Channel Impulse Response Estimation Research Articles

Subcarrier modulation identification of underwater acoustic OFDM based on block expectation maximization and likelihood

The low identification rate of the Orthogonal frequency division multiplexing (OFDM) based subcarrier modulation in underwater acoustic multipath channel is an important issue. Therefore, we proposed a novel Expectation Maximization-Block-Quasi Hybrid Likelihood Ratio Test (EM-Block-QHLRT) method which effectively improved the identification rate while using the blind channel impulse response (CIR) estimation and likelihood. Initially, CIR is obtained by using clustering, and then the CIR is updated iteratively by EM-Block method. Further, the subcarrier modulation is identified using QHLRT. The influence of iteration times, the length of symbol and influence of the number of blocks in EM are analyzed by using extensive simulation. The identification rate of subcarrier modulation of binary phase shift keying (BPSK), QPSK, 8PSK and 16QAM are presented using different signal noise ratio (SNR). In addition, the identification rate of subcarrier modulation based on Average Likelihood Rate Test (ALRT) as well as QHLRT with EM are compared. Simulation results showed that the identification rate of the proposed EM-Block-QHLRT method can reach to more than 90% when SNR is higher than 5 dB. Finally, the performance of the proposed EM-Block-QHLRT method is verified using sea trial data.

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.

Joint Adaptive Blind Channel Estimation and Data Detection for MIMO-OFDM Systems

In order to track a changing channel in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, it is a priority to estimate channel impulse response adaptively. In this paper, we propose an adaptive blind channel estimation method based on parallel factor analysis (PARAFAC). We used an exponential window to weigh the past observations; thus, the cost function can be constructed via a weighted least squares criterion. The minimization of the cost function is equivalent to the decomposition of a third-order tensor, which consists of the weighted OFDM data symbols. By preserving the Khatri-Rao product, we used a recursive least squares solution to update the estimated subspace at each time instant, then the channel parameters can be estimated adaptively, and the algorithm achieves superior convergence performance. Simulation results validate the effectiveness of the proposed algorithm.

Improvement of FER using Differential Space-Time Block Code

Over recent years, various modulation and coding techniques have been proposed in MIMO wireless communication systems. A MIMO system uses the concept of spatial diversity which is very successful and promising technique. When a coherent transmission system is considered, the estimation of radio channel impulse response is done precisely. In MIMO systems, the radio channel is estimated among every transmitting and receiving antennas such that the complexity can be increased. For this reason, in MIMO systems differential modulation schemes are estimated. A Differential Space-Time Block Code (DSTBC) is useful in the Raleigh fading channel as they do not require channel estimates. Space-time coding with MIMO antennas at transmitting and receiving reduces the consequences of fading in multiple paths and therefore the performance of digital transmission throughout wireless radio channel can be improved. So it has been presumed that perfect CSI is available at the receiver and coherent detection is employed. This paper presents improvement of Frame Error Rate (FER) for differential space-time block code using various Doppler spectra. When the channels estimates are not available the DSTBC system noticed that at SNR of 10 dB, for two transmitting and four receiving antennas the FER is 0.0067 for rounded Doppler spectrum. The differential schemes attains full transmit diversity owing to orthogonal designs. However, the receiver or the transmitter needs the channel state information so these differential schemes are 3 dB worse than the STBC with coherent detection.

MIMO Time Domain Equalizer Design for Long Reach xDSL MIMO Channel Shortening

In discrete multi-tone (DMT) transmission based digital subscriber line (DSL) systems, a cyclic prefix (CP) is added to each symbol before transmission, where the length of the CP is larger than the estimated channel impulse response (CIR) length. This ensures the elimination of inter-symbol interference (ISI) and inter-carrier interference (ICI) between the carriers of the same symbol, and allows for single tap frequency domain equalizers and crosstalk cancellation at the receiver. Recently, long reach xDSL (LR-xDSL) has been proposed to extend the reach of conventional DSL systems. With the extended loop lengths, the required CP length increases, in order to match the larger CIR length. The longer CP adds a large overhead and results in overall throughput loss. A more efficient way to deal with extended loop lengths is to use a channel shortening filter - commonly referred as a time domain equalizer (TEQ), to reduce the length of the CIR to the length of CP. This paper focuses on minimum mean square error (MMSE) based multiple input multiple output (MIMO) TEQ design for LR-xDSL MIMO channel shortening. Constraints are applied to the minimization problem to eliminate the trivial solution. This paper proposes two new constraints for the MMSE based MIMO TEQ design for upstream scenarios, which result in a lower complexity and provide better (or similar) performance compared to existing MMSE based MIMO TEQ design methods. Furthermore, a diagonal MIMO TEQ with lower memory requirement and lower computational complexity is presented based on the proposed constraints, which can be applied in upstream as well as downstream scenarios.

Channel Estimation Based on Adaptive Denoising for Underwater Acoustic OFDM Systems

Underwater acoustic (UWA) communications systems suffer from a low signal-to-noise ratio (SNR) and a doubly selective channel, which are caused by many limitations, such as high propagation loss, slow propagation speed, and time-varying environmental factors. To overcome a low SNR, various diversity techniques are often adopted in UWA communications. However, such diversity can only be exploited with knowledge of the channel. Accordingly, channel estimation needs to be performed without obtaining the benefit of diversity. Moreover, accurate side information that can support a channel estimator (CE) is difficult to acquire at a low SNR under doubly selective channel. In this article, a novel CE based on an adaptive denoising is proposed for UWA orthogonal frequency-division multiplexing (OFDM) systems. The proposed method exploits two different types of pilot symbols. Channel impulse response (CIR) is estimated based on primary pilot symbols. By minimizing the squared error of the received secondary pilot symbols, a near-optimal denoising window is adaptively determined based on the channel length for the given CIR estimate. The proposed method does not require a priori information about channel statistics and SNR values. Analysis on the effect of denoising and the performance of the proposed denoising window estimator are also presented. Simulation and at-sea experiments verify that the proposed method has superior performance, compared with conventional CEs over diverse channel conditions. Complexity analysis shows that the proposed method is computationally efficient. Therefore, the proposed method is effective for real-time UWA OFDM systems under a harsh UWA channel with strong noise.

Time-Varying Sparse Channel Estimation Based on Adaptive Average and MSE Optimal Threshold in STBC MIMO-OFDM Systems

Channel estimation is still a challenge for space time block coding (STBC) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in time-varying environments. To estimate the channel state information (CSI) precisely without increasing complexity in any significant way, this paper utilizes the sparsity and the inherent temporal correlation of the time-varying wireless channel, and proposes a novel channel estimation method applied to STBC MIMO-OFDM systems. The proposed method consists of two schemes: adaptive multi-frame averaging (AMA) and improved mean square error (MSE) optimal threshold (IMOT). First, the temporal correlation of the time-varying channel is modeled by a linear Gauss-Markov (LGM) model, and the AMA scheme is incorporated to refine the initial estimated channel impulse response (CIR) through noise reduction. Based on the LGM model, the optimal average frame number is adaptively determined by minimizing the MSE of the denoised CIR. Then, the sparsity of the wireless channel is utilized to model the CIR as a sparse vector, and the IMOT scheme is performed to further remove the noise effect by discarding most of the noise-only CIR taps. Specifically, the IMOT scheme is achieved by recovering the CIR support across the optimal “tap-to-tap” threshold derived by minimizing the MSE of each CIR tap. Moreover, the prior confidence level of the tap to be active is calculated through multi-frame statistics to further improve the performance of the IMOT scheme. Simulation results verify that the proposed AMA-IMOT channel estimation method can achieve better performance than comparison methods.

Joint channel and Doppler estimation for OSDM underwater acoustic communications

Orthogonal signal-division multiplexing (OSDM) has recently emerged as a promising modulation scheme for underwater acoustic communications. Although providing more flexibility in system design, it suffers from a special interference structure over time-varying channels. To enable reliable OSDM equalization, we propose a joint channel impulse response and Doppler estimation algorithm in this paper. Compared to the existing method, which solves a nonconvex bilinear optimization problem iteratively in the frequency domain without a guarantee of global optimality, the proposed algorithm transforms the problem into a convex quadratic minimization in the time domain and can achieve a closed-form solution.

A Time-Domain Approach to Channel Estimation and Equalization for the SC-FDM System

Similar to orthogonal frequency division multiplexing receiver, the frequency-domain (FD) channel estimation (CE) and equalization are indispensable parts in the coherent single carrier frequency division multiplexing (SC-FDM) receiver. When the channel varies slowly, the FD processing is cost effective. For the applications with high mobility, the traditional implementation of the SC-FDM receiver causes significant performance degradation. In this paper, we propose to estimate time-varying pieces of channel taps for pilot symbols based on basis expansion model (BEM), and subsequently to reconstruct time-domain (TD) channel response for data symbols by utilizing the Slepian sequences-based piece-wise interpolation. Furthermore, two simplified schemes, i.e., the Slepian sequences-based multiple-point interpolation and the segmented BEM, are developed to reduce the computational complexity of the TD-CE. The Cramer-Rao lower bound (CRLB) of channel impulse response (CIR) estimation is also analyzed. In the light of the sparsity of TD channel gain matrix, we design an iterative least-squares QR decomposition algorithm to equalize the SC-FDM symbols. In the simulated SC-FDM system, when considering the carrier frequency of 5.9 GHz and the velocity of about 510 km/h, we observe that the traditional FD methods cause the demodulation failure, while the proposed TD processing schemes achieve ideal error-probability performance and preserve a relatively low complexity.

Performance Evaluation of OFDM System using Pseudo-Pilots with Particle Swarm & Moth Flame (Hybrid) Optimization

This paper is focused on the advanced signal processing techniques for the multi-carrier modulation especially on the orthogonal frequency division multiplexing (OFDM). OFDM has high data rate and robust against the frequency selective multi-path channels. Channel estimation is crucial for the receiver design in coherent detection. In this paper we are investigating and examining the pilot aided system for channel estimation and its special effects on the performance of the OFDM based system. In this paper new technique is proposed which make use of conventional Least Square (LS) and Recursive Least Square (RLS) hybridization techniques for the channel estimation afterwards we apply Particle Swarm Optimization (PSO) and Hybrid PSO (PSO+ Moth Flame Optimization(MFO)) optimization for obtaining more optimize techniques. In the proposed system, to estimate channel impulse response we are using pseudo-pilots instead to pilots as it is useful to overcome the pilot overhead and decrease the complexity by using pseudo random symbol (PRS). The performance of the proposed techniques and the weighted scheme are compared and verified using computerized simulation carried out using Matrix Laboratory (MATLAB) software. It is demonstrated on the basis of graph between BER and signal to Noise Ratio (Eb/NO), that the proposed technique reduces the execution time and increase the transmission rate with low or zero overhead.

Generalized CAZA sequences

ABSTRACTConstant amplitude zero auto-correlation (CAZA) sequences and periodic sequences have been widely used as preambles for synchronization and channel estimation in the literature. In this paper, we first construct generalized CAZA (GCAZA) sequences in the time domain. Then, their Cramer-Rao bounds containing no matrix inversion for the purpose of joint carrier frequency offset and channel impulse response estimation are derived. The properties of auto-correlation and the discrete Fourier transform of a GCAZA are then presented analytically. The latter also explains the relationship between the conventional frequency-domain CAZA and the proposed time-domain GCAZA. Finally, we conduct numerical simulations and compare the results with existing sequences in the literature for fading channels.

Prototype Filter Design to Minimize Stopband Energy With Constraint on Channel Estimation Performance for OQAM/FBMC Systems

In this paper, we propose a prototype filter design method to minimize the stopband energy of the filter with guaranteed time-domain channel estimation performance for offset quadrature amplitude modulation based filter bank multicarrier (OQAM/FBMC) systems. First, we analytically derive the total mean squared error (MSE) of estimated channel impulse response with two channel estimation schemes, i.e., the linear minimum mean square error and weighted least square, where the impact of intrinsic interference from data symbols is taken into account. Then, we formulate an optimization problem of filter coefficients to minimize the stopband energy with constraint on the total MSE. By exploiting the Heisenberg–Gabor uncertainty principle, the original optimization problem is transformed into an equality-constrained one. Finally, we employ the Lagrange multiplier method to solve the transformed problem and obtain the filter coefficients with the Newton’s method. Simulation results demonstrate that the proposed filter outperforms the isotropic orthogonal transform algorithm and extended Gaussian function filters considering both the MSE and stopband energy performances.

Improved LTE like initial uplink synchronization via reduced problem dimension

Initial uplink synchronization (IUS) is a random access process in LTE that enables the eNodeB to detect, and uplink synchronize new user equipment. In future networks with huge number of devices, the number of simultaneous IUS users will increase significantly. In addition, it is desirable to serve users moving at high speed. We exploit the structure of the physical random access channel (PRACH) in LTE to reduce the dimension of the underlying data model. This reduction gives a very compact representation of channel impulse response (CIR). We utilize this representation to develop an efficient algorithm which can work in presence of large multiple access interference (MAI) and high carrier frequency offsets (CFO). When compared with the state of the art methods, the proposed method is capable of detecting a significantly higher number of IUS users and can allow high values of CFO. In addition, it produces very reliable estimates of both CIR and CFO of the detected users.

A Pipelined Turbo Receiver for MU-MIMO Communication Systems

The MU-MIMO (multi-user multiple-input-multiple-output) communications can enhance the capacity of mobile communication channels. This paper considers the uplink MU-MIMO transmission systems. We jointly design the multiuser detection, turbo equalization and channel estimation for the considered systems. Our proposed system is with low computational complexity. Existing methods can’t meet the requirements of practical implementation. Pipelined receiver can reduce latency and improve throughput, thus pipelined receiver is a good choice for implementation. For the very-large-scale integration (VLSI) hardware implementation, the pipelined architecture can be used to separate the equalizer and decoder into several sub-blocks. The processing time can be shortened and the throughput of system can be increased. The proposed iterative receiver is pipelined structure. The proposed method improves the accuracy of the channel impulse response estimation and the multiuser detection (MUD).

Blind deconvolution based equalizer for underwater acoustic communications

Reliable and efficient underwater acoustic communications are challenging problems because of complex underwater channel properties. With the recent advancements in underwater technologies, there is a need for a robust underwater communication system which can endure high data rate and optimize the use of resources like bandwidth and time. In this work, a blind deconvolution based equalizer is proposed, which uses the received signal to compute the channel impulse response estimates and equalizes the received signal using these channel impulse response estimates. The performance of the proposed system is tested with both simulated and real data. The real data were obtained during an experiment in September 2017 in TrondheimFjord. In the case of real data, the difference in performance in terms of mean square error between the proposed equalizer and the probe-based channel estimation equalizer is only 0.68 dB. This suggests that the proposed blind deconvolution technique can provide good channel estimates to equalize the underwater channel effects. In addition to that, it can save the resources allocated for the probe signal estimation.

Independence-Checking Coding for OFDM Channel Training Authentication: Protocol Design, Security, Stability, and Tradeoff Analysis

In wireless OFDM communications systems, pilot tones, due to their publicly known and deterministic characteristic, suffer significant jamming/nulling/spoofing risks. Thus, the convectional channel training protocol using pilot tones could be attacked and paralyzed, which raises the issue of anti-attack channel training authentication (CTA), i.e., verifying the claims of identities of pilot tones and channel estimation samples. In this paper, we consider one-ring scattering scenarios with large-scale uniform linear arrays (ULA) and develop an independence-checking coding (ICC) theory to build a secure and stable CTA protocol, namely, ICC-based CTA (ICC-CTA) protocol. In this protocol, the pilot tones are not only merely randomized and inserted into subcarriers but also encoded as diversified subcarrier activation patterns (SAPs) simultaneously. Those encoded SAPs, though camouflaged by malicious signals, can be identified and decoded into original pilots for high-accuracy channel impulse response (CIR) estimation. The CTA security is first characterized by the error probability of identifying legitimate CIR estimation samples. The CTA instability is formulated as the function of probability of stably estimating CIR against all available diversified SAPs. A realistic tradeoff between the CTA security and instability under the discretely distributed AoA is identified and an optimally stable tradeoff problem is formulated, with the objective of optimizing the code rate to maximize security while maintaining maximum stability for ever. Solving this, we derive the closed-form expression of optimal code rate. Numerical results finally validate the resilience of proposed ICC-CTA protocol.

Deep Neural Networks for Channel Estimation in Underwater Acoustic OFDM Systems

Orthogonal frequency division multiplexing (OFDM) provides a promising modulation technique for underwater acoustic (UWA) communication systems. It is indispensable to obtain channel state information for channel estimation to handle the various channel distortions and interferences. However, the conventional channel estimation methods such as least square (LS), minimum mean square error (MMSE) and back propagation neural network (BPNN) cannot be directly applied to UWA-OFDM systems, since complicated multipath channels may cause a serious decline in performance estimation. To address the issue, two types of channel estimators based on deep neural networks (DNNs) are proposed with a novel training strategy in this paper. The proposed DNN models are trained with the received pilot symbols and the correct channel impulse responses in the training process, and then the estimated channel impulse responses are offered by the proposed DNN models in the working process. The experimental results demonstrate that the proposed methods outperform LS, BPNN algorithms and are comparable to the MMSE algorithm in respect to bit error rate and normalized mean square error. Meanwhile, there is no requirement of prior statistics information about channel autocorrelation matrix and noise variance for our proposals to estimate channels in UWA-OFDM systems, which is superior to the MMSE algorithm. Our proposed DNN models achieve better performance using 16QAM than 32QAM, 64QAM, furthermore, the specified DNN architectures help improve real-time performance by saving runtime and storage resources for online UWA communications.

Iterative Per-Vector Equalization for Orthogonal Signal-Division Multiplexing Over Time-Varying Underwater Acoustic Channels

Orthogonal signal-division multiplexing (OSDM) is a promising modulation scheme that provides a generalized framework to unify orthogonal frequency-division multiplexing (OFDM) and single-carrier frequency-domain equalization. By partitioning each data block into vectors, it allows for a flexible configuration to trade off resource management flexibility with peak-to-average power ratio. In this paper, an OSDM system is proposed for underwater acoustic communications. The channel Doppler effect after front-end resampling is modeled as a common time-varying phase on all propagation paths. It leads to a special signal distortion structure in the OSDM system, namely, intervector interference, which is analogous to the intercarrier interference in the conventional OFDM system. To counteract the related performance degradation, the OSDM receiver performs iterative detection, integrating joint channel impulse response and phase estimation, equalization, and decoding in a loop. Meanwhile, to avoid inversion of large matrices in channel equalization, frequency-domain per-vector equalization is designed, which can significantly reduce the computational complexity. Furthermore, the performance of the proposed OSDM system is evaluated through both numerical simulations and a field experiment, and its reliability over underwater acoustic channels is confirmed.

Joint Channel Estimation and Impulsive Noise Mitigation Method for OFDM Systems Using Sparse Bayesian Learning

The impulsive noise can deteriorate sharply the performance of orthogonal frequency division multiplexing (OFDM) systems. In this paper, we propose a novel joint channel impulse response estimation and impulsive noise mitigation algorithm based on compressed sensing theory. In this algorithm, both the channel impulse response and the impulsive noise are treated as a joint sparse vector. Then, the sparse Bayesian learning framework is adopted to jointly estimate the channel impulse response, the impulsive noise, and the data symbols, in which the data symbols are regarded as unknown parameters. The Cramer–Rao Bound is derived for the benchmark. Unlike the previous impulsive noise mitigation methods, the proposed algorithm utilizes all subcarriers without any a priori information of the channel and impulsive noise. The simulation results show that the proposed algorithm achieves significant performance improvement on the channel estimation and bit error rate performance.

Enhancement of Localization Systems in NLOS Urban Scenario with Multipath Ray Tracing Fingerprints and Machine Learning.

A hybrid technique is proposed to enhance the localization performance of a time difference of arrival (TDOA) deployed in non-line-of-sight (NLOS) suburban scenario. The idea was to use Machine Learning framework on the dataset, produced by the ray tracing simulation, and the Channel Impulse Response estimation from the real signal received by each sensor. Conventional localization techniques mitigate errors trying to avoid NLOS measurements in processing emitter position, while the proposed method uses the multipath fingerprint information produced by ray tracing (RT) simulation together with calibration emitters to refine a Machine Learning engine, which gives an extra layer of information to improve the emitter position estimation. The ray-tracing fingerprints perform the target localization embedding all the reflection and diffraction in the propagation scenario. A validation campaign was performed and showed the feasibility of the proposed method, provided that the buildings can be appropriately included in the scenario description.