Adjacent Orthogonal Frequency Division Multiplexing Symbols Research Articles

Cluster-aware channel estimation with deep learning method in deep-water acoustic communications.

In underwater acoustic (UWA) communications, channels often exhibit a clustered-sparse structure, wherein most of the channel impulse responses are near zero, and only a small number of nonzero taps assemble to form clusters. Several algorithms have used the time-domain sparse characteristic of UWA channels to reduce the complexity of channel estimation and improve the accuracy. Employing the clustered structure to enhance channel estimation performance provides another promising research direction. In this work, a deep learning-based channel estimation method for UWA orthogonal frequency division multiplexing (OFDM) systems is proposed that leverages the clustered structure information. First, a cluster detection model based on convolutional neural networks is introduced to detect the cluster of UWA channels. This method outperforms the traditional Page test algorithm with better accuracy and robustness, particularly in low signal-to-noise ratio conditions. Based on the cluster detection model, a cluster-aware distributed compressed sensing channel estimation method is proposed, which reduces the noise-induced errors by exploiting the joint sparsity between adjacent OFDM symbols and limiting the search space of channel delay spread. Numerical simulation and sea trial results are provided to illustrate the superior performance of the proposed approach in comparison with existing sparse UWA channel estimation methods.

Adaptive Channel Estimation for Underwater Acoustic OFDM System in Impulsive Noise Environment

Applying the orthogonal matching pursuit (OMP) to estimate the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) channels is attractive because of its high estimation accuracy and low computational cost. However, most existing OMP-based algorithms suffer the limited estimation accuracy in impulsive noise (IN) cases. Through the studies can be found, only part of channels’ estimation is affected due to the random IN which appears transient and intermittent in time and frequency. Based on this observation, joint time-frequency OMP (JTF-OMP) method is proposed, where the estimation of the affected channels benefits adaptively from that of adjacent channels in time or frequency. It is well known that preliminary Doppler scale estimation is key to the subsequent OMP algorithm, which is difficult to deal with due to the IN. To solve this problem, an adaptive Doppler scale estimation (ADSE) method is proposed. It involves generating two shorter identical cyclic prefixes (CPs) for each OFDM symbol, placed before two adjacent OFDM symbols. The repetition pattern can adaptively defend the IN which appears randomly and shortly in time. Simulation results show that the proposed algorithms integrating JTF-OMP with ADSE can achieve much higher estimation accuracy and better system reliability than the OMP in the IN environment.

200 Gbit/s Photonics-Aided MMW PS-OFDM Signals Transmission at W-Band Enabled by Hybrid Time-Frequency Domain Equalization

Millimeter-wave (MMW) transmission has the advantage of larger available bandwidth compared with traditional wireless communications. Compared with the electrical algorithms, photonics-aided MMW signals generation can support a large modulation bandwidth and is regarded as a potential candidate for future RoF link. In this experiment, we demonstrate a W-band (75 GHz ~ 110 GHz) photonics-aided MMW communication system. To overcome the high-frequency power loss induced by the limited bandwidth of photoelectric devices, we adopt orthogonal frequency division multiplexing (OFDM) signal with hybrid quadrature amplitude modulation (QAM) formats. Moreover, the probabilistic constellation shaping (PCS) technique is used to further improve the system capacity. In the offline digital signal processing (DSP), a time-domain Volterra series based nonlinear equalizer with I/Q multiple input multiple output (MIMO) structure can compensate for the nonlinearity during the fiber and wireless transmission. Finally, a hybrid time-frequency domain least mean square (LMS) equalizer is proposed to eliminate the crosstalk in the adjacent OFDM symbols and subcarriers. With the aid of advanced modulation and hybrid time-frequency domain equalization, 30 Gbaud OFDM MMW signals with hybrid PS-512QAM and PS-128QAM at 92.5 GHz can be successfully transmitted over 1-m wireless link, whose raw transmission rate is 208.4 Gbit/s. It is worth noting that the hybrid time-frequency domain LMS equalizer can increase the maximal AIR to 178.8 Gbit/s.

Parallel Iterative Inter-carrier Interference Cancellation in Underwater Acoustic Orthogonal Frequency Division Multiplexing

In underwater acoustic orthogonal frequency division multiplexing (OFDM) communication system, channel variations would introduce inter-carrier interference (ICI). This gets more severe as mobile speed, carrier frequency or OFDM symbol duration increases. In this paper, we propose a kind of parallel iterative ICI cancellation algorithm to combat that problem. The proposed method extracts channel variations from the adjacent OFDM symbols, and constantly improves the estimation accuracy of ICI through iteration. Finally, simulation and trial in real lake channel verify the availability and stability of the algorithm in the underwater acoustic time varying channel. In addition, this algorithm avoids the matrix inversion, reducing the complexity of the algorithm, and has the obvious advantage in accuracy and speed.

Covariance-Fitting-Based Blind Carrier Frequency Offset Estimation Method for OFDM Systems

In this paper, a new unsupervised carrier frequency offset (CFO) estimation method is proposed for orthogonal frequency division multiplexing (OFDM) systems using constant modulus constellations. Under the assumption that the channel variation is slow within two adjacent OFDM symbols, the influence of the channel on corresponding subcarriers of the two OFDM symbols will be the same. Utilizing this feature, a robust cost function is formulated based on covariance fitting criterion between two nearby OFDM symbols, and the CFO is estimated by minimizing this objective function. The mean square error (MSE) and bit error rate (BER) performance of the proposed covariance-fitting-based CFO estimation method is compared with that of the prominent conventional estimation schemes under noisy multipath channels with high delay spreads and Doppler spreads. The effect of channel length on the performance of the proposed estimator is also investigated. Based on Monte Carlo simulations, it is shown that the proposed method is robust under different channel conditions and provides more accurate CFO estimates.

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.

Robust Blind Carrier Frequency Offset Estimation Algorithm for OFDM Systems

In this paper, a joint covariance power fitting and phase based blind estimation method for carrier frequency offset (CFO) is proposed for orthogonal frequency division multiplexing (OFDM) systems using constant modulus constellations. Based on the assumption that the channel varies slowly within two adjacent OFDM symbols, the influence of the channel on corresponding covariance values and phase of the two OFDM symbols will be the same. Utilizing this, a robust method is formulated based on covariance power fitting criterion and phase information between two nearby OFDM symbols. The mean square error and bit-error-rate performance of the proposed estimation method is compared with that of the prominent conventional estimation schemes under noisy multipath channels with high delay spreads and Doppler spreads. Based on Monte Carlo simulations, it is shown that the proposed method is robust under different channel conditions and provides more accurate CFO estimates.

A New Sparse Channel Estimation and Tracking Method for Time-Varying OFDM Systems

We propose a simple sparse channel estimation and tracking method for orthogonal frequency-division multiplexing (OFDM) systems based on a dynamic parametric channel model, where the channel is parameterized by a small number of distinct paths, each characterized by path delay and path gain, and all parameters are time varying. In the proposed method, we adaptively choose the delay grid and estimate each channel path delay iteratively. To further reduce the complexity, we also propose a tracking algorithm based on the fact that the changes in the channel path number and path delays are small over a few adjacent OFDM symbols. After the physical path delays are estimated, we then estimate the channel path gains by using the polynomial basis expansion model (P-BEM). Simulation results demonstrate the effectiveness of the proposed channel estimation and tracking method in dynamic environments. Compared with the compressive-sensing-based channel estimator using the orthogonal matching pursuit (OMP) algorithm, the new technique proposed here has much lower computational complexity while offering comparable performance.

A Novel Iterative Algorithm for Time-varying Channel Estimation in OFDM systems

A novel iterative channel estimation algorithm for orthogonal frequency-division multiplexing (OFDM) systems in fast time-varying environment is proposed. Instead of directly estimate the original channel taps, the proposed estimation focus on the rest channel part from the original channel taps subtracted by the prior estimation results in the absence of any channel information. The initial results are obtained by time-domain least squares (LS) estimation by inserting zeros-padded training symbols between adjacent OFDM symbols. Monte Carlo simulations demonstrate the superiority of the proposed algorithm after a few iterations over the conventional methods based on complex-exponential basis expansion model (CE-BEM) or generalized complex-exponential basis expansion model (GCEBEM).

An SNR-Assisted Decision-Directed RCFO Estimation Algorithm for Wireless OFDM Systems

An effect of decision errors on the decision-directed (DD) algorithm for residual carrier frequency offset (RCFO) estimation is analyzed, and a new signal-to-noise ratio (SNR)-assisted DD algorithm is proposed to estimate the RCFO in wireless orthogonal frequency-division multiplexing (OFDM) systems. In the algorithm, the received data on the subchannels with high SNRs are employed in the cross-correlation operation between the adjacent OFDM symbols, and the data of the cross correlation on the subchannels are hard decided. With these decisions and the SNR-weighted factors on the subchannels, the new algorithm estimates the RCFO in high accuracy. The simulation results indicate that the proposed scheme achieves a much better performance than the conventional DD schemes.

A novel CDD-OFDM scheme with pilot-aided channel estimation

Cyclic delay diversity (CDD) is a low-complexity standard-conformable transmit diversity scheme for coded orthogonal frequency division multiplexing (OFDM) systems. However, it makes channel estimation more challenging due to the increased frequency-selectivity of the equivalent single-input single-output channel. In this paper, we propose a novel CDD-OFDM scheme with pilot-aided channel estimation for any number of transmit antennas. By alternating and optimizing the cyclic delay parameter over adjacent OFDM symbols, we design a simple yet efficient channel estimation scheme and illustrate its excellent performance for the DVB-T application.