Architecture of mmWave edge cloud in 5G-MiEdge (invited)

Gia Khanh Tran1, Hiroaki Nishiuchi1, Valerio Frascolla2, Koji Takinami3, Antonio De Domenico4, Emilio Calvanese Strinati4, Thomas Haustein5, Kei Sakaguchi1,5, Sergio Barbarossa6, Sergio Barberis7, Katsuo Yunoki8

1Tokyo Institute of Technology, 2Intel, 3Panasonic, 4CEA-Leti, 5Fraunhofer HHI, 6Sapienza University, 7TIM , 8KDDI Research

Abstract— This paper starts presenting the vision of 5G-MiEdge, a research project leveraging the benefits of merging Multi-access Edge Computing (MEC) and millimeter-wave (mmWave) technologies. Then, the three chosen key enabling technologies, i.e. mmWave edge cloud, liquid control-plane (C-plane), application-/user-centric orchestration, and the five defined use cases and application scenarios are briefly described. Based on these, a high-level system architecture based on 3GPP and ETSI functional blocks is proposed to integrate MEC and mmWave into the 5G network architecture. Finally, as a Proof-of-Concept, numerical results using the novel C-plane for Omotenashi services are presented to show the benefit of the proposed architecture.

Keywords—5G; mmWave; edge cloud; C-lplane; prefetching

I. INTRODUCTION AND STANDARDIZATION STATUS The convergence of different technologies, a key aspect of

the forthcoming 5G networks, paves the way to new services and business models [1]. Though, those new opportunities come at the cost of increased complexity and more stringent or new requirements. The ongoing standardization [2] of the 5G Phase 1 features (see Fig. 1) introduces some new services, like massive Machine Type Communications (mMTC), enhanced Mobile BroadBand (eMBB) and ultra-Reliable and Low Latency Communications (uRLLC), which can only partially address all those new needs. In fact additional enhancements are needed to fulfil other more complex as well as more appealing services, like Augmented-/Virtual-Reality (AR/VR) or the spread availability of 1 Gbps wireless connections. 3GPP has recently defined the 5G Phase 2 to accommodate such advanced services, like the combination of eMBB and uRLLC into a new class, called Ultra High Speed Low Latency Communications (uHSLLC) [3] (see Fig. 2).

In particular, a key transformation of the communication architecture is represented by the deployment of the Multi-access Edge Computing (MEC) [4], a technology that brings cloud services at the edge of mobile networks, together with the millimeter-wave (mmWave) technology. The latter is identified as a key enabler of 5G systems [5], due to its very high data rate and effectiveness in handling interference. Moreover, since the accessibility of MEC services from terminals increases the uplink traffic [6], uplink mmWave connectivity is foreseen to be essential for effective service provisioning in 5G networks.

Leveraging on those two enabling technologies, this paper unveils the vision of 5G-MiEdge (Millimeter-wave Edge Cloud as an Enabler for 5G Ecosystem) a 3-years EU-Japan co-funded research project [7] focusing on 5G Phase 2, and identifies services and use cases where such technologies can provide substantial benefits for wireless system users.

The rest of the paper is organized as follows: Section II

details the 5G-MiEdge vision and specifies the chosen use cases and scenarios. Section III presents the high-level architecture to realize 5G-MiEge technologies over 3GPP networks. Section IV describes an example application as PoC of the proposed architecture, and finally Section V draws the conclusions.

II. THE 5G-MIEDGE VISION 5G-MiEdge has the ambitious goal of looking beyond the

current scope of 5G, focusing on the uHSLLC services, so to address new use cases and create additional values for 5G users. The distinctive feature of the 5G-MiEdge vision is to exploit the benefit of combining mmWave edge cloud, liquid

Figure 1. Timeline of 3GPP Releases and 5G Phases [1]

Figure 2. 5G Phase 1 and Phase 2 main services and key system KPIs [3]

Figure 3. Ultra High Speed Low Latency Communications use cases (left), and key 5G-MiEdge technologies (right) [11]

RAN C-plane, and user/application centric orchestration techniques. The final target is to demonstrate a first 5G implementation through testbeds in the city of Berlin [8] and at the 2020 Tokyo Summer Olympics.

Current 5G enhancements build on a radical increase of system capacity by incorporating massive MIMO techniques, dense deployment of radio access points (AP), and much wider bandwidth (new spectrum), all aspects facilitated by the use of mmWave communications. However, the improvements that can be achieved at the access stratum will still be insufficient to meet the challenging new 5G requirements. Therefore, to provide an efficient platform serving several different new applications, a paradigm shift is needed, putting applications at the center of the system design. Virtualization of network functionalities and MEC are key tools of this application-centric networking, where mobile edge applications run as virtual machines (VM) on top of a virtualization infrastructure, provided by the mobile edge host. MEC and mmWave technologies complement each other well: mmWave access benefits from the distributed computation and storage capabilities of MEC to optimize the communication strategies, incorporating cache prefetching [9], and orchestration of APs at the edge. MEC benefits from the high data rate proximity access to the edge cloud of mmWave, thus reducing latency and improving the Quality of Experience (QoE).

A. Key Technologies for 5G Phase II The fusion of MEC with mmWave access is seen as an

effective way to support applications requiring at the same time extreme high data rates, low latency end-to-end (E2E) service provisioning, and full mobility support [10]. Indeed, it is such merging that enables the design of an application-centric scalable system. Mobile applications can run in terminals, if resources are sufficient, in the nearest MEC server, or in a

cluster of MEC servers, depending on latency constraints and energy availability. Effective traffic and computation offloading mechanisms are made possible by enabling fast access to the edge cloud, through high capacity mmWave APs. At the same time proactive strategies like data caching are used to meet the demanding E2E latency of applications foreseen in 5G verticals like Automotive, Industry 4.0, and Media & Entertainment.

Table 1 summarizes the key technologies developed by 5G-MiEdge so to make uHSLLC a reality. A newly defined ultra-lean and inter-operable control-signaling plane, called ‘liquid RAN C-plane’, is proposed, where ‘liquid’ stems from its capability of enabling services and connections that follow and adapt to users’ needs, like a liquid adapts to the form of its container. The liquid C-plane is meant to provide ubiquitous allocation of communication and computation resources in a user-/application-centric perspective. Acquisition of context information and service requests forecast are key steps to enable a proactive orchestration of radio and computation resources in 5G-MiEdge enhanced 5G networks.

Table 1. 5G-MiEdge key technologies [11]

Technology Description

mmWave Edge Cloud Pre-fetch and cache user data/applications /computation results and provide them to MiEdge AP and users

Liquid RAN C-Plane Collect context info (location, habits) to provide traffic forecast to users and application providers

User/Application Centric Orchestration

Network orchestration fulfilling the applications requirements/specifications

B. Typical Use Cases / Scenarios in 5G-MiEdge

5G-MiEdge proposes five use cases that best show the benefits of merging MEC and mmWave technologies [11]:

1) Tokyo 2020 Olympic Games The new national stadium for Tokyo Olympics 2020 will

contain seats for more than 60,000 people. In the stadium visitors are expected to pass under the six entrance gates (see Figure 4) with a very high frequency. Each gate is subdivided in a large number of multiple access ports, equipped with turnstiles. To accelerate the flow rate, spectator electronic tickets may be read from fixed APs. When passing through a gate, the visitor can download data hungry event-specific applications, e.g., event schedule, related videos of past events, players’ profiles, etc., so to enjoy new unique applications of AR/VR while watching the game.

2) Omotenashi services Omotenashi is a concept that describes the Japanese style of

hospitality, i.e., make tourists have fun and feel satisfied by providing a service adjusted to their needs. On the way to their destination tourists may arrive at the airport two hours prior to departure of their flights, and after check-in they may go shopping before moving towards the departure gates. Usually, when tourists arrive at airports or stations, they may want to download large volumes of data, such as 3D virtual tour videos or games. Omotenashi services in 5G-MiEdge aim to offer ultra-fast wireless connections, so that visitors can enjoy high quality services without suffering from throughput limitation. Since waiting areas are highly crowded, the currently deployed wireless networks cannot fulfil users’ needs. In order to achieve ultra-high-speed throughput, the proposed MiEdge system combines mmWave access with MEC. In particular, MEC enables to pre-fetch the most requested contents to the local edge server in order to prevent backhaul congestion.

C. Moving Hotspot A moving hotspot describes a wireless communication

system for passengers making a long trip on train/bus/airplane and requires wireless link as a backhaul. Therefore, it is needed to have communication measures for synchronizing and sharing contents between the local server on train/bus/airplane and service servers in the cloud, while stopping at train station/bus stop/airport or while passing some spots along the way. The mobile hotspot consists of (legacy) WLAN/mmWave APs, local MEC servers, and other components to deliver video content and games to passengers. Often passengers upload to social network servers (SNS) photos/videos, which are temporarily stored in local MEC server and will be transferred to the cloud when suitable backhaul bandwidth becomes available.

D. Dynamic Crowd This use case focuses on a metropolitan city center area

where thousands of people spend part of their day. The area is provided with several outdoor hotspots. Users close by such outdoor hotspots might download large volume contents, such as tourist information, high definition (HD) 3D live broadcast of events happening at a stadium, or upload and share through SNS photos/videos. Typical location specific applications for this use case are public surveillance and HD video broadcast services. The key difference of this use case compared to the other ones is that the traffic pattern changes very dynamically during a day, in accordance to users’ activities, e.g., from light to heavy traffic.

E. Automated Driving Automated driving systems require highly resolved and

dynamic maps in order to maneuver safely. The resolution of current maps is definitely not sufficient for autonomous driving, hence high resolution and real-time maps, also called dynamic HD maps, become indispensable. Examples of such HD maps are the ones generated through a LiDAR (Light Detection and Ranging) sensor used to monitor the car surroundings. The enhanced V2V/V2X communication targeting automated driving requires a data rate of 1 Gbps per link, E2E latency < 10 ms per link, and a communication range > 150 m. With the use of mmWave technology the latency between two links goes below 0.5 ms, and, due to the proximity of mobile edge cloud computing, the data can be sent to the MEC for processing and is delivered to the destination without exceeding the 10 ms latency constraint. The high density of cars on a highway requires highly focused beams, easily generated by small mmWave antenna arrays which also provide positioning data with few mm accuracy. With MEC the HD maps are stored locally, reducing latency by continuously updates with the latest sensor data. The current state of the art technology simply cannot fulfill these requirements. 3GPP has just recently initiated related work in Release 15 under the enhanced V2X feature. As a consequence, mmWave and MEC technologies become increasingly important for the field of automated driving.

III. HIGH LEVEL SYSTEM ARCHITECTURE OF 5G-MIEDGE To efficiently implement these edge cloud functionalities,

our architecture uses a novel liquid RAN C-plane, which collects network and user context, such as user position, network load, and data popularity, and enables the network to orchestrate its radio and cloud resources. Accordingly, the operator can proactively instantiate network slices based on traffic forecasts, to reduce CAPEX and OPEX, and use SDN controllers to efficiently route the traffic towards the final destination. It is worth to underline that this architecture is user- and application-centric. In contrast with the classic orchestration approach, which is network-centric, our solution aims to deploy a novel management approach that adapts the network parameters based on the need of each end user and running application, thanks to the novel liquid RAN C-plane.

Figure 4 highlights the main functions and the interfaces

needed to extend the 5G architecture so to realize mmWave edge cloud, liquid RAN C-plane, and user/application centric

Figure 4. Extensions to the 5G system architecture

orchestration. It describes at an abstract level the required logical functionalities of the architecture proposed by 5G-MiEdge.

The proposed MiEdge RAN (the blue box) describes the mmWave edge cloud function, which is located close to UE and consists of a mmWave RAN, an User Plane Function (UPF) and some of the MEC functionalities. The MEC Service Function (MSF) functionality (colored in red), is a main extension to the existing 5G system architecture as currently discussed in standards bodies. Other important extensions are the proposed orange interfaces for the liquid RAN C-plane and the red interfaces to enable the user/application centric orchestration. The MSF manages all functions and procedures to enable MEC as a part of 5G system. For example, MEC capabilities are exposed to an Application Server (AS) by using NL4 and NL5 interfaces via a Network Exposure Function (NEF). The AS (or application provider) creates scenario specific applications running on the MEC and provides them to the UE via the logical interface NO2. If the UE activates that applications (without downloading), that UE is registered to the MSF via the NO1 interface. When the UE approaches the location of the scenario, an Access & Mobility Management Function (AMF) triggers MEC services via the NL1 interface, then user/application centric orchestration starts via the NO3 interface. When the UE enters the scenario, the MSF manages UE association of both access and computation, and also steers the session by using a Session Management Function (SMF) via the NL3 interface.

A summary of the network functions required for the proposed 5G-MiEdge system architecture is given in Table 2.

Table 2. Roles of network functions in 5G-MiEdge system architecture Acronym Network Functions Roles

AMF Access and Mobility management Function

Registration management, Connection management, Reachability management, Mobility management, Access authentication, Access authorization, Security context management

SMF Session Management Function

Session management, UE IP address management, Selection and control of UPF, Control and coordination of charging data collection at UPF

UPF User Plane Function

Anchor point for Intra-/Inter-RAT mobility, External PDU session point of interconnect to Data Network, Packet routing & forwarding, Traffic usage reporting, QoS handing for user plane

NEF Network Exposure Function

Exposition of services and capabilities to 3rd party, e.g. Application Function, Edge Computing, Mobility Pattern, Communication Pattern

MSF MEC Service Function

MEC service management, MEC capability exposition (via NEF) to Application Server (Service), MEC (LADN) registration for UE, UE location based MEC management and orchestration (long term and short term)

IV. EXAMPLE APPLICATION OF MIEDGE ARCHITECTURE In this section, numerical analysis on an exemplary

application of Omotenashi services is conducted to show the effectiveness of the proposed 5G-MiEdge architecture.

A. Evaluation Models 1) Network Model

We consider a HetNet structure where mmWave smallcells are overlaid on the conventional LTE macro cells and the system is evaluated at the center macro cell, surrounded by six interference macro cells, as shown in Fig. 5. Each smallcell BS is installed at the center of the hotspot area where Omotenashi services exist and has a 3-sector mmWave access, a wired backhaul line and an Edge Cloud storage. The mmWave communication protocol is based on the IEEE 802.11ad standard [12] using a frequency channel in the 57-66 GHz band. This paper assumes only one sub-channel among the four sub-channels of 802.11ad to be used commonly at all the three sectors of each smallcell BS. In each sector, the transmit signal is steered to the desired user by beam forming protocol, using massive antennas equipped in mmWave smallcell BSs.

In addition, by employing the proposed novel C-plane architecture, mmWave Edge Cloud system can gather abundant user context information such as mobility, traffic generation time, traffic quantity, traffic type etc. This paper assumes that the network can grasp such context information perfectly and UE have dual U-plane connectivity to both smallcell and macro cell. Based on the context information, if a UE’s traffic demand is small or of a type which cannot be stored in the edge storage, the macro cell BS will take charge of the UE’s U-plane. Otherwise, the data can be gradually transmitted and prefetched at the Edge Cloud storage installed at the site of mmWave smallcell BS. That helps to reduce user experienced latency and enables communication through high-speed mmWave access, even under low-capacity backhaul.

2) Mobility Model

A new user mobility model is developed for facilitating the evaluation of our proposed network with MEC. This model is an extension of the 3GPP hotspot model [13]. More specifically, there are several hotspots in the evaluation macro cell and users move at a constant speed within the macro cell partitioned into grids of regular distance. One loop of user movement within the algorithm consists of the following steps: 1) A user selects one hotspot area randomly as a destination when entering the evaluation macro cell. 2) The user moves to the destination and stays there for a certain time. 3) The user exits the macro cell. 4) A new user enters the macro cell as a new user and the loops repeats itself again and again until the evaluation time stops. Table 3 shows the parameters chosen for our mobility model.

Figure 5. Evaluation network architecture

Table 3 – Parameters of the mobility model Parameter Value

Macro cell radius 250 m Hotspot area radius 40 m

User speed 1 m/s Road interval 25 m Staying time Exponential distribution of avg. 1800 s

Table 4 – Parameters of traffic model Parameter Value

Traffic quantity Gamma distribution of shape 0.2892

and scale 2.012 ×105

Traffic bias 4 kbit Traffic interval Exponential distribution of avg. 8 s

Figure 6 shows the spatial distribution of users. The color represents the probabilistic density, therefore the integration of these values within and out of hotspot areas will provide exactly the preferred hotspot ratio as defined by 3GPP.

3) Traffic Model

A traffic model should be linked with a mobility model,i.e. users might demand large traffic in the hotspot area defined in the mobility model. This assumption is reasonable since nomadic (or static) users usually demand large traffic (e.g., video data, backup, etc.), contrarily to walking users who demand small traffic (e.g., mail, voice call, etc.). Regarding our considered traffic distribution, the traffic quantity is based on measurement data [14] fitted with gamma distribution and the average parameters are multiplied by a factor of 1000 in the anticipation of the traffic increase in the next ten years. Figure 7 shows the traffic distribution of one user where the red traffic is generated inside hotspots, and the blue one outside of hotspots. In this paper, we assume that all types of generated traffic can be stored in the storage. Traffic generation interval follows an exponential distribution. Table 4 shows the chosen parameters with respect to the traffic model.

4) C-plane for data prefectching Using the functional blocks defined in Section III, the

control architecture for data prefetching in this example scenario is presented in Figure 8. In this figure, when there are multiples co-located MiEdge RANs with diversified applications provided by different Application Servers (AS), a 3rd party broker called “portal” in this paper should exist to orchestrate between these RANs and ASs based on predefined policies. First, UE’s destination is assumed to be predicted by

specific application e.g. the UE’s private calendar or agenda. Also, this application will predict the type and amount of traffic the UE will demand at the predicted destination. At the next step, based on the information about the UE destination, the 5G operator can predict its received SINR using a power map measured in advance (e.g. at the time of deployment of smallcell BSs) and pre-associating the UE to the smallcells yields the highest SINR to the UE for facilitating the prefetching process. When a UE is close to the destination, 5G operator’s mobility management will track the UE’s movement and predict the time when the UE will arrive at the destination, and download a traffic. If this time is smaller than a certain threshold, called “prefetching window” in this paper, the future traffic will be prefetched. The control signaling to realize such prefetching of specific applications to MEC host (MEH) of specific MiEdge RAN can be done through the NO3 between MSF and AS in advance.

B. Evaluation results:

We evaluate the performance of the mmWave Edge Cloud system through numerical simulation. Table 5 shows the chosen simulation parameters. The macro cell BS and the smallcell BS basically conform to the standard specifications of 3GPP and IEEE802.11ad. For user association, we apply multiband cell association method, as presented in [15].

Figure 6. Illustration of spatial distribution of users

Figure 7. Illustration of traffic model by CDF of generated traffic

Figure 8. C-plane architecture for the example scenario

Table 5 - Simulation parameters

Parameter Value Bandwidth (Macro/Small) 10 MHz / 2.16 GHz

Carrier freq. (Macro/Small) 2.1 GHz / 60 GHz

Number of BS (Macro/Small) 7 (1 evaluation, 6 interference) / 84 (12 evaluation, 72 interference)

Number of BS sectors (Macro/Small) 3 / 3 Number of antenna elements

(Macro/Small/UE) 4 / 128 / 2

Antenna height (Macro/Small/UE) 25 m / 4 m / 1.5 m Antenna beam pattern

(Macro/Small/UE) 3GPP macro [13] / IEEE802.11ad

/ Half wave dipole Number of users 200

Tx power (Macro/Small) 46 dBm / 10 dBm LOS probability (Macro/Small)

[17] / [18] Path loss model (Macro/Small) Shadowing std. (Macro/Small)

Channel model QuaDRiGa Channel Model 0 Backhaul capacity 0 bps, 1 Mbps - 30 Gbps

Prefetching window 500 s PF coefficient 10

Figure 9 shows the system rate against backhaul capacity with several prefetching algorithms [16]. Circle, triangle, and square markers represent Weighted Proportional Fair (WPF), Round-Robin (RR), and without prefetching, respectively. In case of 0 bps-backhaul capacity, system rate means only macro cell rate, which is about 100 Mbps. On the other hand, the rightmost part of the figure shows that when backhaul capacity is not constrained, prefetching gain is trivial. The figure also reveals the following three points. Firstly, system rate with prefetching is always higher than that without prefetching. Secondly, system rate with WPF is always higher than that with RR. Thirdly, WPF with 1Gbps-backhaul capacity can achieve about 90% of the ideal system rate realized with 10Gbps-backhaul capacity.

V. CONCLUSIONS

This paper introduces the vision of the 5G-MiEdge project, focusing on 5G Phase II features and uHSLLC applications. Three pillar technologies and five typical use cases and application scenarios were briefly described. Based on these, a

high-level system architecture based on 3GPP functional blocks is proposed to integrate MEC and mmWave into the 5G network architecture. Finally, as a Proof-of-Concept, numerical analysis using the novel C-plane for Omotenashi services was conducted to show the benefit of the proposed architecture. Numerical results showed that better system rate can be achieved when MEC is introduced into the 5G network with the novel C-plane architecture. Our future work is to integrate this architecture into our developed testbed presented in [20].

ACKNOWLEDGMENT The research leading to these results is jointly funded by the

European Commission (EC) H2020 and the Ministry of Internal affairs and Communications (MIC) in Japan under grant agreements N° 723171 5G-MiEdge in EC and 0159-{0048, 0049, 0050}, 0155-0062 in MIC.

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Figure 9. System rate against backhaul capacity