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5G PPP mmMAGIC
Architectural enablers and concepts for mm-wave
RAN integration
Date: 2017-03-29 Version: 1.0
Editor:
Krystian Safjan - Nokia Bell-Labs
Authors:
Patrik Rugeland, Miurel Tercero – Ericsson
Yilin Li, Jian Luo – Huawei
Claudio Fiandrino, Joerg Widmer – IMDEA
Miltiadis Filippou, Honglei Miao – Intel
Krystian Safjan, Arnesh Vijay – Nokia Bell-Labs
Isabelle Siaud, Anne-Marie Ulmer-Moll – Orange
Rui Li, Mehrdad Shariat – Samsung
Javier Lorca, María Teresa Aparicio – Telefónica I+D
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Executive Summary
The following white paper discusses key architectural aspects of the mm-wave RAT
(Radio Access Technology) working in frequency bands from 6 GHz to 100 GHz
integrated with new and other legacy technologies. Five architectural enablers were
identified. The first enabler is multi-connectivity that allows the integration of mm-
wave technology with low-band system and contributes for the improvement both in
terms of reliability and performance. Second key enabler is a new mobility state,
namely RRC_INACTIVE, which helps protect system from extensive signalling related
to infrequent small packet data transmissions. The third enabler is mm-wave cell
clustering, rendering a solution for dealing with propagation blockages and frequent
changes of the serving access point. Mm-wave cell clustering helps to perform cell
switching in a rapid fashion without introducing overwhelming amount of signalling
towards the core network. A fourth enabler is network slicing, which will allow
multiple logical networks to share a common physical infrastructure. The last enabler
is self-backhauling which, when coping with ultra-dense and cost-effective
deployments, is the best transport network solution in this scenario at the moment of
writing. Apart from these key enablers, we present new network functions that bring
significant benefits to mm-wave system operation. These functions are: power
efficiency oriented KPIs, upper layer optimizations for mobility; reference signal
design to support active mode mobility in beam-based Radio Access Network (RAN);
low frequency-assisted initial access beam training; user position prediction; user
localization; and environment mapping to improve mobility.
Table of Contents
1 Introduction ........................................................................................................................... 5
2 Generic Architecture ............................................................................................................ 6
3 Vertical Multi-RAT/RAN management .............................................................................. 7
4 Architectural enablers .......................................................................................................... 9
5 RAN functions and network integration........................................................................... 16
6 Conclusions .......................................................................................................................... 23
7 Acknowledgement ............................................................................................................... 24
8 References ............................................................................................................................ 24
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List of Acronyms and Abbreviations
4G Fourth generation
AoA Angle of Arrival
AP Access Point
AS Access Stratum
AWGN Additive White Gaussian
Noise
BRS Beam Reference Signal
CH Cluster Head
CN Core Network
CP Control Plane
CSI Channel State Information
DC Dual connectivity
DL Downlink
ECM EPC Connection Management
EIRP Emitted Isotropic Radiated
Power
eNB evolved Node-B
EPC Evolved Packet Core
E-
UTRAN
Evolved UTRAN
FEC Forward Error Correction
GLB Green Link Budget
gNB NR base station
GPRS General Packet Radio Service
GPS Global Positioning System
GTP GPRS Tunneling Protocol
ID Identity number
KPI Key Performance Indicator
LA Link Adaptation
LoS Line Of Sight
LT Luby Transform
LTE Long Term Evolution
MAC Medium Access Control
MBB Mobile Broadband
MC Multi-Connectivity
MCG Master Cell Group
MCM Multipath Channel Margin
MCS Modulation and Coding
Scheme
MRS Mobility Reference Signals
NAS Non-access stratum
NG Next generation
NLoS Non Line Of Sight
NR New Radio
PDCP Packet Data Convergence
Protocol
PDU Protocol Data Unit
PE Power Efficient
PHY Physical layer
PLCP Physical Layer Convergence
Procedure
PLM Path Loss Margin
PSS Primary Synchronization
Signal
QoE Quality of Experience
QoS Quality of Service
RA Random Access
RACH Random Access Procedure
RAN Radio access network
RAT Radio Access Technology
RF Radio Frequency
RLC Radio link Control
RRC Radio resource control
RRM Radio Resource Management
RS Reference Signal
RSSI Received Signal Strength
Indicator
SBH Self-backhaul
SCG Secondary Cell Group
SDN Software defined Network
SGW Serving Gateway
SS Synchronization Signal
SSS Secondary Synchronization
Signal
TAI Tracking Are Identifier
TCP Transmission Control Protocol
TM Transmission Mode
TRP Transmission reception point
TTI Transmission Time Interval
UDP User Datagram Protocol
UE User Equipment
UL Uplink
UP User Plane
UTRAN UMTS Terrestrial Radio
Access Network
VA Virtual Access Point
WLAN Wireless Local Ara Network
WT WLAN Termination
xMBB Extreme Mobile Broadband
Xn Inter-node interface
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1 Introduction
The expectations on performance towards future 5G systems lead to giving attention to
high-frequency bands, specifically, so-called mm-waves whose frequencies are defined
between 6 GHz and 100 GHz in mmMAGIC 1. The range of use cases envisioned for
the new generation of communication system, has expanded way beyond voice and
mobile broadband applications. Designing new system (or planning an evolution)
comes across various levels; starting from propagation analysis through various
network layers and up to end-user applications. In this document, we are pointing at
envisioned mandatory architectural elements (architectural enablers) for mm-wave
RATs, working in frequency bands from 6 GHz to 100 GHz and integrated with other
new and legacy technologies.
There have been use case families envisioned for 5G [NGMN15][Nok15], but during
3GPP standardization process the use cases belonging to extreme mobile broadband
(xMBB) gained the highest prioritization (also called enhanced mobile broadband,
eMBB in 3GPP). In xMBB use cases defined in [MMMAG15-D11], such as “media on
demand”, “cloud services”, “immersive early 5G experience” and “smart office”, both
high connection density and high data rates are the challenges to be addressed from
the RAN architecture perspective. In this white paper we present several architectural
enablers which are specific for the mm-wave RAT, and we complement them with
optional technology components and RAN functions. The connection density, traffic
density and data rate challenges are to a large extent handled by densification of the
network, and mm-wave self-backhauling is a key enabler for cost-efficient ultra-dense
deployments. Another enabler helping to cope with these challenges is low-band
integration e.g. help propagate control signalling and to speed-up initial access. A high
number of connections can bring challenging episodes of intense control signalling—
this is mitigated with new, intermediate mobility state RRC_INACTIVE.
Apart from new architectural solutions developed for 5G we need to integrate mm-
wave system with other RATs, primarily for reliability reasons. Integration of mm-
wave systems with LTE using multi-connectivity gives the opportunity to provide
more reliable control signalling and faster initial access in beam-based RAN.
1 Strict definition of mm-wave bands include frequencies between 30 and 300 GHz, but the industry often use a
looser definition including any frequency above 10 GHz. In mmMAGIC project the mm-wave range is referring to
even larger range of frequencies: from 6 to 100GHz
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2 Generic Architecture
Figure 2-1 Generic mm-wave system architecture diagram - RAN perspective
The 5G network architecture shown in Figure 2-1, is expected to consist several RAN
entities. The RAN entities will in-turn comprise existing LTE eNBs (evolved NodeBs),
as well as access points (APs) supporting the next generation RAT, denoted “New
Radio” (NR), which will be capable of supporting mm-wave frequencies. The name for
the NR APs has recently been coined as “gNB” [3GPP TR 38.801]. A brief description of
the various 5G architecture elements such as gNB, Next Generation Core network and
Interfaces are described in this section.
gNB
The gNB is an enhanced version of the LTE Rel-13 eNB, which will be capable of
supporting low and high frequency bands. Whilst the full set of existing features and
functions supported by this entity can be obtained from [3GPP TS 36.401]; some of its
distinguishing features are: facility to support network slicing, tight interworking with
E-UTRAN, capability to support multi-connectivity, session management, and its
ability to support existing and new interfaces. Additionally, it is worthwhile to
mention that the gNB in 5G systems can be expected to include one or more
transmission/reception points (TRPs),Some gNB functionalities can be distributed
across different TRPs, while others are centralized, leaving the flexibility and scope for
specific deployments to fulfil the requirements for specific use cases.
Next Generation Core Network (NG-CN)
The NG-CN must be capable of supporting CP signalling towards the LTE and NR
APs. Here, it is important for the NG-CN to store the UE context for both the LTE and
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NR APs. To maintain mobility between different access networks, the NG-CN will be
in-charge of establishing and retaining the inter-CN signalling. It will also be in-charge
of supporting mobility features like, tracking area list management, UE time zone and
location mapping. UE reachability in Idle mode, QoS and session management are the
other features that must be supported. Additionally, the feature of service flow
management and user gateway support functionality will also be included.
Interfaces
With enhancements in the functional blocks, the interfaces must be capable of
establishing logical connections with several APs tailored to different technological
systems. From the view point of 5G access points, two main interfaces shall be
introduced: NG and Xn interface. The NG interface shall be open and must support the
exchange of signalling information between the 5G-RAN and NG-CN. Whilst, the Xn
interface must offer logical connectivity between eNB and gNB. The NG interface must
be capable of supporting CP and UP separation, at the same time have separate radio
network and transport layer specifications. While on the other hand, the Xn interface
must support the exchange of signalling information and data forwarding between the
endpoints and gNBs. Lastly, the NG interface must be capable of carrying interface
management, UE connect and mobility management functions; in addition to the
enhanced features to support the transportation of NAS messages, paging and PDU
session management. One rule applicable to both cases, is that they must be future
proof to fulfil diverse requirements, services, features, and functionality.
Specifically, for the 5G mm-wave RATs both standalone and non-standalone should be
supported, i.e. mm-wave RAT should be fully operational without support of other
RATs (standalone deployment); however, mm-wave RAT must be benefitted from
tight integration with other RATs (typically low-band systems with better coverage
properties), e.g. improved initial access or improved reliability due to usage of multi-
connectivity with low-band system such as LTE.
3 Vertical Multi-RAT/RAN management
The management of several RATs in a heterogeneous multi-RAT network involves the
use of dedicated Key Performance Indicator (KPI) to switch from one technology to
another one, following dedicated criteria (power efficiency, flexible QoS, multiple
Access Point (AP) connection to send and receive the data) and the integration in
multi-RAT architectures. The generic architecture described in Section 2, encompasses
gNBs as well as eNBs that require multi-RAT management to perform mm-wave and
LTE-A carrier aggregation. The control-plane may then forward the metric decision
evaluated at the PHY layers to the gNB or eNB and the decision is then activated to
achieve data transport between communication entities. For that purpose, dedicated
link adaptation metrics in charge of air interface and transmission mode (TM) selection
have to be designed, evaluated and forwarded in the multi-RAT management engine
followed by integration in the generic architectures detailed in this paper.
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3.1 Multi-RAT multi-layer management
The multi-RAT/RAN management resorts from a vertical multi-layer management of
interfaces and TMs depending on involved technologies in the RATs management
[SVG+16][SUMP16] and optimization criteria (power and spectral efficiency, radio
coverage, etc..). It may cover three independent abstraction layers, where the activated
abstraction layer depends on functional blocks that are required to change the air
interface and TMs to perform the transmission between the two communication
entities.
Link adaptation metrics are the input of the multi-RAT/RAN management processing
in order to choose and activate the appropriate air interface and transmission mode,
depending on propagation conditions and optimization criteria. The green link budget
(GLB) metric [SUM16] is the candidate link adaptation metric for power efficiency
optimization in the multi-RAT context where independent interfaces may be
considered to carry out the transmission. The GLB metric allows a link budget based
comparison between interfaces exhibiting independent power sensitivity levels and
different radio frequency spectrum operations. Innovative KPIs and multi-radio
interface engine as recently introduced in the ETSI Reconfigurable Radio System (ETSI
RRS) technical committee, are computed at the lowest layer, typically at the PHY layer
based on Received Signal Strength Indicator (RSSI) and link budget elements deduced
from involved interfaces in the multi-RAT process.
Figure 3-1 illustrates the architecture using 5G RAT link adaptation metrics to select the
most appropriate technology (technology 1, 2 or 3 following a generic approach), to
establish communications between the transmitter and the receiver. Link adaptation
(LA) metrics are computed using available PHY parameters as the RSSI and context
information provided by Physical Layer Convergence Procedure (PLCP) headers and
signalling headers of every concerned RAT. Metrics are then forwarded to the
appropriate layer to initiate air interface switching. The selection is done considering
equivalent throughput schemes in accordance with the transported services [SUM16].
The power efficient link adaptation metric adopted in mmMAGIC to optimize power
and cost efficiency is described in [SUM16], exhibiting important transmit radiated
power gains for mm-wave and Wi-Fi hot spot deployments [mm-MAGIC D3.1,16]. To
transport decision, the existing X2-S1 and Uu interfaces are differently exploited,
depending on emulated abstraction layer activation.
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Figure 3-1 Generic multi-layer architecture using 5G RAT link adaptation metrics for multiple RAT management
Depending on involved technologies in the multi-RAT scenario, each layer is able to
manage interfaces having their own capabilities to exchange context information
together.
The abstraction layer 1 exploits PHY and MAC protocols to exchange information and
forward the radio interface engine decision. Switching from one Modulation and
Coding Scheme (MCS) to another in a single RAT may be possible in this
configuration. A switching between IEEE802.11 ac TMs and IEEE802.11 ad TMs may be
also implemented using the fast session transfer protocol designed in the IEEE802.11
ad standard.
The abstraction layer-2 requires a L2.5 layer to manage the independent interfaces that
do not benefit of a common context information exchange. The I-MAC layer [KBN12]
which was designed in the ICT-FP7 OMEGA project, illustrates a concrete hardware
and software implementation for indoor communications.
The abstraction layer-3 utilises typically the generic architecture exposed in section 2
with control and data plane architectures using S and X1 interfaces to carry out multi-
RAT carrier aggregation. An illustration of multi-RAT abstraction layer-3 is detailed in
[SUMP16] embracing mm-wave components in innovative control and user plane
splitting schemes . The adaptor represented in Figure 3-1 is similar to the “WT” (WLAN
Termination) specified in 3GPP Release 13 for LTE/WLAN RAN-level aggregation. The
same GTP-U tunnelling is then utilized for data splitting.
4 Architectural enablers
4.1 Multi-connectivity
A widely acknowledged limitation of mm-wave systems is the increased path-loss
associated with the higher carrier frequencies [MMMAG16-D21]. Because of this, mm-
5G R
AT L
A m
etri
cs, R
RM
and
NM
met
rics
Abstraction layer-2
Abstraction layer-1
Abstraction layer-3
5G RAT LA metrics computation and feedback
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wave NR cannot be relied upon to provide ubiquitous coverage from a single
transmission point. Mm-wave often has to provide line-of-sight (LOS) between the
access point and UE, which can frequently be prone to blocking and fast fading.
However, the strict 5G requirements on e.g. throughput will necessitate the use of the
wider bands available at mm-wave frequencies. To address the coverage issues, mm-
wave NR need to support multi-connectivity where a UE can be connected to multiple
nodes at once to facilitate either aggregation of carriers for increased throughput, fast
switching between nodes to enable seamless mobility, or redundant transmission
schemes where the same information is sent over multiple link to increase the
reliability.
In LTE Rel-12 there were two options for dual connectivity (DC): 1A (MCG and SCG
bearer) and 3C (MCG-split bearer). In [MMMAG16-D31] we proposed that these
options should be used for mm-wave NR. In addition, we introduced an alternative,
namely SCG-split bearer which allows the user plane (UP) traffic to be sent over both
links (similar to MCG-split bearer), without straining the processing capacity of the
master node.
Figure 4-1 Multi-connectivity bearer options.
The proposed dual connectivity concept is a mandatory solution for the network
design, in order to provide sufficient reliability for standalone mm-wave NR and to
leverage on LTE coverage for non-standalone deployments. Which of the three options
to use is a matter of optimization, and recently began to be discussed in 3GPP NR
Study Item and are now part of [3GPP TR 38.801].
Another extension of the DC concept, is the possibility to add additional cell groups
beyond the master cell group (MCG) and the secondary cell group (SCG), also known
as a multi-connectivity (MC). The complexity of balancing the load between multiple
links will increase significantly compared to DC, but there are benefits to have a
preconfigured backup link with redundant coverage. This will allow a quick handover
in case of radio link failure on any of the initial links and will be especially useful for
standalone mm-wave NR. The proposed 5G node, known as “gNB” will support both
distributed and centralized deployments, where multiple transmission/reception
points (TRPs) contain a configurable part of the protocol stack. This can provide
pooling gains with centralized functionalities, for instance mobility handling or
scheduling decisions, resulting in more demanding requirements on the backhaul in
terms of e.g. capacity and synchronization. The deployment of the TRPs should
provide some level of redundant coverage to enable this.
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Yet another proposal is control plane (CP) multi-connectivity, also known as RRC
diversity. This was studied for LTE Rel-12 [3GPP TR 36.842] and is considered by
mmMAGIC, given that it will play an important role in a mm-wave system, especially
for standalone mm-wave case. In LTE, the RRC signal is always transmitted directly
from the master eNB (MeNB) to the UE, even for signalling related to the secondary
eNB (SeNB). With RRC diversity, part of the RRC message, or the entire RRC message
can be sent via the primary link, the secondary links, or both. This allows for improved
reliability using redundant messages, or reduced latency by transmitting independent
messages related to the secondary node directly from the SeNB to the UE. However, an
important aspect when considering RRC diversity will be how to handle race
conditions, when multiple, contradicting, RRC messages are received via different
links. The RRC diversity solution can be seen as optional feature for mm-wave NR
which can increase the reliability at mm-wave frequencies.
Since it will be challenging to provide ubiquitous mm-wave coverage with a
reasonable deployment density it will be imperative to supplement the connectivity
with low frequency support. As the mm-wave NR will initially be deployed in many
areas already serviced by LTE, it will be beneficial to leverage on the incumbent
installations and support a gradual deployment of mm-wave NR. By harmonizing the
protocol stacks of LTE and NR, it will be possible to have a tight interworking
between LTE and NR which will for instance, enable aggregation of carriers or fast
switching between the RATs, proposed in mmMAGIC [MMM16-D31]. Work has since
begun in 3GPP to support the interworking between LTE (and its future releases) and
NR (which will operate in both low frequencies and mm-wave frequencies) [3GPP
TR.38.801].
Initial simulation results show that by co-deploying LTE at 2.6 GHz and NR at 15 GHz
in a dense urban environment with DC capabilities, it will provide synergy effects
greater than the sum of the capacity of either RATs as can be seen in Figure 4-2.
Figure 4-2 Downlink performance for LTE-NR interworking.
A similar evaluation comparing LTE DC at 2.6 GHz with LTE-NR DC at 2.6 and 28
GHz respectively show that the mm-wave RAT improves the median throughput by
up to 17 times at high loads and between 1.5 and 2 times for the 5th percentile
throughput.
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4.2 New mobility state: RRC_INACTIVE
In [MMMAGIC16-D31] it was proposed to introduce a new RRC state to complement
the existing states, RRC_IDLE and RRC_CONNECTED. The new state is referred to as
RRC_INACTIVE and allows a UE to benefit from several aspects of the two original
states. Similar to RRC_IDLE, the UE would perform cell-reselection based on
measurements of reference signals without providing the network with measurement
reports. Additionally, when the network needs to reach the UE, e.g. when DL traffic
has arrived, the network pages the UE which in turn performs a random access (RA) to
connect to the network. Likewise, when the UE needs to initiate UL traffic, it performs
a RA to the current cell to synchronize and connect to the network. What differs for
RRC_INACTIVE compared to RRC_IDLE is that the UE and gNB maintains
configurations obtained in RRC_CONNECTED related to e.g. AS context, security, and
radio bearers so that after the RA, the UE can resume its old configurations without
much delay. In addition, the gNB can maintain the CN/RAN interface (NG-C and NG-
U), further reducing the resumption latency. Since the UE resumption from
RRC_INACTIVE to RRC_CONNECTED assumes that the old UE context can be
reused, whichever cell the UE has re-selected must be able to retrieve the context from
the old cell. If the context fetch fails, the network can instruct the UE to perform a RRC
Connection Setup similar to the one performed from RRC_IDLE.
Figure 4-3: State transition diagram
Since the RAN/CN connection can be maintained in RRC_INACTIVE; the CN will
assume that the UE is in ECM_CONNECTED. Whenever the network needs to reach
the UE, e.g. when there is DL data available, the network will need to page the UE, as
the RRC connection is suspended. However, as the CN assumes that the UE is in
connected mode, the CN cannot initiate the page, but rather the RAN will have to
initiate the notification. To facilitate a more efficient paging scheme, the RAN can
assign a limited area, covering one or more cells, within which the UE can be paged by
the RAN. While the UE moves within this RAN area it does not need to notify the
network of its location. It is only when the UE moves outside the RAN area that it will
have to signal the network of its new location and be assigned a modified RAN area.
As the RAN notification area can be smaller than the CN Tracking Area, the RAN
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paging message can be sent out in a smaller number of cells than a typical CN paging.
This can also be used in conjunction with the Smart Paging procedures introduced in
LTE Rel-13, where the UE reports its previously visited cells and time spent in these
cells. This information can be used for statistical analysis to estimate a probable
location of the UE (e.g. a stationary UE could be paged in only from its previous cell).
4.3 Mm-wave cell clustering
In mm-wave communication it is challenging to provide continuous connectivity for
the active user in dynamic environment especially due to changing position of the UE
and other objects in the scene. In case of such beam-based prone to obstruction links, it
is crucial to provide mechanism that can handle switching the serving cells in quick
and transparent manner to CN. The mm-wave node clustering is therefore mandatory
element of the network architecture. Detailed mm-wave cell clustering description has
been provided in [D.31] and previous white paper [MMMAG16-WP31]. Here, we focus
on architectural enablers related to mm-wave cell clustering.
The layout and architecture of the cluster will depend on the quality of backhaul and
coverage of the different nodes. If the backhaul is ideal with very low latency, the
cluster can be coordinated by a central node, handling all scheduling between the
nodes, deployed with a non-ideal backhaul, which may preclude a central scheduler.
Instead, in such cases each node is responsible for the lower layers (MAC and PHY),
and can relay packets through an evolved RLC layer to other nodes when a UE needs
to switch APs. In the cluster one AP with the sufficient processing power and CN
connection quality to support the cluster, will coordinate the mobility within the
cluster. This implies provision of CN connection to that APs that allows flexible
formulation of clusters and ensuring that each mm-wave node can a part of valid
cluster.
To ensure connectivity within the cluster, it may be necessary to rely on the wide area
coverage of low-frequency RATs, e.g. LTE-A, when the mm-wave RAT has limited
reliability e.g. due to signal blockage. The lower frequency can then relay traffic and
control signals from the CH to the UE, and assist in intra-cluster mobility. This makes
strong connection between inter-frequency multi-connectivity and mm-wave
clustering.
Additionally, the mm-wave access clustering is expected to work even with wireless
self-backhauling, where the nodes may relay traffic using the mm-wave air interface.
However, this may introduce additional latencies in the system which needs to be
considered.
4.4 Network slicing
Network slicing will be an important aspect of 5G networks, where multiple services
and business operations can be realized independently on a shared infrastructure
(including shared processing, storage, transport, radio spectrum, and hardware
platforms). This will allow for a more cost- and energy-efficient asset utilization where
the logical separation allows for a flexible and independent configuration and
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management of slices without compromising stability and security. However, it is
important that the configuration and maintenance of the slices add a minimum
overhead as not to waste scarce RAN resources. It should be possible for an operator to
configure a specific slice with a customized logical network optimized for a specific use
case while still not preventing the operation of other slices. Even though a slice may be
optimized for a single use case, the notion of network slicing should not be confused
with the concept of different services. It is possible that a single slice supports multiple
services with e.g. differing numerologies or KPIs, and it is likely that multiple slices on
the same network provide the same service, e.g. multiple operator offer mobile
broadband (MBB) services with independent logical networks on the same physical
infrastructure.
Since different network slices are to be operated as independent networks, it is
important to ensure slice protection to prevent shortage of shared resources, (e.g.
common signalling resources). This could be achieved using slice specific access class
barring where the network configures UEs already associated to a specific slice with
e.g. modified back-off timers.
To facilitate an optimized slice selection, a UE can provide the network with a
configured slice ID, which is obtained after its initial attach. On the absence of a valid
slice ID, the UE should access using default configurations and the network will
configure and redirect the UE to a proper slice.
4.5 Self-backhauling
The new level of densification in 5G will require innovative approaches in radio
resource, mobility, and/or interference management. A centralized operation of mobile
networks, as implemented by C-RAN, allows for obtaining a globalized view on
mobility and interference management in order to optimize the resource usage
[BDO+13]. Aiming at centralization of the mobile network operation; high capacity
links among access points of small cells and the centralized base station of macro cell
are required, which is usually satisfied by optical fibre connections. Nevertheless, it
may be too expensive or impractical to equip every cell with fibre connectivity. As an
attractive, cost efficient alternative, wireless backhauling enables direct, low latency
connections amongst access points and base stations and, hence provide them with a
possibility for enhanced cooperation to achieve better performance, in addition to
providing high data rate throughput to small cells.
A further step of wireless backhauling is self-backhauling, which refers to a set of
solutions to provide technology- and topology-dependent coverage extension and
capacity expansion utilizing same frequency band for both backhaul and access links,
as shown in Figure 4-4. Self-backhauling provides an efficient way to combat
infrastructure constraints especially in dense network deployment, where access to
fibre may be limited to only some APs. However, over time as the fixed infrastructure
will become more available, the self-backhauling will gradually evolve.
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Figure 4-4 Concept of self-backhauling.
The dynamics and self-autonomy of self-backhauling solutions can gradually evolve
into Software Defined Networking (SDN)-based solutions, where one logical controller
is supposed to monitor topology changes, node-to-node radio channel status and all
the traffic needs in a real-time manner. In this case, backhaul networking for densely
deployed small cells could be characterized by a ringed-tree topology with multiple
backhaul links per node and different levels of backhaul links [SGV+16]. An example of
a ringed-tree backhaul networking is illustrated in Figure 4-5.
Figure 4-5 An example of ringed-tree self-backhauling.
As shown in the Figure 4-5, a network node can have more than one backhaul link, and
vertical links would have higher priorities in route selections than horizontal ones.
Focusing on this backhaul networking, a high-level radio resource management
procedure is considered as follows
1. Start-up configuration: Each network node decides if its backhaul links should
be always-active or improvised, e.g., for high-level vertical backhaul links, they
may be always-active, where other candidate backhaul links are improvised to
reduce the signaling overhead of all on the scenario. Furthermore, radio
measurement procedure and reference signal sounding are configured, and
maximum number of simultaneous backhaul links (dependent on RF chains)
for a specific node are specified.
2. AP side configuration: channel measurement for each possible backhaul link,
and reporting the channel state information to the controller. Reporting
bandwidth demands for access and backhaul respectively are also included.
Core
Network
Self-Backhauled Node
Node with
dedicated backhaul
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3. Controller side procedure: end-to-end routing procedure to form backhaul
networking for a cluster of nodes, plus routing policy broadcasting and radio
resource allocation.
5 RAN functions and network integration
5.1 RAN functions
5.1.1 Power efficiency oriented KPIs
A power efficient link adaptation metric has been designed [SUM16] to perform
dynamic multi-RAT management under power and cost efficiency criteria
guaranteeing QoS and radio coverage. This metric, denoted Green Link Budget (GLB)
metric, carries out a selection of the most power efficient transmission mode and air
interface by computing two normalized sub-metrics, the and sub-metric. The -
metric covers extra power requirements to guarantee QoS on a given transmission
mode when passing from AWGN to multi-path propagation conditions i.e. the
Multipath Channel Margin, (MCM) and the extra required radiated power i.e. the Path-
Loss Margin (PLM)), which is necessary to have a received power level equivalent to a
free space path-loss situation. The selected TMs are associated with the minimum
sub-metric values of concerned interfaces. The-metric computes the difference
between the received power and the required power for the transmission mode
initially selected by the -metric. A power control is then done by the use of
numerical-metric value to adjust and limit the Emitted Isotropic Radiated Power
(EIRP) at the AP or the gNB in small or macro-cell deployment.
Figure 5-1 Multipath Channel Margin (left) and Path Loss Margin (PLM) metrics visual interpretation
Figure 5-1 gives the definition of the -metric. MCM is derived from link level
performance in a multipath versus AWGN case for a given TM and technology
1E-6
1E-5
1E-4
1E-3
1E-2
1E-1
1E+0
0 5 10
BE
R
SNR
MCM
AWGN
Multipath
Channel
70
75
80
85
20 21 22 23 24 25 26 27 28 29 30
Pro
pag
atio
n L
oss
(dB
)
distance d (m)
PLM
Free space path-loss
Multipath path-loss
α = MCM + PLM
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delivering the desired throughput to transmit data with a QoS translated in a Bit Error
Rate target. PLM is the variation of measured RSSI and an idealistic RSSI linked to free
space path-loss without any obstacles.
The GLB metric has been applied upon the mm-MAGIC multi-band system integration
model in which each interface is enabling to operate upon several RF bands
[MMMAG17-D13]. The GLB metric is then in charge of selecting the most power
efficient RF band in connection with the environment to establish radio communication
and available technologies in the multi-RAT scenario. Another application is the access
point selection to perform communications in multi-RAT handover scenarios. The
metric has been also integrated in radio engineering tools in order to optimize inter-cell
distance for 5G multi-RAT/RAN deployment [UMS15] [MMMAG17-D13].
5.1.2 Transport layer optimization to improve mobility
mm-wave signals are more outage-prone compared to low-frequency carriers;
blockage can be induced by trees, street furniture, transport traffic and even human
body. Signal blockage (in either control or data channel) may lead to an abrupt
reduction in link quality or to Radio Link Failures (RLFs) with drastic impacts on
transport layer control protocols (e.g., TCP) resulting in degraded quality of experience
(QoE) for end-users. In the context of mm-wave RAN, signal outages or RLFs are not
only triggered in cell boundaries in case of high mobility, but also in any locations
within the coverage area of a mm-wave AP as soon as the strong LOS or reflection
channel component is blocked by dynamics of environment (even if the UE is
stationary).
One way to remedy the QoE from user perspective is to apply efficient forward error
correction (FEC) schemes, known as Fountain codes to counterbalance the outage
impacts. Fountain codes have been designed for lossy and varying channels with
erasures. Luby transform codes (LT codes) are the first class of universal erasure codes
out of them [MLU02]. The source for fountain codes will encode a file into streams of
packets, each containing random parts of the original file. The fountain source keeps
sending these encoded packets to the destination, without knowing which packets will
be received. At the receiver’s side, when the number of packets received is slightly
higher than the original file size, the source file can be recovered. Combining such FEC
schemes at application level, facilitates utilising simpler transport protocols (e.g. UDP)
without congestion management or error check / control at transport layer. This in-turn
can additionally improve user QoE by avoiding unintended cross-layer interactions
when facing abrupt link quality changes (particularly, in mm-wave bands) as outlined.
Figure 5-2 Sequence number per file received over time for TCP (left) vs. LT (right)
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Our simulation analysis in equivalent settings suggests that LT codes can achieve
complete and reliable file transmissions over UDP with lower levels of overhead (i.e.
better throughput) in mm-wave bands (as shown in the figure above- right hand side).
The red line depicts completion of each file (52 MB) in sequence. Furthermore, analysis
also shows that in outage regime (of mm-wave bands) application on top of TCP barely
receives all the packets transmitted out of each file as a large number of them are lost
during the outages (as in the figure above- left hand side). On the contrary, LT (over
UDP) provides complete file deliveries thanks to forward error correction mechanism,
resulting in more consistent QoE.
5.1.3 Reference signal design to allow active mode mobility in a
beam based RAN
As the mm-wave access will need to rely on beam-formed connectivity to provide
connectivity, coverage, and capacity to the UEs due to the increased path loss at higher
frequencies, the mobility procedures need to be adapted to cope with this. In LTE, the
mobility related measurements were based on periodic reference signals, transmitted
omni-directionally by cells. If a UE was in RRC_IDLE state, it would select the best cell
to camp on and if the UE were in RRC_CONNECTED, then the UE would send a
measurement report to the network, if the signals surpassed certain network
configured threshold, and the network would select the target cell for handover. For
NR, it has been agreed that there will be two levels of mobility, with and without RRC
involvement. Mobility without RRC involvement will be limited to scenarios where the
mobility is between transmission/reception points (TRPs) belonging to the same gNB,
where tight synchronization can be assumed. In these cases, beam management
procedures, similar to intra-gNB procedures utilizing channel state information
reference signals (CSI-RS) will suffice. To cater to the wide range of mobility
requirement of the 5G use cases, the network will be able to configure the periodicity of
the CSI-RS from a few milliseconds up to several seconds, or turn off completely, if
there are no UEs active in the cell.
However, as tight synchronization between nodes cannot be assumed to be ubiquitous,
an asynchronous mobility procedure is needed which is provided by the RRC based
mobility. These mobility reference signals (MRS) will need to contain a synchronization
signal (SS) as well as a beam identifier (BRS (beam reference signal)) for the UE to be
able to distinguish beams with different synchronization (e.g. from different nodes).
The active mode mobility in NR requires frequent transmissions of reference signals in
narrow beams to ensure prompt switching in case of poor coverage. However, if these
reference signals were provided in every beam with the strictest periodicity required,
the overhead and added interference would be prohibitive, not to mention the wasted
energy in transmitting superfluous signals not used by the UE. Thus, unlike LTE, there
is a need to distinguish between idle mode and active mode mobility. The requirement
for the idle mode mobility is to provide means for accessing the network, which is
much more latency tolerant than the active mode mobility where e.g. using a
periodicity of 100 ms would be acceptable compared to 5-10 ms for high speed user
during active mode mobility.
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5.1.4 Low frequency assisted initial access beam training
The initial access process comprises the three tasks of downlink timing and frequency
synchronization, system information acquisition and uplink timing synchronization.
During initial access a UE has to establish a RRC connection with the corresponding
mm-wave AP. The performance of this procedure directly impacts the user experience.
Therefore, on PHY layer a beam alignment must be achieved within short time.
Exploitation of the limited a-priori information on the preferred transmission direction
at both ends of the link will support this. In a non-standalone deployment, i.e., a
heterogeneous network, where mm-wave small cells are located within the coverage
area of a macro cell operating at low frequency, low frequency RAT assistance can
improve initial access performance significantly. Especially UE power consumption
and latency can be reduced.
In the following, the three mentioned tasks of low frequency RAT assistance are
highlighted.
5.1.4.1 Downlink synchronization
For downlink synchronization the UE exploits synchronization signals transmitted by
the AP. These are in particular time-frequency resources with a certain periodicity,
which allow acquisition of symbol, slot and sub-frame timing. After achieving that, the
UE is able to obtain the cell ID. If the UE is located in a low frequency RAT coverage
area, the low frequency RAT can transmit information about frequency and cell IDs of
mm-wave small cells within its coverage area. With this signalling, the UE does not
need to perform an exhaustive search over the whole small cell ID space, but it only
tries to detect the signalled cell IDs. As a consequence, the UE power consumption for
downlink synchronization is significantly reduced.
5.1.4.2 System information transmission
The second task of the initial access procedure is to acquire the system information
which provides all the essential information for accessing the network to the UE. The
coverage of the system information determines the coverage of the cell. Some of the
system information components, e.g. the system frame number, are changing fast on
the basis of one or several mm-wave RAT frames. Other system information
components vary relatively slowly, so information about system bandwidth, random
access resources, paging resources and scheduling of other system information
components is typically semi-static. For this reason, it can be energy efficient to convey
some of the slowly varying system information by exploiting the existing low
frequency RAT. The fast changing system information components, however, need to
be transmitted by the mm-wave RAT.
5.1.4.3 Uplink synchronization
It is important that efficient uplink (UL) data transmission in the mm-wave RAT is
supported as well, especially for “UL data traffic dominant” use cases, e.g., uploading
content, such as high-resolution videos to social media during sports events, concerts
etc. UL synchronization needs to be achieved prior to any UL packet transmission to
ensure that all the co-scheduled UEs’ UL signals are time-aligned at the eNB. A RACH
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procedure, similar to that standardized in LTE can be used. Based on the RACH
preamble transmitted by the UE, the eNB can determine the timing advance value for
the UE. The radio resources for the preamble transmission are typically part of the
system information and such system information can be signalled by the low
frequency RAT. This can be viewed as a basic assistance to the UL synchronization. To
ensure a certain UL preamble coverage, if several preamble formats are supported by
the system, the low frequency RAT can signal a particular preamble format to the UE
in order to realize the network assisted preamble format selection. In case of contention
free RACH, the low frequency RAT can signal the exact preamble sequence to be used
by the UE.
During the LTE-like RACH procedure, the RACH response signal can be also
transmitted by the low frequency RAT. In addition to the above mentioned options for
UL synchronization assistance, the low frequency RAT may also offer assistance to the
possible beam alignment operations during the initial UL synchronization procedure.
5.1.5 User movement prediction
Good propagation conditions and beam steering are necessary to achieve high data
rates. In order to achieve this, accurate position estimation and position tracking is
needed, especially for dense urban and high mobility scenarios. This poses a number of
challenges related to the capability of accurately estimating the position and following
the movement of the users, in order to maintain a stable mm-wave connection. Also,
the beam-training overhead per user is independent of the one related to other users
and depends only on the user’s mobility. As the number of users increases, so does the
beam training overhead. In high density mobile scenarios, this overhead may become
prohibitively large, unless more intelligent beam-training strategies are used. From this
perspective, mobility and user density are equivalent issues to be tackled by beam
training and tracking algorithms, and special care is required when highly mobile
users associate to APs that already serve a large number of mm-wave terminals. In
particular, these scenarios yield three related issues: first, beam training procedures
upon AP association can be too slow and result in suboptimal beam pattern choices,
which in turn would lead to unstable channel and data rates; second, the changes of
the optimal beam pattern induced by the movement of the users must be tracked in
order to consistently maintain a sufficiently high link rate; third, links can be easily
broken due to the users moving behind an obstacle or some blocking material, such as
a building, vegetation, vehicles, or other users. In these cases, agile, possibly proactive
AP re-association mechanisms should be provided, in order to avoid that a user loses
connectivity over long time periods, and a complete beam training procedure needs to
be re-initiated from scratch.
Embedding history information about the users’ movement patterns into the beam
training and tracking process at mm-wave AP can considerably improve the
performance of mm-wave links and relieve part of the time burden caused by beam
training procedures [PDW17]. The prediction of the movement of the users can be fully
estimated at the AP side, without requiring any explicit feedback of position
information from the users to the AP. This yields the two-fold advantage that it incurs
no overhead, and that no interface is required between mm-wave communication
systems and other positioning subsystems embedded in user terminals or vehicles,
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such as Global Positioning System (GPS) receivers. In fact, no precise location
information is required from external sources: the same can be reliably estimated by
APs, using only some information from current beam pattern choices that would help
drive future beam tracking procedures.
5.1.6 User localization and environment mapping to improve
mobility
mm-wave technology will play an essential role in indoor localization and mobility
because of its unique characteristics. Propagation occurs in quasi-optical patterns,
whereby reflections off the boundaries of indoor surfaces and obstacles are subject to
limited scattering and the line-of-sight (LoS) component tends to be predominant over
non-LoS (NLoS) components even in the presence of obstacles. Mm-wave signals are
characterized by short wavelength and large bandwidth, thus even directional
transmission may generate multiple reflected paths reaching the moving receiver with
different delays and angles of arrival (AoAs). The information extracted by the phased
antenna arrays typically used for mm-wave devices fits well with the purpose of
localization. In a generic environment where different mm-wave APs are present, the
signals transmitted by each AP typically reach a node via both LoS and NLoS paths.
The antenna array of the node can be used to estimate the AoA of each multipath
arrival from each AP, thereby providing a so-called AoA spectrum for each AP that
illuminates the node. The AoA spectrum information can be directly passed on by a
node’s receiving hardware, or can be derived by processing beam tracking information
(i.e., the sector ID of the phased antenna array). The latter can be forwarded by MAC
protocols such as 802.11ad, which are aware of the sector ID. The algorithm estimates
the location a mobile user in an indoor space working without any a priori knowledge
about the surrounding and the location and number of access points available
[PCW17]. Once the user location and the anchors have been estimated with sufficient
accuracy, it is possible to reconstruct the shape of environment determining the
location of reflective surfaces and walls. The intuition is as follows : the geometric
relationship between physical APs and virtual APs (VAs) permits to estimate the
location of the point on the wall where the signal of the physical AP reflects. The
accuracy of the estimation is enhanced taking into account different user locations to
see the reflection point on the wall from different angles. Accurate localization and
tracking can also serve as a proxy for physical communication functions, such as
beamforming, handovers and context switching.
In Section 3.4 baseline architecture for the mm-wave AP clustering was described, in
this section we extend it to support localization function. To optimize the cluster
management, it could be beneficial to consider the extent of UE mobility and
implement location information and heuristics to predict when and where a UE should
perform handover; considering link quality and the overhead associated with the
handover. Additionally, as the mm-wave RAT will be heavily reliant on beam-based
transmission, the beam training and beam width adaptation strategies need to be
evaluated to optimize the handover procedure for various mobility scenarios. As some
of the APs within a cluster may be serving multiple UEs using overlapping beams will
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be beneficial to coordinate the beam steering between the APs based on network
statistics, to minimize the beam interference. Therefore, clustering might introduce
new, optional RAN functions such as UE location tracking and mobility event
prediction entity.
5.2 Network integration
In the previous sections, five architectural enablers for mm-wave system were
identified as: multi-connectivity, new mobility state, mm-wave cell clustering, network
slicing, and self-backhauling. To design architecture for such a system it is necessary to
understand relation between the enablers and integrate system in the way that all these
relations are managed. The multi-connectivity concept is related to mm-wave cell
clustering where UE is primarily using one active connection to mm-wave AP, but
simultaneous connectivity to the low-band system might be highly beneficial to
properly distribute control signalling from the CN. Additionally, the way how the UE
handles cluster of mm-wave APs has much in common in maintaining APs used in
multi-connectivity. If network slicing is supported, the UE will be configured to use
and be served on a given slice, thus the UE will be able to continue performing multi-
connectivity or connecting to the different mm-wave cell clustering using the
configured slice. At the network side, coordination will be needed between the master
and the secondary nodes in order to server the UE on the same slice. The same slice
need to be supported by both nodes in order to perform dual connectivity.
The mm-wave cell clustering is also heavily dependent on available backhaul -
performance of backhaul connection implies possible architectures in mm-wave
cluster, i.e. better the backhaul connection, the more centralized the architecture can be.
For dense mm-wave networks, self-backhauling is a solution envisioned for backhaul
provisioning to the mm-wave APs. The integration of these two architectural enablers
is mainly about making the right decision on the split between the cloud and radio,
and about deploying the right type of TRPs that are handling either RF up to MAC, or
up to the PDCP, in case where self-backhauling performance is far from ideal. In case
of centralized deployment, the central node may be configured to support a multitude
of slice instances, with flexible configuration of the slice availability in the distributed
nodes.
Self-backhauling is an enabler for multi-connectivity since connection between base
stations over Xn interface is needed. Multi-connectivity needs to be integrated with
self-backhauling which will enable fast data forwarding for buffer synchronization
between the APs involved (in case where service flow is split on the PDCP level). Self-
backhauling should be transparent to the UE, when network slicing is used in a
heterogeneous network. However, at the network level the RRM could turn to be
complex, especially when the spectrum will be shared between multiple slices but also
between self-backhauling and access.
The new mobility state RRC_INACTIVE requires integration with several other
enablers. The RRC-INACTIVE state requires measurements and other information
from UE for its proper configuration. In case of inter-RAT multi-connectivity, available
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measurements might vary and integration requires taking proper measurements and
processing them, in order to have information that can be used to change mobility
states and configure RRC_INACTIVE state to achieve gains from using this new
mobility state. In case of network slicing, it is expected that the slice availability will be
consistent at least within the tracking areas, which a UE can be configured with and a
UE in inactive mode can freely reselect to other cells within the configured tracking
area index list (TAI-list) and resume its connection in its previous slice. If a UE in
RRC_INACTIVE selects a cell outside the configured TAI-list, it will resume its
connection and perform a tracking area update, whereupon the CN can redirect it to
the proper slice, if needed. When the UE resumes the connection, the UE may need to
consider slice-specific access control policies broadcasted by the network.
When considering operation of mm-wave clusters under presence of RRC_INACTIVE
state, the impact on cluster reconfiguration, cluster updates and buffer synchronization
needs to be considered.
Self-backhauling needs to be aware of RRC_INACTIVE and APs need to keep S1
connection to the core under mobility of the UE in the new state.
In Section 3.1, we described a framework that helps to combine various architectural
solutions, via introduction of additional abstraction layers.
6 Conclusions
In this paper we have described the challenges and viewpoints identified by
mmMAGIC project related to the successful deployment of mm-wave networks.
Focusing on a generic RAN architecture (still under study within 3GPP Working
Groups), both for standalone and non-standalone deployments, a multi-RAT multi-
layer management framework is first proposed based on so-called Green Link Budget
metric. Five architectural enablers are then described which can be a key for successful
deployment of mm-wave networks, namely: multi-connectivity, a new mobility state –
the RRC_INACTIVE, mm-wave cell clustering, network slicing and self-backhauling.
Network slicing is briefly introduced as the basic glue where multiple services and
business operations are realized independently on a shared infrastructure. Some
technical enablers are then presented with technical detail, that are considered to bring
significant benefits to mm-wave system operation. Finally, interrelations between the
above mentioned technical enablers are also explained, to round up the different
elements into a holistic concept for mm-waves.
To conclude the paper, we highlighted the identified architectural enablers envisioned
for 3GPP LTE Rel-13. The multi-connectivity concept extends dual connectivity in
terms of number of base stations that can be involved for connection (in dual
connectivity two, and for multi-connectivity, two or more APs).
Since mm-wave systems should effectively operate in standalone deployments, the
modification in the protocol stack is needed. To handle infrequent transmissions of
small packets (e.g. traffic generated by smartphone applications working in the
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background) the new RRC_INACTIVE state needs to be introduced to complement the
states standardized for those present in existing technologies.
The novelty in the architecture related to the mm-wave cell clustering is mainly due to
introduction of the new logical entity and named cluster head. The cluster of mm-wave
cells is different from the group of cells used in dual-connectivity (LTE) or multi-
connectivity (5G) and the cluster head has responsibilities beyond those of MeNB e.g.
coordinating the beams, managing buffers of APs in the cluster.
The mm-wave system will also support network slicing, where the same physical
infrastructure can be configured as multiple logical networks operated separately.
Self-backhauling does not exist in LTE Rel-13 – something that resembles it is LTE-Rel-
10 relaying but the self-backhauling strives for simplification and doesn’t have
overhead coming from backward compatibility. Relays were introduced for mitigating
coverage problems and self-backhauling can help both in extending capacity and
coverage. The self-backhauling is based on dynamic scheduling (different from LTE’s
semi static scheduling). The architecture for self-backhauling supports backhaul
mobility and backhaul multi-connectivity. LTE relaying was standardized for
distributed deployments whereas self-backhauling works both for centralized and
decentralized deployments with various split points.
All five key architectural enablers introduce novelty when compared to LTE Rel-13
architecture and enables operation that will satisfy requirements identified for the use
cases envisioned for mm-wave RAT.
7 Acknowledgement
The research leading to these results received funding from the European Commission
H2020 programme under grant agreement n°671650 (mmMAGIC project).
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