Self-Organizing Networks for 5G and Beyond: A View from the Top
1.1. Motivation and Main Drivers for SON Deployment in Next-Generation Networks
- B5G networks further increase the complexity of network planning and optimization processes since the former must coexist and cointeract with the existing networks (2G/3G/4G/5G) in parallel. The network parameter design, tuning, and management lead to a high complexity in terms of smooth coordination among all existing networks, and it is obvious that as more complex architectures and B5G evolve, network management shall not be feasible without the use of automated SON functions operating in real time and taking direct feedback from the network [8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23].
- The standardization of network traffic characteristics and parameters in order to support the basic 5G use cases (i.e., e-MBB, URLLC and MTC) should be embedded in SON systems and applications so that already set targets for 5G, such as low latency and high reliability, can be achieved.
- The benefits of AI and ML for the operation of next-generation networks should be applied as the basic building blocks for B5G intelligent operation. SON platform advancements and network automation are clearly dependent on advanced ML algorithms and their implementations [8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,30]. Simplistic automation solutions do not enable a distributed approach and they are not effective and efficient in complex environments.
- Academia and the telecommunications industry to combine 5G use cases and intelligent techniques, such as C-RAN, mmWave, cognitive radio (CR), network slicing, spectrum sharing, NFV/SDN, mm-Wave (Millimeter-Wave), automated backhauling and massive MIMO (massive multiple-input and multiple-output). with next-generation SON platforms for maximum performance.
1.2. Paper Structure and Contribution
2. SON Operation Rationale, Use Cases, Standardization, Architectures and Dimensioning
2.1. Manual RAN Planning and Optimization Activities Replaced by SON
2.2. SON EU Research Programs, the NGMN Alliance and 3GPP Standards
2.3. Essential SON Use-Cases
2.4. SON Systems Basic Architectures and Operation Rationale
- Centralized (C-SON).
- Distributed (D-SON).
- Hybrid (H-SON).
2.5. Principles of Dimensioning and Designing a SON System
- Network features applied in the RAN per vendor.
- RAN parameter settings/values. Key points include:
- Handover and reselection policy.
- Admission control policy (admission control is the process that evaluates the existing resources to check if these are sufficient, prior to the establishment of a new connection).
- Load balancing policy (load balancing aims to transfer the load from overloaded cells to the neighboring less loaded ones so that end-user experience and network performance is improved).
- Scrambling codes planning strategy in UMTS and Physical Cell ID (PCI) planning strategy in LTE and 5G.
- Neighbor relation planning strategy (unidirectional/bidirectional) for Intra-Frequency/Inter-Frequency and Inter-RAT and number of maximum neighbors per cell for all technologies.
- Number of maximum number of cells supported per BSC/RNC/MME/AMF.
- Inter-carrier Layer Management Strategy (LMS).
- Frequency bands and number of carriers used per technology (UMTS 2100 MHz, LTE 900 MHz, 1800 MHz, 2600 MHz, etc., according to the spectrum usage acquisition and spectrum refarming policy for 5G).
- Radio Frequency (RF) hardware equipment in the network (antenna types, Remote Electrical Tilt (RET) types).
- Pre-SON network KPIs and the expected result after SON deployment.
- Performance Monitoring (PM) counters activated per vendor.
- Desired IP planning information for SON platform by considering all needed interconnections and ports that must be open.
- Current and expected OSS dimensioning information. (Moreover, full read/write execution rights to the OSS assigned to the SON orchestrator should be ensured.)
- The geolocation information (latitude and longitude) of the cells as well as antenna azimuth information.
- Possible network expansion and site rehoming plans from a BSC/RNC/MME/AMF to a neighboring one, or the use of new RAN technologies.
- Network size in terms of the number of subscribers and number of active cells per technology, number of network controllers (BSCs, RNCs, MMEs, and AMFs) and number of OSS systems per vendor. A typical real case scenario for a medium-sized operator in Europe prior to 5G commercial launch might include 6,000,000 subscribers in total, 60,000 GSM cells, 90,000 UMTS cells, 80,000 LTE cells, 25 BSCs and RNCs and 5 MMEs. The number of OSS subsystems depends on the different vendors an operator might use.
- Decision regarding the hardware infrastructure needs of the SON system to be deployed and prediction for possible hardware expansion (mainly based on the input collected regarding the number of cells per technology as well as existing network controllers (BSC/RNC/MME/AMF).
- Decision about the needed SON applications and tuning of the latter according to the current network planning and optimization strategy.
- Decision and prediction about the expected KPI improvement.
3. SON Applications
3.1. Basic SON Applications in 2G3G/4G/5G Networks and Operation Rationale
- Automatic neighbor relation (ANR).
- Automatic radio network configuration (initial radio and core parameters setting).
- Mobility Load Balancing optimization (MLB).
- Coverage and capacity optimization (CCO).
- Mobility Robustness Optimization (MRO).
- Automated configuration of physical cell identity (Cell ID (CI) and PCI optimization).
- Interference reduction.
- Minimization of drive testing (MDT).
- Energy saving (ES).
- RACH (Random Access Channel) optimization.
- Inter-cell interference coordination (ICIC).
3.2. The Key SON Applications Leading to Full Automation
3.2.1. SON (ANR/Handover Success Rate) and NCL (The Need behind ANR)
- Intra-frequency (same technology and same frequency) cells of the serving and neighbor cells.
- Inter-frequency (same technology and different frequency) cells of the serving and neighbor cells.
- Inter-RAT (different radio access technology) cells of the serving and neighbor cells.
- All the above cell interactions of the serving and neighbor cells in the same Base Transceiver Station (BTS)/NodeB/eNodeB/gNB.
3.2.3. Input for ANR and Triggering
- DCR (Drop Call Rate) for a defined sample period or bad quality (SNR ratio).
- Minimum number of calls served (low or high usability of a cell).
- Importance and usability of existing neighbor relations according to the ratio of handovers for a specific neighbor relation compared to the total number of handovers of the cell.
- Missing neighbor events (collection of network counters reporting missing neighbors).
- Intersection (coverage pattern of each cell and overlap among them).
- Cell coverage including propagation delay counters collected from the OSS.
- Layer Management Strategy (LMS): Policy followed by the operator regarding the inter-frequency relations (i.e., three carriers are used (F1, F2, and F3) if a handover is allowed).
3.2.4. Mobility Load Balancing (MLB) and Traffic Steering (TS)
- Radio resource load related to blocking due to a lack of resources.
- BTS/NodeB/eNodeB/gNB hardware resource load.
- Transport/backhaul network resource load.
3.2.5. Scenarios and Impairments That Trigger MLB Operation
- Offload loaded cells towards non-loaded ones, achieve a better UE throughput and QoS (Quality of Service), and ensure optimum cell capacity while users are moving within an area of interest, as a scenario assumes that an operator uses three carriers for one radio access technology. When a high load is observed in one of the three carriers, load balancing is achieved by forwarding traffic from one of the three carriers to the other through a reselection or handover.
- Offload higher power/capacity macro cells towards low-power/capacity micro/pico or femto cells. This scenario is deployed through the decreasing or increasing cell coverage by adjusting cell power. The same stands in a case where cells operate at high power, but traffic is low.
- Ping pong effects limitation (cases where handovers take place constantly). This phenomenon is seen especially in cases such as highways with fast moving UEs trying to camp on lower cells.
3.2.6. SON MLB and TS Applications—Conflicts among MLB and MRO (Mobility Robustness Optimization)
3.2.7. SON Coverage and Capacity Optimization (CCO), Interference Management and Adaptive Antennas
- When electrical tilt is decreased (uptilt), the coverage area of the cell might overlap with a neighboring cell, and thus interference is created together with capacity issues. This specific case creates the so-called “over-shooting cells”.
- On the other hand, when electrical tilt is increased (down-tilt), the coverage area of the cell is reduced, leading to interference mitigation towards the neighbor cells, traffic offloading because of capacity limitations, and the elimination of over-shooting cells. This tradeoff must be constantly monitored, and operators must perform such changes in a central manner through SON CCO application.
4. Machine Learning Algorithms for SON
4.1. ML Algorithms Taxonomy
4.1.1. Supervised Learning (SL)
- Bayesian: Bayesian algorithms are based on Bayes theorem and their aim is to calculate probabilities according to pre-existing probabilities. Additionally, they do not require a large training sequence.
- Neural networks: This specific category comes from biology (brain neural networks). Neural networks are trained by taking examples and generating identification characteristics from the training input that they are given. A neural network consists of a set of interconnected nodes, while the processing ability of the nodes’ network is described through the node connection weights, created by the adaptation of a learning process obtained by training patterns provided as input .
- Decision trees (DT).
- Hidden Markov Models (HMM).
- Support Vector Machines (SVMs).
- K-nearest neighbors (k-NN).
4.1.2. Unsupervised Learning (UL)
- Self-organizing maps (SoM): A SoM intends to identify groups of data so that a representation of the input data is created. This technique is called clustering. The main idea behind SoM is to group cells with similar network parameter settings and trigger changes for the specific group uniformly.
- Game theory: Known since the 1950s and applied later in biology, social science and economy/political sciences during 1970s game theory have been used for network systems modelling. According to game theory, several players, nodes or artificial agents (in the case of computer science) perform actions that affect each other while the target is to get a zero-sum result by subtracting the losses of one player and adding the gains of the winning player . According to the authors of , operators can avoid zero human interaction by using game theory approaches in mobile networks but limited literature concerning game theory applicability in LTE networks exists.
4.1.3. Reinforcement Learning (RL)
- Environment (e): A real-life scenario that an agent must interact with.
- Agent: An entity or a node that performs specific actions in an environment in order to gain some reward.
- State (s): The current situation in the environment.
- Reward (R): A return provided to the agent when a specific action is taken.
- Policy (π): The rationale that the agent follows in order to decide on the next action according to the current state.
- Value (V): The long-term reward to the agent compared to the short-term reward.
- Value Function: The value of a state as a total amount of reward gained by the agent.
- Q value (Q) or Action Value: An estimation of how good it is to take the action at each state.
- Action performed by the agent is “a”.
- State instance by performing an action by the agent is “s”.
- The reward received for each beneficial action or not is “R”.
- The discount factor is Gamma “γ”.
- V(s): Corresponds to the value calculated at a specific point.
- R(s,a): The reward at a particular state after an action is performed.
- γ = Corresponds to the discount factor that determines if the agent as regards the beneficial the rewards received in the near future compared to those in the immediate future.
- V(s′) = The value obtained at the previous state.
4.2. Docitive Learning (DL)
5. Future SON Research Directions for B5G
- The development of Virtual SON (V-SON) platforms since NFV and SDN architectures have been suggested as one of the key drivers for SON deployment in 5G networks as well as the binding of SON with C-RAN.
- The development of new ML algorithms and the binding of SON with big data and deep learning technologies.
- The binding of SON with key 5G eMBB enablers such as mmWave.
- The application of SON in IoT network infrastructures, and massive Machine-Type Communication (mMTC).
- The backhaul network management through SON.
- The risk assessment and the development of security solutions and policies related to SON systems in 5G networks.
- The development of hybrid SON solution targeting in URLLC services.
- Network Slice Selection Function (NSSF).
- Network Exposure Function (NEF).
- Network Repository Function (NRF).
- Policy Control Function (PCF).
- Unified Data Management (UDM).
- Application Function (AF).
- Network Slice Specific Authentication and Authorization Function (NSSAAF).
- Authentication Server Function (AUSF).
- Access and Mobility Management Function (AMF).
- Session Management Function (SMF).
- Service Communication Proxy (SCP).
- User Equipment (UE).
- (Radio) Access Network ((R)AN).
- User Plane Function (UPF).
- Data Network (DN), e.g., operator services, Internet access or third party services.
5.1. NFV and SON (vSON) for 5G Networks—RAN Virtualization/C-RAN Architectures and SON
- Virtualized Network Function (VNF): VNF refers to a standard network function, non-virtualized prior to NFV introduction, which can be fully or partially virtualized. Typical examples might be the MME or AMF, the SGW (Serving Gateway), or even the eNodeB/gNB. In our case, SON might be one of the VNFs .
- Element Management (EM): Refers to the management operations strategy of the VNFs.
- NFV Infrastructure (NFVI): VNFI refers to the common hardware resources (servers and storage) that host the NVFs. Routers, switches and wireless links interconnecting the main servers can be regarded as part of the NFV infrastructure as well.
5.2. Empowering SON for 5G with Big Data and New ML Algorithms
5.3. SON, mmWave and Massive MIMO Technologies for 5G Networks
5.4. SON for Backhaul Management in 5G
5.5. SON for IoT
5.6. SON Platforms Risk Assessment and Security Concerns
5.7. SON for 5G Key Use Cases—The URLLC Use Case
5.8. SON for 6G Networks
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|LTE to 3G layer IRAT handover rules||SON-related application according to 3GPP defined use cases|
|Circuit Switching (CS) fallback to 2nd carrier||ANR (Automatic Neighbor Relations)|
|Packet Swiching (PS) redirection to 2nd carrier|
|Cell reselection to 1st carrier|
|GSM to 3G layer IRAT handover rules||SON-related application according to 3GPP defined use cases|
|GSM to UMTS: Cell reselection only (no handover)||ANR (Automatic Neighbor Relations)|
|UMTS to GSM: Cell reselection and no handover|
|Define the value of admission control parameters (UMTS)||SON-related application according to 3GPP defined use cases|
|Power admission control settings: 1st carrier: 85%; 2nd and 3rd carriers: 75%||MLB (Mobility Load Balancing)|
|Codes admission control: 90%|
|UL/DL channel elements admission control: 95%|
|Describe the load balancing features activated||SON-related application according to 3GPP defined use cases|
|Example: Intrafrequency load sharing||MLB (Mobility Load Balancing)|
|SON Application||Machine Learning Technique||Algorithms That Can Be Used|
|Mobility Load Balancing (MLB)||RL or DRL, UL, SL||Q-learning|
|Mobility Robustness Optimization (MRO)||RL or DRL, UL, SL||Q-learning|
|Pattern identification SOM|
|Coverage and Capacity Optimization (CCO)||RL or DRL, UL||Fuzzy Q-learning|
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Papidas, A.G.; Polyzos, G.C. Self-Organizing Networks for 5G and Beyond: A View from the Top. Future Internet 2022, 14, 95. https://doi.org/10.3390/fi14030095
Papidas AG, Polyzos GC. Self-Organizing Networks for 5G and Beyond: A View from the Top. Future Internet. 2022; 14(3):95. https://doi.org/10.3390/fi14030095Chicago/Turabian Style
Papidas, Andreas G., and George C. Polyzos. 2022. "Self-Organizing Networks for 5G and Beyond: A View from the Top" Future Internet 14, no. 3: 95. https://doi.org/10.3390/fi14030095