30 results
9 - Adaptive Compression in C-RANs
- from Part II - Physical-Layer Design in C-RANs
- Edited by Tony Q. S. Quek, Singapore University of Technology and Design, Mugen Peng, Osvaldo Simeone, New Jersey Institute of Technology, Wei Yu, University of Toronto
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- Cloud Radio Access Networks
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- 02 February 2017, pp 200-224
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Summary
Cloud radio access networks (C-RANs) provide a promising architecture for the future mobile networks needed to sustain the exponential growth of the data rate. In C-RAN, one data processing center or baseband unit communicates with users through distributed remote radio heads, which are connected to the baseband unit (BBU) via high-capacity low-latency so-called fronthaul links. The architecture of C-RAN, however, imposes a burden of fronthaul bandwidth because raw I/Q samples are exchanged between the RRHs and the BBU. Therefore, signal compression is required on fronthaul links owing to their limited capacity. This chapter exploits the advance of joint signal processing to reduce the transmission rate on fronthaul uplinks. In particular, we first propose a joint decompression and detection (JDD) algorithm which exploits the correlation among RRHs and jointly performs decompressing and detecting. The JDD algorithm takes into consideration both fading and quantization effects in a single decoding step. Second, the block error rate of the JDD algorithm is analyzed in a closed form by using pairwise error probability analysis under both deterministic and Rayleigh fading channel models. Third, on the basis of the analyzed block error rate (BLER), we introduce adaptive compression schemes subject to quality of service constraints to minimize the fronthaul transmission rate while satisfying the predefined target QoS. The premise of the proposed compression methods originates from practical scenarios, where most applications tolerate a non-zero BLER. As a dual problem, we also develop a scheme to minimize the signal distortion subject to the fronthaul rate constraint. We finally consider the counterparts of these two adaptive compression schemes for Rayleigh-fading channels and analyze their asymptotic behavior as the constraints approach extremes.
Introduction
Cloud radio access networks have been widely accepted as a new architecture for future mobile networks to sustain the ever increasing demand in the data rate [1]. In a C-RAN, one centralized processor or BBU communicates with users distributed in a graphical area via a number of remote radio heads (RRHs), which act as “soft” relaying nodes and are connected to the BBU via high-capacity and low-latency fronthaul links. By moving all baseband processing functions from RRHs to a centralized processor, the C-RAN enables adaptive load balancing via a virtual base station pool [2] and effective network-wide inter-cell interference management thanks to multi-cell processing [3, 4].
19 - Field Trials and Testbed Design for C-RAN
- from Part IV - Networking in C-RANs
- Edited by Tony Q. S. Quek, Singapore University of Technology and Design, Mugen Peng, Osvaldo Simeone, New Jersey Institute of Technology, Wei Yu, University of Toronto
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- 02 February 2017, pp 451-471
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Summary
Introduction
Since the proposal of C-RAN [1-3] in 2009, China Mobile (CMCC) has been committed to developing various kinds of proof-of-concept (PoC), test-beds, and field trials to demonstrate C-RAN's benefits and verify the key enabling technologies. This chapter gives a comprehensive introduction to these activities. In particular, we will demonstrate not only the feasibility and reliability of wavelength-division-multiplexing (WDM)-based fronthaul (FH) solutions but also how a noticeable coordinated multiple-points (CoMP) gain can be achieved with the C-RAN architecture. In addition, a virtualized C-RAN system is elaborated, including the design principles, the architecture, and the field trial results.
Field-Trial Verification of FH Solutions
19.2.1 Centralization Field Trials in 2G and 3G Networks
The first step toward C-RAN was baseband unit (BBU) centralization which is relatively easy to implement and can be tested with the existing 2G, 3G, and 4G systems. In the past few years, extensive field trials have been carried out in more than 10 cities in China using commercial 2G, 3G, and pre-commercial TD-LTE networks with different centralization scales. The main objective of C-RAN deployment in 2G and 3G is to demonstrate the deployment benefits of centralization, including accelerated site construction and reduced power consumption. For example, one trial took place in the city of Changchun where 506 2G BSs in five counties were upgraded to a C-RAN-type architecture centralized in several sites. In the largest of these, 21 BSs were aggregated to support 101 RRUs with a total of 312 carriers. It was observed that power consumption was reduced by 41% owing to shared air-conditioning. In addition, system performance in terms of the call-drop rate as well as the downlink data rate was enhanced using multiple RRU-co-cell technologies. For the results and benefits from using centralization in 2G and 3G trials, the reader is referred to [4]. When it comes to TD-LTE, centralization becomes more challenging owing to the high data rate in the FH connection. For example, the data rate of the most widely used FH interface in the industry, the common public radio interface (CPRI), could be as high as 9.8 Gb/s for an TD-LTE carrier with a 20 MHz bandwidth and eight antennas.
Index
- Edited by Tony Q. S. Quek, Singapore University of Technology and Design, Mugen Peng, Osvaldo Simeone, New Jersey Institute of Technology, Wei Yu, University of Toronto
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3 - The Tradeoff of Computational Complexity and Achievable Rates in C-RANs
- from Part II - Physical-Layer Design in C-RANs
- Edited by Tony Q. S. Quek, Singapore University of Technology and Design, Mugen Peng, Osvaldo Simeone, New Jersey Institute of Technology, Wei Yu, University of Toronto
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Summary
Introduction
The trend of increased centralization holds the potential to transform mobile networks in two ways. First, centralization enables the exploitation of common channel knowledge, which in turn allows for significant improvements in the performance of a communication channel by, for instance, performing the joint transmission and reception of signals or allocating resources jointly amongst adjacent cells [1]. Second, centralized processing leverages the trend towards deploying mobile networks on low-cost commodity hardware that is running commodity or open-source software solutions. Deploying software-based implementations increases implementation flexibility, reduces service-creation time, and enables the flexible usage of processing resources through virtualization. In this chapter we use the term Cloud-RAN (C-RAN) to refer to a flexible use of commodity solutions that combines gains in both the telecommunication and information technology domains.
Before implementing the protocol stack of a RAN on a cloud-computing platform, we must also take the required effort into account, e.g., commodity hardware is considered to be less performant and energy efficient than dedicated hardware such as ASIC, DSP, or FPGA. Furthermore, resource virtualization implies an overbooking of resources while satisfying joint resource requirements of all processed base stations (BSs), which is in contrast with fulfilling individual processing constraints at each BS. Centralized signal processing may further impose stringent requirements on the fronthaul network between a radio access point (RAP) and the data center.
So far, research in the area of Cloud-RAN has focused on the telecommunication domain, e.g., the applicability of joint processing approaches, gains from centralization, and optimal degrees of centralization under different side constraints. In this chapter the focus is on the impact of limiting and virtualizing the data processing resources on the communication rate, i.e., the quantitative coupling of the required computational resources and communication rates [2]. After introducing basic notation and definitions, we consider metrics and an analytical framework that allows one to determine the data processing demand Interestingly, the data processing requirements depend not only on the number of information bits but also to a large extent on the quality of a user's communication channel. In this chapter we discuss and quantify multi-user gains, which lower the requirements on the data processing resources to be provided.
1 - Overview of C-RAN
- from Part I - Architecture of C-RANs
- Edited by Tony Q. S. Quek, Singapore University of Technology and Design, Mugen Peng, Osvaldo Simeone, New Jersey Institute of Technology, Wei Yu, University of Toronto
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Summary
Introduction
In 2008, as the specification for long-term evolution (LTE) Release 8 was frozen in the Third Generation Partner Project (3GPP), operators began to shift the network deployment focus to 4G. In 2009, the world's first commercial LTE network was launched by TeliaSonera in Norway and Sweden. As of today, there are several hundred LTE networks in operation, providing unprecedented user experiences to customers. Consequently, we are witnessing the recent mobile traffic explosion in the telecom industry. It is expected that by 2020 consumer Internet traffic will increase by a factor of over one thousand [1].
As operators roll out and expand 4G networks, more and more challenges arise. First, network deployment is becoming more and more difficult simply due to an insufficient number of equipment rooms. Traditional base stations (BSs) comprise either a co-located baseband unit (BBU) with a radio unit or a distributed BBU with a remote radio unit (RRU) connected via fiber. For either case, a separate equipment room with supporting facilities such as air conditioning is required in order for BS deployment. However, since the operating frequency of LTE is usually higher than that of 2G and 3G, the coverage of an LTE cell is smaller than that of a 2G or 3G cell. As a result, more LTE cells are needed to cover the same area, meaning that more equipment rooms are required. Unfortunately, this is increasingly difficult since available real estate is becoming scarcer and more expensive. Traditional deployment puts a lot of pressure on capital expenditure (CAPEX).
Second, in a society where people are promoting energy conservation and environment protection, power consumption has become a sensitive word and a major concern for operators. It is estimated that the carbon footprint of the ICT industry accounts for 2% of the global total, which is the same as that of the aviation industry. For the telecom industry, further analysis has shown that a large percentage of power consumption in mobile networks comes from radio access networks (RANs) [1, 2]. Take China Mobile's networks, for example. The largest mobile network in the world consumed over 14 billion kWh of energy in 2012 in its network of 1.1 million base stations. It can be seen that saving energy in RANs could directly lower the operating expense (OPEX) of the network.
8 - Fronthaul Compression in C-RANs
- from Part II - Physical-Layer Design in C-RANs
- Edited by Tony Q. S. Quek, Singapore University of Technology and Design, Mugen Peng, Osvaldo Simeone, New Jersey Institute of Technology, Wei Yu, University of Toronto
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- 02 February 2017, pp 179-199
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Summary
Introduction
The C-RAN architecture relies on fronthaul links to connect each remote radio head (RRH) to the managing baseband unit (BBU). In particular, for the uplink, the fronthaul links allow the RRHs to convey their respective received signals, either in analog format or in the form of digitized baseband samples, to the BBU. For the downlink, the BBU transfers the radio signal that each RRH is to transmit on the radio interface, in analog or digital format, on the fronthaul links to the RRHs. It is this transfer of radio or baseband signals that makes possible the virtualization of the baseband and higherlayer functions of the (RRHs) at the BBU, which defines the C-RAN architecture. The analog transport solution is typically implemented by means of radio-over-fiber (see, e.g., [1]) but solutions based on copper LAN cables are also available [2]. In contrast, the digital transmission of baseband, or IQ, samples is currently carried out by following the common public radio interface (CPRI) specification [3]. This ideally requires fiber optic fronthaul links, although practical constraints motivate the development of wireless-based digital fronthauling [4]. The digital approach seems to have attracted the most interest owing to the traditional advantages of digital solutions, their including resilience to noise and to hardware impairments as well as flexibility in the transport options (see, e.g., [5]). Furthermore, the connection between an RRH and the BBU may be direct, i.e., single-hop, or it may take place over a cascade of fronthaul links, i.e., be multi-hop, as illustrated in Fig. 8.1.
In this chapter we provide an overview of the state of the art on the problem of transporting digitized IQ baseband signals on the fronthaul links. As mentioned, the current de facto standard that defines analog-to-digital processing and transport options is provided by the common public radio interface (CPRI) specification [3]. This specification is widely understood to be unsuitable for the large-scale implementation of C-RAN owing to its significant fronthaul bit rate requirements under common operating conditions. As an example, as reported in [5], the bit rate needed for an LTE base station that serves three cell sectors with carrier aggregation over five carriers and two receive antennas exceeds even the 10 Gbits/s provided by standard fiber optic links.
4 - Cooperative Beamforming and Resource Optimization in C-RANs
- from Part II - Physical-Layer Design in C-RANs
- Edited by Tony Q. S. Quek, Singapore University of Technology and Design, Mugen Peng, Osvaldo Simeone, New Jersey Institute of Technology, Wei Yu, University of Toronto
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Summary
Cloud radio access network (C-RAN) architecture offers two key advantages as compared with traditional radio access networks (RANs) from the physical-layer transmission point of view. First, the centralization and virtualization of RANs allow the coordination of base stations (BSs) across a large geographic area, thereby enabling coordinated physical-layer resource allocation across the BSs. The physical-layer resources here refer to the frequency, time, and spatial dimensions that can be utilized by radio transmission. Second, and more importantly, the C-RAN architecture also opens up the possibility of the joint transmission and joint reception of user signals across multiple BSs, thereby fundamentally addressing the issue of inter-cell interference. As interference is the main bottleneck in modern densely deployed wireless networks, the C-RAN architecture offers significant advantages in that it provides the possibility of interference mitigation leading to performance enhancement without the need for additional site and bandwidth acquisition.
This chapter provides an optimization framework for cooperative beamforming and resource allocation in C-RANs. We begin by identifying frequency, time, and spatial resources in wireless cellular networks and defining the overall spectrum allocation, scheduling, and beamforming problem in a cooperative network. The chapter then provides a network model for the C-RAN architecture and illustrates typical network objective functions and constraints for network utility maximization. A key characteristic of the C-RAN architecture is that the fronthaul connections between the cloud and the BSs may have limited capacities. One of the main goals of this chapter is to illustrate the impact of limited fronthaul capacity on the cooperative beamforming and resource allocation in C-RANs.
The chapter explores the optimization of the design variables associated with CRANs, depending on the transmission strategies at the cooperative BSs. For the uplink C-RAN, we illustrate compress-forward as the main strategy at the BSs and focus on the impact of the choice of quantization noise levels at the BSs and possible joint transmit optimization strategies. For the downlink C-RAN, we compare a compression-based strategy and a data-sharing strategy and illustrate the problem formulation and solution strategy in both cases. Throughout the chapter, key optimization techniques for solving resource-allocation problems in C-RANs are presented.
Acknowledgments
- Edited by Tony Q. S. Quek, Singapore University of Technology and Design, Mugen Peng, Osvaldo Simeone, New Jersey Institute of Technology, Wei Yu, University of Toronto
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Preface
- Edited by Tony Q. S. Quek, Singapore University of Technology and Design, Mugen Peng, Osvaldo Simeone, New Jersey Institute of Technology, Wei Yu, University of Toronto
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Summary
Cloud radio access networks (C-RANs) refer to a wireless cellular architecture in which all network functionalities of conventional base stations, apart from radio frequency operations and possibly analog-digital conversions, are carried out at a central cloud processor. The idea was relegated for many years to the realm of information- and communication-theoretic studies, which promised gains in terms of spectral efficiency thanks to the possibility of implementing joint baseband processing at the central processor. The main obstacles to the deployment of C-RAN-type systems were thought to be the high complexity of the necessary cloud processor as well as the limited availability of high-speed backhaul links connecting edge and cloud.
In recent years, advances in cloud computing and a more pervasive deployment of fiber optic cables and high-frequency wireless backhaul links towards the network edge have spurred the reconsideration, and eventually the implementation, of cloud-based radio access systems. In fact, as argued in the seminal white paper by China Mobile, not only can the C-RAN architecture reap the spectral efficiency gains promised by academic studies, it can also crucially reduce capital and operating expenses. This is a consequence of the centralization of network resources in the cloud: the complexity and cost of edge nodes can be drastically reduced with respect to conventional base stations, and updates and maintenance can be performed solely at the cloud.
As C-RAN moves from paper to the real world, industry and academia are working towards the definition of protocols and algorithms at all layers of the communication protocol stack, so as to enable cost-effective and high-performance cloud-based systems to be widely adopted as a leading solution for 5G networks.
This book is intended to provide a broad overview of the current research activity in the industry and academia on the subject of C-RANs. While this is an active field of study, involving theoreticians and practitioners, the editors believe that the current state of the art is sufficiently mature to warrant a monographic treatment. The book covers the architecture, physical-layer design, resource allocation, and networking of C-RAN systems, in separate parts each consisting of various chapters authored by leading researchers in both industry and academia.
It is our hope that this book will serve as a useful reference for engineers and students and that it will motivate more researchers to undertake the numerous open problems highlighted in the following pages.
7 - Large-Scale Convex Optimization for C-RANs
- from Part II - Physical-Layer Design in C-RANs
- Edited by Tony Q. S. Quek, Singapore University of Technology and Design, Mugen Peng, Osvaldo Simeone, New Jersey Institute of Technology, Wei Yu, University of Toronto
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Summary
Introduction
7.1.1 C-RANs
The proliferation of “smart” mobile devices, coupled with new types of wireless applications, has led to an exponential growth in wireless and mobile data traffic. In order to provide high-volume and diversified data services, C-RAN [1, 2] has been proposed; it enables efficient interference management and resource allocation by shifting all the baseband units (BBUs) to a single cloud data center, i.e., by forming a BBU pool with powerful shared computing resources. Therefore, with efficient hardware utilization at the BBU pool, a substantial reduction can be obtained in both the CAPEX (e.g., via low-cost site construction) and the OPEX (e.g., via centralized cooling). Furthermore, the powerful conventional base stations are replaced by light and low-cost remote radio heads (RRHs), with the basic functionalities of signal transmission and reception, which are then connected to the BBU pool by high-capacity and low-latency optical fronthaul links. The capacity of C-RANs can thus be significantly improved through network densification and large-scale centralized signal processing at the BBU pool. By further pushing a substantial amount of data, storage, and computing resources (e.g., the radio access units and end-user devices) to the edge of the network, using the principle of mobile edge computing (i.e., fog computing) [3], heterogeneous C-RANs [4], as well as Fog-RANs and MENG-RANs [5] can be formed. These evolved architectures will further improve user experience by offering on-demand and personalized services and location-aware and content-aware applications. In this chapter we investigate the computation aspects of this new network paradigm, and in particular focus on the large-scale convex optimization for signal processing and resource allocation in C-RANs.
7.1.2 Large-Scale Convex Optimization: Challenges and Previous Work
Convex optimization serves as an indispensable tool for resource allocation and signal processing in wireless networks [6-9]. For instance, coordinated beamforming [10] often yields a convex optimization formulation, i.e., second-order cone programming (SOCP) [11]. The network max-min fairness-rate optimization [12] can be solved through the bisection method [11], in polynomial time; in this method a sequence of convex subproblems needs to be solved.
16 - Mobility Management for C-RANs
- from Part IV - Networking in C-RANs
- Edited by Tony Q. S. Quek, Singapore University of Technology and Design, Mugen Peng, Osvaldo Simeone, New Jersey Institute of Technology, Wei Yu, University of Toronto
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Summary
Introduction
Cloud-RAN is a promising wireless network architecture in 5G networks, and it was first proposed by the China Mobile Research Institute [1]. In C-RANs, baseband processing is centralized in a baseband unit (BBU) pool, while radio frequency (RF) processing is distributed in remote radio heads (RRHs). The C-RAN network architecture can reduce both the capital expenditure (CAPEX) and operating expenditure (OPEX) for mobile operators, because fewer BBUs are potentially required in the C-RAN architecture, and the consumed power is lowered [2].
Heterogeneous small-cell networks have attracted much attention owing to the explosion in demand of users' data requirements. In heterogeneous small-cell network, low-power small cells (such as pico-cells, relay cells and femto-cells), together with macro cells, can improve the coverage and capacity of cell-edge users and hotspots by exploiting the spatial reuse of spectrum [3]. Small cells can also offload the explosive growth of wireless data traffic from macro cells. For example, in an indoor environment WiFi and femtocells can offload most data traffic from macro cells [4]. For mobile operators, small cells such as femtocells can reduce the CAPEX and OPEX because of the self-installing and self-operating features of femto base stations.
The combination of a heterogeneous small-cell network and a C-RAN, which is called a heterogeneous cloud small-cell network (HCSNet), benefits from employing both C-RAN and a small-cell network [5]. First, C-RAN reduces the power and energy cost in HCSNet by lowering the number of BBUs in densely deployed heterogeneous small-cell networks. Second, BBUs can be added and upgraded without much effort in the BBU pool, and network maintenance and operation can also be performed easily. Third, many radio resource management functions can be facilitated in the BBU pool with little delay. In HCSNet, cloud-computing-enabled signal processing can be fully utilized to mitigate interference and to improve spectrum efficiency in 5G networks.
In the literature, HCSNet has been studied extensively. In [5], state-of-the-art research results and challenges were surveyed for heterogeneous C-RANs, and promising key techniques were investigated to improve both spectral and energy efficiencies. To mitigate the interference for cell-edge users, coordinated multi-point (CoMP) transmission and reception is also investigated in a C-RAN environment. The C-RAN network architecture is effective for implementing CoMP. Energy-efficient resource optimization was studied in [7] for C-RAN-enabled heterogeneous cellular networks.
2 - Advanced C-RAN for Heterogeneous Networks
- from Part I - Architecture of C-RANs
- Edited by Tony Q. S. Quek, Singapore University of Technology and Design, Mugen Peng, Osvaldo Simeone, New Jersey Institute of Technology, Wei Yu, University of Toronto
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Summary
Introduction
Motivated by the increase in user demand for high data rates and new service applications due to the fast market penetration of smartphones, a large number of mobile operators in the world are introducing long-term evolution (LTE) into their networks [1]. In accordance with the further growth of mobile data traffic, these operators are deploying, or plan to deploy, their LTE networks with multiple-frequency-band operation in order to provide satisfactory user experience to their customers. Therefore, from the viewpoint of mobile operators, technologies that achieve high capacity LTE networks deployed with multiple-frequency-band operation are essential.
In order to achieve high capacity by utilizing multiple LTE frequency bands, carrier aggregation (CA) was specified as one of the new features for LTE in 3GPP Release 10 (i.e., LTE-advanced) [2]. The CA feature will enable operators to provide improved user throughput in their LTE networks by simultaneously using multiple LTE carriers. It can support large bandwidths (up to 100 MHz) and the flexible use of a fragmented spectrum in different frequency bands, where multiple LTE carriers do not have to be contiguous in a frequency band and can even be located in different frequency bands. The increase in user throughput with CA is achieved by assigning available radio resources over multiple LTE carriers to a single user. However, in a high-load network condition due to a large number of connected users, the increase in user throughput would be limited as the radio resources that could be assigned to a single user would not be changed irrespectively of whether CA is employed. Therefore, the utilization of CA only will not contribute to an increase in network capacity.
One conventional way to increase network capacity is to increase the number of cell sites in a certain area (i.e., to employ a densification of cells). However, the densification way of using macro cell deployment is becoming less efficient especially in dense urban areas since it has become difficult to find sites (a building or tower) in which new macro base stations can be installed. To cope with this problem, the deployment of heterogeneous networks, in which multiple small cells are deployed over a macro-cell area, is considered to be a promising option. In this deployment, the frequency band of the small cells is the same as that of the macro cell.
15 - Optimal Repeated Spectrum Sharing by Delay-Sensitive Users
- from Part III - Resource Allocation and Networking in C-RANs
- Edited by Tony Q. S. Quek, Singapore University of Technology and Design, Mugen Peng, Osvaldo Simeone, New Jersey Institute of Technology, Wei Yu, University of Toronto
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Summary
Introduction
The spectrum is becoming an increasingly scarce resource, owing to the emergence of a plethora of bandwidth-intensive and delay-critical applications (e.g. multimedia streaming, video conferencing, and gaming). To achieve the gigabit data rates required by next-generation wireless systems, we need to manage efficiently the interference among a multitude of wireless devices, most of which have limited computational capability. Central to interference management are spectrum-sharing policies, which specify when and at which power level each device should access the spectrum. Given the heterogeneity and the huge number of distributed wireless devices, it is computationally hard to design efficient spectrum sharing policies.
Cloud-RANs present a promising network architecture for designing spectrum-sharing policies. They consist of two components, a pool of baseband processing units (BBUs) and remote radio heads (RRHs), and allocate most demanding computations to the BBU pool (i.e., the “cloud”) [1-7]. In this way, C-RANs open up opportunities for designing efficient (even optimal) spectrum-sharing protocols. However, these opportunities come with the following challenges in C-RANs [1-7]:
How to allocate the computations between the BBU pool and the RRHs and minimize message exchange between them?
How to cope with dynamic entry and exit in large networks?
How to support the delay-sensitive applications that constitute amajority of the traffic in C-RANs?
This chapter presents advances made in the past years on a systematic design methodology for spectrum-sharing protocols that are particularly suitable for C-RANs. The spectrum-sharing protocols designed by the presented methodology can be implemented naturally in two phases:
• the first phase, of determining the optimal network operating point, which requires
• most computation and can be done in the BBU pool; and
• the second, phase of distributed implementation by RRHs with very limited computational capability.
Requiring limited message exchange between the BBU pool and the RRHs, the presented methodology results in provably optimal spectrum-sharing policies for C-RANs in interference-limited scenarios. More importantly, the presented methodology is general and can flexibly reconfigure the BBU pool to compute different optimal operating points in a variety of different C-RAN deployment scenarios.
Part I - Architecture of C-RANs
- Edited by Tony Q. S. Quek, Singapore University of Technology and Design, Mugen Peng, Osvaldo Simeone, New Jersey Institute of Technology, Wei Yu, University of Toronto
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13 - C-RAN Using Wireless Fronthaul: Fast Admission Control and Large System Analysis
- from Part III - Resource Allocation and Networking in C-RANs
- Edited by Tony Q. S. Quek, Singapore University of Technology and Design, Mugen Peng, Osvaldo Simeone, New Jersey Institute of Technology, Wei Yu, University of Toronto
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Summary
Introduction
The fifth generation (5G) mobile communication systems are expected to provide ultrahigh data rate services and seamless user experiences across the whole network [1, 2]. The 5G system capacity is expected to be 1000 times greater than current fourth generation (4G) mobile systems. In order to meet such demanding requirements, as well as to reduce the capital investment and operational cost, C-RAN has been proposed as a promising network architecture for future mobile communication systems. In C-RAN, most signal processing functions are performed at the centralized baseband unit (BBU) pool, while data transmission to the users is provided by remote radio heads (RRHs), which are usually low-power nodes serving local area users. It is known that the hyper-dense deployment of RRHs will be a key factor in serving large numbers of users and achieving tremendous capacity enhancement in future mobile systems [3]. The transportation of user data and control signals between the BBU pool and the RRHs is carried out in the fronthaul [4].
The fronthaul is a major constraint for the practical implementation of C-RANs. In order to provide high quality-of-experience services to users in the network, fast and reliable fronthaul connections between the BBU pool and the RRHs must be established [5, 6].Wired fronthaul, using optical fiber cables, can provide high-rate data links between fixed stations. However, the cost of providing wired fronthaul to all RRHs may be prohibitive when the number of RRHs is large. Moreover, certain locations that are difficult to reach by wired access may restrict the universal deployment of wired fronthaul. Wireless fronthaul, which can overcome many drawbacks of wired fronthaul, offers a cost-effective alternative [7-9]. Compared with wired fronthaul, the management of wireless fronthaul resources, e.g., their power and spectrum, is more complicated owing to finite power and radio spectrum constraints.
Recent works have proposed analysis and design methods for fronthaul and backhaul technologies from many aspects. A linear programming framework for determining the optimum routing and scheduling of data flows in wireless mesh backhaul networks was proposed in [10]. Zhao et al. [11] considered the problem of minimizing backhaul user data transfer in multi-cell joint processing networks, where algorithms involving the joint design of transmit beamformers and user data allocation at base stations (BSs) were proposed to effectively reduce backhaul user data transfer.
Frontmatter
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10 - Resource Management of Heterogeneous C-RANs
- from Part III - Resource Allocation and Networking in C-RANs
- Edited by Tony Q. S. Quek, Singapore University of Technology and Design, Mugen Peng, Osvaldo Simeone, New Jersey Institute of Technology, Wei Yu, University of Toronto
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- Cloud Radio Access Networks
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- 23 February 2017
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- 02 February 2017, pp 227-254
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Summary
Introduction
Mobile cellular infrastructures, which have been deployed in recent decades, successfully provide seamless and reliable streaming (voice or video) services for billions of mobile users. From GSM/GPRS, UMTS, to LTE/LTE-A, transmission data rates have been enhanced a million-fold. The recent deployment of heterogeneous networks (Het-Nets) consisting of macro cells, small cells (femtocells, picocells), and/or further relay nodes ubiquitously support basic multimedia and Internet browsing applications. As a result, primitive human-to-human (H2H) communication applications using existing network architectures and technologies seem satisfactory. However, to substantially facilitate human daily activities in addition to basic voice or video and Internet access services, achieving full automation and everything-to-everything (X2X), had been regarded as an ultimate goal not only for the future information communication industry but also for financial transactions, economics, social communities, transportation, agriculture, and energy allocation. Full automation implies a significant enhancement of human beings' sensory and processing capabilities, which embraces unmanned or remotely controlled vehicles, robots, offices, factories, augmented or virtual reality, and sensory human interactions of cyber-physical-social systems. The goal is to employ distributed autonomous control to relieve or simplify network control and evolutive, by which resource utilization can be boosted in dynamic complex networks and be re-optimized after major environmental changes. However, X2X connection implies that diverse entities including human beings and machines are able to form general sense communities other than H2H, such as social networks that are human-to-machine (H2M) or machine-to-machine (M2M), facilitating the ultimate cyber-physical-social systems. Application scenarios include intelligent transportation systems (ITSs), volunteer information networks, the Internet of Things (IoT), smart grids, and much more.
To enable these various applications, boosting transmission data rates is just one of the diverse requirements. The performance in terms of end-to-end transmission latency, energy efficiency, reliability, scalability, cost efficiency as well as stability should also be fundamentally enhanced. As the data traffic from the Internet has gradually been dominating the traffic volume in mobile communication systems, in addition to an improvement in air-interface the migration to more efficient network architecture is definitely a must in technology development.
Part II - Physical-Layer Design in C-RANs
- Edited by Tony Q. S. Quek, Singapore University of Technology and Design, Mugen Peng, Osvaldo Simeone, New Jersey Institute of Technology, Wei Yu, University of Toronto
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- Book:
- Cloud Radio Access Networks
- Published online:
- 23 February 2017
- Print publication:
- 02 February 2017, pp 33-34
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Cloud Radio Access Networks
- Principles, Technologies, and Applications
- Edited by Tony Q. S. Quek, Mugen Peng, Osvaldo Simeone, Wei Yu
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- Published online:
- 23 February 2017
- Print publication:
- 02 February 2017
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This unique text will enable readers to understand the fundamental theory, current techniques, and potential applications of Cloud Radio Access Networks (C-RANs). Leading experts from academia and industry provide a guide to all of the key elements of C-RANs, including system architecture, performance analysis, technologies in both physical and medium access control layers, self-organizing and green networking, standards development, and standardization perspectives. Recent developments in the field are covered, as well as open research challenges and possible future directions. The first book to focus exclusively on Cloud Radio Access Networks, this is essential reading for engineers in academia and industry working on future wireless networks.
17 - Caching in C-RAN
- from Part IV - Networking in C-RANs
- Edited by Tony Q. S. Quek, Singapore University of Technology and Design, Mugen Peng, Osvaldo Simeone, New Jersey Institute of Technology, Wei Yu, University of Toronto
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- Book:
- Cloud Radio Access Networks
- Published online:
- 23 February 2017
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- 02 February 2017, pp 407-430
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Summary
Introduction
Mobile traffic has shown an exponential growth during the past decade and is expected to increase by five times in the upcoming five years [1]. This growth is mainly driven by the proliferation of smart devices such as smartphones, mobile tablets, and cameras, coupled with the rising popularity of social networks. Most of the generated traffic is for mobile videos; this is envisioned to represent 80% to 90% of the total generated traffic by 2019 [1, 2]. To support this rapidly growing traffic and meet the strict quality of serivce (QoS) requirements of video streaming users, wireless networks have evolved into a distributed heterogeneous architecture [3]. This structure ismet by a dense deployment of low-power and low-coverage small base stations (SBSs), which offload the traffic from the conventional macro base stations [4]. The use of dense small base stations allows boosting of the network capacity by reducing the distance between the SBSs and the end-user. Moreover, it gives the possibility of sharing the spectrum more efficiently by an improved spectrum-reuse ratio across multiple cells, ensuring higher transmission rates for the users. However, as the network becomes denser, inter-cell and intra-cell interference become more important and cause the overall system performance to deteriorate [5]. Different approaches have been proposed to alleviate this mutual interference, especially leveraging the cooperation between base stations (BSs) through coordinated multi-point (CoMP) transmission techniques [6, 7]. In CoMP, the base stations exchange information about channel state and cooperate to form a group that serve a given user. Even though this approach can significantly improve the spectral efficiency and increase the system throughput, its performance depends on the number of base stations in the network as well as the capacity of the backhaul links. In fact, cooperation between the distributed base stations requires an important amount of signaling and exchange of control information between the BSs, which limits the performance of CoMP in capacity-limited backhaul networks [6].
Recently, a centralized (or cloud) network architecture known as C-RAN [8] was introduced as an energy-efficient and low-cost paradigm with high-processing capabilities. In C-RAN the cooperation between the BSs can be implemented in a centralized and efficient way.