a survey of testing for 5g: solutions, opportunities,

China Communications • January 2019 69

Keywords: 5G; testing, channel modelling; over-the-air testing

I. INTRODUCTION

The mobile wireless communication technolo-gy is going through a revolutionary change ev-ery ten years. Reviewing the development his-tory of wireless communication system, each generation of mobile communication system can be defined by the symbolic competence indicators and key technologies. For example, the key technology of the first generation mo-bile communication system was the frequency division multiple access (FDMA), which can only support the voice service. Then, the sec-ond generation mobile communication system was charactered with time division multiple access (TDMA), which can supply the digital voice and low-speed data service. Next, the third generation mobile communication sys-tem was featured by code division multiple access (CDMA), which can provide the mul-timedia data services with different peak data rate per user. Nowadays, to further improve the spectral efficiency and increase the trans-mission rate, the orthogonal frequency divi-sion multiple access (OFDMA) was applied in the fourth generation mobile communication system (4G), which can provide various mo-

Abstract: With the development of wireless communication technology, the fifth gener-ation mobile communications system (5G) emerges at a historic moment and devotes it-self to open the curtain of the information age. Recently, in order to satisfy the requirement of different applications, various advanced 5G technologies have been developed in full swing. However, before applying these 5G related technologies in practical systems, ef-fective testing methods are needed to evaluate these technologies in a real, comprehensive, rapid and flexible manner. However, the test-ing methods are faced with new challenges along with the continuous development of the new 5G technologies. In this paper, we present a survey of 5G testing, including solutions and opportunities. In particular, two cases are considered, i.e., channel modelling and over-the-air (OTA) testing of antenna systems. Specifically, a non-stationary channel model is proposed to characterize and test massive mul-tiple-input multiple-output (MIMO) channel. In addition, we propose two probe subset se-lection algorithms for three-dimensional (3D) OTA testing, which minimizes the number of probe antennas while ensuring the accuracy of the target channel emulation. Finally, future research directions and challenges on 5G test-ing are given.

待聂老师补充Revised: Mar. 16,2018Editor: Shi Jin

REVIEW PAPERS

A Survey of Testing for 5G: Solutions, Opportunities, and ChallengesPing Zhang1,*, Xiaoli Yang1, Jianqiao Chen1, Yuzhen Huang1,2

1 State Key Laboratory of Network and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China2 Artificial Intelligence Research Center, National Innovation Institute of Defense Technology, Beijing 100166, China* The corresponding author, email: [email protected]

China Communications • January 201970

ios or use cases in three broad categories, named enhanced mobile broadband (eMBB), ultra-reliable and low latency communica-tions (URLLC), and massive machine type communications (mMTC). Since it is diffi-cult to satisfy these KPIs, different advanced technologies will be collaboratively applied in future 5G system as indicated in Table 1 [2]. For instance, millimeter wave (mmWave) can be deployed in hotspots and indoor areas [3], massive multiple-input multiple-output (MIMO) antenna system can simultaneous-ly boost the spectrum efficiency and energy efficiency of the system [4], heterogeneous ultra-dense networks enable ultra-high data rates and ultra-low latency [5], mobile edge computing (MEC) can reduce the latency by offloading tasks to local computations [6], and new network architecture may accommodate different services [7].Currently, the Third Generation Partnership Project (3GPP) plans to complete the Rel-15 in mid-2018 while to startup Rel-16. In addition, ITU is collecting these 5G candidate proposals which aims to

bile broadband data services with 100Mbps-1Gbps peak data rate per user.

However, with the rapid development of Mobile Internet and Internet of Things (IoT), the explosive growth of data traffic has made the existing mobile communication system difficult to support. Therefore, to tackle with this, the fifth generation of mobile commu-nication systems (5G) has been regarded as a promising solution, which can satisfy more diverse requirements than ever before [1]. The key performance indicators (KPIs) for 5G are defined by International Telecommunication Union (ITU): 1) The spectral efficiency is expected to increase by a factors of 5 to 15 compared to 4G; 2) To satisfy the demands of massive connectivity for IoT, the connectivity density target is ten times higher than that of 4G, i.e., at least 106/km2; 3) 5G is also expect-ed to satisfy the requirements of a low latency (radio latency ≤1 ms), low cost (≥100 times the cost efficiency of 4G), and support diverse compelling services. Hence, 5G is envisaged to support a diverse variety of usage scenar-

This paper provides a survey of testing for 5G and identified many of the corre-sponding technical challenges, including the new air interface, channel measurement and modelling, anten-na system, network architecture and NB-IoT.

Table I. The relationship between 5G key technologies and requirements.

Goals Technologies5G wireless access requirements

Spectrum efficiency

Peak rate

Peak rate of users

Delay MobilitySwitching

timeRegional capacity

Energy efficiency

Throughputenhancement

Millimeter wave √ √ √

Enhanced carrier aggregation √ √

Cognitive radio √ √ √

Enhanced small cell √ √

Large scale antennas (3D beam forming, massive MIMO...)

√ √ √

Modulation and coding techniques (high or-der modulation, advanced channel coding...)

√ √

Advanced multiple access technology (NOMA, FBMC, FTN...)

√ √

Advanced interference management (net-work coding, joint sending and receiving, SIC, interference alignment)

√ √

Flexibletopology

Advanced relay technology √ √

Wireless backhaul, mobile network √

Virtual cell (fast and seamless handover) √ √ √

Low cost andenergy con-

sumption

Wireless local area network and wireless personal area network based on joint trans-ceiver

√ √

D2D communication √ √


thermore, [12] elaborated on the first hand results and analysis of the large-scale field trials carried out in China. However, most of the previous works only focus on field trials, which are short on details, particularly on the new technologies and new equipment testing that in controllable and repeatable ways. Mo-tivated by these, in this paper, we provide a comprehensive discussion and summarization of 5G testing from key technologies, archi-tectures and application, such as the new air interface, channel measurement and model-ling, antenna system, network architecture and Narrow Band Internet of Things (NB-IoT). Among them, the paper focuses on channel modelling and antenna performance testing.

The rest of this paper is organized as fol-lows. In Section II, an overview of the testing for 5G is provided, which includes new air interface, channel, antenna system, network architecture and NB-IoT. Massive MIMO channel modelling and probe subset selection algorithms for 3D OTA testing are further in-vestigated in Section III. Finally, Section IV discusses future research directions and con-cludes this paper.

II. 5G TESTING OVERVIEW

According to the requirements of 5G sys-tem, 5G testing can be classified into the new

complete the technical specification in 2020. In order to bring these new technologies from theory to practice, many efforts have been de-voted to design systems to test these technol-ogies from both academia and industrial [8] - [10]. Notably, 5G testing is an essential step to verify these new technologies, starting from early research prototypes, and design optimi-zation. As indicated in [11], the benefits of 5G testing can be classified as:Exploring new waveforms, application sce-

narios, and network framework.Accelerating the development and produc-

tion of 5G products.Promoting the development of 5G stan-

dards and communications industry.In view of the importance of 5G testing,

many countries have developed a time sched-ule for 5G testing. For example, 5G technol-ogy research and development (R&D) testing will be carried out in 2016-2018 in China [12] [13], which consists of three stages, i.e., key technology testing, technology program verification and system verification. The first and second stage have now been completed and the third stage is under way. On the other hand, the U.S. National Institute of Stan-dards and Technology (NIST) has launched a new communications technology laboratory, which aims at identifying and developing new measurement sciences related to 5G wireless communications [14]. In addition, the 5G In-frastructure Association has designed a plan in 2016, which aims to establish a strategy for developing a Pan-European 5G Trials Road-map and prepare the comprehensive Trials Roadmap [15].

In the 5G era, the testing methods are faced with new challenges along with the continu-ous development of the new 5G technologies [8] [9] [12]. Specifically, in [8], the authors discussed new developments in channel mod-els and the current state of play in terms of achievable performance with current trials and testbeds. The 5G trials, developments status and field performance testing of manufac-turers, such as Nokia, Samsung, Qualcomm, were discussed and summarized in [9]. Fur- Fig. 1. 5G testing overview.

5GTechnology Testing

Application Testing

Architecture Testing

Equipment Testing

Advanced 5G Multiple Access

Adaptive Beamforming

Full Duplex Radio Technology

Channel model ……

Advanced Air Interface

Centralized Architecture

Network slicing ……

Next Generation Smart Antenna System

Wireless Terminal

Energy Aware Base Station ……

NB-IoT

D2D Communications

M2M Communications

Vehicular Communication

Health-Car & Wearable ……


proposals provided by different operators [8] [12] [18].

As an active contributor in ITU, China started 5G research in early stage through the program of China International Mobile Telecommunications (IMT)-2020. IMT-2020 designs three steps to test 5G techniques in China. The first step is to demonstrate the fea-sibility of various building block technologies and showed that it is feasible to achieve some ITU 5G requirements. In 2016, IMT-2020 has organized manufacturers such as Huawei, ZTE, Datang Telecom, and Nokia to complete the nation-wide 5G field trial in Chengdu, which aims to test the performance of new air interface technologies as in Table II. The de-tail testing results that compared to LTE-A are summarized as follows:The NOMA, e.g., sparse code multiple

access (SCMA) multi-user shared access (MUSA), achieves more than 86\% spectral efficiency improvement in the downlink. In addition, for the uplink, SCMA, MUSA and pattern division multiple access (PDMA) achieves 3 times about the number of con-nected devices.

As a new waveform, the filtered-OFDM (f-OFDM) can improve the system through-put in both uplink and downlink even if there exists the inter-band interference due to the asynchronous transmission.

Compared to Turbo, Polar Code obtain 0.3-0.6 dB in static and moving scenes, and combined with high frequency, achieve more than 20Gpbs rate.The second step is the technical scheme

verification that has been completed in 2017. Thereinto, the air interface technology in dif-ferent scenarios has different configurations e.g., the cell peak throughput >8.29Gbps in eMBB, latency is 0.4ms in URLLC and con-nection capability >4M/MHz/cell in mMTC. Furthermore, the third step has commenced in 2018, i.e., the system verification, which will launch typical application fusion test to promote 5G business and application develop-ment.

On the other hand, Nokia delivered a 5G

technology testing, architecture testing, new equipment and application testing as shown in figure 1. In this section, we provide a detail overview of 5G testing in terms of new air interface, channel, antenna system, network architecture and NB-IoT, respectively.

2.1 New air interface testing

The air interface is the key distinction between different generations of mobile communica-tion [16]. Like in 3G and 4G, the revolution-ary technologies of air interface are also intro-duced into 5G system. The objective of air in-terface of 5G is to achieve higher transmission rate, flexible access ways, improved spectral efficiency and better user experience [17]. In addition, it will support multiple services cov-ering enhanced mobile broadband and vertical industries, which bring about highly diverse requirements.

The key technologies of new air interface are waveform, channel coding, non-orthogo-nal multiple access, as well as massive anten-na techniques [12]. These technologies have been in progress for several years, developed by research institutes, vendors, operators and 5G related forums. Compared to 4G, new waveforms have been proposed to enhance the spectrum utilization and also facilitate flexible band sharing between different services. New multiple access schemes are designed to boost system efficiency and to support massive con-nectivity without increasing extra spectrum resource. Table II captures the main technical

Table II. Candidate technologies for 5G new air interface.Tech.

OperatorNew waveform

Non-orthogonalmultiple access

Channel coding

HuaweiFiltered-OFDM

(f-OFDM)Sparse code multiple

access (SCMA)Polar code

QualcommFourier transform

spread OFDM (DFT-S-OFDM)

Resource spread mul-tiple access (RSMA)

Low densityparity check code

(LDPC)

ZTEFilter bank-OFDM

(FB-OFDM)Multi-user sharedaccess (MUSA)

-

DatangTelecom

-Pattern division multi-

ple access (PDMA)-

NTT DOCOMO

-Non-orthogonal multi-

ple access (NOMA)-


in massive MIMO and mmWave channel mea-surements and modelling as well as identify-ing future challenges and research directions. As to some other channel characteristics, such as high-speed, dual-mobility, and vehicular traffic density (VTD), in vehicle-to-vehicle (V2V) and high-speed railway communication systems are omitted due to space limitation, which can be seen in [19] [20] [21] for a more comprehensive overview.

1) Channel characteristics:According to the channel measurements in

[22] and [23], some new characteristics should be considered for the massive MIMO channel. Firstly, the near-field region is always assumed to be less than or equal to the Rayleigh dis-tance defined by 2 /L2 λ, where L and λ denote the maximum antenna dimension and carrier wavelength, respectively. As a base station equipped with a large number of antennas significantly enlarges the aperture of antenna array, mobile stations (or scatterers) are more likely to locate within the Rayleigh distance. So, the wave-front should be assumed as spherical instead of plane to describe the near-field effect. Although channel modelling under spherical wavefront assumption proves to be accurate, the computational complexity is very high. In [24], the parabolic wavefront as-sumption is adopted to approximate spherical wavefront assumption in order to reduce the complexity. Secondly, spatial non-stationary properties of clusters refer to the phenomenon that different sets of clusters may be observed by different antenna elements along the large-scale array. Specifically, certain clusters are only visible to a part of the large antenna ar-ray caused by their locations, occlusion, and distance between them and the array. Thus, the wide-sense stationary (WSS) assumption of clusters for conventional MIMO is not satisfied for massive MIMO channels. This character is often described by two kinds of algorithms, namely birth-death process and visibility region method. In [25], the author extended WINNER+ standard channel model by adopting the birth-death process to describe spatial non-stationary properties of clusters,

radio access system, and conducted the trial at the AT&T Labs facility in Middletown, New Jersey. Ericsson and Qualcomm Technologies, a subsidiary of Qualcomm Incorporated, an-nounced that they are working with Vodafone to test 5G interoperability and conduct an over-the-air field trial. The trial, taking place in the United Kingdom, will showcase 5G new radio (NR) technologies that utilize wide bandwidths to increase network capacity and achieve multigigabit-per-second data rates.

Polar code and low density parity check code (LDPC) code have been accepted by 3GPP as channel coding schemes for control and data channels, respectively. Meanwhile, Cyclic Prefix (CP)-OFDM based waveform also has been accepted by 3GPP to support eMBB and URLLC services at least up to 40 GHz. Moreover, the other technologies have not yet been determined. Therefore, the tech-nology which will form the basis of the global 5G new air interface standards leads to fierce competition among the producers. It is expect-ed that 5G NR will use a unified air interface technology, this is not only a challenge to test-ing, but also to technology.

2.2 Channel measurements and modelling

Recently, research and development works on 5G systems are ongoing, which aims to meet the requirements of higher data rates, lower latency, more reliable connectivity, etc. [8]. As stated above, these capabilities are expect-ed to be realized via some new technologies. mmWave frequencies are used to offer unprec-edented spectrum and high data rates for trans-mission. Massive MIMO, equipped with tens or even hundreds of antennas to serve users in the same time-frequency slot, will enormously improve the spectral efficiency and capacity.

The above-mentioned technologies set new requirements to further researches into channel characteristics for the sake of 5G system de-sign and performance evaluation, in particular those that have not already been explored for earlier-generation systems. This section pro-vides a brief review of the latest developments


sliding correlator based direction-scan-sound-er, are always be adopted. In order to extract the channel multipath parameters, such as am-plitudes, delays, angle of departures (AODs) and angle of arrivals (AOAs), the joint esti-mation algorithm based on Space-Alternat-ing Generalized Expectation-maximization (SAGE) is often utilized in step 3, which is crucial for realize the channel modelling.

Measurements for massive MIMO channels mainly focus on benefi ts and effects caused by the enormously number of antennas, which re-sults in a signifi cant spatial non-stationarity of the parameters of propagation paths. On one hand, by referring to the angular power spec-trum over the array in the measured channels, spherical wave-fronts over the large array were observed [22]. Lots of researches show that spherical wave assumption has signifi cant effects on the channel response matrix and capacity [30] [31]. On the other hand, in [23], measurements demonstrated that the AoAs and powers of multipath components (MPCs) evolve along the antenna array by using both a virtual linear array with 128 antenna elements and a physical cylindrical array in outdoor scenarios. The birth death process of multipath clusters is observed in angular domains with respect to antenna locations on the array axis [32]. Based on the measurements, although a lots of analysis models based on geome-try-based stochastic channel models (GSCM) have been proposed, there is no widely accept-ed model so far.

Channel measurement campaigns in mmWave bands, such as 28 GHz, 60 GHz and 70-73 GHz have been widely conduct-ed recently, and some popular international project groups, such as 3GPP, METIS, Mi-WEBA, mmMAGIC, 5GCM, have released some standard channel models [33] - [37]. As a whole, mmWave channel measurements are conducted to investigate high attenuation and poor diffraction effects. First, measurements have demonstrated that the pathloss coeffi cient and variance around the distance-dependent mean are considerably large at mmWave fre-quencies, which result in higher probability of

which improves the accuracy of standard channel model. However, it increases the com-plexity of channel model.

Compared with sub-6 GHz frequency bands, mmWave bands suffer from addition-al high path loss and poor diffraction under different propagation effects. Thus, these at-tenuation and dispersion characteristics have significant effects on system performance. According to the extensive measurements and publications [26], some representative propa-gation characteristics, such as large-scale path loss (including free space pathloss, atmospher-ic attenuation, vegetation attenuation, outdoor to indoor (O2I) penetration), shadow fading (SF), probability of line-of-sight (LOS), and spatial consistency, are measured. Correspond-ingly, the studies of channel characteristics of mmWave bands mainly focus on modelling large-scale path loss, O2I penetration loss and probability of LOS [27]. Additionally, mmWave channels are sparse. In other words, the percentage of delay/angle bins with sig-nifi cant energy is rather low in many environ-ments.

2) Channel measurements and models:A block diagram is illustrated in figure 2

with three steps of the procedure to carry on channel measurements. The steps include: a) selection of test scenarios, b) planning and im-plementation of the measurement, c) test data processing and analysis. Generally, the step 2 consists of selecting the appropriate type of antenna and measuring platform. Compared with the actual antenna array, virtual antenna array is often used for channel measurements due to simplicity and low cost, especially for the massive MIMO channel measurements [28] [29]. Two kinds of measuring platforms, namely vector network analyzer (VNA) and

Fig. 2. A block diagram of the procedure of channel measurements.

selection of test scenarios

planning and implementation of the measurement

Test datatest data processing

and analysis


mobility, vehicular traffic density and compli-cated distribution of scatterers. In conclusion, there still needs further research to overcome these deficiencies.

2.3 Antenna system testing

Antenna system testing is an essential step of the radio performance evaluation. Conducted testing has been widely used before 4G. The test system configuration of conducted testing is relatively simple, only need to replace the antennas from antenna connectors by radio frequency (RF) cables. Of the wide palette of 5G features the special interest to this section are the mmWave frequency bands and massive MIMO antenna arrays. Accordingly, some researchers predicted that conducted testing is no longer applicable for 5G antenna systems [38] [39]. For massive MIMO antenna system, it may be equipped with hundreds of antenna elements. Therefore, hundreds of RF cables are required to connect the antenna system in conducted testing, it makes the whole testing system both complex and costly. For antenna system in mmWave frequency bands, it would be nearly impossible to test with RF cables, because the antenna system in mmWave fre-quency bands is becoming more miniaturized and integrated, the antenna connector may not be used. Furthermore, the antenna array signal processing, e.g., beamforming, null steering, cannot be evaluated in the conducted setup.

Currently, OTA testing of the antenna per-formance is one of the most promising testing techniques for 5G. Compared to the conducted testing, it has a great advantage, namely, it can reproduce desired channel environment in a controllable, debuggable and repeatable way in lab. OTA test methods for MIMO capable mobile terminals have been developed and researched for many years [40] [41]. Where several OTA methods were proposed, e.g., the reverberation chamber (RC) [42], the radiated two-stage (RTS) [43], and the multiprobe an-echoic chamber (MPAC) method [44]. In this section, basic principles of OTA test methods and their applicability to 5G antenna systems are discussed.

outage. Second, a large range of delay spreads have been measured in outdoor and indoor en-vironments. Third, the O2I penetration taking account of the building penetration loss has been studied a lot.

3) Challenges and research directions for channel measurements:

Versatile scenarios, large bandwidth, high frequency band and numerous antennas pose great challenges for massive MIMO and mmWave channel measurements. Firstly, in 5G, there are various and complicated scenari-os, such as indoor-hotspots, O2I, urban micro-cell (UMi) and urban macrocell (UMa), which make it difficult to conduct channel measure-ments comprehensively. Secondly, different antenna configurations are of critical impor-tance for the performance of channel sounder, which further affect the accuracy of collecting raw measurement data enormously. So far, uniform linear array (ULA) configuration is commonly used in channel measurement. So, other types of antenna configurations, such as spherical, cylindrical, and rectangular antenna arrays, need to be adopted to conduct channel measurements in practice. Meanwhile, as an-tenna arrays of massive MIMO system tend to be miniaturized, the mutual coupling effect be-comes a serious factor that affects the channel capacity. However, as to the commonly used virtual multi-antenna array configurations, the mutual coupling effect cannot be consid-ered. So, channel measurement with a large physical antenna array is required. Thirdly, different types of antenna configurations, large bandwidth and high frequency bands together challenge the design of channel sounder and collection of channel measurement data, such as generation and reception of high frequency signals, mass data storage, channel detection speed and system calibration. Last, incorpo-rating massive MIMO and mmWave technol-ogies into other 5G scenarios, such as high-speed train (HST) and V2V communication, can improve wireless access and throughput tremendously. But, it poses greater challenges to channel measurement in order to obtain accurate channel characteristics, such as high


in 3GPP [40]. The RTS method is based on the two steps. The fi rst step is to measure the MIMO device complex antenna patterns. In the second step, according to the measured an-tenna patterns, solve the inverse matrix of the calibration matrix, then with the measured pat-terns and the inverse matrix applied, conduct the throughput measurement. The objective is to generate signals that the spatial fi eld is same with the MPAC method. RTS may in principle support also for massive MIMO, but it has two drawbacks. Firstly, it is not well suited for adaptive antenna systems, e.g., analog beam-forming. Secondly, the second step which required probe and fading emulator resource are directly proportional to the number of de-vice under test (DUT) antennas. The test setup may become non-feasible, if the DUT with tens or hundreds antennas. Nonetheless, 3GPP currently discuss the possibility of using RTS method with no antenna pattern, but it requires that the DUT has reporting capabilities [45].

3) MPAC:The MPAC based method, which is stan-

dardized in CTIA [41] for its capability of reproducing standard channel models, i.e. GSCMs as described above. The 2D setup, as shown in fi gure 3, based on a fading emulator, an anechoic chamber and a number of OTA probe antennas. The signals emitted from the probe antennas are controlled in the spatial channel emulator such that the emulated chan-nels experienced by the DUT will mimic the target channel models. It is expected that the MPAC has the highest potential for being the 5G antenna system OTA testing, not only for massive MIMO, but also for mmWave. The challenge with MPAC method is to realize a test zone that is large enough to enclose a large DUT, while at the same time keeping the number of OTA probes and the number of fad-ing generators low.

The OTA techniques with MPAC meth-od for massive MIMO or mmWave device evaluation have been discussed in [38] [39] [46] [47]. [38] specifi ed a sectored 3D probe configuration for massive MIMO testing, as illuminated in fi gure 4, the probe antennas are

1) RC:An RC is an enclosed metallic cavity typ-

ically equipped with metallic paddles and turntables. It is today a well proven method to characterize antenna performance. The prin-ciple of the method is to revolved the mode stirrers, the electromagnetic modes inside the chamber create a large set of random simulta-neous incoming waves that impinge into the test zone. This creates a fading process which is very similar to theoretic Rayleigh fading. The method is useful to characterize antenna radiation parameters that are independent of angular spread. However, RC method might be less suitable for OTA testing of massive MIMO and adaptive mmWave antenna sys-tems, because the channel models of mas-sive MIMO and mmWave system are spatial profiles, in particular, mmWave channels are highly sparse as discussed above. Neverthe-less, researchers are currently investigating the technology possibility of using highly anisotropic environments with RC method for testing directional channel [14].

2) RTS:RTS method has been approved as an al-

ternative MIMO OTA test method to MPAC

Fig. 3. The system setup of OTA testing for UE.

Fig. 4. The system setup of OTA testing for massive MIMO BS.

BS Emulator

SpatialChannelEmulator

Test Area

Probe Probe Antenna Antenna Antenna

UE

UE/Signal Tester

SpatialChannelEmulator

Anechoic Chamber

Probe Antenna ArrayProbe Antenna ArrayProbe Antenna ArrayProbe Antenna Array

Massive MIMO BS


sion of cloud computing paradigm from the core network to the edge network. Secondly, Traditional BS consists of a baseband unit (BBU) and a remote radio unit (RRU), and C-RAN architecture could facilitate the imple-mentation of cooperative algorithms such as Coordinated Multi-point (CoMP) to improve spectral efficiency for wireless networks [49] [50]. While in 5G, a new structure with central unit (CU) and distributed unit (DU) is pro-posed and has been endorsed by 3GPP. Mean-while, it can provide different communication services by deployment CU and DU with inte-grated and distributed architecture. Recently, service based-architecture (SBA) has been ac-cepted by 3GPP as the 5G core network, that was proposed by China Mobile combined with 26 companies (14 operators and 12 network equipment vendors) in May 2017.

The key techniques that are not shown in the diagram are described below. Network slice is considered as the key characteristics of network function virtualization (NFV) appli-cation in 5G era. According to the 5G scene, it can be divided into slice for eMBB, URLLC, mMTC and so on. In particular, a network slice will build an end-to-end logical network to provide one or more network services flex-ibly. Furthermore, as a key technology, the control plane (CP) / user plane (UP) separa-tion proposed by IMT-2020 has also entered

arranged in a sector on one side of the anecho-ic chamber. It also presented simulation results for the minimal physical dimensions of the setup. Numerous figures of merit were used in the evaluations, from direction of arrival esti-mation accuracy up to multi-user MIMO sum rate capacity error. Work on the probe config-urations was continued in [46], simulations were performed with 2D probe configurations only with two channel model scenarios used for 4G evaluations (SCME UMi and UMa). Furthermore, literature [47] described methods for mapping radio channel models onto the probe configuration and discussed the differ-ences to the former 4G case. As for mmWave antenna system, to explore the spatial sparsity of mmWave channel profiles, a cost-effective simplified 3D sectored MPAC system with an OTA antenna selection scheme was proposed in [39]. Several metrics to validate system performance were described for evaluation of mmWave devices, including both BS and user equipment (UE).

The radiated OTA testing methodology is essential for performance evaluation of 5G antenna systems. The MPAC mothed is tech-nically sound for OTA testing of 5G antenna systems. However, a major concern is its system cost and and the calibration procedure for practical setups when scaled to mmWave channels.

2.4 Other key techniques testing

1) Network architecture:Due to the extreme 5G requirement on ex-

perience, efficiency and performance, as well as the vision of “everything connected”, 5G network architecture now is facing new chal-lenges and opportunities [48].

Compared to 4G network architecture, 5G network architecture will not only use some technologies of 4G, but also adopt some new solutions and technologies. There are two major changes in 5G network architecture as shown in figure 5. Firstly, the evolved packed core (EPC) of 4G is split between new core and MEC in 5G. Besides, as an alternative for latency-intolerant services, MEC is an exten- Fig. 5. The difference between 4G and 5G network architecture.

4G Network Architecture 5G Network Architecture Changes

EPC

Cloud BBU

RRU RRU

CU

DUCU+DU

AAU AAU AAU AAU

C-RAN

C-RAN

Backbone NetworkBackbone Network

Metropolitan Area Network

Metropolitan Area Network

backhaul

middlehaul

fronthaul

fronthaul

backhaul

New Core

MEC MEC

MEC


erage range of 20 dB compared to legacy GPRS systems.

Low power consumption. The battery life of IoT devices reaches up to 10 years with 5 Watt-hours battery capacity and 120 min-utes for uplink reporting interval.

Support for a massive number of devices. The number of devices supported per cell is around 55,000.

Ultra low device cost. IoT devices should be very cheap such that they can be de-ployed in mass or even in disposable man-ner.In recent years, NB-IoT has developed rap-

idly. As an active participant in NB-IoT, Hua-wei has introduced the commercial NB-IoT chips. In order to serve the NB-IoT end-to-end business better, it also built a dedicated open lab. Meanwhile, Qualcomm believes that the massive IoT will depend on the LTE NB-IoT technology, if it keep the advantages of low cost, low power consumption, etc. It also pre-dicted that the technology development will lay a solid foundation for IoT in the next five years. In addition, mainstream operators in many countries, such as China, South Korea, Europe, and North America, have launched trials of NB-IoT technology.

In order to speed up the commercialization, and to achieve ultra-low communication mod-ule costs of NB-IoT, testing is essential. But apart from the test methods for the technical characteristics themselves, the biggest differ-ence between NB-IoT and any previous mo-bile communication test scheme is to reduce the test cost significantly.

NB-IoT testing and certification solutions include chip test, module test and terminal test. Among them, to ensure the basic wire-less communication function, NB-IoT chip test adopts conformance test by 3GPP. Key functions and performance test, such as power consumption and RF performance, are covered by NB-IoT module test. Furthermore, NB-IoT functional test focuses on antenna perfor-mance.

A typical test scheme is to integrate many test functions into a suite of systems, such

the 3GPP standard.Currently, 5G network architecture is in

the key technology research and verification stage. To verify the feasibility of the proposed technology of different solutions, some ven-dors such as Huawei, Ericsson, ZTE, Intel and Datang completed the tests in 3 months. The testing contents of different technologies mainly include:Network slicing. Virtualization platform

test, basic slice LCM and performance.MEC. Prove efficiency in reducing data

latency, and support network service expo-sure.

CP/UP separation. Verify CP/UP interface solutions.

Network functions reconstruction. Test a simple prototype of SBAThese tests will help to form the standard

consensus in 5G network. Moreover, IMT-2020 released the 5G network technology test specification in June 2017. Among them, the core network subsystem has been designed 39 test cases, including the SBA architecture, enhanced core network function, network slic-ing, MEC and a series of platforms. Moreover, the specific test contents include functional testing, performance testing, platform capabil-ity testing, etc.

For standardization work, 3GPP SA2 has established a study item called NextGen (TR 23.799), which is responsible for the Rel-14 phase of 5G network architecture standard-ization. Overall 5G network architecture stan-dardization work will be completed by Rel-14/15/16 versions.

2) NB-IoT testing:NB-IoT is proposed to provide IoT func-

tionality using a cellular radio access system. Standardization of NB-IoT was completed during 3GPP’s RAN\#72 plenary meeting, and all features that are part of Release 13 have been frozen [51] [52]. Compared with other IoT technologies, some of the main benefits of NB-IoT [53] are:Coverage enhancement. The aim is to offer

a data rate of at least 160 bits per second in uplink and in downlink at extended cov-


k-th antenna of the receive array. Examples are given in figure 7. On one hand, Clustern+1 is observable to Antl

T but it is not observable to Antk

R. Conversely, Clustern+2 is observable to Antk

R but it is not observable to AntlT On the

other hand, Clustern is observable to both the Antl

T and AntkR. Thus, these situations imply

that different antenna elements may observe different sets of clusters.

In our scheme, a novel channel model is proposed for massive MIMO communication systems by incorporating spherical wave-front assumption and non-stationary properties of clusters on both the array and time axes [54]. Spherical wave-front is assumed to character-ize near-field effects resulting in AoA shifts and Doppler frequency variations on the antenna array. Additionally, a novel visibili-

as protocol conformance test, radio resource management (RRM) performance test and RF performance test. As shown in the figure 6 this scheme can effectively reduce the cost of the terminal from production to commercial, while shortening test cycle and maintain a higher test efficiency.

However, some characteristics of the NB-IoT will bring great challenges to the test. Test scenarios and terminal forms are diverse, because it will support different vertical in-dustries, such as wireless exceeded, environ-mental monitoring, smart home, etc.. A large number of connection terminals and it special working methods will also exacerbate the difficulty of the test, for example, in some sce-narios, the device works only once a day, and the rest time is in sleep mode. Moreover, the security of NB-IoT is also a big challenge for testing.

III. TESTING ENVIRONMENT AND DESIGN

3.1 A brief description of proposed massive channel model

Our proposed channel model for massive MIMO is illustrated in figure 7. When calcu-lating the channel impulse response, two im-portant characteristics, namely spherical wave assumption and non-stationary properties of clusters, are considered. First, an example of the n-th cluster of the receive antenna array under the spherical wave-front assumption is shown in figure 7. The spherical wave-front of each wireless link resulting in AoA shifts and the Doppler frequencies on the antenna array are no longer the same for each antenna element, and they should therefore be deter-mined by geometrical relationships. Second, the non-stationary properties of clusters on the antenna array mean that a cluster may only be observed by a partial set of antennas on the antenna array in massive MIMO channel models. Let Antl

T represent the l-th antenna of the transmit array and Antk

R represent the

Fig. 6. An illustration of integrated general test system.

Fig. 7. An illustration of our proposed channel model for massive MIMO systems.

Power Supply

RF Switching Box

CW Signal Generator

Spectrum Analyzer

(1) System Simulator(2) Vector Signal Generator(3) AWGN Generator(4) Channel Emulator

NB-IoT Wireless Test Set

DUT 1

DUT 2

Protocol Test

NS-IOT

RRM Test

RF Test

NB-IoT Terminal Integration Test SystemSystem Manage

Computer

nCluster

TlAnt

RkAnt

1nCluster

2nCluster


ber of rays that captures channel characteris-tics as accurate as possible is proposed. Last, the impacts of cluster evolution and spherical wave-front assumption on the statistical prop-erties of the channel model are investigated. As an example, the absolute receiver spatial cross-correlation function (CCF) and auto-cor-relation function (ACF) of channel model are shown in fi gure 8 and fi gure 9, respectively. M represents the number of rays within a cluster, and other detailed simulation parameters re-fer to [54]. In conclusion, numerical analysis shows that our proposed non-stationary chan-nel model is effective to capture characteristics of massive MIMO channel.

3.2 Probe subset selection in 3D multiprobe OTA setup

To emulate a 3D realistic environment for massive MIMO and mmWave antenna sys-tems, 3D OTA test setup both over elevation and azimuth dimensions is essential. More specifically, the number of probe antennas needed for 3D OTA test is more than 2D, which will increase the output ports of the channel emulator. But the number of available output ports of the channel emulator is limited, several channel emulators are often required, which will dramatically increase the setup cost and complexity. Therefore, finding the right way to limit the number of probe antennas while still ensuring the effective emulation accuracy of the target channel will make the implementation of the test system simpler and cheaper.

Very few contributions have been addressed on this issue. In [55], three probe subset se-lection algorithms were proposed. The goal of the three algorithms was to fi nd a certain num-ber of probe subsets. However, they do not minimize the number of selected probe sets. Motivated by these problems, we propose two probe subset selection algorithms [56], where the goal is to minimize the number of probe antennas while ensuring the accuracy of the target channel emulation.

1) Probe weighting:We use the prefaded signals synthesis

ty region method is proposed to capture the non-stationary properties of clusters at the re-ceiver side, and combined with the birth-death process, a novel cluster evolution algorithm is proposed.

Meanwhile, corresponding to the theoretical model, a simulation model with a fi nite num-

Fig. 8. Absolute Rx spatial CCF of the proposed channel model in terms of the number of rays within the cluster at the Rx side (M R = ×(32 32), MT = ×(32 32), t s=1 , a m1 =120 , f m=100 , σ = °10 , b =σ / 2 , α π= / 3 , β π= / 3 , γ π= / 3 , λ = 0.15m, f Hzmax = 66.66 , θ πv = / 6, ξ πv = / 6, Laplacian distribution, NLOS).

0 0.5 1 1.5 2 2.5

Normalized antenna spacing

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Rec

eive

r abs

olut

e sp

atia

l CC

F

Theoretical modelM=90M=50M=30

Fig. 9. Absolute Rx temporal ACF of the proposed channel model in terms of the number of rays within the cluster at the Rx side (M R = ×(32 32), MT = ×(32 32), t s=1 , a m1 =120 , f m=100 , σ = °10 , b =σ / 2 , α π= / 3 , β π= / 3 , γ π= / 3 , λ = 0.15m, f Hzmax = 66.66 , v m sc =10 / , θ πvc

= / 3, ξ πvc= / 6, Laplacian distribu-

tion, NLOS).

0 0.01 0.02 0.03 0.04 0.05

Time difference

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Rec

eive

r abs

olut

e te

mpo

ral A

CF

Theoretical modelM=90M=50M=30


number of probes in middle ring is the sum of of those in the upper and lower rings. Fur-thermore, the upper and lower elevation rings are symmetrical about the middle ring, and the elevation angle of the upper and lower rings are ± °18 . For simplicity, the distance between the location pair, i.e., the test volume size sets 1λ. A single cluster channel model is adopted in this part. The power azimuth spectrum and power elevation spectrum are both Laplacian distribution. Azimuth angle of arrival and ele-vation angle of arrival are 15° and 0°, and the angle spread of them are 35° and 10°, respec-tively.

Figure 10(a) show the target spherical power spectrum for target channel model, Fig. 10(b) show the emulated spherical pow-er spectrum with all probe and the selected subset probe by two selection algorithms. The threshold of RMS error sets to ε 0.05= .

(PFS) technique to emulate the target channel models, as detailed in [44]. The idea of a PFS is to radiate independent fading signals from multiple probes on the basis of power weights determined by the emulated channel, and then to obtain optimum power weights to recreate the target channel spatial characteristics. It is able to create radio propagation environments for OTA testing. The objective functions of PFS can be written as to minimize the summa-tion over the total reconstruction errors across all the location pairs in the test zone

min . ( )

s t g. . 1, 0, ,G

G G ΛG

ρ G ρˆ

1= ≥ =

−

k

2

(1)

where Λ = diag cos ,cos( θ θ1， K ) is diagonal matrix. The average power weight of each probe is expressed as G = (g g1, , K )

T, ρ and ρ̂ is the target spatial correlation and the re-constructed spatial correlation, respectively.

The emulation accuracy of the OTA setup can be measured as RMS correlation error be-tween ρ and ρ̂ over L location pairs within a test area

ε ρ ρρ = −L1∑

l=

L

1

ˆ ( ) ( ) .l l (2)

2) Probe subset selection algorithm:As a benchmark, reducing the number of

probes will lead to the reduction of channel simulation accuracy.

The channel emulation with all available probes can be simply treated as a performance upper bound. Decremental selection algorithm (DSA) selects probe subset in a decremental manner, and the detailed process is summa-rized in Algorithm 1.

Error threshold selection algorithm based on alternating search (SAAS) uses the idea of alternating search. In each iteration, the probe with the least emulation accuracy is selected, the probe with the largest emulation is removed to reduce the range of search. The detailed process is summarized in Algorithm 2.

3) Probe subset selection simulation results:In the simulation of this article, the total

number of available probe is K = 48, and the

Algorithm 1. Decremental selection algorithm.

1: Input: Porbe index vecter Ω, performance upper bound, threshold ε2: Output: Selected probe subset vector S, ερ

3: while ε ερ ≥ do

4: Remove the probe with least power values5: Update selected probe index vector S6: Compute theoretical spatial correlation ρ and Q7: Solve (1) and compute emulated spatial correlation ρ̂8: Compute ερ

9: end while

Algorithm 2. Error threshold selection algorithm based on alternating search.

1: Input: Porbe index vecter Ω, performance upper bound, threshold ε, S =∅2: Output: Selected probe subset vector S, ερ

3: Select the probe with largest power values n1, S S= ∪ n1, Ω Ω= \ n1

4: while ε ερ ≥ do

5: for n=1:1:length(Ω) do6: Update S S= ∪Ω (n)7: Compute theoretical spatial correlation ρ and Q8: Solve (1) and compute emulated spatial correlation ρ̂9: Compute ερ (n)10: end for11: Select the probe with least ερ while remove the probe with largest ερ

12: Update S and Ω13: end while


IV. CONCLUSIONS AND FUTURE DIRECTION

This paper provides a survey of testing for 5G and identifi ed many of the corresponding technical challenges, including the new air interface, channel measurement and model-ling, antenna system, network architecture and NB-IoT. In particular, we give two important aspects for 5G testing, namely channel model-ling and OTA testing design. Furthermore, we also identify several key challenges and open issues for future research.

With standardization and development of various advanced communication technologies in 5G systems, it poses great challenges for 5G testing. The major open issues are as follows.Fundamental understanding of propagation

characteristics of the underlaying channel is critical for evaluating and optimizing the performance of 5G communication systems. Therefore, the impacts of all the channel characteristics need further study through channel measurement and channel modelling, especially for massive MIMO and mmWave channels.

Mass ive MIMO an tenna sys tem in mmWave frequencies will be tested by the OTA method, presenting a huge challenge to the test environment, equipment and methods.

5G will adopt the new network architec-ture, namely the SBA, which brings new demands and challenges for multi-vendor environment interworking, interoperability and business applications testing.

5G network will support many new appli-cations, such as NB-IoT and vehicular com-munication, the test of these applications is also a key part of the 5G test.In conclusion, testing is of great signifi-

cance for driving the development of 5G, es-pecially for standardization and development of various 5G technologies. our survey will provide a concise review of the recent advanc-es in the development of 5G testing, so that researchers have a comprehensive grasp of the research progress.

As shown in fi gure 10, the shape of the emu-lated discrete spherical power spectrum with all probe matches well with the shape of the continuous target spherical power spectrum. The probe with relatively large power values are all selected based on both two selection algorithms, and only a few probes with small power values are selected differently. Above all, the number of selected probe by DSA is 10, but SAAS is 9. It means that the number of probe selected by SAAS is less than DSA. In the MPAC setup, confi guring one probe less will save two ports of the channel simulators as well as some hardware overhead. Therefore, the performance of SAAS is better than DSA.

Fig. 10. Target and emulated spherical power spectrum.

090

60

0.5

360

Nor

mal

ized

SP

S

30

Elevation [degree]

0 240

Azimuth [degree]

1

-30120-60

-90 0

020

0.08

0.16

10 400

0.24

0.32

300

Elevation [degree]

0

0.4

Azimuth [degree]

200-10 100

-20 0

No SelectionDSASAAS

(a) Target spherical power spectrum

(b) Emulated spherical power spectrum with probe subset selection algorithms


Lu, B. Zhang, L. Hu, L. Gu, B. Li, H. Yang, H. Shen, T. Tian, Z. Luo, and K. Wei, “Introduction on IMT-2020 5G trials in China,” IEEE J. Sel. Areas Commun., vol. 35, no. 8, pp. 1849-1866, Aug. 2017.

[13] IMT-2020 Promotion Group, [Online]. Available: http://www.imt-2020.org.cn/

[14] K. A. Remley et al., “Measurement challenges for 5G and beyond: An update from the Na-tional Institute of Standards and Technology,” IEEE Microw. Mag., vol. 18, no. 5, pp. 41-56, Jun. 2017.

[15] 5G-PPP Trials Roadmap Version 2.0, [Online]. Available: https://5g-ppp.eu/5g-trials-roadmap/

[16] M. Agiwal, A. Roy, and N. Saxena, “Next gener-ation 5G wireless networks: A comprehensive survey,” IEEE Commun. Surveys Tuts., vol. 18, no. 3, pp. 1617-1655, Feb. 2016.

[17] S. Y. Lien, S. L. Shieh, Y. Huang, B. Su, Y. Hsu, and H. Wei, “5G new radio: waveform, frame struc-ture, multiple access, and initial access,” IEEE Commun. Mag., vol. 55, no. 6, pp. 64-71, Jun. 2017.

[18] L. Dai, B. Wang, Z. Ding, Z. Wang, S. Chen, and L.Hanzo, “A Survey of Non-Orthogonal Multiple Access for 5G,” IEEE Commun. Surveys Tuts., 2018. (Early Access)

[19] C. Wang, J. Bian, J. Sun, W. Zhang, and M. Zhang, “A Survey of 5G Channel Measurements and models,” IEEE Commun. Surveys Tuts., 2018. (Early Access)

[20] P. Harris, S. Malkowsky, J. Vieira, E. Bengtsson, F. Tufvesson, W. B. Hasan, L. Liu, M. Beach, S. Armour, and O. Edfors, “Performance charac-terization of a real-time massive MIMO system with LOS mobile channels,” IEEE J. Sel. Areas Commun., vol. 35, no. 6, pp. 1244-1253, Apr. 2017.

[21] J. Zhao, L. Wang, K. Wong, M. Tao, and T. Mah-moodi, “Energy and Latency Control for Edge Computing in Dense V2X Networks,” arXiv pre-print arXiv: 1807.02311, Jul. 2018.

[22] S. Payami and F. Tufvesson, “Channel measure-ments and analysis for very large array systems at 2.6 GHz,” in Proc. EUCAP, pp. 433-437, Mar. 2012.

[23] X. Gao, F. Tufvesson, O. Edfors, and F. Rusek, “Measured propagation characteristics for very-large MIMO at 2.6 GHz,” in Proc. ASILOMAR, pp. 295-299, Nov. 2012.

[24] C. F. Lopez and C. X. Wang, “Novel 3D Non-sta-tionary Wideband Models for Massive MIMO Channels,” IEEE Trans. Wireless Commun., vol. 17, no. 5, pp. 2893-2905, Mon. 2018.

[25] J. Bian, J. Sun, C. X. Wang, R. Feng, J. Huang, Y. Yang, and M. Zhang, “A WINNER+ Based 3-D Non-Stationary Wideband MIMO Channel Model,” IEEE Trans. Wireless Commun., vol. 17, no. 3, Mar. 2018, pp. 1755-1767.

[26] T. S. Rappaport, Y. Xing, G. R. MacCartney, A.

ACKNOWLEDGEMENTS

This work was supported by the National Natural Science of Foundation for Creative Research Groups of China (No. 61421061).

References[1] Minimum Requirements Related to Technical

Performance for IMT-2020 Radio Interface(s), document ITU-R M. [IMT-2020.TECH PERFREQ], Oct. 2016.

[2] C. X. Wang, F. Haider, X. Gao, X. You, Y. Yang, D. Yuan, H. M. Aggoune, H. Haas, S. Fletcher, and E. Hepsaydir, “Cellular architecture and key technologies for 5G wireless communication networks,” IEEE Commun. Mag., vol. 52, no. 2, pp. 122-130, Feb. 2014.

[3] T. S. Rappaport, S. Sun, R. Mayzus, H. Zhao, Y. Azar, K. Wang, G. N. Wong, J. K. Schulz, M. Samimi, and F. Gutierrez, “Millimeter wave mobile communications for 5G cellular: It will work!,” IEEE Access, vol. 1, pp. 335-345, May 2013.

[4] L. Lu, G. Y. Li, A. L. Swindlehurst, A. Ashikhmin, and R. Zhang, “An overview of massive MIMO: Benefits and challenges,” IEEE J. Sel. Areas Com-mun., vol. 8, no. 5, pp. 742-758, Oct. 2014.

[5] L. Wang, K. K. Wong, S. Jin, G. Zheng, and R. W. Heath Jr., “A new look at physical layer security, caching, and wireless energy harvesting for het-erogeneous ultra-dense networks,” IEEE Com-mun. Mag., vol. 56, no. 6, pp. 49-55, Jun. 2018.

[6] Y. Yu, “Mobile edge computing towards 5G: Vision, recent progress, and open challenges,” China Commun., vol. 13, pp. 89-99, 2016.

[7] V. Nguyen, A. Brunstrom, K. Grinnemo, and J. Taheri, “SDN/NFV-based mobile packet core network architectures: A survey,” IEEE Commun. Surveys Tuts., vol. 19, no. 3, pp. 1567-1602, Apr. 2017.

[8] M. Shafi, A. F. Molisch, P. J. Smith, T. Haustein, P. Zhu, P. D. Silva, F. Tufvesson, A. Benjebbour, and G. Wunder, “5G: A tutorial overview of standards, trials, challenges, deployment, and practice,” IEEE J. Sel. Areas Commun., vol. 35, no. 6, pp. 1201-1221, Apr. 2017.

[9] M. Pätzold, “5G developments are in full swing [Mobile Radio], “ IEEE Veh. Technol. Mag., vol. 12, no. 2, pp. 4-12, May 2017.

[10] M. Rumney, P. Cain, T. Barratt, A. L. Freire, W. Yuan, E. Mellios, and M. Beach, ”Testing 5G: Evolution or revolution?,” Radio Propagation and Technologies for 5G (2016), pp. 1-9, Oct. 2016.

[11] Y. Yang, J. Xu, G. Shi, and C. Wang, “Evolution of Testing Technology,” in 5G Wireless Systems, Springer, Cham, 2017, pp. 21-44.

[12] L. Dong, H. Zhao, Y. Chen, D. Chen, T. Wang, L.


Millimeter-Wave Beam-steerable Devices in a Cost-Effective Measurement Setup,” IEEE Com-mun. Mag., 2018. [Online]. Available: https://www.researchgate.net/publication/322636170

[40] 3GPP, “verification of radiated multi-antenna reception performance of user equipment,” TR 37.977, Tech. Rep. V14.5.0, Sep. 2017.

[41] CTIA, “Test Plan for 2x2 Downlink MIMO and Transmit Diversity Over-the-Air Performance,” Tech. Rep. Version 1.1.1, Apr. 2017.

[42] X. Chen, “Throughput modelling and measure-ment in an isotropic-scattering reverberation chamber,” IEEE Trans. Antennas Propag., vol. 62, no. 4, pp. 2130-2139, Apr. 2014.

[43] W. Yu, Y. Qi, K. Liu, Y. Xu, and J. Fan, “Radiated two-stage method for LTE MIMO user equip-ment performance evaluation,” IEEE Trans. Elec-tromagn. Compat., vol. 56, no. 6, pp. 1691-1696, Dec. 2014.

[44] P. Kyösti, T. Jämsä, and J. P. Nuutinen, “Channel modelling for multiprobe over-the-air MIMO testing,” Int. J. Antennas Propag., Mar. 2012.

[45] 3GPP, “Study on test methods for New Radio,” TR 38.810, Tech. Rep. V2.0.0, Mar. 2018.

[46] W. Fan, I. Carton, P. Kyösti i, A. Karstensen, T. Jämsä, M. Gustafsson, and G. F. Pedersen, “A step toward 5G in 2020: Low-cost OTA perfor-mance evaluation of massive MIMO base sta-tions.” IEEE Antennas Propag. Mag., vol. 59, no. 1, pp. 38-47, Feb. 2017.

[47] P. Kyösti, L. Hentila, W. Fan, and M. Latva-aho, “On Radiated Performance Evaluation of Mas-sive MIMO devices in Multi-Probe Anechoic Chamber OTA Setups,” IEEE Trans. Antennas Propag., 2018. Online [Available]: https://www.researchgate.net/publication/322385980

[48] IMT-2020 Promotion Group, “White paper on 5G concept,” IMT-2020,Tech. Rep., Feb. 2015.

[49] L. Liu, Y. Zhou, V. Garcia, L. Tian, and J. Shi, “Load Aware Joint CoMP Clustering and Inter-cell Resource Scheduling in Heterogeneous Ultra Dense Cellular Networks,” IEEE Trans. Veh. Tech-nol., vol. 67, no. 3, pp. 2741-2755, Mar. 2018.

[50] V. Garcia, Y. Zhou, and J. Shi, “Coordinated Multipoint Transmission in Dense Cellular Net-works with User-Centric Adaptive Clustering,” IEEE Trans. Wireless Commun., vol. 13, no. 8, pp. 4297-4308, Aug. 2014.

[51] 3GPP, Draft Meeting Report, TSG-RAN Meet-ing \#72, 2016. [Online]. Available: http://ww-w.3gpp.org/ftp/tsg - ran/TSG - RAN/TSGR -72/Report/RP-161883.zip

[52] 3GPP TR 36.802. “Narrowband Internet of Things (NB-IoT),” Technical Report for BS and UE radio transmission and reception (Relase 13). 2016.

[53] M. Chen, Y. Miao, Y. Hao, and H. Kai, “Narrow band Internet of Things,” IEEE Access, vol. 5, pp. 20557-20577, Sep. 2017.

[54] J. Chen, Z. Zhang, T. Tang, and Y. Huang, “A

F. Molisch, E. Mellios, and J. Zhang, “Overview of millimeter wave communications for 5G wireless networks-with a focus on propagation models,” IEEE Trans. Antennas Propag., vol. 65, no. 12, pp. 6213-6230, Aug. 2017.

[27] T. Bai and R. W. Heath Jr., “Coverage and rate analysis for millimeterwave cellular networks,” IEEE Trans. Wireless Commun., vol. 14, no. 2, pp. 1100-1114, Feb. 2015.

[28] J. Zhang, Z. Zheng, Y. Zhang, J. Xi, X. Zhao, and G. Gui, “3D MIMO for 5G NR: Several Observa-tions from 32 to Massive 256 Antennas Based on Channel Measurement,” IEEE Commun. Mag., vol. 56, no. 3, pp. 62-70, Mar. 2018.

[29] J. Huang, C. Wang, R. Feng, J. Sun, W. Zhang, and Y. Yang, “Multi-frequency MmWave Mas-sive MIMO Channel Measurements and Char-acterization for 5G Wireless Communication Systems,” IEEE J. Select. Areas Commun., vol. 35, no. 7, pp. 1591-1605, Jul. 2017.

[30] X. Pu, S. Shao, K. Deng, and Y. Tang, “Analysis of the capacity statistics for 2X2 3D MIMO chan-nels in short-range communications,” IEEE Com-mun. Lett, vol. 19, no. 2, pp. 219-222, Feb. 2015.

[31] M. M. Tamaddondar and N. Noori, “Plane wave against spherical wave assumption for non-uni-form linear massive MIMO array structures in LOS condition,” in Proc. ICEE, pp. 1802-1805, Jul. 2017.

[32] X. Gao, O. Edfors, F. Rusek, and F. Tufvesson, “Massive MIMO performance evaluation based on measured propagation data,” IEEE Trans. Wireless Commun., vol. 14, no. 7, pp. 3899-3911, Jul. 2015.

[33] 3GPP, “Study on channel model for frequencies from 0.5 to 100 GHz,” 3rd Generation Partner-ship Project (3GPP), TR 38.901, V14.0.0, May. 2017. [Online]. Available: http://www.3gpp.org/DynaReport/38901.htm

[34] METIS2020, “METIS channel model,” Tech. Rep. METIS2020, Deliverable D1.4 v3, Jul. 2015. [On-line]. Available: https://www.metis2020.com/wpcontent/uploads/deliverables/METIS D1.4 v3.0.pdf

[35] MiWEBAD5.1, “Channel modelling and charac-terization,” v1.0, FP7 ICT 608637, 2014. [Online]. Available: http://www.miwebaeu

[36] mmMAGIC, “Measurement results and final mmmagic channel models,” Tech. Rep. H2020-ICT 671650-mmMAGIC/D2.2, May 2017. [On-line]. Available: https://5g-mmmagic.eu/results/

[37] 5GCM, “5G Channel Model for bands up to 100 GHz,” Tech. Rep., Oct. 2016. [Online]. Available: http://www.5gworkshops.com/5GCM.html

[38] P. Kyösti, W. Fan, G. F. Pedersen, and M. Lat-va-aho, “On dimensions of OTA setups for mas-sive MIMO base stations radiated testing,” IEEE Access, vol.4, pp. 5971-5981, Sep. 2016.

[39] F. Wei, P. Kyösti, R. Moray, X. Chen, and G.F. Pedersen, “Over-the-air Radiated Testing of


Xiaoli Yang, received the B.E.degree from the Electronics and Information Engineering, Hubei University, Wuhan, Chi-na, in 2009, and is currently pursuing the Ph.D.degree in In-formation and Communication Engineering, Beijing University

of Posts and Telecommunication. Her research inter-ests are in the area of channel modelling and wire-less communications.

Jianqiao Chen, received his M.S. degree from the Institute of Communications Engineer-ing, Xidian University, China in 2014, and is currently pursuing the Ph.D. degree with the De-partment of Information and Communication Engineering,

Beijing University of Posts and Telecommunications, China. His research interests are in the area of chan-nel modelling and wireless communications.

Yuzhen Huang, received his B.S. degree in Communications Engineering, and Ph.D. degree in Communications and Infor-mation Systems from College of Communications Engineer-ing, PLA University of Science and Technology, in 2008 and

2013, respectively. Now, he has been with the Artifi-cial Intelligence Research Center, National Innovation Institute of Defense Technology, and currently as an Research Associate. He also is a Post-Doctoral Re-search Associate with the School of Information and Communication, Beijing University of Posts and Tele-communications, Beijing. His research interests focus on channel coding, MIMO communications systems, cooperative communications, physical layer security, and cognitive radio systems. He has published nearly 70 research papers in international journals and con-ferences. He and his coauthors have been awarded a Best Paper Award at the WCSP 2013. He received an IEEE Communications Letters exemplary reviewer certificate for 2014.

non-stationary channel model for 5G massive MIMO systems,” Frontiers of Information Tech-nology \& Electronic Engineering, vol. 18, no. 12, pp. 2101-2110, 2017.

[55] W. Fan, F. Sun, J. Nielsen, X. Carreno, J. S. Ashta, M. B. Knudsen, and G. F. Pedersen, “Probe Se-lection in Multiprobe OTA Setups,” IEEE Trans. Antennas Propag., vol. 62, no. 4, pp. 2109-2120, Jan. 2014.

[56] X. Yang, P. Zhang, J. Chen, N. Ma, and B. Liu, “Probe Subset Selection in 3D Multiprobe OTA Setup,” in Proc. PIMRC, 2018

BiographiesPing Zhang, received his Ph.D. degree in electrical from Bei-jing University of Posts and Telecommunications (BUPT), China in 1990. He is a Professor at the School of Information and Communication Engineer-ing of BUPT. He is an Executive

Associate Editor-in-Chief on information sciences of Chinese Science Bulletin, Reviewer and TCP Member of many magazines, journals and conferences. He is a Member of next-generation broadband wireless communication network in National Science and Technology Major Project, a Member of the 5th Advi-sory Committee of National Natural Science Founda-tion of China, the Chief Scientist of “973” National Basic Research Program of China, a Member of the 11th Beijing Municipal Committee of Chinese Peo-ple’s Political Consultative Conference, and the owner of the Special Government Allowance of State Coun-cil of China. He received the Second Award for Na-tional Science and Technology Prize twice, the Sec-ond Award for National Science and Technology In-vention Prize once, the Provincial Science and Tech-nology Awards many times, and the Title of Out-standing Science and Technological Workers in 2010. His research interests include broadband wireless communication, new technologies on cognitive wire-less networks, TD-LTE, MIMO, OFDM etc.

a survey of testing for 5g: solutions, opportunities,

Documents