• Journal of Communications and Networks
• /
• v.18 no.2
• /
• pp.135-149
• /
• 2016

Cloud radio access network (C-RAN) refers to the virtualization of base station functionalities by means of cloud computing. This results in a novel cellular architecture in which low-cost wireless access points, known as radio units or remote radio heads, are centrally managed by a reconfigurable centralized "cloud", or central, unit. C-RAN allows operators to reduce the capital and operating expenses needed to deploy and maintain dense heterogeneous networks. This critical advantage, along with spectral efficiency, statistical multiplexing and load balancing gains, make C-RAN well positioned to be one of the key technologies in the development of 5G systems. In this paper, a succinct overview is presented regarding the state of the art on the research on C-RAN with emphasis on fronthaul compression, baseband processing, medium access control, resource allocation, system-level considerations and standardization efforts.

• Journal of Communications and Networks
• /
• v.15 no.4
• /
• pp.398-406
• /
• 2013

In this paper, we investigate two types of in-phase and quadrature-phase (IQ) data transfer methods for cloud multiple-input multiple-output (MIMO) network operation. They are termed "after-precoding" and "before-precoding". We formulate a cloud massive MIMO operation problem that aims at selecting the best IQ data transfer method and transmission strategy (beamforming technique, the number of concurrently receiving users, the number of used antennas for transmission) to maximize the ergodic sum-rate under a limited capacity of the digital unit-radio unit link. Based on our proposed solution, the optimal numbers of users and antennas are simultaneously chosen. Numerical results confirm that the sum-rate gain is greater when adaptive "after/before-precoding" method is available than when only conventional "after-precoding" IQ-data transfer is available.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.10
• /
• pp.4618-4639
• /
• 2018

One of the most innovative paradigms for the next-generation of wireless cellular networks is the cloud-radio access networks (C-RANs). In C-RANs, base station functions are distributed between the remote radio heads (RHHs) and base band unit (BBU) pool, and a communication link is defined between them which is referred as the fronthaul. This leveraging link is expected to reduce the CAPEX (capital expenditure) and OPEX (operating expense) of envisioned cellular architectures as well as improves the spectral and energy efficiencies, provides the high scalability, and efficient mobility management capabilities. The fronthaul link carries the baseband signals between the RRHs and BBU pool using the digital radio over fiber (RoF) based common public radio interface (CPRI). CPRI based optical links imposed stringent synchronization, latency and throughput requirements on the fronthaul. As a result, fronthaul becomes a hinder in commercial deployments of C-RANs and is seen as one of a major bottleneck for backbone networks. The optimization of fronthaul is still a challenging issue and requires further exploration at industrial and academic levels. This paper comprehensively summarized the current challenges and requirements of fronthaul networks, and discusses the recently proposed system architectures, virtualization techniques, key transport technologies and compression schemes to carry the time-sensitive traffic in fronthaul networks.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.3
• /
• pp.1174-1192
• /
• 2021

With the assistance of non-orthogonal multiple access (NOMA), the spectrum efficiency and the number of users in cloud radio access network (CRAN) can be greatly improved. In this paper, the system performance of NOMA-assisted CRAN is investigated. Specially, the outage probability (OP) and ergodic sum rate (ESR), are derived for performance evaluation of the system, respectively. Based on this, by minimizing the OP of the system, a suboptimal power allocation (PA) scheme with closed-form PA coefficients is proposed. Numerical simulations validate the accuracy of the theoretical results, where the derived OP has more accuracy than the existing one. Moreover, the developed PA scheme has superior performance over the conventional fixed PA scheme but has smaller performance loss than the optimal PA scheme using the exhaustive search method.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.8
• /
• pp.3821-3841
• /
• 2019

Cloud radio access networks (C-RANs) have been regarded in recent times as a promising concept in future 5G technologies where all DSP processors are moved into a central base band unit (BBU) pool in the cloud, and distributed remote radio heads (RRHs) compress and forward received radio signals from mobile users to the BBUs through radio links. In such dynamic environment, automatic decision-making approaches, such as artificial intelligence based deep reinforcement learning (DRL), become imperative in designing new solutions. In this paper, we propose a generic framework of autonomous cell activation and customized physical resource allocation schemes for energy consumption and QoS optimization in wireless networks. We formulate the problem as fractional power control with bandwidth adaptation and full power control and bandwidth allocation models and set up a Q-learning model to satisfy the QoS requirements of users and to achieve low energy consumption with the minimum number of active RRHs under varying traffic demand and network densities. Extensive simulations are conducted to show the effectiveness of our proposed solution compared to existing schemes.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.2
• /
• pp.3-9
• /
• 2016

In this paper, we propose a Radio over Fiber system for small cell applications and for mobile fronthaul networks supporting cloud-radio access networks(RAN). We built a system with a downlink employing orthogonal frequency division multiplexing (OFDM) techniques and an uplink using single carrier-frequency multiple access(SC-FDMA). System parameters are evaluated for various subcarrier modulations and the results of link performance measurements are analyzed.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.4
• /
• pp.1837-1860
• /
• 2020

We propose a general framework for studying resource allocation problem and the tradeoff between spectral efficiency (SE) and energy efficiency (EE) for downlink traffic in power domain-non-orthogonal multiple access (PD-NOMA) and device to device (D2D) based heterogeneous cloud radio access networks (H-CRANs) under imperfect channel state information (CSI). The aim is jointly optimize radio remote head (RRH) selection, spectrum allocation and power control, which is formulated as a multi-objective optimization (MOO) problem that can be solved with weighted Tchebycheff method. We propose a low-complexity algorithm to solve user association, spectrum allocation and power coordination separately. We first compute the CSI for RRHs. Then we study allocating the cell users (CUs) and D2D groups to different subchannels by constructing a bipartite graph and Hungrarian algorithm. To solve the power control and EE-SE tradeoff problems, we decompose the target function into two subproblems. Then, we utilize successive convex program approach to lower the computational complexity. Moreover, we use Lagrangian method and KKT conditions to find the global optimum with low complexity, and get a fast convergence by subgradient method. Numerical simulation results demonstrate that by using PD-NOMA technique and H-CRAN with D2D communications, the system gets good EE-SE tradeoff performance.

• International journal of advanced smart convergence
• /
• v.9 no.2
• /
• pp.58-67
• /
• 2020

In this paper, we propose a data-driven-based beam selection scheme for massive multiple-input and multiple-output (MIMO) systems in ultra-dense networks (UDN), which is capable of addressing the problem of high computational cost of conventional coordinated beamforming approaches. We consider highly dense small-cell scenarios with more small cells than mobile stations, in the millimetre-wave band. The analog beam selection for hybrid beamforming is a key issue in realizing millimetre-wave UDN MIMO systems. To reduce the computation complexity for the analog beam selection, in this paper, two deep neural network models are used. The channel samples, channel gains, and radio frequency beamforming vectors between the access points and mobile stations are collected at the central/cloud unit that is connected to all the small-cell access points, and are used to train the networks. The proposed machine-learning-based scheme provides an approach for the effective implementation of massive MIMO system in UDN environment.

• Information and Communications Magazine
• /
• v.33 no.1
• /
• pp.49-53
• /
• 2015

본고에서는 4G/5G/WiFi 무선 통신의 C-RAN(Centralized/Cloud Radio Access Network)을 지원하는 xhaul망에서의 이더넷 응용 기술에 대해 분석한다. xhaul에서의 요구사항을 고찰하고, 이를 충족시키기 위한 이더넷 응용으로서 RoE(Radio over Ethernet)을 중심으로 한 기술 분석과 함께, 그 외 프론트홀 전송 기술을 알아봄으로써, xhaul에서의 이더넷의 응용과 더불어 기타 차세대 무선 망 기술과 함께 유무선 융합 전송 기술에 대한 방향을 타진해 보도록 한다.

• Journal of Information Processing Systems
• /
• v.16 no.4
• /
• pp.896-914
• /
• 2020

With the rapid growth of network traffic, a large number of connected devices, and higher application services, the traditional network is facing several challenges. In addition to improving the current network architecture and hardware specifications, effective resource management means the development trend of 5G. Although many existing potential technologies have been proposed to solve the some of 5G challenges, such as multiple-input multiple-output (MIMO), software-defined networking (SDN), network functions virtualization (NFV), edge computing, millimeter-wave, etc., research studies in 5G continue to enrich its function and move toward B5G mobile networks. In this paper, focusing on the resource allocation issues of 5G core networks and radio access networks, we address the latest technological developments and discuss the current challenges for resource management in 5G.