Owing to the dynamic characteristics of the underwater environment, the set of underwater targets needing to be covered and monitored by sensor nodes often exhibits a variety of irregular distributions, bringing a certain burden to node deployment. In this paper, an improved greedy strategy with a depth-first search mechanism was proposed to address the coverage problem of underwater targets. With the optimal selection criteria of deployment location at each step and a flexible search process, the global solution problem was converted into multiple local optimal solution problems to improve solution efficiency. Thus the performance of coverage and connectivity can be effectively guaranteed in underwater scenarios with irregular target distributions. The simulation results show that the proposed algorithm has superiority in solving the target coverage problem in underwater acoustic sensor networks (UASNs), which is manifested in node deployment cost and coverage efficiency.

Non-orthogonal multiple access (NOMA) is a promising multiple access technique for fifth-generation mobile networks. In this letter, a novel multi-tier NOMA (TNOMA) scheme is proposed for a coordinated multi-point (CoMP) network. This TNOMA scheme aims to improve the coverage of high data rate services in a CoMP network, especially for the case when access points are not able to serve all of the users in the network. To bench-mark the TNOMA scheme against one-tier NOMA-CoMP, the effective sum-rate performances are evaluated. The results show that the performance difference between one-tier only NOMA-CoMP and multi-tier NOMA-CoMP is influenced by the transmission-nodes-selection threshold value ( $\epsilon _{k}$ ) and the target data rate.

Reconfigurable intelligent surface (RIS) represent a novel form of electromagnetic metamaterial that have been extensively studied for user equipment (UE) positioning by exploiting the multipath propagation of signals. A novel RIS-assisted localization prediction (RLP) method based on Bayesian optimization and long short-term memory (BO-LSTM) has been proposed in this paper. This method capitalizes on the predictive advantages of LSTM for data sequence and RIS's flexible and controllable multidimensional feature parameters, establishing a mobile UE localization model in an RIS-assisted wireless communications system based on the interplay between time slot transmission power and user location information. In order to provide a more stable communication environment for data collection during the localization process, a power allocation optimization (PAO) method is proposed for maximizing time slot channel capacity in the RLP system based on the number of RIS reflection elements. The study conducts a thorough comparison of simulation results of BO-LSTM, convolutional neural networks (CNN)-LSTM and improved bidirectional LSTM (BiLSTM) combined with Adaptive boost, employing adaptive moment estimation (Adam) and stochastic gradient descent with momentum (SGDM) optimizers. Experimental results demonstrate that the BO-LSTM-based RLP method exhibits improved prediction accuracy. These findings suggest the effectiveness of the proposed method and highlight its potential for further enhancement.

Reconfigurable intelligent surfaces (RISs)-assisted Internet of Things (IoT) networks have attracted a great deal of interest due to the potential contributions to the next generation wireless networks. In this article, an adaptive RIS clustering system with weak channels or obstacles is investigated. Ideally, there is a suitable RIS that adopts the self-organized RIS-assisted (SORA) scheme between a base station (BS) and a user equipment (UE) for reflection transmission. However, due to the nonideal channel scenarios, a single RIS may not be capable of connecting the BS with the UE. To tackle these nonideal cases, we propose a complementary RIS-cluster-assisted (C-RCA) strategy by reflecting signals among clusters to improve the performance of the overall system. The performances of the two strategies in terms of outage probability (OP) and ergodic capacity (EC), derived using approximate closed-form expressions, are analyzed. By increasing the numbers of complementary RISs and the reflecting elements on each RIS simultaneously, these performance metrics as well as the effective throughput and energy efficiency (EE) are evaluated under different signal-to-noise ratios (SNRs). The obtained results demonstrated that, in contrast to the ideal case, the network EE of the proposed C-RCA strategy is higher than the SORA scheme under the larger target spectral efficiency (SE).

The use of aerial base stations (ABSs) is gaining attention due to its potentials to provide a flexible wireless coverage in adverse scenarios. Investigations on key performance metrics are desirable to ensure the feasibility of these ABS-assisted networks, especially when they are jointly designed with advanced transmission technologies, e.g., cooperative transmissions and non-orthogonal multiple access (NOMA). In this paper, we consider an ABS-assisted cooperative system with NOMA enabled to boost connectivity ability. It is assumed that multiple ABSs hover around a macro base station to relay downlink signals to user equipments, while interfering nodes are randomly distributed on the ground. We assume a more realistic channel model featured with a distance-related probabilistic line-of-sight and non-line-of-sight propagation, as well as non-identical small-scale fading. We derive the outage probability, and study the impacts of various parameters on the system performance. Numerical results unveil that: 1) The reliability of backhauls plays an important role in the system and determines the outage performance floor. 2) Joint transmissions from the sky can bring in a significant performance enhancement for the system. 3) The NOMA based transmission outperforms the traditional orthogonal multiple access with an improved outage performance, provided a properly selected NOMA power allocation coefficient.

The next generation of mobile communication aims to extend the capabilities of traditional communication by reshaping the environment with wireless signals. The channel state information can describe the propagation characteristics in wireless communications, which is beneficial in developing wireless communication networks towards intelligent communication and wireless sensing networks. Raspberry PI with Nexmon firmware patched can extract the channel state information from WiFi signals and realize human activity recognition. However, the phase values on some carriers are susceptible to noise, resulting in phase errors after singular value decomposition. To solve this problem, a method is proposed in this paper to find the optimal phase value by dynamic time warping algorithm utilizing the property of orthogonality between amplitude and phase. In contrast to the conventional recognition strategies, the proposed optimal phase extraction method with commercial WiFi signals can further improve the accuracy of the recognition strategy under different complicated scenarios.