In this paper, we investigate the anti-jamming channel selection problem for interference mitigation (IM)-based dense wireless networks in dynamic environment, in which the active user set is variable due to their specific traffic demands. We jointly consider the mutual interference among users and external jamming in IM-based dense wireless networks, and propose a generalized maximum protocol interference and jamming (GMPIJ) model to accurately capture the mutual interference and external jamming. Then, the anti-jamming channel selection problem is formulated as an anti-jamming dynamic game, and subsequently it is proved to be an exact potential game, which has at least one pure strategy Nash equilibrium (NE). Based on the stochastic learning theory, a distributed anti-jamming channel selection algorithm (DACSA) is proposed to find the NE solution. Moreover, the simulation results are presented to demonstrate the effectiveness of the proposed DACSA algorithm.

This letter investigates the anti-jamming problem with discrete power strategies, and then a Stackelberg game is formulated to model the competitive interactions between the user and jammer. Specifically, the user acts as the leader, whereas the jammer is the follower. Based on their own utilities, the user and jammer select their power strategies and determine their respective optimal strategies. Also, a hierarchical power control algorithm (HPCA) is proposed to obtain the Stackelberg equilibrium, and the asymptotic convergence is analyzed. In addition, we consider the impact of the imperfect information due to the jammer's bounded rationality and inaccurate observation of the user's action. Finally, simulations are conducted to show the effectiveness of the proposed HPCA algorithm, and simulation results demonstrate that the jammer's bounded rationality and limited observation lead to the increase of the user's utility.

Recently, the development of unmanned aerial vehicle (UAV) mobile-edge computing (MEC) networks has brought unprecedented gains and opportunities. In this article, the joint computation offloading, UAV role, and location selection problem in hierarchical multicoalition UAV MEC network is investigated. To capture the hierarchical feature and discrete optimization, the discrete Stackelberg game with multiple leaders and followers is formulated. We prove that both the leader-level and member-level subgames are ordinal potential games (OPGs) with Nash equilibrium (NE). Thus, the Stackelberg equilibrium (SE) is guaranteed. To achieve the SE, the log-linear-based hierarchical learning algorithm (LHLA) is proposed and analyzed. The simulation results show that the LHLA can converge fast and achieve better performance compared with the existing schemes.

In this paper, we study the unmanned aerial vehicle (UAV)-enabled opportunistic data collection in wireless sensor networks (WSNs). The UAV performing remote missions is expected to collect data from the WSN during the return flights. Due to the specified task and safety restrictions, flight trajectory and time of the UAV are strictly constrained, resulting in the limited coverage ability in the data collection process. Moreover, the unknown distribution of active sensors makes it difficult for ground sensors and the UAV to complete the off-line optimization of flight mode and transmission. To tackle these problems, we develop an air-ground collaborative online planning method. On one hand, ground sensors actively form terrestrial transmission clusters to improve the data upload efficiency. After analyzing the line-of-sight (LoS) reliability and transmission correlation, we construct a coalition formation game model for the clustering of ground sensors. We discuss the equilibrium property of the game model, which can be achieved by the proposed distributed coalition formation algorithm. On the other hand, to avoid conflicts during the data collection, a data upload protocol is designed. We further discuss various flight speed planning schemes based on different detection capabilities of the UAV. The simulation results show that the performance of ground coalition-based air-ground collaborative online optimization is much better than that of the unilateral data collection by the UAV. Moreover, UAV flight online planning can further improve data uploading efficiency.

Ultra-dense small-cell network is widely acknowledged as a key enabler for high capacity wireless networks. Some of the key challenges that ultra-dense networks face are profitable deployment distribution under complex traffic loads and efficient radio resource management (RRM) in excessive interference environments. Poor small-cell deployment locations can lead to excessive interference without clear profit margins and inefficient resource utilization. As such, data-driven small-cell deployment and self-organizing RRM of small-cell clusters are regarded as the two main technologies that can improve ultra-dense small-cell services. This paper first reviews the latest research in data-driven small-cell deployment using structured and unstructured social media data. A combination of irregular clustering techniques is used to identify hotspots, and natural language processing algorithms are used to identify blackspots. This paper then reviews recent advances in self-organization of small-cell RRM and analyzes how data can improve self-organization performance. Moreover, the idea of cooperative self-organization is introduced to further promote the self-organization capability. Finally, two ultra-dense small-cell RRM case studies are presented to demonstrate the performance which improves of cooperative self-organization.