The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and intelligence of leakage detection, we propose a leakage identification method that employs the intrinsic mode function, approximate entropy, and principal component analysis to construct a signal feature set and that uses a support vector machine (SVM) as a classifier to perform leakage detection. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify the water pipeline leakage and has lower energy consumption than the networking methods used in conventional wireless sensor networks.

Integrating cognitive radio into Internet of Things (IoT) is conducive to reducing spectrum scarcity for large-scale IoT deployment, where a core technology is the design of spectrum access algorithms for effective assignment of spectrum holes. However, due to the partially observable channels and increased number of users in the cognitive radio Internet of Things (CRIoT) network, the secondary users have difficulty avoiding interferences and accessing the spectrum quickly. This study presents a distributed dynamic spectrum access (DSA) algorithm that employs a priority experience replay deep echo state Q-network (PER-DESQN) for CRIoT networks with multiple users and channels. To accelerate the Q-network convergence, we use an echo state network based on the underlying temporal correlation to estimate Q-values. Then, to resolve the Q-value overestimation and improve prediction accuracy, the estimated Q-value and decision action process are trained using a double deep Q-network (DDQN). Moreover, a priority experience replay mechanism that uses the Sum-Tree combined with importance sampling weights is proposed to optimize the DDQN to address the instability of the Q-value resulting from random sampling. As the simulation results demonstrate, the proposed algorithm can make fast and accurate DSA decisions and boost the network channel capacity significantly.

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) utilizes large antenna arrays and is considered a promising technology for fifth-generation (5G) and beyond wireless communication systems. However, the high-power consumption of the radio-frequency (RF) chains makes it infeasible. To solve this problem, hybrid precoding is proposed, which is a combination of analog and digital precoding. The fully connected architecture hybrid precoding still requires a large number of phase shifters (PSs). The sub-connected architecture can greatly reduce the required power consumption, and however, it cannot obtain a satisfactory achievable rate. To avoid the high energy consumption and obtain a high resolution, we propose a novel partly connected architecture in this paper. In addition, we propose an energy-efficient successive interference cancelation (SIC) hybrid precoding based on the partly connected architecture, which transforms the problem of maximizing the total achievable rate with non-convex constraints into a series of sub-rate optimization problems. Furthermore, a low-complexity energy-efficient SIC hybrid precoding based on the partly connected architecture is developed, which uses the partial singular value decomposition (SVD) to realize the sub-rate optimization and significantly reduce the complexity. Theoretical analysis demonstrates the superiority of the proposed hybrid precoding in terms of complexity. The simulation results indicate that the proposed hybrid precoding algorithms enjoy better energy efficiency and achievable rate performance than some recently proposed hybrid precoding algorithms.

The linear zero-forcing (ZF) precoding can achieve the near-optimal sum-rate performance when the favorable channel propagation is obtained in downlink massive multiple-input multiple-output (MIMO) systems. However, it involves high complexity with the matrix inversion. To significantly reduce the complexity of ZF precoding, we propose a weighted two-stage (WTS) precoding scheme with low complexity based on an iterative method. Specially, the proposed WTS precoding converts the complicated matrix inversion into two half iteration stages, and the result of each stage is weighted by a coefficient to further speed up the convergence and reduce the complexity. Theoretical analysis demonstrates that the proposed WTS precoding enjoys a fast convergence rate and low complexity. Simulation results indicate that the proposed WTS precoding can achieve better bit-error-rate (BER) and sum-rate performance with a smaller number of iterations than the recently proposed schemes.

Obtaining accurate angle parameters using direction-of-arrival (DOA) estimation algorithms is crucial for acquiring channel state information (CSI) in massive multiple-input multiple-output (MIMO) systems. However, the performance of the existing algorithms deteriorates severely due to mutual coupling between antenna elements in practical engineering. Therefore, for solving the array mutual coupling, the array output signal vector is modeled by mutual coupling coefficients and the DOA estimation problem is transformed into block sparse signal reconstruction and parameter optimization in this paper. Then, a novel sparse Bayesian learning (SBL)-based algorithm is proposed, in which the expectation-maximum (EM) algorithm is used to estimate the unknown parameters iteratively, and the convergence speed of the algorithm is enhanced by utilizing the approximate approximation. Moreover, considering the off-grid error caused by discretization processes, the grid refinement is carried out using the polynomial roots to realize the dynamic update of the grid points, so as to improve the DOA estimation accuracy. Simulation results show that compared with the existing algorithms, the proposed algorithm is more robust to mutual coupling and off-grid error and can obtain better estimation performance.

Estimating direction of arrival (DOA) is important in a variety of practical applications. Conventional cyclostationarity-based coherent DOA estimation algorithms are not robust to non-Gaussian α-stable impulsive noise. Additionally, fractional lower-order statistics (FLOS)-based algorithms are tolerant to impulsive noise; however, they experience performance degradation for coherent signals and interference. To overcome these drawbacks, two types of fractional lower-order cyclostationarity-based subspace DOA estimation methods are proposed for coherent signals in the presence of interference and α-stable impulsive noise. The new proposed algorithms exploit the fractional lower-order cyclostationarity properties of the signals and are immune to the impulsive noise and interference. Moreover, they can provide more accurate DOA estimates of coherent signals than conventional cyclostationarity-based and FLOS-based methods. The simulation results illustrate the robustness and effectiveness of the proposed methods for coherent signals based on a comparison with traditional methods. The new algorithms can be used in the presence of a wide range of interference, Gaussian noise, and α-stable distribution impulsive noise environments.