Distributed Radio Access Research Articles

Next Generation Distributed Radio Access Networks With FSO Fronthauling

Next Generation Distributed Radio Access Networks With FSO Fronthauling

Collaborative Cloud and Edge Mobile Computing in C-RAN Systems With Minimal End-to-End Latency

Mobile cloud and edge computing protocols make it possible to offer computationally heavy applications to mobile devices via computational offloading from devices to nearby edge servers or more powerful, but remote, cloud servers. Previous work assumed that computational tasks can be fractionally offloaded at both cloud processor (CP) and at a local edge node (EN) within a conventional Distributed Radio Access Network (D-RAN) that relies on non-cooperative ENs equipped with one-way uplink fronthaul connection to the cloud. In this paper, we propose to integrate collaborative fractional computing across CP and ENs within a Cloud RAN (C-RAN) architecture with finite-capacity two-way fronthaul links. Accordingly, tasks offloaded by a mobile device can be partially carried out at an EN and the CP, with multiple ENs communicating with a common CP to exchange data and computational outcomes while allowing for centralized precoding and decoding. Unlike prior work, we investigate joint optimization of computing and communication resources, including wireless and fronthaul segments, to minimize the end-to-end latency by accounting for a two-way uplink and downlink transmission. The problem is tackled by using fractional programming (FP) and matrix FP. Extensive numerical results validate the performance gain of the proposed architecture as compared to the previously studied D-RAN solution.

Collaborative Cloud and Edge Mobile Computing in C-RAN Systems With Minimal End-to-End Latency

Mobile cloud and edge computing protocols make it possible to offer computationally heavy applications to mobile devices via computational offloading from devices to nearby edge servers or more powerful, but remote, cloud servers. Previous work assumed that computational tasks can be fractionally offloaded at both cloud processor (CP) and at a local edge node (EN) within a conventional Distributed Radio Access Network (D-RAN) that relies on non-cooperative ENs equipped with one-way uplink fronthaul connection to the cloud. In this paper, we propose to integrate collaborative fractional computing across CP and ENs within a Cloud RAN (C-RAN) architecture with finite-capacity two-way fronthaul links. Accordingly, tasks offloaded by a mobile device can be partially carried out at an EN and the CP, with multiple ENs communicating with a common CP to exchange data and computational outcomes while allowing for centralized precoding and decoding. Unlike prior work, we investigate joint optimization of computing and communication resources, including wireless and fronthaul segments, to minimize the end-to-end latency by accounting for a two-way uplink and downlink transmission. The problem is tackled by using fractional programming (FP) and matrix FP. Extensive numerical results validate the performance gain of the proposed architecture as compared to the previously studied D-RAN solution.

Elastic Resource Provisioning for Increased Energy Efficiency and Resource Utilization in Cloud-RANs

Distributed Radio Access Networks (D-RANs), characterized by static deployment of Base Stations (BSs), are facing critical difficulties in handling the temporal and geographical fluctuations of capacity demands. Recently, Cloud RAN (C-RAN) has been introduced as a centralized cloud computing based paradigm for wireless cellular networks in which the BSs are physically decoupled into Virtual Base Stations (VBSs) and Remote Radio Heads (RRHs). In this article, a novel framework is proposed for C-RAN to adapt to the fluctuation in capacity demand while at the same time maximizing the energy efficiency and resource utilization. The RRHs and their corresponding VBSs are divided into clusters within which the active RRH density, transmission power, and size of Virtual Machines (VMs) are adjusted dynamically. To characterize the computational requirements of a VBS, a programmable C-RAN testbed is implemented using the OpenAirInterface (OAI) software platform and USRP boards. Testbed experiment results are presented to describe the consumption and utilization of computing resources in the cloud; simulation results are also presented to illustrate the performance gains of this elastic solution against the current static deployment.

Low-Complexity Distributed Radio Access Network Slicing: Algorithms and Experimental Results

Radio access network (RAN) slicing is an effective methodology to dynamically allocate networking resources in 5G networks. One of the main challenges of RAN slicing is that it is provably an NP-Hard problem. For this reason, we design near-optimal low-complexity distributed RAN slicing algorithms. First, we model the slicing problem as a congestion game, and demonstrate that such game admits a unique Nash equilibrium (NE). Then, we evaluate the Price of Anarchy (PoA) of the NE, i.e., the efficiency of the NE as compared with the social optimum, and demonstrate that the PoA is upper-bounded by 3/2. Next, we propose two fully-distributed algorithms that provably converge to the unique NE without revealing privacy-sensitive parameters from the slice tenants. Moreover, we introduce an adaptive pricing mechanism of the wireless resources to improve the network owner’s profit. We evaluate the performance of our algorithms through simulations and an experimental testbed deployed on the Amazon EC2 cloud, both based on a real-world dataset of base stations from the OpenCellID project. Results conclude that our algorithms converge to the NE rapidly and achieve near-optimal performance, while our pricing mechanism effectively improves the profit of the network owner.

Control-Data Separation With Decentralized Edge Control in Fog-Assisted Uplink Communications

Fog-aided network architectures for 5G systems encompass wireless edge nodes, referred to as remote radio systems (RRSs), as well as remote cloud center (RCC) processors, which are connected to the RRSs via a fronthaul access network. RRSs and RCC are operated via Network Functions Virtualization (NFV), enabling a flexible split of network functionalities that adapts to network parameters such as fronthaul latency and capacity. This work focuses on uplink communications and investigates the cloud-edge allocation of two important network functions, namely the control functionality of rate selection and the data-plane function of decoding. Three functional splits are considered: (i) Distributed Radio Access Network (D-RAN), in which both functions are implemented in a decentralized way at the RRSs, (ii) Cloud RAN (C-RAN), in which instead both functions are carried out centrally at the RCC, and (iii) a new functional split, referred to as Fog RAN (F-RAN), with separate decentralized edge control and centralized cloud data processing. The model under study consists of a time-varying uplink channel in which the RCC has global but delayed channel state information (CSI) due to fronthaul latency, while the RRSs have local but more timely CSI. Using the adaptive sum-rate as the performance criterion, it is concluded that the F-RAN architecture can provide significant gains in the presence of user mobility.

Dynamic Virtual Network Connectivity Services to Support C-RAN Backhauling

Traditional distributed radio access networks (RANs) are evolving toward centralized architectures, with the aim of satisfying in a cost-effective manner the forecast traffic growth that future mobile networks will need to support. Specifically, the cloud RAN (C-RAN) architecture has shown that it can alleviate to some extent the ever-increasing total cost of ownership in mobile networks. The current trend in C-RAN is to separate remote radio heads with radio frequency (RF) functions and baseband unit (BBU) gathering baseband processing. This functional split allows the RF modules to be kept close to the antennas while placing BBUs at centralized locations so they can be shared among different sites and even be virtualized. However, some issues still need to be addressed in future mobile networks, especially due to the dynamicity of services and the strict constraints imposed by the interfaces needed. In fact, connectivity reconfiguration for X2 and S1 backhaul interfaces needs to be provided as an all-or-nothing request to enable mobile resource reconfiguration in a geographical area. In view of this, in this paper we propose dynamic customer virtual network (CVN) reconfiguration to be supported in metro and core network segments. Such CVN requests must include quality of service constraints to ensure specific delay constraints, as well as bitrate guarantees to avoid service interruption. A mathematical formulation and heuristic algorithm are presented for the CVN reconfiguration problem, and exhaustive simulation results study its performance in realistic scenarios.