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Unmanned aerial vehicle (UAV) communications are vulnerable to smart attacks, where the attacker can change the attack mode (e.g., eavesdropping and jamming) via smart radio devices. To ensure secure transmissions against hybrid attacks, UAVs may transmit confidential information and misleading information alternately. In this paper, we consider a dynamic anti-hybrid attack framework with trajectory optimization, where both a UAV and an attacker attempt to find their optimal trajectories without knowing the information type and the attack mode of each other. Given the major challenge due to incomplete knowledge (i.e., each agent knows only its own information), we establish an adversarial game with partial-observation feature to formulate an optimization problem, and propose a counterfactual regret minimization learning scheme to achieve the correlated equilibrium for both the UAV and attacker. Simulation results validate the superiority of our scheme over a benchmark in UAV communication scenarios with partial information. [ABSTRACT FROM AUTHOR]

Benefiting from the global deployment and fast commercialization of cellular systems, cellular-enabled vehicle-to-everything (V2X) opens up a significant prospect in the foreseeable future, for supporting proliferating vehicular services. Reliable and efficient resource allocation is a prerequisite for enjoying excellent quality of vehicular communication experiences, which yet still remains a serious challenge due to the inherent vehicle mobility and network heterogeneity. In this work, taking the rapidly varying channel state information (CSI) into consideration, we investigate the joint time-frequency allocation problem for the heterogeneous cellular V2X network, where resource sharing based on hierarchical reuse among different V2X communication types are granted. To address the high dynamics of V2X environment, a mapped fuzzy space is introduced, in which the uncertain CSI is interpreted as fuzzy numbers. This could effectively avoid the deterioration of such dynamic environment to the stability and convergence of optimization mechanisms. On this basis, we construct a two-side many-to-many fuzzy matching game (MM-FMG) to formulate the optimization problem with the dynamic and uncertain information, and further propose a vehicle-resource dynamic matching algorithm to solve this MM-FMG problem. Our developed scheme can achieve both reliable and efficient resource assignment by excluding the precise CSI, which is hence more suitable to the concerned V2X scenarios. Finally, the superior performances of our designed algorithm are demonstrated via numerical simulations. [ABSTRACT FROM AUTHOR]

The persistent awareness of dynamic links quality is of critical importance to shared access systems, e.g., promoting sensing accuracy or even enabling seamless sharing, which remains yet as a challenging task. In this paper, we consider the shared access in heterogeneous small-cells, which are likely to be the main infrastructure components by utilizing the carrier aggregation (CA) scheme, potentially combining both licensed and unlicensed bands. We suggest a promising sensing scheme, which permits the simultaneous assessment of multiple links quality when detecting vacant spectrum, and hence is able to solve the long-standing hidden terminal problem. We incorporate an additional objective of acquiring dynamic link state information of multiple incumbent transmitters/receivers, and thereby, structure spectrum sensing as a mixed classification and estimation problem. The main challenge is that multiple channels may become similar with each other, and existing joint estimation schemes are unable to distinguish which incumbent is active and what its associated channel state is. To address this indistinguishable problem and alleviate the error propagation in recursive estimations, a novel alternating Bernoulli filtering algorithm is proposed. By fully exploiting the group transitional behavior of incumbents, a multiple-chains inference framework is developed. Numerical simulations validate our new scheme. Occupancy states of multiple incumbents can be identified accurately, by tracking their links quality and exploiting the underlying dynamics, which lays the foundation for more effective shared access. [ABSTRACT FROM AUTHOR]

Dynamic spectrum sharing is considered as one of the key features in the next-generation communications. In this correspondence, we investigate the dynamic tradeoff between the sensing performance and the achievable throughput, in the presence of time-varying fading (TVF) channels. We first establish a unified dynamic state-space model to characterize the involved dynamic behaviors. On this basis, a promising dynamic sensing schedule framework is proposed, whereby the sensing duration is adaptively adjusted based on the estimated real-time TVF channel. We formulate the sensing-throughput tradeoff problem mathematically. We then show that there exists the optimal sensing duration maximizing the throughput for the secondary user (SU), which will change dynamically with channel gains. Relying on our designed recursive sensing paradigm that is able to blindly acquire varying channel gains as well as the PU states, the sensing duration can be then adjusted in line with the evolving channel gains. Numerical simulations are provided to validate our dynamic sensing schedule algorithm, which can significantly improve the SU's throughput by reconfiguring the sensing duration according to dynamic channel conditions. [ABSTRACT FROM AUTHOR]

In this paper, the throughput maximization of millimeter-wave (mm-Wave) ultradense networks (UDN) using dynamic spectrum sharing (DSS) is considered. Most of the existing works only allow temporal-domain access and admit at most one user at each time slot, resulting in significant underutilization of spectrum resource, which will be less attractive to mm-wave UDN applications. A generalized temporal-spatial sharing scheme is proposed in this paper for UDN by exploiting the location information of incumbent devices, where multiple users are allowed to access each channel simultaneously via spatial separations. For distributed applications, the global information exchange among secondary users (SU) tends to be impractical, given the unaffordable signaling overhead and latency. Thus, a noncooperative game with fine-grained two-dimensional reuse is formulated, which leads to a more efficient access strategy. It is then proved to be an ordinary potential game (OPG), which guarantees the existence of the strategy Nash equilibrium (NE). Finally, an improved decentralized reinforcement learning algorithm is designed, with which SUs can learn from wireless environments and adapt toward an NE point, relying on the individual observation and the historical action reward (rather than the global information exchanging). The convergence efficiency of the new scheme is also rigorously proved. Numerical simulations are provided to validate the performances of the proposed schemes. [ABSTRACT FROM AUTHOR]