Aerial base station (ABS) provides a flexible solution for hotspot scenarios in traditional cellular networks, where macro-cell base stations (MBSs) are challenged by overwhelming short-time traffic demands. However, due to stretched transmission distances from sky, the system performance of this ABS-scheme is sometimes questioned. In this paper, we study the system performance of ABS-assisted networks by tools from stochastic geometry. The two-tier network consisting of MBSs and ABSs is modeled as the superposition of a Poisson point process over the infinite plane and a binomial point process within an overlapped finite circular area. We consider a more general probabilistic line-of-sight and non-line-of-sight propagation model and derive coverage probability as well as area spectral efficiency. Based on the proposed analytical framework, we study the impacts of various parameters and compare the ABS-scheme with a benchmark scheme, in which network densification is realized through deploying additional ground base stations (GBSs). Simulation results unveil that: 1) the height and ABS number should be carefully designed to obtain the optimal performance and 2) when the number of assisting BSs is limited, the ABS-scheme can achieve even better performance than the GBS-scheme, which validates the feasibility of enhancing system performance through ABSs in hotspot scenarios.

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.

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.

In this paper, we study the potential of nonorthogonal multiple access (NOMA) for the purpose of interference mitigation in downlink small cell networks (SCNs). Different from prior works, we focus on opportunistically multiplexing different users on the same subchannel to avoid the severe intercell interference brought in by ultra dense networking. Aiming to maximize the network throughput, we formulate a distributed subchannel assignment problem with local information exchange. This problem is analyzed through a locally cooperative game model and the existence of Nash equilibrium (NE) is confirmed by proving that the formulated game is an exact potential game. To solve the problem, we design two concurrent distributed algorithms based on best response (BR) and spatial adaptive play (SAP), respectively. The BR-based algorithm guarantees rapid convergence to an NE, which may not be globally optimal. On the contrary, the SAP-based algorithm can find the global optimum with an arbitrary large probability, although the learning process requires more iterations to converge. Simulation results reveal that the aggregate interference can be more efficiently suppressed in NOMA enhanced networks, which can lead to higher network throughputs. Besides, the superiority of NOMA over orthogonal multiple access is more obvious when the network grows denser.

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.