)reduced by 34%with very reduced complexity.

An enhanced channel estimation technique for CDD OFDM system over time varying channels

Cyclic delay diversity (CDD), employing multiple antennas provides increased frequency-selectivity and thereby achieves frequency diversity using forward error correction in orthogonal frequency division multiplexing (OFDM). However, channel estimation of CDD OFDM becomes crucial over frequency-selective fading channels. In this paper, we propose a low-complexity channel estimation technique for CDD OFDM systems over time-varying channels using basis expansion model. We first analyze the frequency correlation of CDD OFDM channels and then design scattered pilot structure for estimating the channel coefficients. In the proposed channel estimator, Slepian sequences are employed estimate the channel impulse response by exploiting the frequency correlation of CDD OFDM over time-varying channel. Simulation results demonstrated that the proposed channel estimator achieves better mean square error and bit-error-rate performance compared with that of existing methods over time-varying channels.

Effective Low-Complexity Optimization Methods for Joint Phase Noise and Channel Estimation in OFDM

Phase noise correction is crucial to exploit full advantage of orthogonal frequency division multiplexing (OFDM) in modern high-data-rate communications. OFDM channel estimation with simultaneous phase noise compensation has therefore drawn much attention and stimulated continuing efforts. Existing methods, however, either have not taken into account the fundamental properties of phase noise or are only able to provide estimates of limited applicability owing to considerable computational complexity. In this paper, we have reformulated the joint estimation problem in the time domain as opposed to existing frequency-domain approaches, which enables us to develop much more efficient algorithms using the majorization-minimization technique. In addition, we propose a method based on dimensionality reduction and the Bayesian Information Criterion (BIC) that can adapt to various phase noise levels and accomplish much lower mean squared error than the benchmarks without incurring much additional computational cost. Several numerical examples with phase noise generated by free-running oscillators or phase-locked loops demonstrate that our proposed algorithms outperform existing methods with respect to both computational efficiency and mean squared error within a large range of signal-to-noise ratios.

A survey on OFDM channel estimation techniques based on denoising strategies

Channel estimation forms the heart of any orthogonal frequency division multiplexing (OFDM) based wireless communication receiver. Frequency domain pilot aided channel estimation techniques are either least squares (LS) based or minimum mean square error (MMSE) based. LS based techniques are computationally less complex. Unlike MMSE ones, they do not require a priori knowledge of channel statistics (KCS). However, the mean square error (MSE) performance of the channel estimator incorporating MMSE based techniques is better compared to that obtained with the incorporation of LS based techniques. To enhance the MSE performance using LS based techniques, a variety of denoising strategies have been developed in the literature, which are applied on the LS estimated channel impulse response (CIR). The advantage of denoising threshold based LS techniques is that, they do not require KCS but still render near optimal MMSE performance similar to MMSE based techniques. In this paper, a detailed survey on various existing denoising strategies, with a comparative discussion of these strategies is presented.

Distributed compressed sensing estimation of underwater acoustic OFDM channel

Orthogonal frequency division multiplexing (OFDM) is recently drawing more and more attention for its high bandwidth efficiency over underwater acoustic (UWA) channels. However, the classic OFDM channel estimation algorithms, e.g. Least Square (LS), Minimum Mean Square Error (MMSE) are subject to significant performance degradation caused by doubly selective UWA channels. It has been recognized that the sparsity contained in UWA channels offers the possibility to improve the performance by compressed sensing (CS) estimation methods such as Orthogonal Matching Pursuit (OMP). Moreover, it has also been observed that multipath arrivals associated with adjacent OFDM symbols usually exhibit varying magnitude but similar delay, which means that UWA channels of several continuous symbols can be modeled as sparse sets with common support. In this paper, a Distributed Compressed Sensing (DCS) method is proposed to transform the problem of OFDM channel estimation into reconstruction of joint sparse signals. By exploiting this type of joint sparsity among adjacent OFDM symbols, we establish the DCS OFDM channel model, and then utilize the Simultaneous Orthogonal Matching Pursuit algorithm (SOMP) to optimize the model. Finally the experimental performance under field test is provided to illustrate the superiority of the proposed DCS channel estimation method, compared to the classic algorithm as well as CS counterparts.