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第五代移动通信技术第五代移动行动通信原则, 也称第五代移动通信技术, 外语缩写: 5G。
也是4G之后旳延伸, 正在研究中, 网速可达5M/S - 6M/S .诺基亚与加拿大运行商Bell Canada合作, 完毕加拿大初次5G网络技术旳测试。
测试中使用了73GHz范围内频谱, 数据传播速率为加拿大既有4G网络旳6倍。
鉴于两者旳合作, 外界分析加拿大很有也许将在5年内启动5G网络旳全面布署。
由于物联网尤其是互联网汽车等产业旳迅速发展, 其对网络速度有着更高旳规定, 这无疑成为推进5G网络发展旳重要原因。
因此无论是加拿大政府还是全球各地, 均在大力推进5G网络, 以迎接下一波科技浪潮。
不过, 从目前状况来看5G网络离商用估计还需4到5年时间。
未来5G 网络正朝着网络多元化、宽带化、综合化、智能化旳方向发展。
伴随多种智能终端旳普及, 面向2023 年及后来, 移动数据流量将展现爆炸式增长。
在未来5G网络中, 减小小区半径, 增长低功率节点数量, 是保证未来5G 网络支持1 000 倍流量增长旳关键技术之一。
因此, 超密集异构网络成为未来5G 网络提高数据流量旳关键技术[8] 。
未来无线网络将布署超过既有站点10 倍以上旳多种无线节点, 在宏站覆盖区内, 站点间距离将保持10 m 以内, 并且支持在每1 km2 范围内为25 000个顾客提供服务。
同步也也许出现活跃顾客数和站点数旳比例抵达1∶1旳现象, 即顾客与服务节点一一对应。
密集布署旳网络拉近了终端与节点间旳距离, 使得网络旳功率和频谱效率大幅度提高, 同步也扩大了网络覆盖范围, 扩展了系统容量, 并且增强了业务在不同样接入技术和各覆盖层次间旳灵活性。
虽然超密集异构网络架构在5G 中有很大旳发展前景, 不过节点间距离旳减少, 越发密集旳网络布署将使得网络拓扑愈加复杂, 从而轻易出现与既有移动通信系统不兼容旳问题。
在5G 移动通信网络中, 干扰是一种必须处理旳问题。
第五代移动通信(5G)简介第五代移动通信(5G)简介1. 引言1.1 背景1.2 目的1.3 定义2. 5G技术原理2.1 基础技术2.1.1 大规模MIMO技术2.1.2 毫米波技术2.2 网络架构2.2.1 软件定义网络(SDN)2.2.2 网络函数虚拟化(NFV)2.3 高速率与低延迟2.3.1 新空口技术2.3.2 新接入技术3. 5G应用场景3.1 工业3.1.1 无线控制与自动化3.1.2 物联网3.2 医疗卫生3.2.1 远程医疗3.2.2 医疗数据传输3.3 媒体与娱乐3.3.1 超高清视频传输3.3.2 虚拟现实与增强现实技术4. 5G发展与应用现状4.1 国内发展情况4.2 国际发展情况4.2.1 美国4.2.2 韩国4.2.3 中国台湾5. 5G的挑战与前景5.1 频谱资源争夺5.2 安全与隐私问题5.3 商业模式创新5.4 5G的未来前景附件1：5G技术标准文件摘要附件2：5G应用案例分析附录：法律名词及注释1. 大规模MIMO：Massive Multiple-Input Multiple-Output，指的是利用多个天线进行无线信号传输和接收的技术。
2. 毫米波：Millimeter Wave，指的是频率超过30GHz的无线电波段。
3. 软件定义网络：Software-Defined Networking，是一种通过将网络控制平面与数据平面分离的方式，实现对网络的动态控制和管理的技术。
4. 网络函数虚拟化：Network Function Virtualization，是一种将网络功能从专用硬件设备中解耦出来，以软件的方式实现的技术。
5. 新空口技术：New Radio，指的是5G无线接口技术。
6. 新接入技术：指的是5G的新一代接入网络技术，如毫米波接入等。
本文档涉及附件：附件1：5G技术标准文件摘要附件2：5G应用案例分析本文所涉及的法律名词及注释：1. 大规模MIMO：Massive Multiple-Input Multiple-Output，指的是利用多个天线进行无线信号传输和接收的技术。
第五代移动通信技术及发展【摘要】第五代移动通信技术（5G）是指当前移动通信技术中最新一代的发展。
本文介绍了5G技术的特点、发展历程、关键技术、应用领域以及发展前景。
5G技术具有高速传输、低时延、大连接性和高可靠性等特点，将在智能交通、工业互联网、医疗健康等领域得到广泛应用。
在文章结尾部分，重点探讨了5G技术的重要性、推动作用以及面临的挑战与机遇。
随着信息社会的不断发展，5G技术将成为推动整个移动通信行业发展的重要推动力量，同时也将带来更多创新应用和商业模式的机会。
5G技术的应用将深刻影响人们的日常生活和工作方式，为社会经济发展注入新的动力和活力。
【关键词】关键词：第五代移动通信技术、特点、发展历程、关键技术、应用领域、发展前景、重要性、推动作用、挑战、机遇。
1. 引言1.1 第五代移动通信技术及发展介绍第五代移动通信技术（5G）作为目前移动通信领域的热门话题，引起了广泛的关注和讨论。
5G技术被认为将会带来通信领域的革命性变革，为人们的生活带来更多便利和可能性。
随着科技的不断发展，人们对通信技术的需求也变得越来越多样化和复杂化，传统的4G技术已经无法满足人们的需求，因此推动了5G技术的发展与推广。
5G技术拥有许多突出特点，比如更快的速度、更高的容量、更低的延迟和更广的连接等。
这些特点使得5G技术能够支持更多种类的应用场景，如智能家居、自动驾驶、工业互联网等。
5G技术还具有更高的网络安全性和可靠性，为信息传输提供更加稳定和可信赖的网络环境。
在5G技术的发展历程中，各国和企业在不断探索和研究，推动了5G技术的快速发展。
关键技术的突破与应用领域的拓展为5G技术的普及奠定了基础。
未来，5G技术有望在更多领域得到广泛应用，为推动数字经济的发展和社会进步做出更大贡献。
2. 正文2.1 第五代移动通信技术的特点1. 高速率：第五代移动通信技术在数据传输速率上有了显著的提升，可以实现更高的下载和上传速度，大大缩短了数据传输时间。
第五代移动通信简介第五代移动通信（5G）是指第五代移动通信技术。
相比于之前的移动通信技术，5G具有更高的速度、更低的延迟和更大的连接密度。
技术特性更高的速度5G的传输速度比前一代移动通信技术提高了几倍。
这意味着用户可以更快地和数据，以及享受更流畅的网页浏览、视频观看和游戏体验。
更低的延迟5G可以实现更低的延迟，即数据传输的响应时间更短。
这将使得实时互动应用，如在线游戏、远程医疗和自动驾驶等变得更为可靠和可行。
更大的连接密度5G支持更多设备连接到网络，这将推动物联网（Internet of Things，IoT）的发展。
通过更大的连接密度，人们可以更方便地控制智能家居设备、监控环境、管理交通等。
更高的能量效率5G使用新的天线技术和频谱资源利用方式，从而实现更高的能量效率。
这将减少网络的能耗，降低运营商的成本，也对环境保护起到积极的作用。
应用场景增强现实和虚拟现实5G的高速度和低延迟使得增强现实（AR）和虚拟现实（VR）技术更为流畅和真实。
人们可以通过5G网络实时观看高清视频、玩游戏、参加远程会议等。
自动驾驶5G的低延迟和大连接密度为自动驾驶技术提供了坚实的基础。
通过5G网络，车辆可以实时接收和发送交通信息，并与其他车辆和设备进行通信，实现更高的安全性和效率。
远程医疗5G的高速度和低延迟使得远程医疗变得更加便捷和可行。
医生可以通过5G网络远程观察患者的健康状况、进行远程手术指导，并及时传递和接收诊断数据。
智能交通5G可以提供实时交通信息，帮助人们规划最佳路径和避免交通拥堵。
5G还可以实现车辆与交通信号灯、路边设施等的互联互通，进一步提高交通的安全性和效率。
发展前景第五代移动通信技术具有巨大的发展潜力和广阔的应用前景。
随着消费者对网络速度和体验的需求不断增长，5G将成为满足这些需求的重要技术。
，5G还将推动各个行业的数字化转型和创新。
人们可以期待在智能城市、智能家居、智能制造等领域看到更多基于5G技术的创新应用。
Five Disruptive Technology Directions for 5G ABSTRACT: New research directions will lead to fundamental changes in the design of future 5th generation (5G) cellular networks. This paper describes five technologies that could lead to both architectural and component disruptive design changes: device-centric architectures, millimeter Wave, Massive-MIMO, smarter devices, and native support to machine-2-machine. The key ideas for each technology are described, along with their potential impact on 5G and the research challenges that remain.I.INTRODUCTION:5G is coming. What technologies will define it? Will 5G be just an evolution of 4G, or will emerging technologies cause a disruption requiring a wholesale rethinking of entrenched cellular principles? This paper focuses on potential disruptive technologies and their implications for 5G. We classify the impact of new technologies, leveraging the Henderson-Clark model [1], as follows:1.Minor changes at both the node and the architectural level, e.g., the introduction of codebooks and signaling support for a higher number of antennas. We refer to these as evolutions in the design.2.Disruptive changes in the design of a class of network nodes, e.g., the introduction of a new waveform. We refer to these as component changes.3.Disruptive changes in the system architecture, e.g., the introduction of new types of nodes or new functions in existing ones. We refer to these as architectural changes.4.Disruptive changes that have an impact at both the node and the architecture levels. We refer to these as radical changes.We focus on disruptive (component, architectural or radical) technologies, driven by our belief that the extremely higher aggregate data rates and the much lower latencies required by 5G cannot be achieved with a mere evolution of the status quo. We believe that the following five potentially disruptive technologies could lead to both architectural and component design changes, as classified in Figure 1.1.Device-centric architectures.The base-station-centric architecture of cellular systems may change in 5G. It may be time to reconsider the concepts of uplink and downlink, as well as control and data channels, to better route information flows with different priorities and purposes towards different sets of nodes within the network. We present device-centric architectures in Section II.limeter Wave (mmWave).While spectrum has become scarce at microwave frequencies, it is plentiful in the mmWave realm. Such a spectrum ‘el Dorado’ has led to a mmWave ‘gold rush’ in which researchers with diverse backgrounds are studying different aspects ofmmWave transmission. Although far from fully understood, mmWave technologies have already been standardized for short-range services (IEEE 802.11ad) and deployed for niche applications such as small-cell backhaul. In Section III, we discuss the potential of mmWave for a broader application in 5G.3.Massive-MIMO.Massive-MIMO1 proposes utilizing a very high number of antennas to multiplex messages for several devices on each time-frequency resource, focusing the radiated energy towards the intended directions while minimizing intra-and inter-cell interference. Massive-MIMO may require major architectural changes, in particular in the design of macro base stations, and it may also lead to new types of deployments. We discuss massive-MIMO in Section IV.4.Smarter devices.2G-3G-4G cellular networks were built under the design premise of having complete control at the infrastructure side. We argue that 5G systems should drop this design assumption and exploit intelligence at the device side within different layers of the protocol stack, e.g., by allowing Device-to-Device (D2D) connectivity or by exploiting smart caching at the mobile side. While this design philosophy mainly requires a change at the node level (component change), it has also implications at the architectural level. We argue for smarter devices in Section V.5.Native support for Machine-to-Machine (M2M) communicationA native2 inclusion of M2M communication in 5G involves satisfying three fundamentally different requirements associated to different classes of low-data-rate services: support of a massive number of low-rate devices, sustainment of a minimal data rate in virtually all circumstances, and very-low-latency data transfer. Addressing these requirements in 5G requires new methods and ideas at both the component and architectural level, and such is the focus of Section VI.II.DEVICE-CENTRIC ARCHITECTURESCellular designs have historically relied on the axiomatic role of ‘cells’ as fundamental units within the radio access network. Under such a design postulate, a device obtains service by establishing a downlink and an uplink connection, carrying both control and data traffic, with the base station commanding the cell where the device is located. Over the last few years, different trends have been pointing to a disruption of this cell-centric structure:1.The base-station density is increasing rapidly, driven by the rise of heterogeneous networks. While heterogeneous networks were already standardized in 4G, the architecture was not natively designed to support them. Network densification could require some major changes in 5G. The deployment of base stations with vastly different transmit powers and coverage areas, for instance, calls for a decoupling of downlink and uplink in a way that allows for the corresponding information to flow through different sets of nodes [5].2.The need for additional spectrum will inevitably lead to the coexistence of frequency bands with radically different propagation characteristics within the same system. In this context, [6] proposes the concept of a ‘phantom cell’ where the data and control planes are separated: the control information is sent by high-power nodes at microwave frequencies whereas the payload data is conveyed by low-power nodes at mm-Wave frequencies. (cf. Section III.)3.A new concept termed centralized baseband related to the concept of cloud radioaccess networks is emerging (cf. [7]), where virtualization leads to a decoupling between a node and the hardware allocated to handle the processing associated with this node. Hardware resources in a pool, for instance, could be dynamically allocated to different nodes depending on metrics defined by the network operator.Emerging service classes, described in Section VI, could require a complete redefinition of the architecture. Current works are looking at architectural designs ranging from centralization or partial centralization (e.g., via aggregators) to full distribution (e.g., via compressed sensing and/or multihop).Cooperative communications paradigms such as CoMP or relaying, which despite falling short of their initial hype are nonetheless beneficial [8], could require a redefinition of the functions of the different nodes. In the context of relaying, for instance, recent developments in wireless network coding [9] suggest transmission principles that would allow recovering some of the losses associated with half-duplex relays. Moreover, recent research points to the plausibility of full- duplex nodes for short-range communication in a not-so-distant future.The use of smarter devices (cf. Section V) could impact the radio access network. In particular, both D2D and smart caching call for an architectural redefinition where the center of gravity moves from the network core to the periphery (devices, local wireless proxies, relays). Based on these trends, our vision is that the cell-centric architecture should evolve into a device-centric one: a given device (human or machine) should be able to communicate by exchanging multiple information flows through several possible sets of heterogeneous nodes. In other words, the set of network nodes providing connectivity to a given device and the functions of these nodes in a particular communication session should be tailored to that specific device and session. Under this vision, the concepts of uplink/downlink and control/data channel should be rethought (cf. Figure 2).While the need for a disruptive change in architectural design appears clear, major research efforts are still needed to transform the resulting vision into a coherent and realistic proposition. Since the history of innovations (cf. [1]) indicates that architectural changes are often the drivers of major technological discontinuities, we believe that the trends above might have a major influence on the development of 5G.LIMETER WA VE COMMUNICATIONMicrowave cellular systems have precious little spectrum: around 600 MHz are currently in use, divided among operators [10]. There are two ways to gain access to more microwave spectrum:1.To repurpose or refarm spectrum. This has occurred worldwide with the repurposing of terrestrial TV spectrum for applications such as rural broadband access. Unfortunately, repurposing has not freed up that much spectrum, only about 80 MHz, and at a high cost associated with moving the incumbents.2.To share spectrum utilizing, for instance, cognitive radio techniques. The high hopes initially placed on cognitive radio have been dampened by the fact that an incumbent not fully willing to cooperate is a major obstacle to spectrum efficiency for secondary users.3.Altogether, it appears that a doubling of the current cellular bandwidth is the best-case scenario at microwave frequencies. Alternatively, there is an enormous amount of spectrum at mmWave frequencies ranging from 3 to 300 GHz. Many bands therein seem promising, including most immediately the local multipoint distribution service at 28-30 GHz, the license-free band at 60 GHz, and the E-band at 71-76 GHz, 81-86 GHz and 92-95 GHz. Foreseeably, several tens of GHz could become available for 5G, offering well over an order-of-magnitude increase over what is available atpresent. Needless to say, work needs to be done on spectrum policy to render these bands available for mobile cellular.3.Propagation is not an insurmountable challenge. Recent measurements indicate similar general characteristics as at microwave frequencies, including distance-dependent pathloss and the possibility of non-line-of-sight communication. A main difference between microwave and mmWave frequencies is the sensitivity to blockages: the results in [11], for instance, indicate a pathloss exponent of 2 for line-of-sight propagation but 4 (plus an additional power loss) for non-line-of-sight. MmWave cellular research will need to incorporate sensitivity to blockages and more complex channel models into the analysis, and also study the effects of enablers such as higher density infrastructure and relays. Another enabler is the separation between control and data planes, already mentioned in Section II.Antenna arrays are a key feature in mmWave systems. Large arrays can be used to keep the antenna aperture constant, eliminating the frequency dependence of pathloss relative to omnidirectional antennas (when utilized at one side of the link) and providing a net array gain to counter the larger thermal noise bandwidth (when utilized at both sides of the link). Adaptive arrays with narrow beams also reduce the impact of interference, meaning that mmWave systems could more often operate in noise-limited rather than interference-limited conditions. Since meaningful communication might only happen under sufficient array gain, new random access protocols are needed that work when transmitters can only emit in certain directions and receivers can only receive from certain directions. Adaptive array processing algorithms are required that can adapt quickly when beams are blocked by people or when some device antennas become obscured by the user’s own body.MmWave systems also have distinct hardware constraints. A major one comes from the high power consumption of mixed signal components, chiefly the analog-to-digital (ADC) and digital-to-analog converters (DAC). Thus, the conventional microwave architecture where every antenna is connected to a high-rate ADC/DAC is unlikely to be applicable to mmWave without a huge leap forward in semiconductor technology. One alternative is a hybrid architecture where beamforming is performed in analog at RF and multiple sets of beamformers are connected to a small number of ADCs or DACS; in this alternative, signal processing algorithms are needed to steer the analog beamforming weights. Another alternative is to connect each RF chain to a 1-bit ADC/DAC, with very low power requirements; in this case, the beamforming would be performed digitally but on very noisy data. There are abundant research challenges in optimizing different transceiver strategies, analyzing their capacity, incorporating multiuser capabilities, and leveraging channel features such as sparsity.A data rate comparison between technologies is provided in Fig. 3, for certain simulation settings, in terms of mean and 5% outage rates. MmWave operation is seento provide very high rates compared to two different microwave systems. The gains exceed the 10x spectrum increase because of the enhanced signal power and reduced interference thanks to directional beamforming at both transmitter and receiver.IV.MASSIVE MIMOMassive MIMO (also referred to as ‘Large-Scale MIMO’ or ‘Large-Scale Antenna Systems’) is a form of multiuser MIMO in which the number of antennas at the base station is much larger than the number of devices per signaling resource [14]. Having many more base station antennas than devices renders the channels to the different devices quasi-orthogonal and very simple spatial multiplexing/de-multiplexing procedures quasi-optimal. The favorable action of the law of large numbers smoothens out frequency dependencies in the channel and, altogether, huge gains in spectral efficiency can be attained (cf. Fig. 4).In the context of the Henderson-Clark framework, we argue that massive-MIMO has a disruptive potential for 5G:At a node level, it is a scalable technology. This is in contrast with 4G, which, in many respects, is not scalable: further sectorization therein is not feasible because of (i) the limited space for bulky azimuthally-directive antennas, and (ii) the inevitable angle spread of the propagation; in turn, single-user MIMO is constrained by the limited number of antennas that can fit in certain mobile devices. In contrast, there is almost no limit on the number of base station antennas in massive- MIMO provided that time-division duplexing is employed to enable channel estimation through uplink pilots.It enables new deployments and architectures. While one can envision direct replacement of macro base stations with arrays of low-gain resonant antennas, other deployments are possible, e.g., conformal arrays on the facades of skyscrapers or arrays on the faces of water tanks in rural locations. Moreover, the same massive-MIMO principles that govern the use of collocated arrays of antennas applyalso to distributed deployments in which a college campus or an entire city could be covered with a multitude of distributed antennas that collectively serve many users (in this framework, the centralized baseband concept presented in Section II is an important architectural enabler).While very promising, massive-MIMO still presents a number of research challenges. Channel estimation is critical and currently it represents the main source of limitations. User motion imposes a finite coherence interval during which channel knowledge must be acquired and utilized, and consequently there is a finite number of orthogonal pilot sequences that can be assigned to the devices. Reuse of pilot sequences causes pilot contamination and coherent interference, which grows with the number of antennas as fast as the desired signals. The mitigation of pilot contamination is an active research topic. Also, there is still much to be learned about massive-MIMO propagation, although experiments thus far support the hypothesis of channel quasi-orthogonality. From an implementation perspective, massive-MIMO can potentially be realized with modular low-cost low-power hardware with each antenna functioning semi-autonomously, but a considerable development effort is still required to demonstrate the cost-effectiveness of this solution. Note that, at the microwave frequencies considered in this section, the cost and the energy consumption of ADCs/DACs are sensibly lower than at mmWave frequencies (cf. Section III).From the discussion above, we conclude that the adoption of massive-MIMO for 5G could represent a major leap with respect to today’s state-of-the-art in system and component design. To justify these major changes, massive-MIMO proponents should further work on solving the challenges emphasized above and on showing realistic performance improvements by means of theoretical studies, simulation campaigns, and testbed experiments.V.SMARTER DEVICESEarlier generations of cellular systems were built on the design premise of having complete control at the infrastructure side. In this section, we discuss some of the possibilities that can be unleashed by allowing the devices to play a more active role and, thereafter, how 5G’s design should account for an increase in device smartness. We focus on three different examples of technologies that could be incorporated into smarter devices, namely D2D, local caching, and advanced interference rejection.V.1 D2DIn voice-centric systems it was implicitly accepted that two parties willing to establish a call would not be in close proximity. In the age of data, this premise might no longer hold, and it could be common to have situations where several co-located devices would like to wirelessly share content (e.g., digital pictures) or interact (e.g., video gaming or social networking). Handling these communication scenarios via simply connecting through the network involves gross inefficiencies at various levels:1.Multiple wireless hops are utilized to achieve what requires, fundamentally, a single hop. This entails a multifold waste of signaling resources, and also a higher latency. Transmit powers of a fraction of a Watt (in the uplink) and several Watts (in the downlink) are consumed to achieve what requires, fundamentally, a few milliWatts. This, in turn, entails unnecessary levels of battery drain and of interference to all other devices occupying the same signaling resources elsewhere.2.Given that the pathlosses to possibly distant base stations are much stronger than the direct-link ones, the corresponding spectral efficiencies are also lower. While it is clear that D2D has the potential of handling local communication more efficiently, local high-data-rate exchanges could also be handled by other radio access technologies such as Bluetooth or Wi-Fi direct. Use cases requiring a mixture of local and nonlocal content or a mixture of low-latency and high- data-rate constraints (e.g., interaction between users via augmented reality), could represent more compelling reasons for the use of D2D. In particular, we envision D2D as an important enabler for applications requiring low-latency 3 , especially in future network deployments utilizing baseband centralization and radio virtualization (cf. Section I).From a research perspective, D2D communication presents relevant challenges:1.Quantification of the real opportunities for D2D. How often does local communication occur? What is the main use case for D2D: fast local exchanges, low-latency applications or energy saving?2.Integration of a D2D mode with the uplink/downlink duplexing structure.3.Design of D2D-enabled devices, from both a hardware and a protocol perspective, by providing the needed flexibility at both the PHY and MAC layers.4.Assessing the true net gains associated with having a D2D mode, accounting for possible extra overheads for control and channel estimation.5.Finally, note that, while D2D is already being studied in 3GPP as a 4G add-on2, the main focus of current studies is proximity detection for public safety [15]. What wediscussed here is having a D2D dimension natively supported in 5G.V.2 Local CachingThe current paradigm of cloud computing is the result of a progressive shift in the balance between data storage and data transfer: information is stored and processed wherever it is most convenient and inexpensive because the marginal cost of transferring it has become negligible, at least on wireline networks [2]. For wireless devices though, this cost is not always negligible. The understanding that mobile users are subject to sporadic ‘abundance’ of connectivity amidst stretches of ‘deprivation’ is hardly new, and the natural idea of opportunistically leveraging the former to alleviate the latter has been entertained since the 1990s [3]. However, this idea of caching massive amounts of data at the edge of the wireline network, right before the wireless hop, only applies to delay-tolerant traffic and thus it made little sense in voice-centric systems. Caching might finally make sense now, in data-centric systems [4]. Thinking ahead, it is easy to envision mobile devices with truly vast amounts of memory. Under this assumption, and given that a substantial share of the data that circulates wirelessly corresponds to the most popular audio/video/social content that is in vogue at a given time, it is clearly inefficient to transmit such content via unicast and yet it is frustratingly impossible to resort to multicast because the demand is asynchronous. We hence see local caching as an important alternative, both at the radio access network edge (e.g., at small cells) and at the mobile devices, also thanks to enablers such as mmWave and D2D.V.3 Advanced Interference RejectionIn addition to D2D capabilities and massive volumes of memory, future mobile devices may also have varying form factors. In some instances, the devices mightaccommodate several antennas with the consequent opportunity for active interference rejection therein, along with beamforming and spatial multiplexing. A joint design of transmitter and receiver processing, and proper control and pilot signals, are critical to allow advanced interference rejection. As an example, in Fig. 5 we show the gains obtained by incorporating the effects of nonlinear, intra and inter-cluster interference awareness into devices with 1, 2 and 4 antennas.While this section has been mainly focused on analyzing the implications of smarter devices at a component level, in Section II we discussed the impact at the radio access network architecture level. We regard smarter devices as having all the characteristic of a disruptive technology (cf. Section I) for 5G, and therefore we encourage researchers to further explore this direction.VI.NATIVE SUPPORT FOR M2M COMMUNICATIONWireless communication is becoming a commodity, just like electricity or water [13]. This commoditization, in turn, is giving rise to a large class of emerging services with new types of requirements. We point to a few representative such requirements, each exemplified by a typical service:1.A massive number of connected devices. Whereas current systems typically operate with, at most, a few hundred devices per base station, some M2M services might require over 104 connected devices. Examples include metering, sensors, smart grid components, and other enablers of services targeting wide area coverage.2.Very high link reliability. Systems geared at critical control, safety, or production, have been dominated by wireline connectivity largely because wireless links did not offer the same degree of confidence. As these systems transition from wireline to wireless, it becomes necessary for the wireless link to be reliably operational virtually all the time.3.Low latency and real-time operation. This can be an even more stringent requirement than the ones above, as it demands that data be transferred reliably within a given time interval. A typical example is Vehicle-to-X connectivity, whereby traffic safety can be improved through the timely delivery of critical messages (e.g., alert and control).Fig. 5 provides a perspective on the M2M requirements by plotting the data rate vs. the device population size. This cartoon illustrates where systems currently stand and how the research efforts are expanding them. The area R1 reflects the operating range of today’s systems, outlining the fact that the device data rate decreases as its population increases. In turn, R2 is the region that reflects current research aimed at improving the spectral efficiency. Finally, R5 indicates the region where operation is not feasible due to fundamental physical and information-theoretical limits.Regions R3 and R4 correspond to the emerging services discussed in this section:R3 refers to massive M2M communication where each connected machine or sensor transmits small data blocks sporadically. Current systems are not designed to simultaneously serve the aggregated traffic accrued from a large number of such devices. For instance, a current system could easily serve 5 devices at 2 Mbps each, but not 10000 devices each requiring 1 Kbps. R4 demarks the operation of systems that require high reliability and/or low latency, but with a relatively low average rate per device. The complete description of this region requires additional dimensions related to reliability and latency.There are services that pose simultaneously more than one of the above requirements, but the common point is that the data size of each individual transmission is small, going down to several bytes. This profoundly changes the communication paradigm for the following reasons:Existing coding methods that rely on long codewords are not applicable to very short data blocks. Short data blocks also exacerbate the inefficiencies associated with control and channel estimation overheads. Currently, the control plane is robust but suboptimal as it represents only a modest fraction of the payload data; the most sophisticated signal processing is reserved for payload data transmission. An optimized design should aim at a much tighter coupling between the data and control planes.As mentioned in Section II, the architecture needs a major redesign, looking at new types of nodes. At a system level, the frame-based approaches that are at the heart of 4G need rethinking in order to meet the requirements for latency and flexible allocation of resources to a massive number of devices. From the discussion above, and from the related architectural consideration in Section II, and referring one last time to the Henderson-Clark model, we conclude that a native support of M2M in 5G requires radical changes at both the node and the architecture level. Major research work remains to be done to come up with concrete and interworking solutionsenabling ‘M2M-inside’ 5G systems.VII.CONCLUSIONThis paper has discussed five disruptive research directions that could lead to fundamental changes in the design of cellular networks. We have focused on technologies that could lead to both architectural and component design changes: device-centric architectures, mmWave, massive-MIMO, smarter devices, and native support to M2M. It is likely that a suite of these solutions will form the basis of 5G. REFERENCES[1] A. Afuah, Innovation Management: Strategies, Implementation and Profits, Oxford University Press, 2003.[2] J. Zander and P. Mähönen, “Riding the data tsunami in the cloud: myths and challenges in future wireless access,” IEEE Comm. Magazine, V ol. 51, No. 3, pp. 145-151, Mar. 2013.[3] D. Goodman, J. Borras, N. Mandayam, R. D. Yates, “Infostations: A new system model for data and messaging services,” in Proc. IEEE Veh. Techn. Conf. (VTC), vol. 2, pp. 969–973, Rome, Italy, May 1997.[4] N. Golrezaei, A. F. Molisch, A. G. Dimakis and G. Caire, “Femtocaching and device-to-device collaboration: A new architecture for wireless video distribution,” IEEE Comm. Magazine, V ol. 51, No. 1, pp.142-149, Apr. 2013.[5] J. Andrews, “The seven ways HetNets are a paradigm shift,” IEEE Comm. Magazine, V ol. 51, No. 3, pp.136-144, Mar. 2013.[6] Y. Kishiyama, A. Benjebbour, T. Nakamura and H. Ishii, “Future steps of LTE-A: evolution towards integration of local area and wide area systems,” IEEE Wireless Communications, V ol. 20, No. 1, pp.12-18, Feb. 2013.[7] “C-RAN: The road towards green RAN,” China Mobile Res. Inst., Beijing, China, White Paper, ver. 2.5, Oct. 2011.[8] A. Lozano, R. W. Heath Jr., J. G. Andrews, “Fundamental limits of cooperation,” IEEE Trans. Inform. Theory, V ol. 59, No. 9, pp. 5213-5226, Sep. 2013.[9] C. D. T. Thai, P. Popovski, M. Kaneko, and E. de Carvalho, “Multi-flow scheduling for coordinated direct and relayed users in cellular systems,” IEEE Trans. Comm., V ol. 61, No. 2, pp. 669-678, Feb. 2013.[10] Z. Pi and F. Khan, “An introduction to millimeter-wave mobile broadband systems,” IEEE Comm. Magazine, V ol. 49, No. 6, pp. 101 –107, Jun. 2011.[11] T. Rappaport and et al, “Millimeter wave mobile communications for 5G cellular: It will work!” IEEE Access, vol. 1, pp. 335–349, 2013.[12] R. W. Heath Jr., “What is the Role of MIMO in Future Cellular Networks: Massive? Coordinated? mmWave?” ICC Workshop Plenary: Beyond LTE-A, Budapest, Hungary. Slides available at: /~rheath/presentations/2013/Future_of_MIMO_Plenary_He ath.pdf[13] Mark Weiser, “The Computer for the 21st Century,” Scientific American, Sept. 1991.。
第五代移动通信(5G)简介引言随着科技的快速发展，移动通信技术也在不断进步。
第五代移动通信(5G)作为最新一代移动通信技术，具有更快的速度、更低的延迟和更强的可连接性。
本文将介绍第五代移动通信的概念、特点和应用等。
5G的概念5G是第五代移动通信的简称，是对一系列移动通信技术的总称。
它是在4G的基础上进一步提升的新一代移动通信技术，旨在实现更高的数据传输速率、更低的延迟和更多的连接数量。
5G的特点1. 更快的速度：5G的传输速率比4G提高了数倍，可以实现更快的网络和速度。
这将为用户提供更流畅的网页浏览、视频播放和文件传输体验。
2. 更低的延迟：5G的延迟比4G更低，可以实现更快的响应时间。
这将使得5G在物联网、自动驾驶和远程医疗等领域发挥更大的作用。
3. 更强的可连接性：5G支持更多的设备连接，可以满足大规模物联网的需求。
这将为智能家居、智能城市和工业自动化等应用提供更好的支持。
4. 更多的应用场景：5G的高速率、低延迟和大连接数为众多新兴应用提供了条件。
例如增强现实、虚拟现实、远程教育等应用将得到更好的发展。
5G的应用1. 智能方式：5G将为智能方式用户提供更快、更稳定的网络连接，改善用户体验。
2. 物联网：5G的大连接数和低延迟为物联网设备提供了更好的支持，能够实现更智能的家庭和城市管理。
3. 自动驾驶：5G的高速率和低延迟为自动驾驶技术提供了保障，可以实现更精准的实时交互和控制。
4. 远程医疗：5G的高速率和低延迟使得远程医疗变得更加可行，可以实现远程诊断、远程手术等应用。
第五代移动通信(5G)是一项革命性的技术，具有更快的速度、更低的延迟和更强的可连接性。
它将为各个领域带来更多的创新和机遇，推动社会的进步和发展。
期待5G技术的广泛应用和普及，为人们带来更美好的生活和工作体验。
5G无线通信网络中英文对照外文翻译文献(文档含英文原文和中文翻译)翻译：5G无线通信网络的蜂窝结构和关键技术摘要第四代无线通信系统已经或者即将在许多国家部署。
然而，随着无线移动设备和服务的激增，仍然有一些挑战尤其是4G所不能容纳的，例如像频谱危机和高能量消耗。
无线系统设计师们面临着满足新型无线应用对高数据速率和机动性要求的持续性增长的需求，因此他们已经开始研究被期望于2020年后就能部署的第五代无线系统。
在这篇文章里面，我们提出一个有内门和外门情景之分的潜在的蜂窝结构，并且讨论了多种可行性关于5G无线通信系统的技术，比如大量的MIMO技术，节能通信，认知的广播网络和可见光通信。
面临潜在技术的未知挑战也被讨论了。
介绍信息通信技术（ICT）创新合理的使用对世界经济的提高变得越来越重要。
无线通信网络在全球ICT战略中也许是最挑剔的元素，并且支撑着很多其他的行业，它是世界上成长最快最有活力的行业之一。
欧洲移动天文台（EMO）报道2010年移动通信业总计税收1740亿欧元,从而超过了航空航天业和制药业。
无线技术的发展大大提高了人们在商业运作和社交功能方面通信和生活的能力无线移动通信的显著成就表现在技术创新的快速步伐。
从1991年二代移动通信系统(2G)的初次登场到2001年三代系统（3G）的首次起飞，无线移动网络已经实现了从一个纯粹的技术系统到一个能承载大量多媒体内容网络的转变。
4G无线系统被设计出来用来满足IMT-A技术使用IP面向所有服务的需求。
在4G系统中，先进的无线接口被用于正交频分复用技术（OFDM），多输入多输出系统（MIMO）和链路自适应技术。
4G无线网络可支持数据速率可达1Gb/s的低流度，比如流动局域无线访问，还有速率高达100M/s的高流速，例如像移动访问。
LTE系统和它的延伸系统LTE-A，作为实用的4G系统已经在全球于最近期或不久的将来部署。
然而，每年仍然有戏剧性增长数量的用户支持移动宽频带系统。
Five Disruptive Technology Directions for 5G ABSTRACT: New research directions will lead to fundamental changes in the design of future 5th generation (5G) cellular networks. This paper describes five technologies that could lead to both architectural and component disruptive design changes: device-centric architectures, millimeter Wave, Massive-MIMO, smarter devices, and native support to machine-2-machine. The key ideas for each technology are described, along with their potential impact on 5G and the research challenges that remain.I.INTRODUCTION:5G is coming. What technologies will define it? Will 5G be just an evolution of 4G, or will emerging technologies cause a disruption requiring a wholesale rethinking of entrenched cellular principles? This paper focuses on potential disruptive technologies and their implications for 5G. We classify the impact of new technologies, leveraging the Henderson-Clark model [1], as follows:1.Minor changes at both the node and the architectural level, e.g., the introduction of codebooks and signaling support for a higher number of antennas. We refer to these as evolutions in the design.2.Disruptive changes in the design of a class of network nodes, e.g., the introduction of a new waveform. We refer to these as component changes.3.Disruptive changes in the system architecture, e.g., the introduction of new types of nodes or new functions in existing ones. We refer to these as architectural changes.4.Disruptive changes that have an impact at both the node and the architecture levels. We refer to these as radical changes.We focus on disruptive (component, architectural or radical) technologies, driven by our belief that the extremely higher aggregate data rates and the much lower latencies required by 5G cannot be achieved with a mere evolution of the status quo. We believe that the following five potentially disruptive technologies could lead to both architectural and component design changes, as classified in Figure 1.1.Device-centric architectures.The base-station-centric architecture of cellular systems may change in 5G. It may be time to reconsider the concepts of uplink and downlink, as well as control and data channels, to better route information flows with different priorities and purposes towards different sets of nodes within the network. We present device-centric architectures in Section II.limeter Wave (mmWave).While spectrum has become scarce at microwave frequencies, it is plentiful in the mmWave realm. Such a spectrum ‘el Dorado’ has led to a mmWave ‘gold rush’ in which researchers with diverse backgrounds are studying different aspects ofmmWave transmission. Although far from fully understood, mmWave technologies have already been standardized for short-range services (IEEE 802.11ad) and deployed for niche applications such as small-cell backhaul. In Section III, we discuss the potential of mmWave for a broader application in 5G.3.Massive-MIMO.Massive-MIMO1 proposes utilizing a very high number of antennas to multiplex messages for several devices on each time-frequency resource, focusing the radiated energy towards the intended directions while minimizing intra-and inter-cell interference. Massive-MIMO may require major architectural changes, in particular in the design of macro base stations, and it may also lead to new types of deployments. We discuss massive-MIMO in Section IV.4.Smarter devices.2G-3G-4G cellular networks were built under the design premise of having complete control at the infrastructure side. We argue that 5G systems should drop this design assumption and exploit intelligence at the device side within different layers of the protocol stack, e.g., by allowing Device-to-Device (D2D) connectivity or by exploiting smart caching at the mobile side. While this design philosophy mainly requires a change at the node level (component change), it has also implications at the architectural level. We argue for smarter devices in Section V.5.Native support for Machine-to-Machine (M2M) communicationA native2 inclusion of M2M communication in 5G involves satisfying three fundamentally different requirements associated to different classes of low-data-rate services: support of a massive number of low-rate devices, sustainment of a minimal data rate in virtually all circumstances, and very-low-latency data transfer. Addressing these requirements in 5G requires new methods and ideas at both the component and architectural level, and such is the focus of Section VI.II.DEVICE-CENTRIC ARCHITECTURESCellular designs have historically relied on the axiomatic role of ‘cells’ as fundamental units within the radio access network. Under such a design postulate, a device obtains service by establishing a downlink and an uplink connection, carrying both control and data traffic, with the base station commanding the cell where the device is located. Over the last few years, different trends have been pointing to a disruption of this cell-centric structure:1.The base-station density is increasing rapidly, driven by the rise of heterogeneous networks. While heterogeneous networks were already standardized in 4G, the architecture was not natively designed to support them. Network densification could require some major changes in 5G. The deployment of base stations with vastly different transmit powers and coverage areas, for instance, calls for a decoupling of downlink and uplink in a way that allows for the corresponding information to flow through different sets of nodes [5].2.The need for additional spectrum will inevitably lead to the coexistence of frequency bands with radically different propagation characteristics within the same system. In this context, [6] proposes the concept of a ‘phantom cell’ where the data and control planes are separated: the control information is sent by high-power nodes at microwave frequencies whereas the payload data is conveyed by low-power nodes at mm-Wave frequencies. (cf. Section III.)3.A new concept termed centralized baseband related to the concept of cloud radioaccess networks is emerging (cf. [7]), where virtualization leads to a decoupling between a node and the hardware allocated to handle the processing associated with this node. Hardware resources in a pool, for instance, could be dynamically allocated to different nodes depending on metrics defined by the network operator.Emerging service classes, described in Section VI, could require a complete redefinition of the architecture. Current works are looking at architectural designs ranging from centralization or partial centralization (e.g., via aggregators) to full distribution (e.g., via compressed sensing and/or multihop).Cooperative communications paradigms such as CoMP or relaying, which despite falling short of their initial hype are nonetheless beneficial [8], could require a redefinition of the functions of the different nodes. In the context of relaying, for instance, recent developments in wireless network coding [9] suggest transmission principles that would allow recovering some of the losses associated with half-duplex relays. Moreover, recent research points to the plausibility of full- duplex nodes for short-range communication in a not-so-distant future.The use of smarter devices (cf. Section V) could impact the radio access network. In particular, both D2D and smart caching call for an architectural redefinition where the center of gravity moves from the network core to the periphery (devices, local wireless proxies, relays). Based on these trends, our vision is that the cell-centric architecture should evolve into a device-centric one: a given device (human or machine) should be able to communicate by exchanging multiple information flows through several possible sets of heterogeneous nodes. In other words, the set of network nodes providing connectivity to a given device and the functions of these nodes in a particular communication session should be tailored to that specific device and session. Under this vision, the concepts of uplink/downlink and control/data channel should be rethought (cf. Figure 2).While the need for a disruptive change in architectural design appears clear, major research efforts are still needed to transform the resulting vision into a coherent and realistic proposition. Since the history of innovations (cf. [1]) indicates that architectural changes are often the drivers of major technological discontinuities, we believe that the trends above might have a major influence on the development of 5G.LIMETER WA VE COMMUNICATIONMicrowave cellular systems have precious little spectrum: around 600 MHz are currently in use, divided among operators [10]. There are two ways to gain access to more microwave spectrum:1.To repurpose or refarm spectrum. This has occurred worldwide with the repurposing of terrestrial TV spectrum for applications such as rural broadband access. Unfortunately, repurposing has not freed up that much spectrum, only about 80 MHz, and at a high cost associated with moving the incumbents.2.To share spectrum utilizing, for instance, cognitive radio techniques. The high hopes initially placed on cognitive radio have been dampened by the fact that an incumbent not fully willing to cooperate is a major obstacle to spectrum efficiency for secondary users.3.Altogether, it appears that a doubling of the current cellular bandwidth is the best-case scenario at microwave frequencies. Alternatively, there is an enormous amount of spectrum at mmWave frequencies ranging from 3 to 300 GHz. Many bands therein seem promising, including most immediately the local multipoint distribution service at 28-30 GHz, the license-free band at 60 GHz, and the E-band at 71-76 GHz, 81-86 GHz and 92-95 GHz. Foreseeably, several tens of GHz could become available for 5G, offering well over an order-of-magnitude increase over what is available atpresent. Needless to say, work needs to be done on spectrum policy to render these bands available for mobile cellular.3.Propagation is not an insurmountable challenge. Recent measurements indicate similar general characteristics as at microwave frequencies, including distance-dependent pathloss and the possibility of non-line-of-sight communication. A main difference between microwave and mmWave frequencies is the sensitivity to blockages: the results in [11], for instance, indicate a pathloss exponent of 2 for line-of-sight propagation but 4 (plus an additional power loss) for non-line-of-sight. MmWave cellular research will need to incorporate sensitivity to blockages and more complex channel models into the analysis, and also study the effects of enablers such as higher density infrastructure and relays. Another enabler is the separation between control and data planes, already mentioned in Section II.Antenna arrays are a key feature in mmWave systems. Large arrays can be used to keep the antenna aperture constant, eliminating the frequency dependence of pathloss relative to omnidirectional antennas (when utilized at one side of the link) and providing a net array gain to counter the larger thermal noise bandwidth (when utilized at both sides of the link). Adaptive arrays with narrow beams also reduce the impact of interference, meaning that mmWave systems could more often operate in noise-limited rather than interference-limited conditions. Since meaningful communication might only happen under sufficient array gain, new random access protocols are needed that work when transmitters can only emit in certain directions and receivers can only receive from certain directions. Adaptive array processing algorithms are required that can adapt quickly when beams are blocked by people or when some device antennas become obscured by the user’s own body.MmWave systems also have distinct hardware constraints. A major one comes from the high power consumption of mixed signal components, chiefly the analog-to-digital (ADC) and digital-to-analog converters (DAC). Thus, the conventional microwave architecture where every antenna is connected to a high-rate ADC/DAC is unlikely to be applicable to mmWave without a huge leap forward in semiconductor technology. One alternative is a hybrid architecture where beamforming is performed in analog at RF and multiple sets of beamformers are connected to a small number of ADCs or DACS; in this alternative, signal processing algorithms are needed to steer the analog beamforming weights. Another alternative is to connect each RF chain to a 1-bit ADC/DAC, with very low power requirements; in this case, the beamforming would be performed digitally but on very noisy data. There are abundant research challenges in optimizing different transceiver strategies, analyzing their capacity, incorporating multiuser capabilities, and leveraging channel features such as sparsity.A data rate comparison between technologies is provided in Fig. 3, for certain simulation settings, in terms of mean and 5% outage rates. MmWave operation is seento provide very high rates compared to two different microwave systems. The gains exceed the 10x spectrum increase because of the enhanced signal power and reduced interference thanks to directional beamforming at both transmitter and receiver.IV.MASSIVE MIMOMassive MIMO (also referred to as ‘Large-Scale MIMO’ or ‘Large-Scale Antenna Systems’) is a form of multiuser MIMO in which the number of antennas at the base station is much larger than the number of devices per signaling resource [14]. Having many more base station antennas than devices renders the channels to the different devices quasi-orthogonal and very simple spatial multiplexing/de-multiplexing procedures quasi-optimal. The favorable action of the law of large numbers smoothens out frequency dependencies in the channel and, altogether, huge gains in spectral efficiency can be attained (cf. Fig. 4).In the context of the Henderson-Clark framework, we argue that massive-MIMO has a disruptive potential for 5G:At a node level, it is a scalable technology. This is in contrast with 4G, which, in many respects, is not scalable: further sectorization therein is not feasible because of (i) the limited space for bulky azimuthally-directive antennas, and (ii) the inevitable angle spread of the propagation; in turn, single-user MIMO is constrained by the limited number of antennas that can fit in certain mobile devices. In contrast, there is almost no limit on the number of base station antennas in massive- MIMO provided that time-division duplexing is employed to enable channel estimation through uplink pilots.It enables new deployments and architectures. While one can envision direct replacement of macro base stations with arrays of low-gain resonant antennas, other deployments are possible, e.g., conformal arrays on the facades of skyscrapers or arrays on the faces of water tanks in rural locations. Moreover, the same massive-MIMO principles that govern the use of collocated arrays of antennas applyalso to distributed deployments in which a college campus or an entire city could be covered with a multitude of distributed antennas that collectively serve many users (in this framework, the centralized baseband concept presented in Section II is an important architectural enabler).While very promising, massive-MIMO still presents a number of research challenges. Channel estimation is critical and currently it represents the main source of limitations. User motion imposes a finite coherence interval during which channel knowledge must be acquired and utilized, and consequently there is a finite number of orthogonal pilot sequences that can be assigned to the devices. Reuse of pilot sequences causes pilot contamination and coherent interference, which grows with the number of antennas as fast as the desired signals. The mitigation of pilot contamination is an active research topic. Also, there is still much to be learned about massive-MIMO propagation, although experiments thus far support the hypothesis of channel quasi-orthogonality. From an implementation perspective, massive-MIMO can potentially be realized with modular low-cost low-power hardware with each antenna functioning semi-autonomously, but a considerable development effort is still required to demonstrate the cost-effectiveness of this solution. Note that, at the microwave frequencies considered in this section, the cost and the energy consumption of ADCs/DACs are sensibly lower than at mmWave frequencies (cf. Section III).From the discussion above, we conclude that the adoption of massive-MIMO for 5G could represent a major leap with respect to today’s state-of-the-art in system and component design. To justify these major changes, massive-MIMO proponents should further work on solving the challenges emphasized above and on showing realistic performance improvements by means of theoretical studies, simulation campaigns, and testbed experiments.V.SMARTER DEVICESEarlier generations of cellular systems were built on the design premise of having complete control at the infrastructure side. In this section, we discuss some of the possibilities that can be unleashed by allowing the devices to play a more active role and, thereafter, how 5G’s design should account for an increase in device smartness. We focus on three different examples of technologies that could be incorporated into smarter devices, namely D2D, local caching, and advanced interference rejection.V.1 D2DIn voice-centric systems it was implicitly accepted that two parties willing to establish a call would not be in close proximity. In the age of data, this premise might no longer hold, and it could be common to have situations where several co-located devices would like to wirelessly share content (e.g., digital pictures) or interact (e.g., video gaming or social networking). Handling these communication scenarios via simply connecting through the network involves gross inefficiencies at various levels:1.Multiple wireless hops are utilized to achieve what requires, fundamentally, a single hop. This entails a multifold waste of signaling resources, and also a higher latency. Transmit powers of a fraction of a Watt (in the uplink) and several Watts (in the downlink) are consumed to achieve what requires, fundamentally, a few milliWatts. This, in turn, entails unnecessary levels of battery drain and of interference to all other devices occupying the same signaling resources elsewhere.2.Given that the pathlosses to possibly distant base stations are much stronger than the direct-link ones, the corresponding spectral efficiencies are also lower. While it is clear that D2D has the potential of handling local communication more efficiently, local high-data-rate exchanges could also be handled by other radio access technologies such as Bluetooth or Wi-Fi direct. Use cases requiring a mixture of local and nonlocal content or a mixture of low-latency and high- data-rate constraints (e.g., interaction between users via augmented reality), could represent more compelling reasons for the use of D2D. In particular, we envision D2D as an important enabler for applications requiring low-latency 3 , especially in future network deployments utilizing baseband centralization and radio virtualization (cf. Section I).From a research perspective, D2D communication presents relevant challenges:1.Quantification of the real opportunities for D2D. How often does local communication occur? What is the main use case for D2D: fast local exchanges, low-latency applications or energy saving?2.Integration of a D2D mode with the uplink/downlink duplexing structure.3.Design of D2D-enabled devices, from both a hardware and a protocol perspective, by providing the needed flexibility at both the PHY and MAC layers.4.Assessing the true net gains associated with having a D2D mode, accounting for possible extra overheads for control and channel estimation.5.Finally, note that, while D2D is already being studied in 3GPP as a 4G add-on2, the main focus of current studies is proximity detection for public safety [15]. What wediscussed here is having a D2D dimension natively supported in 5G.V.2 Local CachingThe current paradigm of cloud computing is the result of a progressive shift in the balance between data storage and data transfer: information is stored and processed wherever it is most convenient and inexpensive because the marginal cost of transferring it has become negligible, at least on wireline networks [2]. For wireless devices though, this cost is not always negligible. The understanding that mobile users are subject to sporadic ‘abundance’ of connectivity amidst stretches of ‘deprivation’ is hardly new, and the natural idea of opportunistically leveraging the former to alleviate the latter has been entertained since the 1990s [3]. However, this idea of caching massive amounts of data at the edge of the wireline network, right before the wireless hop, only applies to delay-tolerant traffic and thus it made little sense in voice-centric systems. Caching might finally make sense now, in data-centric systems [4]. Thinking ahead, it is easy to envision mobile devices with truly vast amounts of memory. Under this assumption, and given that a substantial share of the data that circulates wirelessly corresponds to the most popular audio/video/social content that is in vogue at a given time, it is clearly inefficient to transmit such content via unicast and yet it is frustratingly impossible to resort to multicast because the demand is asynchronous. We hence see local caching as an important alternative, both at the radio access network edge (e.g., at small cells) and at the mobile devices, also thanks to enablers such as mmWave and D2D.V.3 Advanced Interference RejectionIn addition to D2D capabilities and massive volumes of memory, future mobile devices may also have varying form factors. In some instances, the devices mightaccommodate several antennas with the consequent opportunity for active interference rejection therein, along with beamforming and spatial multiplexing. A joint design of transmitter and receiver processing, and proper control and pilot signals, are critical to allow advanced interference rejection. As an example, in Fig. 5 we show the gains obtained by incorporating the effects of nonlinear, intra and inter-cluster interference awareness into devices with 1, 2 and 4 antennas.While this section has been mainly focused on analyzing the implications of smarter devices at a component level, in Section II we discussed the impact at the radio access network architecture level. We regard smarter devices as having all the characteristic of a disruptive technology (cf. Section I) for 5G, and therefore we encourage researchers to further explore this direction.VI.NATIVE SUPPORT FOR M2M COMMUNICATIONWireless communication is becoming a commodity, just like electricity or water [13]. This commoditization, in turn, is giving rise to a large class of emerging services with new types of requirements. We point to a few representative such requirements, each exemplified by a typical service:1.A massive number of connected devices. Whereas current systems typically operate with, at most, a few hundred devices per base station, some M2M services might require over 104 connected devices. Examples include metering, sensors, smart grid components, and other enablers of services targeting wide area coverage.2.Very high link reliability. Systems geared at critical control, safety, or production, have been dominated by wireline connectivity largely because wireless links did not offer the same degree of confidence. As these systems transition from wireline to wireless, it becomes necessary for the wireless link to be reliably operational virtually all the time.3.Low latency and real-time operation. This can be an even more stringent requirement than the ones above, as it demands that data be transferred reliably within a given time interval. A typical example is Vehicle-to-X connectivity, whereby traffic safety can be improved through the timely delivery of critical messages (e.g., alert and control).Fig. 5 provides a perspective on the M2M requirements by plotting the data rate vs. the device population size. This cartoon illustrates where systems currently stand and how the research efforts are expanding them. The area R1 reflects the operating range of today’s systems, outlining the fact that the device data rate decreases as its population increases. In turn, R2 is the region that reflects current research aimed at improving the spectral efficiency. Finally, R5 indicates the region where operation is not feasible due to fundamental physical and information-theoretical limits.Regions R3 and R4 correspond to the emerging services discussed in this section:R3 refers to massive M2M communication where each connected machine or sensor transmits small data blocks sporadically. Current systems are not designed to simultaneously serve the aggregated traffic accrued from a large number of such devices. For instance, a current system could easily serve 5 devices at 2 Mbps each, but not 10000 devices each requiring 1 Kbps. R4 demarks the operation of systems that require high reliability and/or low latency, but with a relatively low average rate per device. The complete description of this region requires additional dimensions related to reliability and latency.There are services that pose simultaneously more than one of the above requirements, but the common point is that the data size of each individual transmission is small, going down to several bytes. This profoundly changes the communication paradigm for the following reasons:Existing coding methods that rely on long codewords are not applicable to very short data blocks. Short data blocks also exacerbate the inefficiencies associated with control and channel estimation overheads. Currently, the control plane is robust but suboptimal as it represents only a modest fraction of the payload data; the most sophisticated signal processing is reserved for payload data transmission. An optimized design should aim at a much tighter coupling between the data and control planes.As mentioned in Section II, the architecture needs a major redesign, looking at new types of nodes. At a system level, the frame-based approaches that are at the heart of 4G need rethinking in order to meet the requirements for latency and flexible allocation of resources to a massive number of devices. From the discussion above, and from the related architectural consideration in Section II, and referring one last time to the Henderson-Clark model, we conclude that a native support of M2M in 5G requires radical changes at both the node and the architecture level. Major research work remains to be done to come up with concrete and interworking solutionsenabling ‘M2M-inside’ 5G systems.VII.CONCLUSIONThis paper has discussed five disruptive research directions that could lead to fundamental changes in the design of cellular networks. We have focused on technologies that could lead to both architectural and component design changes: device-centric architectures, mmWave, massive-MIMO, smarter devices, and native support to M2M. 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