Vehicular edge computing (VEC) is a critical enabler of low-latency and computation-intensive vehicular applications by offloading tasks from vehicles to edge servers. However, the dynamic nature of vehicular networks introduces significant uncertainty in task characteristics and network conditions. This paper proposes a robust task offloading scheme for VEC within the open radio access network (O-RAN) architecture. The proposed scheme integrates large-timescale computational resource allocation (CRA) with small-timescale task partitioning and radio resource allocation (RRA) using O-RAN’s hierarchical control framework. Our scheme minimizes the network-wide resources under latency constraints that are subjected to demand uncertainty. We employ the cutting-set method to address the demand uncertainty in the large-timescale CRA. We obtain a closed-form solution to the optimal task partitioning problem and provide a heuristic approach for the small-timescale RRA. Simulation results show that the small-timescale RRA succeeds to counteract the demand uncertainty at the large-timescale CRA, that is, no outage occurs when demands are in the assumed uncertainty set, whereas the non-robust scheme exhibits as high as 50% outage probability. Moreover, our slotted scheme consumes about 50% less bandwidth than the conventional non-slotted robust solution.

Hybrid beamforming (HB) with quality-of-service (QoS) provisioning per stream in millimeter waves is indispensable in 5G/6G networks. HB includes baseband and radio frequency (RF) beamforming, and requires error-free channel state information (CSI), which is erroneous in practice. So there is a need for efficient, feasible, robust, and QoS-aware HB. To achieve this, we mitigate CSI uncertainty via baseband beamforming, and we steer the RF beamformer by using the estimates of the channel’s eigenvectors. In doing so, we consider the effective channel’s uncertainty region instead of the uncertainty region of the channel itself, as the former is smaller than the latter, requiring less transmit power to satisfy the QoS constraint. We also detect and eliminate the infeasible data streams. Our iterative scheme (which is based on the cutting-set method) for baseband beamforming satisfies the mean-squared error (MSE) constraint per stream, where a limited number of constraints are considered instead of infinitely many constraints. In our low-complexity scheme, we derive a simple sufficient condition to check the feasibility of each stream, we diagonalize the effective channel at the baseband precoder, and we use minimum MSE combining at the baseband combiner. Extensive simulations validate our formulations and theoretical derivations.

In this paper, we propose a novel resource management scheme that jointly allocates the transmit power and computational resources in a centralized radio access network architecture. The network comprises a set of computing nodes to which the requested tasks of different users are offloaded. The optimization problem minimizes the energy consumption of task offloading while takes the end-to-end-latency, i.e., the transmission, execution, and propagation latencies of each task, into account. We aim to allocate the transmit power and computational resources such that the maximum acceptable latency of each task is satisfied. Since the optimization problem is non-convex, we divide it into two sub-problems, one for transmit power allocation and another for task placement and computational resource allocation. Transmit power is allocated via the convex-concave procedure. In addition, a heuristic algorithm is proposed to jointly manage computational resources and task placement. We also propose a feasibility analysis that finds a feasible subset of tasks. Furthermore, a disjoint method that separately allocates the transmit power and the computational resources is proposed as the baseline of comparison. A lower bound on the optimal solution of the optimization problem is also derived based on exhaustive search over task placement decisions and utilizing Karush–Kuhn–Tucker conditions. Simulation results show that the joint method outperforms the disjoint method in terms of acceptance ratio. Simulations also show that the optimality gap of the joint method is less than 5%.