LTE Backhaul: Planning and Optimization

389 Pages·2015·20.365 MB·English

by  Esa Metsälä, Juha Salmelin

Table of Contents
Cover
Title page
List of Contributors
Foreword
Acknowledgments
List of Abbreviations
1 Introduction
1.1 To the reader
1.2 Content
1.3 Scope
Reference
2 LTE Backhaul
2.1 Introduction
2.2 LTE Backhaul Planes
2.3 Radio Features of LTE and LTE-A
2.4 Requirements for LTE Backhaul (SLAs)
2.5 Transport Services
2.6 Planning Problems
2.7 LTE Backhaul Technologies
2.8 Small Cell Backhaul
2.9 Future Radio Features Affecting Backhaul
2.10 Related Standards and Industry Forums
2.11 Operator Example
References
3 Economic Modeling and Strategic Input for LTE Backhaul
3.1 Introduction
3.2 Strategic Input for Planning
3.3 Quantifying benefits
3.4 Quantifying costs
3.5 Case router
3.6 Wireless Backhaul Case Study
References
Further Reading
4 Dimensioning Aspects and Analytical Models of LTE MBH Networks
4.1 Introduction
4.2 Dimensioning Paradigm
4.3 Applications and QoE: Considerations
4.4 Dimensioning Requirements
4.5 Traffic Models
4.6 Network models
4.7 Dimensioning
References
5 Planning and Optimizing Mobile Backhaul for LTE
5.1 Introduction
5.2 Backhaul Network Deployment Scenarios
5.3 Network Topology and Transport Media
5.4 Availability and Resiliency Schemes
5.5 QoS Planning
5.6 Link Bandwidth Dimensioning
5.7 Dimensioning Other Traffic Types
5.8 Base Station Site Solutions
5.9 Security Solutions
5.10 IP Planning
5.11 Synchronization Planning
5.12 Self-Organizing Networks (SON) and Management System Connectivity
5.13 LTE Backhaul Optimization
References
6 Design Examples
6.1 Introduction
6.2 Scenario #1: Microwave
6.3 Scenario #2: Leased Line
Reference
7 Network Management
7.1 Introduction
7.2 NMS Architecture
7.3 Fault Management
7.4 Performance Management
7.5 Configuration Management (CM)
7.6 Optimization
7.7 Self-Organizing Network (SON)
7.8 O&M Protocols
7.9 Planning of Network Management System
References
8 Summary
Index
End User License Agreement
List of Tables
Chapter 02
Table 2.1 Delay and loss requirements for different planes (upper bound)
Table 2.2 Synchronization requirements for LTE mobile standards (see Chapter 6 of
Metsälä and Salmelin, 2012)
Table 2.3 LTE feature-dependent phase synchronization requirements
Table 2.4 DSL standards suitable for LTE backhaul
Table 2.5 Categorization of non-ideal backhaul in 3GPP Release 12 work (3GPP,
2014)
Table 2.6 Categorization of ideal backhaul in 3GPP Release 12 work (3GPP, 2014)
Chapter 03
Table 3.1 Estimates of backhaul capex and opex as percentage of operator revenue
Table 3.2 Opex types relevant to the LTE backhaul
Table 3.3 Annual cash flows of Example 3.7
Table 3.4 Cumulative net cash flows of Example 3.7
Table 3.5 Net present value calculation
Table 3.6 Related cost items for Alternative A (leased lines) and Alternative B
(wireless)
Table 3.7 Cash flows (thousands)
Table 3.8 NPV of Example 3.8 (thousands) for Alternative B (compared against
Alternative A)
Chapter 05
Table 5.1 Availability and corresponding downtime per year
Table 5.2 Availability calculations for different hardware configurations
Table 5.3 Unavailability for different types of links is calculated from the outdoor and
indoor units
Table 5.4 Unavailability comparison for different types of links a
Table 5.5 Availability comparison for different types of links
Table 5.6 Protection latency times from different layers
Table 5.7 A simple example of traffic type to service class mapping, with four classes
Table 5.8 An example of traffic type to service class mapping, with eight classes
Table 5.9 Overheads for S1 user plane transport
Table 5.10 Typical cost of selected S1 signaling procedures for DL and UL, including
SCTP/IP/Eth/L1 overhead. The UL/DL cost is calculated over 60 minutes
Table 5.11 Example control plane traffic model
Table 5.12 Single-server queue types with example applications
Table 5.13 Summary of dimensioning approaches
Table 5.14 Planning parameters for IPsec
Table 5.15 Planning parameters for PKI
Table 5.16 Example for QoS setting for user, control, synchronization and management
planes
Chapter 06
Table 6.1 Input information for both scenarios
Table 6.2 Example eNB IP address allocation
Table 6.3 Summary of IP addresses in the design example
Table 6.4 Summary of availability calculations for eNB in the example topology
Table 6.5 Mapping of traffic types to classes
Table 6.6 Summary of provider service offering of the candidate service providers
List of Illustrations
Chapter 02
Figure 2.1 Network element symbols
Figure 2.2 Definition of network areas
Figure 2.3 LTE related e2e planes of backhaul network
Figure 2.4 LTE U-plane protocol stack
Figure 2.5 LTE C-plane protocol stack
Figure 2.6 Frequency aligned signals
Figure 2.7 Phase and frequency aligned signals
Figure 2.8 Time, phase and frequency aligned signals
Figure 2.9 Management plane protocol stack
Figure 2.10 Principle of carrier aggregation
Figure 2.11 Carrier aggregation improves average cell throughput both in UL and DL
(Nokia Networks, 2014)
Figure 2.12 Principle of MIMO
Figure 2.13 MIMO peak data rates with different antenna configurations (Nokia
Networks, 2014)
Figure 2.14 Principle of CoMP (intra-eNB)
Figure 2.15 Principle of relay nodes
Figure 2.16 Examples of achievable cell throughput gains in some relay node scenarios
(Nokia Networks, 2014)
Figure 2.17 Principle of time domain enhanced inter-cell interference coordination
Figure 2.18 User plane peak capacities of LTE base station for different frequency
bands
Figure 2.19 Average capacity of LTE base station for different frequency bands
Figure 2.20 X2 handover signaling
Figure 2.21 Security architecture
Figure 2.22 Topology example
Figure 2.23 FTP throughput for different shaping configurations
Figure 2.24 Simplified network picture
Figure 2.25 Unsuccessful inter-frequency handover, real time per day for the base
stations shown in Figure 2.24
Figure 2.26 Unsuccessful inter-system handover, real time per day for the base stations
shown in Figure 2.24
Figure 2.27 Different radio KPIs per day for another base station which changed from
in sync to out of sync
Figure 2.28 Chain of L2 and L3 VPNs in case of microwave backhaul
Figure 2.29 L3 VPN in case of optical backhaul
Figure 2.30 High-level planning process
Figure 2.31 Physical media used in the backhaul for 2013 and 2018 (Infonetics
Research, 2014)
Figure 2.32 Radio data rate as a function of modulation with different radio channel
bandwidths
Figure 2.33 Scheduler coordination alternatives (eCoMP)
Figure 2.34 Principle of dual connectivity
Figure 2.35 Alternative architectures of dual connectivity
Figure 2.36 Impact of dynamic ABS muting pattern adjustment on system performance
Figure 2.37 Operator example combining different leased line providers and own
equipment
Chapter 03
Figure 3.1 2015 Estimate for network capex by network component.
Figure 3.2 Telco’s opex.
Figure 3.3 Comparison of alternatives
Figure 3.4 Router investment
Figure 3.5 Annual net cash flows of Example 3.7
Figure 3.6 Example 3.7 net cash flow and cumulative net cash flow
Figure 3.7 Alternative A: Leased line access (shaded area considered)
Figure 3.8 Alternative B: Wireless access (shaded area considered)
Figure 3.9 Cumulative net cash flow (thousands)
Chapter 04
Figure 4.1 The connectivity and interfaces of an LTE eNB
Figure 4.2 The dimensioning process
Figure 4.3 Simulation results showing the measured throughput of TCP CUBIC
connections not in timeout over a 10 Mbps transport link serving a first in, first out
(FIFO) buffer with maximum delay thresholds set to 50, 100 and 200 ms. The buffer
was managed by RED with minimum threshold set to 50% of the maximum delay and
maximum drop probability set to 10%. The number of simultaneous TCP connections
was increased from 30 to 200. The available per connection nominal bandwidth is the
link capacity divided by the number of TCP connections
Figure 4.4 Simulation results showing the ratio of the measured throughput and the
available (nominal) bandwidth of TCP CUBIC connections not in timeout. The
simulation setup was the same as the one shown in Figure 4.3. As the load is increased
by starting more and more connections, an increasing number of connections enter in
timeout, thus the available bandwidth is taken by those connections that are not in
timeout
Figure 4.5 Simulation results showing the ratio of the TCP CUBIC in exponential back-
off. The simulation setup is the same as the one in Figure 4.3. As the load is increased
by starting more and more connections, increased numbers of connections enter timeout
Figure 4.6 The anatomy of an HTTP object download
Figure 4.7 The anatomy of a Web page download
Figure 4.8 Simulation evaluation of the simple capacity calculation. The calculated
capacity is shown in the function of the target download time (the quality requirement).
The results show that at reasonable target download time values the overtime of the
Web page download versus the target download time is less than 20%
Figure 4.9 Web page download rate as a function of time. The same Web page was
downloaded over an LTE system with a point-to-point transport link connecting the S-
GW and the eNB. The link capacity was calculated with the simple capacity
calculation based on the target Web page download time set to 5 s (a) and 20 s (b)
Figure 4.10 The cumulative distribution function of the YouTube media rate in function
of the itag
Figure 4.11 The anatomy of a YouTube download
Figure 4.12 Dimensioning requirements
Figure 4.13 Relationship between the modeling levels
Figure 4.14 Example of the daily load profile
Figure 4.15 The source of multiplexing gain due to geographic diversity
Figure 4.16 Identifying the busy hour: the busy hour is the time of the day when the
highest amount of transport resources are needed. Due to the complexity of the traffic
mix, the identification of the busy hour is difficult as the required amount of transport
resources largely depends on the traffic mix
Figure 4.17 Busy hour load and session level models. The session level models
capture the dynamics of the session arrival process and allow the calculation of the
number of simultaneous sessions during the busy hour
Figure 4.18 The typical tail distribution of the number of simultaneous sessions. In this
example, the average number of simultaneous sessions is 20 and the quantiles Q are
p
shown for p = 0.9, p = 0.99, and p = 0.999
Figure 4.19 Microscopic level behavior of the On–Off traffic source
Figure 4.20 The number of sessions in the ‘On’ state changes dynamically
Figure 4.21 Typical dependence of the required and effective bandwidth on the number
of simultaneous sessions
Figure 4.22 Packet level behavior within an On period
Figure 4.23 Link capacity and the arrival process: if identical streaming sources with
deterministic TTI are aggregated, the required link capacity will be a function of the
aggregated traffic process. In this example, the packets are always served before a new
packet arrives; therefore, the required link capacity is not increasing with the number of
sessions
Figure 4.24 Link capacity and the arrival process: if identical streaming sources with
deterministic TTI are aggregated, the required link capacity will be a function of the
aggregated traffic process. In this example, the packets are not served before a new
packet arrives; therefore, the link capacity is increasing with the number of sessions.
Dimensioning should guarantee that the delay of the second, third, etc. packet is below
the maximum allowed time
Figure 4.25 Link capacity and the arrival process: if identical streaming sources with
deterministic TTI are aggregated, the required link capacity will be a function of the
aggregated traffic process. If there is no strict delay requirement, dimensioning should
guarantee that the buffer content is served during one TTI. The required link capacity in
this case is linearly increasing with the number of connections
Figure 4.26 Packet and burst level queues in the PDF of the buffer occupancy Y
Figure 4.27 The TCP source is probing the system for the available bandwidth. As at
the connection setup it has no knowledge on the system conditions, data transfer is
started at a low rate, which is increased exponentially (slow-start). During this period,
the source is not using the system efficiently. In a similar way, a slow-start is executed
after a timeout or a longer idle period, during which the resources are not fully utilized
Figure 4.28 When the requested object (file, software update, etc.) is large enough, the
TCP connection spends the majority of its lifetime as an elastic connection
Figure 4.29 The TCP connections established during a Web browsing session are not
acting as elastic sources as they spend significant time within the slow-start phase
Figure 4.30 The difference between the real and ideal elastic sources
Figure 4.31 Users may establish TCP connections through several S-GW to various
content servers, that is the TCP connections served by an eNB can have diverse routes
Figure 4.32 Queuing model of a transport node. The figure assumes that the outgoing
link is the bottleneck link; therefore, the node is modeled by its output scheduler
Figure 4.33 An example of delay and waiting packets in a node
Figure 4.34 Example of a transport network topology with uplink and downlink traffic
Figure 4.35 Queueing network model of the reference transport topology. The topology
is converted into two independent queuing networks with directed links corresponding
to the uplink and downlink traffic considered not correlated
Figure 4.36 Example of delay requirements derived from the expectations of different
connections sharing the same link
Figure 4.37 End-to-end protection over two domains
Chapter 05
Figure 5.1 Detailed planning is a continuous process
Figure 5.2 MPLS pseudowire
Figure 5.3 MPLS L2 VPN supports multipoint-to-multipoint connectivity
Figure 5.4 L3 VPN
Figure 5.5 Network deployment example using IP access
Figure 5.6 Network deployment example using Ethernet in the access
Figure 5.7 Simple network topologies
Figure 5.8 Ring availability from an eNB point of view presented as an equivalent
parallel system
Figure 5.9 HSRP/VRRP principle
Figure 5.10 BFD-triggered gateway protection
Figure 5.11 Synchronization protection using IEEE1588v2 timing over packet
Figure 5.12 A simple QoS model for an access layer transport node. Other scheduler
types could also be used
Figure 5.13 WRR/WFQ scheduler with four queues
Figure 5.14 Strict priority queuing scheduler with four queues
Figure 5.15 The operation of traffic shaping. Transport node #2 has a buffer size of four
packets
Figure 5.16 Traffic shaping and policing for the leased line provider use case. Service
provider polices packets entering its network
Figure 5.17 An example of distribution of data traffic over 24 hr for three sites and at
the network level
Figure 5.18 Example load distribution factor per site versus site daily traffic volume.
Each dot is one site