The centralized radio access network (C-RAN) is an effective architecture to promote CAPEX/OPEX reduction and cell cooperation derived from its centralized baseband processing. However, there is a contradiction between centralization gain and transport resource saving, which hinders the vision of a resourceefficient and cost-effective RAN deployment. Advanced RAN architectures with functional splits are then introduced to cope with this challenge. Distinguished with other studies, we are intended to investigate whether a fine-grained functional split architecture could benefit to the RAN evolution, and how it impacts on the converged optical-wireless access networks. To this end, we establish a quantitative model to analyze the performance of this architecture. With the fine-grained split, baseband unit (BBU) is divided into a set of fine-grained units (FU) to be placed in desired processing pools (PP) as a service chain. To analyze the placement performance, we propose a mixed-integer linear programming model (MILP) considering the PP selection, routing, wavelength and bandwidth assignment, as well as latency control to minimize the number of PPs, bandwidth, latency, and functions deployment cost. We compare its performance with other two coarse-grained split architectures, i.e., SBBU (adopt low-PHY split like BBU in 4G) and recently emerged DU-CU in both small-scale and large-scale network scenarios. Our analyses provide insights into the modeling and design of efficient converged opticalwireless access networks in 5G and beyond.

To accommodate ever-growing mobile data and heterogeneous 5G services, considerable challenges have emerged to stimulate new design principles on radio access networks (RAN). Advanced functional splits are then introduced to divide baseband unit (BBU) into two new entities, i.e., distributed unit (DU) and central unit (CU), that DU-CU can be co-located or separated into different processing pools (PP) honoring service requirements. Then, the optical transport network evolves towards a more flexible architecture to support agile DU-CU deployment (i.e., alterable DU-CU locations). However, this agile DU-CU deployment without an effective management strategy may lead to the activation of many unnecessary PPs and lightpaths in the network infrastructure that results in low energy efficiency. Moreover, this problem will be continuously amplified with network scale expansion, which becomes one of the operator's major concerns for OPEX reduction. Thus, in this paper, we are dedicated to solving this problem through turning network devices from “always on” to “on-demand on” pattern to support energy-efficient DU-CU deployment and lightpath provisioning. To this end, we propose a mixed-integer linear programming (MILP) model and auxiliary graph-based heuristic to minimize the power consumption of reconfigurable add/drop multiplexer, Ethernet switch, optical transponder, PP, and DU-CU processor. We analyze the power consumption under different 5G services (i.e., eMBB, URLLC, and mMTC) from a latency perspective in both small-scale and large-scale networks. Moreover, our strategies can also be applied with adjustable deployment that adapts to time-varying user traffics, which reduces power by over 14% compared to conventional static deployment. Our analyses can help provide insights into the modeling and designing of energy-efficient DU-CU deployment and lightpath provisioning in 5G.

In this paper, we propose a deep reinforcement learning (DRL)-based algorithm to generate policies of Baseband Function (BBF) placement and routing. In order to explore the performance of the proposed algorithm in practical systems, the online scenario with the completely random requests is used in the simulation considering C-RAN and NG-RAN architectures. Besides, an Integer Linear Programming (ILP) model is formulated to generate the optimal solution as the benchmark. The simulation results show that DRL-based algorithm converges in a short time, and its performance closes to the optimal benchmark obtained by ILP in terms of latency and bandwidth for the online scenarios. In addition, the performance of the generated policies based on DRL is compared with a classic heuristic algorithm, i.e., first-fit algorithm. The performance of DRL-based algorithm is superior to the first-fit algorithm from above two perspectives. The fast convergence and the near-optimal performance prove that the DRL-based algorithm is a promising approach for the BBF placement and routing of RAN in 5G and Beyond.

Massive multiple input multiple output (mMIMO) enabled beamforming is a promising technology in the next generation of mobile communication. It allows multiple antennas transmit the same data to a user to improve the signal strength and quality. However, beamforming may incur redundant data transmission over fronthaul in the centralized radio access networks. When accommodating a set of beam requests, a key problem is how to coordinate radio and optical resources to maximize the beamforming throughput and minimize the fronthaul bandwidth. In this paper, we study on the joint wavelength, antenna, radio resource block allocation (WARA) in a time and wavelength division multiplexed passive optical network, which is an economic fronthaul solution for the mMIMO. We formulate the WARA into an integer linear programming (ILP) model. Three heuristic algorithms are proposed to optimize fronthaul bandwidth, radio resource block (RB) utilization, and both. We compare ILP with the proposed algorithms under different sizes of antenna arrays. A tradeoff is shown to exist between fronthaul bandwidth and radio RB utilization. Also, we find that the optimization goal depends on the number of antennas in an optical network unit.

The emerging services of 5G and beyond (5G/B5G) have imposed challenging resiliency demands that make reliable slicing in radio access networks (RAN) an imperative concern. In previous literature, service protection schemes for one single domain are well studied, e.g., lightpath protection for transport domain. However, 5G/B5G will evolve towards a converged network where data transport and function processing coordinate together, thus the single-domain protection is inadequate. In this paper, we are dedicated to investigating a multi-domain protection scheme, which can achieve customized protection for specific reliability demands and reduce backup redundancy. To this end, we propose a topology-level based protection scheme (TLPS) to customize virtual networks (VN) for slices to guarantee reliability while reducing redundancy. The designed VNs are then embedded onto the substrate network with our proposed mixed integer linear programming (MILP) model and heuristic, which aim at minimizing processing and transport resources. We also introduce the multi-service function chain (MSFC) scheme for comparison, which is extended from the existing “Primary + Backup” solution. In the simulation, different cases are numerically evaluated where TLPS outperforms MSFC in terms of processing and transport resource consumption. Our analyses can provide insights into achieving customized protection for RAN slicing in 5G and beyond.

Mobile/multi‐access edge computing (MEC) takes advantage of its proximity to end‐users, which greatly reduces the transmission delay of task offloading compared to mobile cloud computing (MCC). Offloading computing tasks to edge servers with a certain amount of computing ability can also reduce the computing delay. Meanwhile, device‐to‐device (D2D) cooperation can help to process small‐scale delay‐sensitive tasks to further decrease the delay of tasks. But where to offload the computing tasks is a critical issue. In this article, we integrate MEC and D2D cooperation techniques to optimize the offloading decisions and resource allocation problem in D2D‐enabled three‐tier MEC networks for Internet of Things (IoT). Mobile devices (MDs), edge clouds, and central cloud data center (DC) make up these three‐tier MEC networks. They cooperate with each other to finish the offloading tasks. Each task can be processed by MD itself or its neighboring MDs at device tier, by edge servers at edge tier, or by remote cloud servers at cloud tier. Under the maximum energy cost constraints, we formulate the cooperative offloading problem into a mixed‐integer nonlinear problem aiming to minimize the total delay of tasks. We utilize the alternating direction method of multipliers (ADMM) to speed up the computing process. The proposed scheme decomposes the complicated problem into 3 smaller subproblems, which are solved in a parallel fashion. Finally, we compare our proposal with D2D and MEC networks in simulations. Numerical results validate that the proposed D2D‐enabled MEC networks for IoT can significantly enhance the computing abilities and reduce the total delay of tasks.