Research challenges and technologies for

Future Radio Network layer

Sílvia Ruiz

WG3 Co-chairTelecommunications and Aerospace EngineeringUniversitat Politècnica de Catalunya (UPC)silvia.ruiz@upc.edu

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WG3 Goal

• Ad-Hoc and V2V Networks• Spectrum Management and Sharing• RRM and Scheduling• HetNet and UDN• C-RAN• SDN and NFV• UAVs and flying platforms• Emerging Services and Applications

Investigate the Network Layer aspects that will characterise the merger of the cellular paradigm and the IoT architectures, in the context of the evolution towards 5G-and-beyond.

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Ad-Hoc and V2V Networks

Hybrid Networks:

• IEEE 802.11p dedicated standard for Wireless Access in Vehicular Environments (WAVE) at licensed 5.9 GHz (short range, flexibility, low latency)

• Overlaying 5G (or LTE) for V2X (wide range coverage, high data rate)

• Traffic efficiency, driver comfort, safety

Question: Future Intelligent Transportation Systems (ITSs), which communication technology to be used? (throughput/latency)

TD(16)01025

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Improving scenarios for dense vehicular Ad-Hoc Networks:

TD-08-027,

Detection of faulty/defective nodes in Delay Tolerant Networks (DTN)

• when nodes are not aware of the status of their sensors • improving Distributed Fault Detection (DFD) algorithms (long delays)• Fully distributed, easily implementable and fast convergent

• Simplifications (good for static networks) fail for a highly mobile channel.

• Simulation of Urban Mobility (SUMO) + Vehicles in Network Simulations (VEINS from OMNET ++ )

• Model for interference caused packet loss (hidden node) as function of the neighbourhood and packet size

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Network topology:

• Ring topology, one of the most resilient.• Because of delay and availability requirements, a wireless network

connected to an aggregation node must sometimes be split into several rings.

• Optimal splitting of nodes into rings.

TD(18)07017

Coverage maps prediction through ML:

• Critical to decide if more BSs in UDN are needed.

• Using sample SNR measurements from smartphones and/or dedicated low cost IoT.

• Radial Basis Network (ANN) 5

• Tools to predict SINR so IC can be performed (Piecewise Cubic Hermite Interpolating Polynomial, PCHIP). TD-09-076, Dublin,

Cognitive radio SINR prediction• Study moving interference source disturbing a mmWave mesh network.

Use Social networks to optimize through SON

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Ad-Hoc Network emulation platforms• Survey of best known emulation testbeds

for MANETs.• Difficult to customize proprietary SW.• Extendable Mobile Ad Hoc Network

Emulator (EMANE) with energy-efficient routing (OLSR) to reproduce the behaviour of real tactical radio with 24-nodes with fixed/adaptable data rate.

Nodes mobility models considering social relations (better than random)• Towards a general theory for mobility models

Energy harvesting-aware sensor selection • Needed due to the reduced number of available sensor-to-FC (fusion

center) channels. Better than JSS-EH and SS-EH (joint/separated sensor selection and power allocation) 7

5G Spectrum Management & Sharing

IoT/MTC using TVWS. • Coexistence (DVB-T2 / NB-LTE IoT

UL/DL) in: smart parking, traffic congestion, smart agriculture, smart farming and eHealth indoor

• Maximum EIRP for NB-LTE depending on the guard band.

TD(16)01020

MBB/MTC• Dynamic resource

partitioning: adjust the amount of available Small Packet Blocks (SPB) for the

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5G has to support extremely different traffic types, from MTC to MBB

access, reducing collisions and increasing throughput 8

Coexistence in novel 5G frequency bands

• 700 MHz, 3.5 GHz and 26 GHz bands.• 700 MHz interferer LTE BS by supplemental

DL (SDL), victim LTE M2M BS, both working at the duplex gap. Minimum distances to guarantee I/N.

• 26 GHz interferer IMT-2020 outdoor urban hotspot BS, victim P-P link• 3.5 GHz interferer LTE eNodeB, victim incumbent FS (Fixed Service

receiver). Co and Adj channel.

TD(18)07012,Cartagena,

• Other 5GNR bands in UHF/SHF and mmWaves up to 73 GHz.• Coexistence wit LoRa, Public Protection and Disaster Relief (PPDR)

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First world LSA (Licensed Shared Access) pilot

TD(16)01026, Lille

TD(17)03039, Lisbon

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• Italy, France and Finland• 7 LTE-TDD BS (5 femto, 3 macro) at 2.3-2.4 GHz• Coexistence with incumbent FS and PMSE (Programme Making and

Special Events). • Rules for protection and restriction/exclusion zones were obtained.

Test show that LSA provides high predictability

Exposure limits• When adding 5G (Ma-MIMO, CA) in sites with other radio

technologies New method to carry out large-scale measurements.

• RRM for D2D scenarios. D2D typically re-use UL resources since they are under utilized. Limitation is the amount of information required at the scheduler (path loss and interference)

RRM and Scheduling

Across different networks 5G, NB-IoT

RRM to balance load via inter-frequency HOs.• Calculating the SNR for different frequency

bands visible to the UEs. • Users’ load Distribution to different bands

provides high QoS with gain in throughput exceeding 8%

Tuning HO margins• LB combined with HO margins tuning, so highly/low congested cells

decrease/increase HO margins. Improves end-user throughput11

Load Balancing using QoE• LB techniques maximizing QoS show limitations and may even worsen

the experience perceived by the users. Future Systems shall also use QoE as the network performance.

TD(17)05003TD(17)05020,

TD(17)03014

dense users environments.

DUDe• Compared with CRE + eICIC. With different cell

selection criteria such as path loss or interference levels. CRE+eICIC slightly outperforms DUDe in UL UE throughput in

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HetNets RRM (5G, NB-IoT)• Static: highly sensitive to the offered traffic load.• Dynamic: drastically improves global performance but does not

automatically provide any guaranteed performance to the over-the-top services. May degrade performance, especially when the offered traffic load is non-uniform.

TD(18)07031,

• Dynamic with reservation: channels are assigned sequentially taking into account the features of NB-IoT.

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Distributed Power Control• With independent control centers, in charge of a small region, slightly

less accurate than centralizedRRM tool: Generic Wireless Network System Modeller (GWNSyM) • based on snapshots. The previous snapshot is used to feed the

following one, to analyse service evolution and dynamics.

Cooperative backhaul• Energy efficient High-speed Cost

Effective Cooperative Backhaul for LTE/LTE-A Small-cells (E-COOP)

TD:(17)05006, Graz

HetNets and UDN

To bring the network close to the UEs

Dynamic clustering• Coordinated Multi-Point focusing on Joint Transmission in C-RAN DL, so

signals interfere constructively at the receivers. • Cooperation only allowed between eNBs belonging to the same cluster• Dynamic clustering algorithm is defined. Improves sum data rate but

also individual data rates.

D:(17)03038,

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Capacity with opportunistic spectrum sharing

TD:(18)07020,

• Comparison of capacity between UHF/SHF bands and millimetre Wavebands.

• Outdoor at 2.6, 3.5, 28, 38, 60,73 GHz.• Assuming that MO have dedicated

spectrum for macro cellular layer whileScells share the access to spectrum in an opportunistic manner.

Interference among MOs• One cell from MO2 overlapped with

cells form MO1 both using the Shared band. Then supported throughput is highly reduced by the interference caused.

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Cost/Revenue in mmWaveband• Cost/revenue trade-off in small

cell networks in the mm Waveband. The revenues are noticeably higher than the costs of all frequencies.

Hyper-Dense Small-cell Network deployments• Realistic urban scenario with full 3D Building models and 3D ray-Optical

path loss predictions. • Compared with 3GPP-like environments this

are too optimistic.• Complementary CDF of RSRP and SINR with

macro / small-cell deployment on the same carrier or standalone deployment.

TD:(16)01024, Lille

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Location-aware SON• using indoor localization systems to provide

an additional source of information to support self-optimization and self-healing.

TD(17)05052,

TD(16)01031,

D(18)06018TD(18)08025

UDN user association problem • New Knapsack optimization (KO) a

combinatorial optimization technique proposed for Mobility Load Balancing (MBL) instead of eICIC with CRE and 20% ABS.

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C-RAN

Efficient RRM to optimize computational resources: • Bargaining in cooperative game theory, show a minimum 83%

improvement in allocation efficiency compared to a fixed resource allocation policy.

• BBU demand is fulfilled more than 98% without any processing delay by using only 43.5% of BBU-pool capacity

Key feature of 5G: Split BBUs and RRHs; integrates BBUs in a BBU-pool

ML for C-RAN optimization• to predict and provision computational

resources adapted to the actual needs of the BBU-pools guaranteeing QoS.

• delay minimization, load balancing based on traffic/number of RRHs, multiplexing gain.

Realistic scenarios Porto, Lisbon, Vienna18

Fog-RAN• Smart IoT devices causes excessive

load on the back-haul. • C-RAN not practical for some

delay-sensitive applications because of the long distance device-cloud center.

TD(19)11040

• Offload comp. tasks from cloud to the network edge by fog computing• Determine the BBU (fog node or in the cloud) eligible to process the RRH

Edge computing • Model to offload processing tasks of

a Mobile Device. • Heavy/light tasks defined according

to the threshold on the processing volume and the threshold on the data size.

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SDN and NFV

VRRM is in charge of satisfying the demands of diff. service slices based on aggregated capacity owned by the IP.

Centralized VRRM model • Isolation between the VNOs to ensure that their contracted SLA will not

be affected by variation of network parameters.• Sharing the aggregated capacity among various service slices on-

demand and in a fair mannerTD(17)05011D(19)10040

TD(17)03037

Network slicing, service customisation and RAN cloudification as important enablers of 5G networks.

VRRM as a convex optimisation problem• Constraints considering individual VNO’s

policies, contracted SLAs (GB, Best Effort with minimum Guaranteed BG and BE).

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IP sharing the resources of a mMIMO cell

Virtualized Networks

TD(16)02008

TD(16)02040Hypervisor customizable Resource Virtualization • for multi-user data scheduling in a LTE C-RAN

deployment, based on the Hypervisor dynamic assignment of resources to the VNOs.

• Orchestrate the interaction between the VNOs and the IP through an auction-based mechanism that allocates spatial streams to the VNOs.

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UAVs and flying platforms

UAV trajectory design and RRM • Optimisation: maximize the number of served UEs, according to

priorities, with constraints as battery duration, speed and data rate• Cost function accounts for: energy, distance, throughput, UEs

density and RRU availability.• Clustering the UEs that remain unserved• Defining a centroid point for each cluster so UAVs moves from

centroid to centroid.

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TD(18)06039D(18)08023

Used as UABS to improve path loss and link budget; to support terrestrial infrastructure or to address user demand.

Mobile Operator Network Orchestrator MANO • Manage a UAV Network Controller• Assigns available Radio Resources• Defines trajectories and missions

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Ultra-Dense Cloud Drone networks • Effect of height and antenna aperture. Radio

Environmental Map (REM). • Coverage ranges from 50 to 200 m radius• Limiting factor: inter-cell interference.

Beamforming is a promising Technology.

D(18)06007

UAVs for emergency disasters scenarios: • tool that determines the number of UAVs

required and the optimal locations. 1100 UAVs is required to cover a city center of 6.85 km2.

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D(16)02001TD(19)11021

Network Flying Platform (NFP)• UAVs, unmanned balloons HAP, MAPs,

LAPs becoming an integrated part of the cellular network. With airbone SON Systems. 23

Emerging Services & Applications

• Smart home, smart Health, smart manufacturing, smart grids, smart vehicles, smart agriculture, smart cities…

Smart metering grids: Wide-Area Monitoring System design• Real-time scalable and reliable monitoring, control and

protection of electric power generation, transmission and Distribution systems.

• With one PRB 2500 nodes can be accommodated within an LTE cell if the maximum allowable delay is relaxed from 1 to 2 s.

Public protection and disaster relief systems

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The future? A network able to satisfy all EWG demands!

SecurityUltra reliable low-latency++Distributed Dynamic clusterizationDistributed IntelligenceFully interactive & vast amount of interactionsMassive connectivityMoving BSs, RRHsLarge volumes of dataOn-the-fly decisions

So not predicting, not forecasting but “nowcasting”: be able to react to events as they happen.

Thank you for being so

active at WG3!

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