This paper investigates the joint resource allocation and group formation for platoon in vehicle-to-everything (V2X) networks under vehicular channel uncertainty. To achieve the high spectrum efficiency and overcome the platoon head communication range limitation, an adaptive multicast-based group cooperation communication model is developed for the platoon with dynamic topology. Considering the heterogeneous characteristics of different types of links, i.e., high capacity for vehicle-to-infrastructure (V2I) links and ultra-reliability for vehicle-to-vehicle (V2V) links, we attempt to maximize the V2I capacity whilst satisfying a probability constraint for ultra-reliable V2V-supported intra-platoon communication. To handle the intractable probability constraint, a support vector clustering (SVC) based method is developed to capture the distributional geometry of massive uncertain channel samples as a sphere in high-dimensional feature space with asymmetric structure. Based on it, the probability constraint is transformed into a tractable linear convex set. After that, an exploration-selection-alternating-iterative algorithm is developed to solve the formulated problem with coupled optimization variables. Specifically, in the exploration process, a two-stage algorithm is proposed for the resource allocation problem under fixed group formation decision, which includes power control and spectrum allocation. During the selection process, a performance difference-based decision transition rate is designed to optimize group formation solution. Simulation results demonstrate the proposed data-driven approach can overcome the over-conservatism of the traditional symmetric-geometry-based uncertainty sets, and the multicast-based group cooperation communication model corresponds to a higher performance on V2I capacity than other traditional schemes.

In this paper, we study the resource allocation in high mobility vehicle-to-everything (V2X) networks with only slowly varying large-scale channel parameters. For satisfying the diversity requirements of different types of links, i.e., low delay for vehicle-to-infrastructure (V2I) connections and ultra-reliability for vehicle-to-vehicle (V2V) connections, we formulate a joint power, spectrum and vehicle local computing ratio allocation problem to minimize the delay of V2I links whilst satisfying the V2V reliability constraint. For solving the formulated problem, a Feasible Region Transformation Method is firstly developed to convert the probabilistic V2V reliability requirement into a computable constraint. In addition, a Robust Signal to Interference Plus Noise Ratio (SINR) Modified Method is proposed to give the computable expression for the V2I throughput. Then, a parallel Deep Neural Network (DNN) framework is designed for the resource allocation in V2X networks, where one is the transmit power control unit and the other is the local computing ratio allocation unit. After that, a Feedback-oriented Learning Method is proposed to train the parallel DNN-based resource allocation framework, in which the output of DNN is used as feedback to dynamically revise the training loss function along with the training process. Afterwards, the Hungarian method is employed to obtain the optimal spectrum matching. Finally, we conduct the simulations to show that the proposed learning-based algorithm has better performance compared with other general algorithms.

This paper investigates the resource allocation and user association for time-varying heterogeneous wireless network (HetNet), where the multi-homed user equipment (UE) utilizes multiple access options (AOs) simultaneously. Firstly, a stochastic optimization model is proposed to maximize the long-term energy efficiency (EE), which is defined as the ratio of long-term total throughput to the long-term corresponding energy consumption. A modified fractional programming method is proposed to convert the long-term EE maximize problem into an instance throughput-minus- energy optimization problem, which is proved as a mixed integer nonlinear optimization (MINO). The continuity relaxation and Lagrange dual method are proposed to solve the MINO problem. Then, a utility-based selection algorithm is developed to determine the optimal associated AO sets for each UE. After that, the dynamic EE-based resource allocation algorithm is developed to allocate the AOs radio resource energy efficiently, which depends only on the current network state information. Our simulation results show that the proposed algorithm achieves EE performance improvement compared to other general algorithms.