Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues

As a promising paradigm to reduce both capital and operating expenditures, the cloud radio access network (C-RAN) has been shown to provide high spectral efficiency and energy efficiency. Motivated by its significant theoretical performance gains and potential advantages, C-RANs have been advocated by both the industry and research community. This paper comprehensively surveys the recent advances of C-RANs, including system architectures, key techniques, and open issues. The system architectures with different functional splits and the corresponding characteristics are comprehensively summarized and discussed. The state-of-the-art key techniques in C-RANs are classified as: the fronthaul compression, large-scale collaborative processing, and channel estimation in the physical layer; and the radio resource allocation and optimization in the upper layer. Additionally, given the extensiveness of the research area, open issues and challenges are presented to spur future investigations, in which the involvement of edge cache, big data mining, social-aware device-to-device, cognitive radio, software defined network, and physical layer security for C-RANs are discussed, and the progress of testbed development and trial test are introduced as well.Comment: 27 pages, 11 figure

Performance Modeling of Next-Generation Wireless Networks

The industry is satisfying the increasing demand for wireless bandwidth by densely deploying a large number of access points which are centrally managed, e.g. enterprise WiFi networks deployed in university campuses, companies, airports etc. This small cell architecture is gaining traction in the cellular world as well, as witnessed by the direction in which 4G+ and 5G standardization is moving. Prior academic work in analyzing such large-scale wireless networks either uses oversimplified models for the physical layer, or ignores other important, real-world aspects of the problem, like MAC layer considerations, topology characteristics, and protocol overhead. On the other hand, the industry is using for deployment purposes on-site surveys and simulation tools which do not scale, cannot efficiently optimize the design of such a network, and do not explain why one design choice is better than another. In this paper we introduce a simple yet accurate analytical model which combines the realism and practicality of industrial simulation tools with the ability to scale, analyze the effect of various design parameters, and optimize the performance of real- world deployments. The model takes into account all central system parameters, including channelization, power allocation, user scheduling, load balancing, MAC, advanced PHY techniques (single and multi user MIMO as well as cooperative transmission from multiple access points), topological characteristics and protocol overhead. The accuracy of the model is verified via extensive simulations and the model is used to study a wide range of real world scenarios, providing design guidelines on the effect of various design parameters on performance

User Association and Interference Management in Massive MIMO HetNets

Two key traits of 5G cellular networks are much higher base station (BS) densities - especially in the case of low-power BSs - and the use of massive MIMO at these BSs. This paper explores how massive MIMO can be used to jointly maximize the offloading gains and minimize the interference challenges arising from adding small cells. We consider two interference management approaches: joint transmission (JT) with local precoding, where users are served simultaneously by multiple BSs without requiring channel state information exchanges among cooperating BSs, and resource blanking, where some macro BS resources are left blank to reduce the interference in the small cell downlink. A key advantage offered by massive MIMO is channel hardening, which enables to predict instantaneous rates a priori. This allows us to develop a unified framework, where resource allocation is cast as a network utility maximization (NUM) problem, and to demonstrate large gains in cell-edge rates based on the NUM solution. We propose an efficient dual subgradient based algorithm, which converges towards the NUM solution. A scheduling scheme is also proposed to approach the NUM solution. Simulations illustrate more than 2x rate gain for 10th percentile users vs. an optimal association without interference management

A Stochastic Analysis of Network MIMO Systems

This paper quantifies the benefits and limitations of cooperative communications by providing a statistical analysis of the downlink in network multiple-input multiple-output (MIMO) systems. We consider an idealized model where the multiple-antenna base-stations (BSs) are distributed according to a homogeneous Poisson point process and cooperate by forming disjoint clusters. We assume that perfect channel state information (CSI) is available at the cooperating BSs without any overhead. Multiple single-antenna users are served using zero-forcing beamforming with equal power allocation across the beams. For such a system, we obtain tractable, but accurate, approximations of the signal power and inter-cluster interference power distributions and derive a computationally efficient expression for the achievable per-BS ergodic sum rate using tools from stochastic geometry. This expression allows us to obtain the optimal loading factor, i.e., the ratio between the number of scheduled users and the number of BS antennas, that maximizes the per-BS ergodic sum rate. Further, it allows us to quantify the performance improvement of network MIMO systems as a function of the cooperating cluster size. We show that to perform zero-forcing across the distributed set of BSs within the cluster, the network MIMO system introduces a penalty in received signal power. Along with the inevitable out-of-cluster interference, we show that the per-BS ergodic sum rate of a network MIMO system does not approach that of an isolated cell even at unrealistically large cluster sizes. Nevertheless, network MIMO does provide significant rate improvement as compared to uncoordinated single-cell processing even at relatively modest cluster sizes.Comment: Accepted for publication at IEEE Transactions on Signal Processin

Cloud Radio Access Network: Virtualizing Wireless Access for Dense Heterogeneous Systems

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 (RUs) or remote radio heads (RRHs), are centrally managed by a reconfigurable centralized "cloud", or central, unit (CU). 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.Comment: To appear on JC

NOMA in 5G Systems: Exciting Possibilities for Enhancing Spectral Efficiency

This article provides an overview of power-domain non-orthogonal multiple access for 5G systems. The basic concepts and benefits are briefly presented, along with current solutions and standardization activities. In addition, limitations and research challenges are discussed.Comment: 6 pages, 1 figure, IEEE 5G Tech Focu

Heterogeneous Cloud Radio Access Networks: A New Perspective for Enhancing Spectral and Energy Efficiencies

To mitigate the severe inter-tier interference and enhance limited cooperative gains resulting from the constrained and non-ideal transmissions between adjacent base stations in heterogeneous networks (HetNets), heterogeneous cloud radio access networks (H-CRANs) are proposed as cost-efficient potential solutions through incorporating the cloud computing into HetNets. In this article, state-of-the-art research achievements and challenges on H-CRANs are surveyed. In particular, we discuss issues of system architectures, spectral and energy efficiency performances, and promising key techniques. A great emphasis is given towards promising key techniques in H-CRANs to improve both spectral and energy efficiencies, including cloud computing based coordinated multi-point transmission and reception, large-scale cooperative multiple antenna, cloud computing based cooperative radio resource management, and cloud computing based self-organizing network in the cloud converging scenarios. The major challenges and open issues in terms of theoretical performance with stochastic geometry, fronthaul constrained resource allocation, and standard development that may block the promotion of H-CRANs are discussed as well.Comment: 20 pages, 6 figures, to be published in IEEE Wireless Communication

Cross Layer Provision of Future Cellular Networks

To cope with the growing demand for wireless data and to extend service coverage, future 5G networks will increasingly rely on the use of low powered nodes to support massive connectivity in diverse set of applications and services [1]. To this end, virtualized and mass-scale cloud architectures are proposed as promising technologies for 5G in which all the nodes are connected via a backhaul network and managed centrally by such cloud centers. The significant computing power made available by the cloud technologies has enabled the implementation of sophisticated signal processing algorithms, especially by way of parallel processing, for both interference management and network provision. The latter two are among the major signal processing tasks for 5G due to increased level of frequency sharing, node density, interference and network congestion. This article outlines several theoretical and practical aspects of joint interference management and network provisioning for future 5G networks. A cross-layer optimization framework is proposed for joint user admission, user-base station association, power control, user grouping, transceiver design as well as routing and flow control. We show that many of these cross-layer tasks can be treated in a unified way and implemented in a parallel manner using an efficient algorithmic framework called WMMSE (Weighted MMSE). Some recent developments in this area are highlighted and future research directions are identified

Sparse Beamforming and User-Centric Clustering for Downlink Cloud Radio Access Network

This paper considers a downlink cloud radio access network (C-RAN) in which all the base-stations (BSs) are connected to a central computing cloud via digital backhaul links with finite capacities. Each user is associated with a user-centric cluster of BSs; the central processor shares the user's data with the BSs in the cluster, which then cooperatively serve the user through joint beamforming. Under this setup, this paper investigates the user scheduling, BS clustering and beamforming design problem from a network utility maximization perspective. Differing from previous works, this paper explicitly considers the per-BS backhaul capacity constraints. We formulate the network utility maximization problem for the downlink C-RAN under two different models depending on whether the BS clustering for each user is dynamic or static over different user scheduling time slots. In the former case, the user-centric BS cluster is dynamically optimized for each scheduled user along with the beamforming vector in each time-frequency slot, while in the latter case the user-centric BS cluster is fixed for each user and we jointly optimize the user scheduling and the beamforming vector to account for the backhaul constraints. In both cases, the nonconvex per-BS backhaul constraints are approximated using the reweighted l1-norm technique. This approximation allows us to reformulate the per-BS backhaul constraints into weighted per-BS power constraints and solve the weighted sum rate maximization problem through a generalized weighted minimum mean square error approach. This paper shows that the proposed dynamic clustering algorithm can achieve significant performance gain over existing naive clustering schemes. This paper also proposes two heuristic static clustering schemes that can already achieve a substantial portion of the gain.Comment: 14 pages, 9 figures, to appear in IEEE Access, Special Issue on Recent Advances in Cloud Radio Access Networks, 201

CB-REFIM: A Practical Coordinated Beamforming in Multicell Networks

Performance of multicell systems is inevitably limited by interference and available resources. Although intercell interference can be mitigated by Base Station (BS) Coordination, the demand on inter-BS information exchange and computational complexity grows rapidly with the number of cells, subcarriers, and users. On the other hand, some of the existing coordination beamforming methods need computation of pseudo-inverse or generalized eigenvector of a matrix, which are practically difficult to implement in a real system. To handle these issues, we propose a novel linear beamforming across a set of coordinated cells only with limiting backhaul signalling. Resource allocation (i.e. precoding and power control) is formulated as an optimization problem with objective function of signal-to-interference-plus-noise ratios (SINRs) in order to maximize the instantaneous weighted sum-rate subject to power constraints. Although the primal problem is nonconvex and difficult to be optimally solved, an iterative algorithm is presented based on the Karush-Kuhn-Tucker (KKT) condition. To have a practical solution with low computational complexity and signalling overhead, we present CB-REFIM (coordination beamforming-reference based interference management) and show the recently proposed REFIM algorithm can be interpreted as a special case of CB-REFIM. We evaluate CB-REFIM through extensive simulation and observe that the proposed strategies achieve close-to-optimal performance.Comment: 20 pages, 8 figures, to appear in IET Communicatio