Compressive sensing-based channel estimation for high mobile systems with delay Doppler effects

Multiphase flow measurements, based on tomographic imaging techniques, have made substantial progress in the past several years. In particular, ultrasonic tomographic reconstruction has shown its high potential in accurately measuring fluid fractions in a flow cross-section based on direct flow measurements. These types of measurements are very attractive as the system is nonradioactive, has direct contact to fluid, yet without flow obstruction. An ultrasonic tomographic multiphase flow meter (UTM) is being developed to capture the fluid flow rates and phase compositions in producing oil wells. The UTM aims to generate cross-sectional images of the flow inside the pipe. To recover these images, complex computations and high processing time are required, based on a limited amount of available data. For this purpose, compressive sensing (CS) reconstruction methods have shown great potential in overcoming the mentioned data quality and quantity issues. CS builds upon the fact that one can recover certain data of interest from potentially very few measurements. Many CS algorithms have been proposed in the literature. In this paper, we seek to find a suitable algorithm by comparing different CS algorithms in terms of the resulting recovered image quality. Specifically, we survey three algorithms in CS, namely basis pursuit, iterative soft thresholding (IST), and orthogonal matching pursuit (OMP). We also compare the results of the CS algorithms to that of a least-square based conventional algorithm. The numerical results show the superiority of CS recovery algorithms over the conventional approach, both qualitatively and quantitatively. The results also suggest that high image quality could potentially be achieved with less complex CS algorithms, i.e., the IST and OMP algorithms. As presented in the paper, our goal is to adapt CS reconstruction algorithms for images generated by the UTM, for the purpose of accurate quantification of oil, gas, and water phase fractions produced from different wells.

In this paper, a novel 2×2 multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) testbed based on an Analog Devices AD9361 highly integrated radio frequency (RF) agile transceiver was specifically implemented for the purpose of estimating and analyzing MIMO-OFDM channel capacity in vehicle-to-infrastructure (V2I) environments using the 920 MHz industrial, scientific, and medical (ISM) band. We implemented two-dimensional discrete cosine transform-based filtering to reduce the channel estimation errors and show its effectiveness on our measurement results. We have also analyzed the effects of channel estimation error on the MIMO channel capacity by simulation. Three different scenarios of subcarrier spacing were investigated which correspond to IEEE 802.11p, Long-Term Evolution (LTE), and Digital Video Broadcasting Terrestrial (DVB-T)(2k) standards. An extensive MIMO-OFDM V2I channel measurement campaign was performed in a suburban environment. Analysis of the measured MIMO channel capacity results as a function of the transmitter-to-receiver (TX-RX) separation distance up to 250 m shows that the variance of the MIMO channel capacity is larger for the near-range line-of-sight (LOS) scenarios than for the long-range non-LOS cases, using a fixed receiver signal-to-noise ratio (SNR) criterion. We observed that the largest capacity values were achieved at LOS propagation despite the common assumption of a degenerated MIMO channel in LOS. We consider that this is due to the large angular spacing between MIMO subchannels which occurs when the receiver vehicle rooftop antennas pass by the fixed transmitter antennas at close range, causing MIMO subchannels to be orthogonal. In addition, analysis on the effects of different subcarrier spacings on MIMO-OFDM channel capacity showed negligible differences in mean channel capacity for the subcarrier spacing range investigated. Measured channels described in this paper are available on request.

Channel estimation for MIMO orthogonal frequency division multiplexing (MIMO-OFDM) systems has been improved observably based on compressive sensing (CS) techniques. Recently, some block-structured CS methods have been utilized in MIMO-OFDM systems. However, the block-coherence which characterizes the performance of a block-structured measurement matrix is still not optimized. In this paper, we proposed a two-step scheme to optimize the pilot locations and power of different transmitting antennas, in order to minimize the block-coherence of the aggregate system matrix. The selection of pilot locations and power can be obtained by two genetic algorithms. Then we introduce a block version of the orthogonal matching pursuit (OMP) algorithm, termed BOMP, to obtain the channel estimation consistent with the block-sparse model. Simulation results demonstrate that the proposed pilot allocation scheme with superimposed pilots design and block-structured CS method performs very well in MIMO-OFDM systems.

The channel estimation techniques based on comb-type pilot arrangement, which is used in multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system, is investigated in this paper. In view of the contradiction of the time-domain least squares (LS) channel estimation bit error rate (BER) performance and the practical realization complexity, a novel optimal pilots design method for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) is proposed. By using this approach no large matrix pseudo-inverse is required, which greatly reduces the complexity of algorithms. Simulation results show that the proposed method provides better BER performance and lower complexity. And it is easy to implement in practical systems.

This paper presents channel measurement results obtained in a bus for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Favourable MIMO-OFDM channels were found throughout the bus. Packet error simulations based on the measured channels show that packet error probability (PEP) of less than 1% is achievable with less than 30 dB signal-to-noise ratio (SNR) by using list sphere detection (LSD) and convolutional coding, with which we achieved 600 Mbit/s and 15 bit/s/Hz bandwidth efficiency using four transmitters and four receivers. The error performances of various schemes in 802.11n draft standard are also examined by simulation in the context of measured channels. (8 pages)

The recently proposed compressive sensing (CS) theory provides a new solution for multiple description coding (MDC) with fine granularity, by treating each random CS measurement as a description. The performance of CS-based MDC (CS-MDC) depends on the efficacy of the CS recovery algorithm. Existing CS recovery algorithms recover the signal in a fixed space (e.g., Wavelet, DCT, and gradient spaces) for the entire duration of the signal, even though a typical multimedia signal exhibits sparsity in time/space variant spaces. To rectify this problem and develop a better CS recovery algorithm for CSMDC, we propose a learning-based framework to conduct the CS recovery in locally adaptive spaces, and carry out a case study on image MDC. A set of prior image models are learned offline from a training set to facilitate the CS recovery in local adaptive bases. Experiments show that the learning-based CS recovery algorithm can significantly improve the performance of the previous CS-MDC technique in both PSNR and visual quality.

In this new information age, high data rate and strong reliability features our wireless communication systems and is becoming the dominant factor for a successful deployment of commercial networks. MIMO-OFDM (multiple input multiple output-orthogonal frequency division multiplexing), a new wireless broadband technology, has gained great popularity for its capability of high rate transmission and its robustness against multi-path fading and other channel impairments. A major challenge to MIMO-OFDM systems is how to obtain the channel state information accurately and promptly for coherent detection of information symbols and channel synchronization. In the first part, this dissertation formulates the channel estimation problem for MIMO-OFDM systems and proposes a pilot-tone based estimation algorithm. A complex equivalent base-band MIMO-OFDM signal model is presented by matrix representation. By choosing equally-spaced and equally-powered pilot tones from sub-carriers in one OFDM symbol, a down-sampled version of the original signal model is obtained. Furthermore, this signal model is transformed into a linear form solvable for the LS (least-square) estimation algorithm. Based on the resultant model, a simple pilot-tone design is proposed in the form of a unitary matrix, whose rows stand for different pilot-tone sets in the frequency domain and whose columns represent distinct transmit antennas in the spatial domain. From the analysis and synthesis of the pilot-tone design in this dissertation, our estimation algorithm can reduce the computational complexity inherited in MIMO systems by the fact that the pilot-tone matrix is essentially a unitary matrix, and is proven an optimal channel estimator in the sense of achieving the minimum MSE (mean squared error) of channel estimation for a fixed power of pilot tones. In the second part, this dissertation addresses the wireless location problem in WiMax (worldwide interoperability for microwave access) networks, which is mainly based on the MIMO-OFDM technology. From the measurement data of TDOA (time difference of arrival), AOA (angle of arrival) or a combination of those two, a quasi-linear form is formulated for an LS-type solution. It is assumed that the observation data is corrupted by a zero-mean AWGN (additive white Gaussian noise) with a very small variance. Under this assumption, the noise term in the quasi-liner form is proved to hold a normal distribution approximately. Hence the ML (maximum-likelihood) estimation and the LS-type solution are equivalent. But the ML estimation technique is not feasible here due to its computational complexity and the possible nonexistence of the optimal solution. Our proposed method is capable

This paper proposes a novel channel tracking method based on radial basis function neural network (RBFNN) and particle filter (PF) in multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system. First, we use the RBFNN to obtain the initial values of the MIMO channels, and then apply the PF method to track the variation of the channels. The fading channels are modeled as autoregressive (AR) process and the transmit signals are encoded by space-time block code (STBC) scheme. Simulation results show that the proposed method has effective tracking performance.

In image fusion techniques based on joint sparse representation (JSR), the composite image is calculated from the fusion of features, which are represented with sparse coefficients. Orthogonal matching pursuit (OMP) and basis pursuit (BP) are the main candidates to estimate the coefficients. Previously OMP is utilized for the advantage of low complexity. However, noticeable errors occur when the dictionary of JSR cannot ensure the coefficients are sparse enough. Alternatively, BP is more robust than OMP in such cases (though suffered from larger complexity). Unfortunately, it has never been studied in image fusion tasks. In this paper, we investigate JSR based on BP for image fusion. The target is to verify that 1) to what extent can BP outperform OMP; and 2) what is the trade-off between BP and OMP. Finally, we conclude, in some cases, fusion with BP obviously outperforms the one with OMP under an affordable computational complexity.

Accurate channel estimation is crucial for millimeter-wave (mmWave) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Conventional grid-based compressive sensing approaches encounter the base mismatch problem, which degrades channel estimation accuracy. To address this issue, this letter proposes a novel gridless channel estimation strategy tailored for mmWave MIMO-OFDM systems. Specifically, the channel estimation problem is formulated as a joint sparse signal recovery problem by exploiting the inherent sparsity in the angle-delay domain of the mmWave channel. We then introduce a Hankel-Toeplitz block model-based new atomic norm minimization (NANM) algorithm with multiple measurement vectors (MMV), representing the formulated problem as a semi-definite programming (SDP) problem with structured sparsity. To efficiently solve the SDP problem, we employ a low-complexity alternating direction multiplier method (ADMM). Simulation results verify that the proposed method significantly enhances channel estimation accuracy with reduced pilot overhead, compared with conventional channel estimation techniques.

To address the problem that in the Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system, the Compressed Sensing (CS) greedy algorithm uses the inner product criterion in the process of selecting atoms for channel estimation will lose part of the original signal information and affect the accuracy of reconstruction, this paper uses the Dice criterion instead of the inner product criterion for atom screening, the Dice criterion can better preserve the original state of the atoms. Due to the problem that the fixed step size of the Sparsity Adaptive Matching Pursuit (SAMP) algorithm affects the accuracy of channel estimation, this paper proposes an improved SAMP algorithm by using the idea of power function variable step size. The simulation results show that the improved SAMP algorithm has better Mean Square Error (MSE) and Bit Error Rate (BER) estimation performance than the traditional compressed sensing greedy algorithm in MIMO-OFDM system.

Massive Multiple Input Multiple Output (MIMO) system has gathered lately a huge interest from researchers and it has been known as a backbone technology for the 5th generation because it can greatly improve the capacity of wireless communication and it can potentially provide energy efficiency and security. Before ensuring the full advantages, massive MIMO system has to overcome many challenges. One of them is to accurate an exact estimation of the Channel Impulse response (CIR) between each transmit-receive antenna pair. The other problem arises when implementing such a system in presence of High Power Amplifiers (HPA) which imposes a nonlinear distortion on the transmitted signals. This paper proposes contributions in three key areas. Firstly, we investigate the nonlinear effects of memoryless HPA, which is modeled using Saleh model, by deriving its Bit Error Rate expression (BER) in massive MIMO Orthogonal Frequency Division Multiplexing (OFDM) system. Secondly, we highlight the necessity of providing the accurate channel characteristics by estimating it using Compressive Sensing (CS) techniques. Moreover, we propose also a CS compensation algorithm based on Orthogonal Matching Pursuit (OMP) to mitigate the nonlinear distortion in the receiver. The proposed CS-based compensation technique has been compared to the Neural Network (NN) pre and post compensation technique and it has shown a good performance not only in terms of BER but also in terms of complexity.

Multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) with an electronically steerable passive array radiator (ESPAR) antenna receiver can improve the bit error rate performance and obtains additional diversity gain without increasing the number of Radio Frequency (RF) front-end circuits. However, due to the large size of the channel matrix, the computational cost required for the detection process using Vertical-Bell Laboratories Layered Space-Time (V-BLAST) detection is too high to be implemented. Using the minimum mean square error sparse-sorted QR decomposition (MMSE sparse-SQRD) algorithm for the detection process the average computational cost can be considerably reduced but is still higher compared with a conventional MIMOOFDM system without ESPAR antenna receiver. In this paper, we propose to use a low complexity submatrix divided MMSE sparse-SQRD algorithm for the detection process of MIMOOFDM with ESPAR antenna receiver. The computational cost analysis and simulation results show that on average the proposed scheme can further reduce the computational cost and achieve a complexity comparable to the conventional MIMO-OFDM detection schemes.

This paper proposes Twin Turbo (T2) MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing). The advanced iterative decoder, called the T2 decoder, decreases the transmission error rate compared to conventional turbo decoders because it uses the correlation information among the bits mapped on an identical symbol of multi-level modulation and updates the channel reliability. When T2 is applied to a MIMO-OFDM, the required symbol energy to noise power density ratio (Es/N0) can be reduced more effectively than when T2 is applied to SISO (Single Input Single Output). This is because T2 can use the correlation among the bits not only mapped on an identical symbol but also transmitted from different antennas. Moreover, T2 achieves good performance in a correlated MIMO channel because the average minimum squared Euclidean distances between symbol replica candidates consisting of signals transmitted from multiple transmitter antennas are reduced. Computer simulations verify that the required Es/N0 of T2 MIMO-OFDM using 16QAM is 1.9dB lower than that of a conventional turbo decoder when the correlation coefficients of transmitter and receiver antennas are 0.8. A computational complexity analysis clarifies the relation between the increase in computational complexity and the reduction in the required Es/N0.

In this paper, we present improved performance compressed sensing (CS) based pulse Doppler radar. CS is an emerging sampling technique which uses very few number of samples to reconstruct original signal. Improvement in performance is achieved using Orthogonal Matching Pursuit (OMP) method with Fourier dictionary at low signal to noise ratio (SNR). OMP is an iterative and fast reconstruction method while Fourier dictionary is used to transform the original signal from time domain to Fourier domain. A significant reduction of sampling frequency by a factor of 360 is achieved at SNR as low as 10 dB, while preserving essential signal information. Low signal reconstruction error leads to good detection performance using Generalized Likelihood Ratio Test (GLRT), a detector based on ratio of probabilities of detection and false alarm. Simulation results demonstrate usefulness of the ideas presented.