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Study of Self-Optimization of Neighbor Cell Listing for eNodeB in Long Term

Evolution (LTE)

Hanan Naeem Thesis Worker

Ericsson Finland

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Study of Self-Optimization of Neighbor Cell Listing for eNodeB in Long Term Evolution (LTE)

Author: Hanan M. Naeem

Supervisor: Prof. Riku Jäntti

Instructor: M.Sc. (Tech) Mira Heiskari

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What is LTE/SAE ?

• Next generation mobile communications technology standard

• LTE - Long Term Evolution– Study and work done by 3GPP to specify the long term evolution of

the 3G radio part referred as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network)

• SAE - System Architecture Evolution– Study and work by 3GPP specifying the long term evolution of the

3G architecture, EPC (Evolved Packet Core)

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LTE Design Targets

• High data rates– DL target: 100 Mbps UL target: 50 Mbps– Cell-edge data rates 2-3 times that of Rel-6 HSPA

• Low delay/latency – User plane RTT: Less than 10 ms – Channel set-up: Less than 100 ms

• High spectral efficiency – Targeting 3 times Rel-6 HSPA

• High performance for broadcast services

• Spectrum flexibility– Operation in a wide-range of spectrum allocations– Support for FDD, Half-duplex FDD and TDD Modes

• Cost-effective migration from current/future 3G systems

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LTE - Radio Access Network

• Decentralized structure

• Single eNodeB encompassing all major functionalities

• DL preferred technique is OFDM, due to its flexible features like robustness, flexible bandwidth allocation and broadcast/multicast transmissions

• SC-FDMA used for UL due to its good PAPR (Peak-to-Average Power Ratio) performance

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SAE Core Network

• IP based Core network

• EPC to be based on a single-node concept (GW) with all necessary functions encompassed in one node except the HSS (Home Subscriber Server)

• MME (Mobility Management Entity) responsible for authentication of the user by interacting with HSS, bearer activation and deactivation and GW assignment during handovers

• Anchors all 3GPP and non-3GPP technologies like GSM,HSPA, WiMax

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LTE Services

• Rich voice• Paid information

• Data messaging• Fast browsing

• Personalization

• TV/ video on demand• High quality music streaming

• Mobile commerce • Mobile data networking

• Gaming

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Objectives of this Thesis

• Study of telecom operators’ requirements for future

• Thorough study of the concept of self-configuration & self-optimization.

• Neighbor Cell List (NCL) self-optimization in cellular networks

• Suggesting a possible NCL self-optimization algorithm

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Contemporary Operators’ Requirements

• Mobile Broadband Access• Seamless access and mobility• Support of Broadcast and Multicast• Personalization• IP Traffic Billing• Network Automation

– Self-planning – Self-configuration – Self-optimization

– Self-testing– Self-healing– Self-protecting

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Autonomic Computing

• Autonomic computing is often referred to as self-CHOP (Self-Configuration, -Healing, - Optimization, and -Protection)

• Automatic: Autonomic system must be able to self control and automatically configure or reconfigure

• Adaptive: An autonomic system must be sensitive and be able to alter its course of action based on the situations confronted based on defined policies

• Aware: An autonomic system must know itself and be able to monitor its operational context

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Self-Configuration

• Configuration of a new node or a radio base station deployed or installed in an already working cellular network

• Node undergoes self-automated management tasks to adjust to the actual confronted environment

• Automated management tasks take place in pre-operational state of the node before entering the operational state

• Referred to ´plug and play´ behavior of the network nodes which simplifies the installation processes

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Self-Configuration Features

• Decentralization: Nodes or entities interact and communicate with each other in a localized manner

• Adaptability: Ability to adapt in parallel with user density and traffic patterns

• Survivability: Capability of a system to fulfill its mission, in a timely manner, in the presence of attacks, failures, or accidents

• Scalability: The network still works with acceptable service quality

and functionalities when the number of nodes grow very large

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Self-Optimization

• Continuation of self-configuration

• Comes into action after self-configuration has been completed and the network enters an operational state

• Purpose is to maintain and improve the efficiency, service quality and performance of the network

• Change suggestions are based on performance indicators and matrices from the network itself sent by the mobile terminals

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Major Self-Optimization Tasks

• Cell Identity Management: – Due to the availability of limited number of 504 physical-layer cell

identities, Cell Identity Management is critical to avoid conflicts

• Neighbor Cell Management: – Self-optimization enables each eNodeB manages a list of immediate

neighboring eNodeBs in the network

• Power Tuning: – Self-optimized power tuning controls coverage of the nodes, interference

levels maintenance, pilot signaling strength in handover (HO) regions, automated antenna tilting, overshooting cell issues and overall network throughput

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NCL Self-Optimization Approaches

• Layer based approach

– Policy based three layered architecture– Graph associated to each layer

functionality

• Range based approach

– Neighbor cells detected within a certain range of the candidate cell are regarded as potential neighbors

– Overlapping identification finalizes the neighbor cell

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NCL Self-Optimization Approaches (Cont.)

• Antenna radiation based approach

– Same as range based technique– Neighbors are added in the NCL based

on the overlapping of antenna radiation patterns

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Suggested Algorithm for NCL Self-Optimization

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Assumptions

• All LTE mobile terminals are GPS equipped.• Self-configuration phase has been completed successfully.• Sectorization is observed throughout E-UTRAN • There are no coverage gaps after self-configuration• Self-configuration phase has allocated each eNodeB cell with a calculated

value of r

• All cell IDs, corresponding IPs and assigned parameter ‘r’ to each cell are stored in a central database.

• UE triggers measurement reports once a new potential neighbor comes across. This information is sent to the eNodeB for NCL calculations.

• Adjacent two cells (sectors) of a cell site are always added in the NCL

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Overlapping Judgment and Cell Addition to NCL

• Geographical coordinates used for angular calculations

• Measurements done with respect to antenna main lobe direction

• Overlapping detected based on different UEs in the field and measurement reports sent

• If the distance ‘d’ between UE and the detected cell is less than r, then its added as a neighbor in the NCL

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Cell Deletion From NCL

• Unwanted and obsolete neighbors are to be deleted to keep the NCL updated

• Conditions for deletion:

– Σi [Φi] = ά, i=1,2, … N

– Identify which cells are not been assigned any angle during the iteration process

– Analyze those cells in the NCL which are tagged with smaller Φ than the newer detected cell.

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Conclusions

• LTE is expected to meet most of the current market requirements

• Self-managing processes would make this communications technology more robust, scalable and adaptable

• With self-optimization of NCL would result in better system performance and throughputs

• Self-managing services would also decrease OPEX for the operators and manual intervention related issues would be avoided

• Geographical coordinates based NCL updating mechanism are simple and easy to implement with more accurate results

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THANK YOU !!