The resource allocation in vehicle-to-everything (V2X) communications face a great challenge for satisfying the heterogeneous quality of service (QoS) requirements of the ultra-reliable safety-related services and the minimum throughput required entertainment services, due to the channel uncertainties caused by high mobility. In this paper, we first consider an optimistic scenario where the distribution of uncertain channel state information (CSI) can be deterministic and accurately known at the eNB. Then, a low-complexity resource allocation approach is developed, in which the probabilistic QoS constraint of V2V is transformed into a computable optimization constraint. To deal with the scenario with unknown uncertain CSI distribution, we develop a distributionally robust resource allocation approach for converting the intractable chance constraint of V2V into a deterministic semidefinite constraint based only on the first- and second-order moments of uncertain CSI. For alleviating the conservatism of above approach, a support-based distributionally robust resource allocation approach is developed to tighten the semidefinite constraint by utilizing the support information of uncertain CSI. Finally, we conduct simulations to show that the effectiveness of the proposed approaches outperforms other state-of-art approaches.

Deploying a radio frequency (RF) access point (AP) to the visible light communication (VLC) system is a promising strategy to overcome the VLC’s limitations, such as limited coverage, strictly line-of-sight transmission and mobility robustness, etc. In this paper, we focus on the energy-aware design of network selection and resource allocation for a heterogeneous network combining with RF and VLC APs. For adapting to different timescale network states and stochastic data arrival, we propose an on-line two-timescale adaptive network resource optimization (ANRO) framework by employing the Lyapunov optimization technique. At the large timescale, we first develop a closedform solution for the subproblem of network selection for user equipment. Second, we design a cost-effective and easy-to-realize algorithm for VLC’s joint transmission scheduling and power control subproblem, which is a nonconvex optimization. While, at the small timescale, we obtain the optimal solution for RF’s joint resource block and power allocation subproblem, which is proven a mixed integer nonlinear optimization. Simulation results demonstrate that the ANRO can achieve a tradeoff between network power consumption and delay. Furthermore, it not only can stabilize the network but also can significantly reduce the energy consumption compared with other existing schemes.

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, channel 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 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.

In this paper, we study the joint platoon partition, power control and spectrum allocation for platoon communication in cellular vehicle-to-everything (V2X) networks with uncertain channel parameters. A distributionally robust (DR) optimization problem is formulated to maximize the vehicle-to-infrastructure (V2I) capacity whilst guaranteeing the reliability of intra-platoon communication. For solving the DR problem, a statistical-based approach is developed to learn the distributional ambiguity set from historical uncertain channels. Then, based on it, a data-driven equivalent transformation approach is proposed to transform the probabilistic vehicle-to-vehicle (V2V) reliability requirement into a deterministic semidefinite expression. Considering that the resource allocation is built on the platoon partition result, the formulated problem is decomposed into a resource allocation problem and a platoon partition problem. Then, a low-complexity two-stage wireless resource allocation strategy is proposed for V2X networks, which contains power control and spectrum matching. After that, the platoon partition is transformed into a specific Markov chain design problem. An exploration-consolidation learning algorithm is proposed to obtain the optimal platoon partition according to the designed Markov transition rate. Finally, simulation results verify that the proposed algorithm is superior to other existing schemes.

Vehicle-to-everything (V2X) communications face a great challenge in delivering not only the low-latency and ultra-reliable safety-related services but also the minimum throughput required entertainment services, due to the channel uncertainties caused by high mobility. This paper focuses on the robust resource management of V2X communications with the consideration of channel uncertainties. First, we formulate a transmit power minimization problem, whilst guaranteeing the different quality-of-service (QoS) requirements. To achieve the robustness of QoS provisions against channel uncertainties, a statistical leaning approach is developed to learn the uncertainties from the data samples of the random channel coefficients as a convex ellipsoid set, which is also called high-probability-region (HPR). Then, the highly intractable power minimization problem is converted into a second-order cone program by the robust optimization approach. Afterwards, we propose a joint set partitioning and reconstruction mechanism to further reduce the total transmit power by pruning the rough HPR into a more precise uncertainty set, which leads to a trackable second-order cone program and a linear program. Finally, we prove that the network performance can be effectively enhanced by the improvement mechanism. Simulation results verify the effectiveness of the robust resource allocation approaches over the non-robust one.