Heterogeneous cloud radio access network (H-CRAN) promises higher energy efficiency (EE) than the conventional cellular networks by centralizing the baseband signal processing into the baseband unit (BBU) pool hosted by cloud computing platforms. Because of the difference between H-CRAN and conventional cellular networks, existing energy-efficient networking mechanisms designed for conventional cellular networks cannot fully leverage H-CRAN in terms of reducing the network energy consumption. In this paper, we bridge this gap by proposing a radio resource management scheme to optimize the network energy efficiency (NEE) of H-CRAN. We develop a network energy consumption model that characterizes the energy consumption of radio access points (RAPs), fronthaul, and the BBU pool in H-CRAN. Based on the network energy consumption model, we formulate the NEE optimization problem with the consideration of the capacity constrained fronthaul. The NEE optimization problem is a mixed integer non-linear programming problem (MINLP). We propose the H-CRAN energy-efficient radio resource management (HERM) algorithm to solve the NEE optimization problem efficiently. Various properties of the proposed solution are derived and extensive simulations are conducted. The simulation results show that the HERM algorithm significantly improves the NEE of H-CRAN. As compared with a baseline algorithm in which the radio resource management is not optimized, HERM boosts the NEE by 59% under the dynamic network traffic. As compared to an energy-efficient radio resource allocation (ERA) algorithm which does not optimize the energy consumption of the BBU pool, the NEE of H-CRAN achieved by the HERM algorithm is up to 51% better than that by the ERA algorithm with network traffic dynamics.

The metaverse is a perpetual, immersive, and shared digital universe that is linked to but is beyond the physical reality, and this emerging technology is attracting enormous attention from different industries. In this article, we define the first holistic realization of the metaverse in the mobility domain, coined as metamobility. We present our vision of what metamobility will be and describe its basic architecture. We also propose two use cases, tactile live maps and metaempowered advanced driver-assistance systems (ADASs), to demonstrate how metamobility will benefit and reshape future mobility systems. Each use case is discussed from the perspective of the technology evolution, future vision, and critical research challenges. Finally, we identify multiple concrete open research issues.

5G networks will be characterized by massive densification of base stations (BSs). To minimize the network power consumption due to the increased BS deployment, we leverage emerging paradigms to propose BS energy-saving strategies applicable to 5G networks. We explore the potentials of virtualized radio access and core networks to enable spectrum sharing among mobile network operators (MNOs) by harnessing inter-band non-contiguous carrier aggregation. We propose the sleep-mode with efficient beamformers and spectrum-sharing (SEBS) strategy, which minimizes BS power consumption of cooperative MNOs. The licensed bandwidth of each MNO is partitioned into private and shared bands to provision active BSs with sufficient spectrum resources for serving all UEs. Moreover, an optimization problem is formulated to obtain the optimal intra- and inter-RAN beamforming vectors for efficient BS transmission power and improved user signal reception. Numerical simulations show that the proposed strategies yield significant reductions of total power consumption as compared to other schemes. We present different bandwidth sharing ratio formulations to motivate the cooperation among MNOs by the spectrum-user support trading. We also show that the trading does not always yield a higher spectral efficiency in the cooperative cellular network.

Autonomous driving perceives surroundings with line-of-sight sensors that are compromised under environmental uncertainties. To achieve real time global information in high definition map, we investigate to share perception information among connected and automated vehicles. However, it is challenging to achieve real time perception sharing under varying network dynamics in automotive edge computing. In this paper, we propose a novel real time dynamic map, named LiveMap to detect, match, and track objects on the road. We design the data plane of LiveMap to efficiently process individual vehicle data with multiple sequential computation components, including detection, projection, extraction, matching and combination. We design the control plane of LiveMap to achieve adaptive vehicular offloading with two new algorithms (central and distributed) to balance the latency and coverage performance based on deep reinforcement learning techniques. We conduct extensive evaluation through both realistic experiments on a small-scale physical testbed and network simulations on an edge network simulator. The results suggest that LiveMap significantly outperforms existing solutions in terms of latency, coverage, and accuracy.

5G network will carry compute-intensive applications from vertical industries. Network slicing and edge computing are key technologies for fulfilling the diverse requirements of these applications efficiently. We define a cellular network with the edge computing capability as cellular edge computing network. Dynamically slicing a cellular edge computing network is challenging because it needs to orchestrate multi-domain resources and ensure isolation among network slices. In this paper, we present the VirtualEdge system that enables the dynamic creation of a virtual node (vNode) on top of a physical cellular edge computing node to serve the traffic and workloads of a network slice. VirtualEdge introduces a realizable multi-domain resource orchestration and virtualization that provides isolation among network slices without losing the efficiency in virtualizing the radio resources. To efficiently orchestrate multi-domain resources, we design a new learning-assisted algorithm that allows the resource orchestrator to optimize the utilization of physical resources without knowing the utility functions of individual vNodes. For the resource virtualization, we develop a heuristic algorithm and a credit-based queue management scheme to dynamically map virtual radio and computing resources to underlying physical resources, respectively. VirtualEdge is developed and implemented based on the OpenAirInterface LTE and CUDA GPU computing platforms, and its performance is validated through both experiments and large-scale simulations.

By leveraging the elasticity of cloud computing, cloud radio access network (C-RAN) facilitates flexible resource management and is one of the key techniques of enabling 5G. In this paper, we study the energy-efficient on-demand resource provisioning in C-RAN by dynamically provisioning the radio and computing resources according to network traffic demands. The network energy consumption of C-RAN is minimized by jointly optimizing the cooperative beamforming, remote radio head (RRH) selection and virtual baseband units (vBBUs) provisioning. It is challenging to resolve the optimization problem because of the interdependence between the RRH selection and vBBU provisioning. We propose the energy-efficient on-demand C-RAN virtualization (REACT) algorithm to solve the problem in two steps. First, we cluster RRHs into groups by using the hierarchical clustering analysis (HCA) algorithm and assign a vBBU to each RRH group for the baseband signal processing. Second, we determine the RRH selection by optimizing the cooperative beamforming. The performance of the proposed algorithm is validated through extensive simulations, which show that the proposed algorithm significantly reduces the network energy consumption.