A typical operational task is to optimize the network according to coverage and capacity. The traditional way is to find the problems by drive tests and use planning tools to find possible solutions. This use case aims at discovering the coverage and capacity problems automatically through the measurements at the eNB and those reported by UEs. It minimizes the human intervention and reduces the feedback delay.
Traditionally, this has been performed based on measurements from the network and using theoretical propagation models in planning tools. It requires extensive data collection from the network including statistics and measurements, such as using extensive drive tests. In current networks, while this task has been semi-automated with the help of Automatic Cell Planning tools, this method is still largely based on measurement estimations. Therefore their results are not very accurate. Furthermore, running such tools is a cumbersome task that requires a significant preparation on the operator side to compile all necessary data inputs, create optimization clusters, and then implement the changes in the network.
Objective:
• Optimization of network coverage
• Maximize the system capacity
Expected results:
• Continuous coverage
• Increased capacity of the system
• Interference reduction
• Controlled cell edge performance
• Savings on drive tests
• Minimized human intervention in network management and optimization tasks
• Self-healing in case of equipment (e.g. eNB) failure by automatic reconfiguration of surrounding eNBs
DESCRIPTION
The term Coverage and Capacity Optimization (CCO) is very generic and covers a broad range of use cases. In reality, many of the SON use cases defined in 3GPP are somehow related to a coverage/capacity optimization effort such as, Cell Outage Compensation, Mobility Load Balancing and the Interaction between Home and Macro eNB. As defined by 3GPP, the CCO use case is understood as automatic cell planning, in which the SON algorithm has to find the optimum antenna and RF parameters for the sectors that serve a certain area for a particular traffic situation. The optimum result should maximize network throughput/capacity while complying with a certain service confidence level, as specified by the operator.
“In the area, where LTE system is offered, users can establish and maintain connections with acceptable or default service quality, according to operator’s requirements. It implies that the coverage is continuous
and users are unaware of cell borders. The coverage must be provided in both, idle and active mode for both, UL and DL. While coverage optimization has higher priority than capacity optimization in Rel-9, the coverage optimization algorithms must take the impact on capacity into account. Since coverage and capacity are linked, a trade-off between the two of them may also be a subject of optimization.”
The term “coverage” could mean coverage of basic service or coverage of user services. Basic service includes reception of DL control channels and setting up a signaling radio bearer. User services refer to services such as speech, video telephony, etc. The term “capacity” could have various interpretations. There are several options for capacity, e.g., cell throughput, median user throughput, xth-percentile cell edge throughput, and number of served users (with a specific service or bit rate requirement).
There is a tradeoff between coverage and capacity. Typically, increasing the coverage results in less spectral efficiency due to deteriorating signal power, resulting in less capacity. One cannot optimize both coverage and capacity at the same time, and therefore there is a need to balance and manage the tradeoff between the two.
The self-optimizing CCO function is a continuously running process which continuously gathers measurements and takes actions, if needed. The operator can specify the desired performance optimization target and the balance between different targets to achieve tradeoffs. Measurements and reports from the network are used by CCO to estimate entities related to coverage and capacity according to the target given by the operator. Minimization of Drive Test reports may be used to monitor and detect coverage problems in the network.
CCO corrective actions comprise changes in radio parameters such as, antenna tilt and UL power control parameters. Deployment of pico cells or coverage/capacity enhancing features are also corrective actions that may be proposed by CCO to reach desired target. The CCO should not react to performance of individual users, but rather compute appropriate CCO actions based on long-term statistics.
As with other aspects of SON development, it is expected that coverage and capacity optimization techniques will change over time, adapting to the maturity level of the networks. In the initial stages of commercial LTE network operation, traffic load will not be a major concern, and it is expected that there will be coverage challenges due to lack of sufficient cell density or configuration errors. Therefore, CCO techniques will primarily be focused around providing service coverage with a certain minimum quality.
The coverage area and capacity of a network, or some cells in the network, may vary due to addition of base stations, malfunctioning base stations, or change in user distribution. Suboptimal coverage area and capacity leads to inefficient use of network resources and lower quality. Furthermore, adapting to network changes manually is very expensive and time consuming. Thus, the CCO function operates continuously to gather measurements and takes actions if needed. CCO should be a slow activity where statistics and measurements are used as basis for decision.
3GPP TS 32.521 specifies the following requirements on CCO:
• Coverage and capacity optimization shall be performed with minimal human intervention.
• Operator shall be able to configure the objectives and targets for the coverage and capacity optimization function.
• Operator shall be able to configure the objectives and targets for the coverage and capacity optimization functions differently for different areas of the network.
• The collection of data used as input into the coverage and capacity optimization function shall be automated to the maximum extent possible and shall require minimum possible amount of dedicated resources.
The 3GPP specifications provide a set of use cases that the CCO function should cover. These use cases are defined in a very generic manner, and simply indicate that the system should have functionality that addresses these particular issues, without providing further guidelines. The following sections describe the specified scenarios.
Traditionally, this has been performed based on measurements from the network and using theoretical propagation models in planning tools. It requires extensive data collection from the network including statistics and measurements, such as using extensive drive tests. In current networks, while this task has been semi-automated with the help of Automatic Cell Planning tools, this method is still largely based on measurement estimations. Therefore their results are not very accurate. Furthermore, running such tools is a cumbersome task that requires a significant preparation on the operator side to compile all necessary data inputs, create optimization clusters, and then implement the changes in the network.
Objective:
• Optimization of network coverage
• Maximize the system capacity
Expected results:
• Continuous coverage
• Increased capacity of the system
• Interference reduction
• Controlled cell edge performance
• Savings on drive tests
• Minimized human intervention in network management and optimization tasks
• Self-healing in case of equipment (e.g. eNB) failure by automatic reconfiguration of surrounding eNBs
DESCRIPTION
The term Coverage and Capacity Optimization (CCO) is very generic and covers a broad range of use cases. In reality, many of the SON use cases defined in 3GPP are somehow related to a coverage/capacity optimization effort such as, Cell Outage Compensation, Mobility Load Balancing and the Interaction between Home and Macro eNB. As defined by 3GPP, the CCO use case is understood as automatic cell planning, in which the SON algorithm has to find the optimum antenna and RF parameters for the sectors that serve a certain area for a particular traffic situation. The optimum result should maximize network throughput/capacity while complying with a certain service confidence level, as specified by the operator.
“In the area, where LTE system is offered, users can establish and maintain connections with acceptable or default service quality, according to operator’s requirements. It implies that the coverage is continuous
and users are unaware of cell borders. The coverage must be provided in both, idle and active mode for both, UL and DL. While coverage optimization has higher priority than capacity optimization in Rel-9, the coverage optimization algorithms must take the impact on capacity into account. Since coverage and capacity are linked, a trade-off between the two of them may also be a subject of optimization.”
The term “coverage” could mean coverage of basic service or coverage of user services. Basic service includes reception of DL control channels and setting up a signaling radio bearer. User services refer to services such as speech, video telephony, etc. The term “capacity” could have various interpretations. There are several options for capacity, e.g., cell throughput, median user throughput, xth-percentile cell edge throughput, and number of served users (with a specific service or bit rate requirement).
There is a tradeoff between coverage and capacity. Typically, increasing the coverage results in less spectral efficiency due to deteriorating signal power, resulting in less capacity. One cannot optimize both coverage and capacity at the same time, and therefore there is a need to balance and manage the tradeoff between the two.
The self-optimizing CCO function is a continuously running process which continuously gathers measurements and takes actions, if needed. The operator can specify the desired performance optimization target and the balance between different targets to achieve tradeoffs. Measurements and reports from the network are used by CCO to estimate entities related to coverage and capacity according to the target given by the operator. Minimization of Drive Test reports may be used to monitor and detect coverage problems in the network.
CCO corrective actions comprise changes in radio parameters such as, antenna tilt and UL power control parameters. Deployment of pico cells or coverage/capacity enhancing features are also corrective actions that may be proposed by CCO to reach desired target. The CCO should not react to performance of individual users, but rather compute appropriate CCO actions based on long-term statistics.
As with other aspects of SON development, it is expected that coverage and capacity optimization techniques will change over time, adapting to the maturity level of the networks. In the initial stages of commercial LTE network operation, traffic load will not be a major concern, and it is expected that there will be coverage challenges due to lack of sufficient cell density or configuration errors. Therefore, CCO techniques will primarily be focused around providing service coverage with a certain minimum quality.
The coverage area and capacity of a network, or some cells in the network, may vary due to addition of base stations, malfunctioning base stations, or change in user distribution. Suboptimal coverage area and capacity leads to inefficient use of network resources and lower quality. Furthermore, adapting to network changes manually is very expensive and time consuming. Thus, the CCO function operates continuously to gather measurements and takes actions if needed. CCO should be a slow activity where statistics and measurements are used as basis for decision.
3GPP TS 32.521 specifies the following requirements on CCO:
• Coverage and capacity optimization shall be performed with minimal human intervention.
• Operator shall be able to configure the objectives and targets for the coverage and capacity optimization function.
• Operator shall be able to configure the objectives and targets for the coverage and capacity optimization functions differently for different areas of the network.
• The collection of data used as input into the coverage and capacity optimization function shall be automated to the maximum extent possible and shall require minimum possible amount of dedicated resources.
The 3GPP specifications provide a set of use cases that the CCO function should cover. These use cases are defined in a very generic manner, and simply indicate that the system should have functionality that addresses these particular issues, without providing further guidelines. The following sections describe the specified scenarios.
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