Since underwater acoustic channels are shared by multiple heterogeneous entities and can suffer from severe interference, underwater acoustic communication networks (UACNs) are faced with the challenge of mitigating interference and improving communication quality by implementing distributed resource allocation approaches. In this work, we introduce the concept of reinforced learning in intelligent control to the UACNs by treating the nodes as intelligent agents and the node networks as multi-agent networks. By partitioning the state space and the action space, we formulate a reward function and a search strategy and propose a distributed resource allocation algorithm based on cooperative Q-Learning. In addition, we verify the convergence of the proposed algorithm. Finally, simulation results in two different underwater application scenarios show that the proposed algorithm outperforms the existing algorithms in improving the network transmission capacity, and can reduce the overhead of resource allocation by using cooperative Q-Learning.

This paper addresses the target localization problems based on received signal strength (RSS) measurements in underwater acoustic wireless sensor network (UWSN). Firstly, the problems based on the maximum likelihood (ML) criterion for estimating target localization in cases of both known and unknown transmit power are respectively derived, and fast implementation algorithms are proposed by transforming the non-convex problems into a generalized trust region subproblem (GTRS) frameworks. A three-step procedure is also provided to enhance the estimation accuracy in the unknown target transmit power case. Furthermore, the Cramer–Rao lower bounds (CRLBs) in both cases are derived. Computer simulation results show the superior performance of the proposed methods in the underwater environment.

This paper proposes a novel scheme to solve the target localization problem by using combined measurements of received signal strength (RSS) and angle-of-arrival (AOA) hybrid measurements in three-dimensional (3D) wireless sensor networks (WSNs). The proposed scheme can work effectively in both cases of known and unknown target transmit power. In the known transmit power case, by using polarized identity, we transform the available AOA measurements into a norm form and establish new relationships between the AOA measurements and the unknown target location, a novel target localization method is then proposed by using the second-order cone programming (SOCP) relaxation technique. Furthermore, we demonstrate that by using mean inequality, the proposed method can be effectively extended to the unknown transmit power case. In this case, we propose a practical alternating estimation procedure which alternatively estimates the target location and transmit power. Computer simulations are carried out to demonstrate that comparing with other methods, the proposed method can achieve significant performance gains for different settings in 3D WSNs.

This study investigates the application of artificial fish swarm algorithm (AFSA) in the power allocation for multiple‐input and multiple‐output orthogonal frequency‐division multiplexing (MIMO‐OFDM) relay underwater acoustic (UWA) communication systems. First, by using the singular value decomposition technique, the two‐hop transmission links are converted into the virtual direct links in an single‐input and single‐output OFDM (SISO‐OFDM) system. Then, a power allocation optimisation problem, together with the assignment of subcarriers and relay nodes, are formulated for the virtual SISO‐OFDM system. Finally, the problem‐solving algorithms are proposed in two parts. Computer simulation results show that the proposed AFSA scheme improves in both power consumptions and diversity gains compared with two existing schemes for UWA communication systems.

In this paper, we consider the received signal strength-based cooperative localization problem in both known and unknown transmit power of target nodes. For the case of known transmit power, we treat the transmit power as a constant and derive a novel non-convex weighted least squares estimator which can be transformed into a second-order cone programming (SOCP) problem for reaching an efficient solution. For the case of unknown transmit power, we treat the transmit power as an additional unknown parameter and propose a hybrid maximum likelihood-SOCP algorithm to alternatively estimate the target node locations and transmit power. Simulation results confirm the effectiveness of the proposed methods in all considered settings.

SUMMARY: Train traffic has been recognized as a powerful noise source for subsurface imaging and monitoring due to its properties of being strong, economic and repeatable. Compared with the traditional train, the high-speed train (HST) runs faster and generates vibrations that also manifest as periodical signals but with a more striking feature of sharp equidistant spectral lines in the frequency spectrum. Like the vibrations generated by the traditional train, the HST-induced vibrations can be utilized after the retrieval of interstation impulse responses using seismic interferometry. However, little attention has been paid to the characteristics of HST-induced vibrations during seismic interferometry. The aim of this study is to investigate the effects of train traffic characteristics on the seismic interferometry method. We first present an alternative derivation of seismic interferometry that is not based on the assumption of uncorrelation between sources at different locations. The derivation is valid for scalar waves in 3-D and for elastic surface waves. After analysing the effects of several HST-related factors, including HST moving direction, HST carriage number, rail structure, HST speed and the stacking number of HST records, on the seismic interferometry, we find that the seismic interferometry using HST traffic noise generates crosstalk that can be effectively attenuated by stacking the interferometric results of several HSTs with slightly different speeds. This finding has been validated by the seismic interferometry of both synthetic and field HST traffic noise. We further demonstrate that the retrieved surface waves can be used to estimate near-surface velocities.