Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems can significantly reduce the number of radio frequency (RF) chains by using lens antenna arrays, because it is usually the case that the number of RF chains is often much smaller than the number of antennas, so channel estimation becomes very challenging in practical wireless communication. In this paper, we investigated channel estimation for mmWave massive MIMO system with lens antenna array, in which we use a mixed (low/high) resolution analog-to-digital converter (ADC) architecture to trade-off the power consumption and performance of the system. Specifically, most antennas are equipped with low-resolution ADC and the rest of the antennas use high-resolution ADC. By utilizing the sparsity of the mmWave channel, the beamspace channel estimation can be expressed as a sparse signal recovery problem, and the channel can be recovered by the algorithm based on compressed sensing. We compare the traditional channel estimation scheme with the deep learning channel-estimation scheme, which has a better advantage, such as that the estimation scheme based on deep neural network is significantly better than the traditional channel-estimation algorithm.

Massive multiple-input–multiple-output (MIMO) systems provide efficient connectivity services for a large number of industrial Internet of Things (IoT) devices. To support numerous IoT devices, this article considers downlink hybrid architecture massive MIMO systems with phase shifters-based and switches-based cases, in which the data stream is first sent to the digital domain and then processed by the analog domain. zero-forcing (ZF) receivers are considered under the assumption of perfect channel state information (CSI). For hybrid architecture MIMO systems under quantized phase shifters and switches, we derive asymptotic approximations of the spectrum efficiency (SE) for massive MIMO systems using random matrix theory and then we propose an iterative algorithm to generate corresponding analog processing matrix, which can be used arbitrary quantized phase shifters. With the help of derived theoretical SE and realistic power consumption model, we further evaluate the total energy efficiency (EE) and provide insights into the tradeoffs between the SE and total EE. Finally, Monte Carlo simulation results are presented to substantiate our theoretical analysis results and showcase that the hybrid architecture provides higher total EE compared to existing all-digital architectures, but its achievable SE is lower than that of the hybrid architecture. Furthermore, the results also show that the switchbased hybrid architecture have ability to achieve the enhanced EE performance with large number of base station antennas and low-signal-to-noise ratio regimes compared to quantized phase shifters.

Using seismic data, logging information, geological interpretation data, and petrophysical data, it is possible to estimate the stratigraphic texture and elastic parameters of a study area via a seismic inversion. As such, a seismic inversion is an indispensable tool in the field of oil and gas exploration and development. However, due to unknown natural factors, seismic inversions are often ill-conditioned problems. One way to work around this unknowable information is to determine the solution to the seismic inversion using regularization methods after adding further a priori constraints. In this study, the nonconvex L${}_{1-2}$ regularization method is innovatively applied to the three-parameter prestack amplitude variation angle (AVA) inversion. A forward model is first derived based on the Fatti approximate formula and then low-frequency models for P impedance, S impedance, and density are established using logging and horizon data. In the Bayesian inversion framework, we derive the objective function of the prestack AVA inversion. To further improve the accuracy and stability of the inversion results, we remove the correlations between the elastic parameters that act as initial constraints in the inversion. Then, the objective function is solved by the nonconvex L${}_{1-2}$ regularization method. Finally, we validate our inversion method by applying it to synthetic and observational data sets. The results show that our nonconvex L${}_{1-2}$ regularization seismic inversion method yields results that are highly accurate, laterally continuous, and can be used to identify and locate reservoir formation boundaries. Overall, our method will be a useful tool in future work focused on predicting the location of reservoirs.

During the propagation of seismic waves in the Earth, the influence of Earth absorption leads to energy attenuation and phase distortion. Traditional seismic acoustic impedance (AI) inversion typically utilizes a constant wavelet to construct a wavelet library for AI inversion, resulting in outcomes that struggle to effectively portray the spatial variation characteristics of underground reservoirs. While using an inverse Q filter for energy compensation before AI inversion can enhance precision, accurately extracting the Q factor before the inverse Q filter is complex. Moreover, the inverse Q filter often amplifies noise interference, impacting the accuracy of the inversion. Therefore, we propose an alternating iterative synchronous inversion method for AI and the quality factor. Compared with the conventional method, the impedance information and attenuation parameters of seismic data can be obtained directly, which improves the efficiency and accuracy of inversion. The method can be divided into two steps. Firstly, we construct the L _1-2 -norm regularization of the AI inversion objective function, which is solved using the difference of convex algorithm (DCA) and the alternating direction method of multipliers (ADMM). Subsequently, the Markov Chain Monte Carlo (MCMC) method is employed to solve the nonstationary forward equation, with the fitting of the centroid frequency of seismic data and synthetic seismic records. Through continuous iteration of AI and the quality factor until the error is below the specified threshold or reaches the designated number of iterations, stable AI and average Q factors are obtained. Numerical simulations demonstrate the reliability of the proposed method, showcasing higher accuracy compared to stable wavelet inversion and constant Q inversion. Real data applications validate the method’s effectiveness, providing more dependable results for reservoir fluid identification. The obtained average Q factors can be employed for seismic data processing and interpretation.

Amplitude variation with offset (AVO) inversion has been widely used in reservoir characterization to predict lithology and fluids. However, some existing AVO inversion methods that use L1 norm regularization may not obtain the block boundary of subsurface layers because the AVO inversion is a severely ill-posed problem. To obtain sparse and accurate solutions, we have introduced the L1−2 minimization method as an alternative to L1 norm regularization. We used L1−2 minimization for simultaneous P- and S-impedance inversion from prestack seismic data. We first derived the forward problem with multiangles and set up the inversion objective function with constraints of a priori low-frequency information obtained from well-log data. Then, we introduced minimization of the difference of L1 and L2 norms, denoted as L1−2 minimization, to solve this objective function. The nonconvex penalty function of the L1−2 minimization method is decomposed into two convex subproblems via the difference of convex algorithm, and each subproblem is solved by the alternating direction method of multipliers. Compared to L1 norm regularization, the results indicate that L1−2 minimization has superior performance over L1 norm regularization in promoting blocky/sparse solutions. Tests on synthetic and field data indicate that our method can provide sparser and more accurate P- and S-impedance inversion results. The overall results confirm that our method has great potential in the detection and identification of fluids.