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As the fog-based internet of vehicles (IoV) is equipped with rich perception, computing, communication and storage resources, it provides a new solution for the bulk data processing. However, the impact caused by the mobility of vehicles brings a challenge to the content scheduling and resource allocation of content dissemination service. In this paper, we propose a time-varying resource relationship graph to model the intertwined impact of the perception, computation, communication and storage resources across multiple snapshots on the content dissemination process of IoV. Based on this graph model, the content dissemination process is modeled as a mathematical optimization problem, where the quality of service of both delay tolerant and delay sensitive services are considered. Owing to its NP-completeness, the optimization problem is decomposed into a joint link and subchannel scheduling subproblem and as well a joint power and flow control subproblem. Then, a cascaded low complexity scheduling algorithm is proposed for the joint link and subchannel scheduling subproblem. Moreover, a robust resource management algorithm is developed for the power and flow control subproblem, where the channel uncertainties in future snapshots are fully considered in the algorithm. Finally, we conduct simulations to show that the effectiveness of the proposed approaches outperforms other state-of-art approaches.

In Vehicle-to-Everything (V2X) communication, the high mobility of vehicles generates the Doppler shift which leads to channel uncertainties. Moreover, the reasons for channel uncertainties also include the finite channel feedback, channels state information (CSI) loss and latency. With this concern, we formulate a joint spectrum and power allocation problem for V2X communication with imperfect CSI. Specifically, the sum capacity of cellular user equipments (CUEs) is maximized subject to the minimum Signal-to-Interference-and-Noise Ratio (SINR) requirements of CUEs and the outage probability constraints of vehicular user equipments (VUEs). Then, two different robust resource allocation approaches are designed to solve the problem. One is Bernstein Approximation-based Robust Resource Allocation approach. More specifically, Bernstein approximations are employed to convert the chance constraint into a calculable constraint, and Bisection search method is proposed to obtain the optimal allocation solution with low complexity. Then, for further reducing the computational complexity, Self-learning Robust Resource Allocation approach, which includes a learning method and an analytical mapping method, is proposed as the second approach. The learning method is devised to learn the uncertainty set which transforms the chance constraint into calculable constraints, and the analytical mapping method is proposed to obtain closed-form solutions of the resource allocation problem. Finally, the simulation results prove that the proposed approaches can improve the capacity of all CUEs effectively whilst ensuring the reliability of the channel.

In this paper, we study the resource allocation in D2D underlaying cellular network with uncertain channel state information (CSI). For satisfying the diversity requirements of different users, i.e. the minimum rate requirement for cellular user and the reliability requirement for D2D user, we attempt to maximize the cellular user's throughput whilst ensuring a chance constraint for D2D user. Then, a robust resource allocation framework is proposed for solving the highly intractable chance constraint about D2D reliability requirement, where the CSI uncertainties are represented as a deterministic set and the reliability requirement is enforced to hold for any uncertain CSI within it. Then, a symmetrical-geometry-based learning approach is developed to model the uncertain CSI into polytope, ellipsoidal and box. After that, we derive the robust counterpart of the chance constraint under these uncertainty sets as the computation convenient convex sets. To overcome the conservatism of the symmetrical-geometry-based uncertainty sets, we develop a support vector clustering (SVC)-based approach to model uncertain CSI as a compact convex uncertainty set. Based on that, the chance constraint of D2D is converted into a linear convex set. Then, we develop a bisection search-based power allocation algorithm for solving the resource allocation in D2D underlaying cellular network with different robust counterparts. Finally, we conduct the simulation to compare the proposed robust optimization approaches with the non-robust one.

Hadrons formed in heavy-ion collisions are not point-like objects, they cannot occupy too close space-time points. When the two bosons are too close to each other, their constituents start to mix and they cannot be considered as bosons subjected to Bose-Einstein statistics, this effect is called excluded volume effect. We study the volume effect on HBT for the sources with various sizes. The effect on HBT was shown in out, side and long directions, and it is more obvious for the source with a narrow space-time distribution. The correlation functions for high transverse momenta are more suppressed by the volume effect. Hence the incoherence parameter may be more suppressed by the volume effect for high transverse momenta in small collision systems.

We study the effect of in-medium mass-shift on transverse-momentum spectrum and elliptic anisotropy of $\phi$ meson. It is found that the mass-shift enhances the $\phi$ yields and suppresses the elliptic flow $v_2$ in large momentum region, and the effects increase with the increasing mass-shift. The effects are various for different sources and decrease with the increasing expanding velocity. We further study the effects for parts of all $\phi$ meson with mass-shift, and the effects decrease with the decreasing probability of $\phi$ meson with mass-shift. Since the different mass-shift lead to different transverse-momentum spectrum and $v_2$, our study may provide a way to restrict the ranges of in-medium mass-shift of $\phi$ meson in high-energy heavy-ion collisions.

We investigate the squeezed back-to-back correlations (BBC) of $K^+$$K^-$, caused by the mass modification of the particle in the dense medium formed in d+Au collisions at $\sqrt{s_{NN}}=200$ GeV and Au+Au collisions at $\sqrt{s_{NN}}=62.4$ GeV. Considering some kaons may be not affected by the medium, we further study the BBC functions of $K^+$$K^-$ when parts of all kaons have a mass-shift. Our results indicate that the BBC functions of $K^+$$K^-$ can be observed when only about 10$\%$ of all kaons have a mass-shift in d+Au collisions at $\sqrt{s_{NN}}=200$ GeV and the peripheral collisions of Au+Au at $\sqrt{s_{NN}}=62.4$ GeV. Since the BBC function is caused by the mass-shift due to the interaction between the particle and the medium, the successful detection of the BBC function indirectly marks that the dense medium has formed in these collision systems. We suggest to measure the BBC function of $K^+$$K^-$ experimentally in d+Au collisions at $\sqrt{s_{NN}}=200$ GeV and peripheral collisions of Au+Au at $\sqrt{s_{NN}}=62.4$ GeV.