Millimeter wave (MmWave) has become the most promising candidate to enable multi-Gbps transmission and high-precision detection due to the large available bandwidth. Because of the serious propagation attenuation, it is necessary for a vehicle-to-infrastructure (V2I) system communicating in the mmWave band to overcome severe path loss by utilizing beamforming technology. However, swift and accurate beam alignment at the transceivers is challenging when considering user mobility and fast-varying wireless environment. In this paper, we propose an Adaptive Discounted Thompson Sampling (ADTS) based beam alignment algorithm without any prior information such as coarse user location information, which ignores historical observations made beyond the past time periods and avoids misleading for the current state. Besides, we conduct performance analysis to determine the achievable performance bound of the proposed algorithm. Using simulation results, we show that our proposed algorithm achieves good performance in terms of the average effective achievable rate and the beam alignment accuracy without prior knowledge.

Network function virtualization (NFV) is a new network architecture concept that simplifies the deployment of network services and improves service management. However, it is challenging for a network service provider (NSP) to decide where to place virtual network functions (VNFs). Most previous studies have considered only single-chain services, wherein the VNFs for a request are executed in sequence. In contrast to previous approaches, we consider more general and practical situations in which the VNFs of a request can be executed in parallel and are represented as a forwarding graph. Our objective is to maximize the profits earned by providing network services while satisfying the delay requirements of requests. We formulate the VNF placement problem as an integer linear programming (ILP) problem. Due to the complexity of this problem, we propose a probability-based approach called PBP, in which the placements of the VNFs are determined based on their probabilities of contributing to the profit. Furthermore, we propose a heuristic reliability-aware algorithm to guarantee service reliability, in which each VNF of a request is assigned a backup that can be shared with other requests. Simulation experiments show that PBP achieves a much shorter computation time than previous algorithms while earning higher profit, and furthermore, our reliability-aware algorithm provides the same reliability as a previous algorithm while yielding much higher profit.