AI vs SON

SON was introduced in 3GPP Release 8 (2008) with a clear mandate: reduce OPEX by automating repetitive network optimization tasks. It has three pillars: Self-Configuration (automatic site setup — plug-and-play eNBs), Self-Optimization (continuous parameter tuning — ANR, MRO, MLB, CCO), and Self-Healing (automatic fault detection and recovery — cell outage compensation). SON uses rule-based algorithms with predefined thresholds: "if RSRP < -110 dBm and load < 30%, increase coverage" type logic.

AI/ML in telecom replaces SON's fixed rules with learned models. Instead of "if RSRP < threshold then action," an ML model learns from millions of historical optimization outcomes to predict the optimal action for any given network state. The three ML paradigms used are: Supervised Learning (train on labeled data — predict KPIs, classify faults), Unsupervised Learning (find patterns without labels — anomaly detection, clustering), and Reinforcement Learning (learn by trial and reward — real-time parameter optimization).

SON has three deployment architectures: Centralized SON (C-SON) runs in the OSS/NMS with a global view but 15-60 min latency. Distributed SON (D-SON) runs on the eNB itself with <100ms latency but limited network view. Hybrid SON combines both. AI adds a fourth: O-RAN RIC-based with Near-RT RIC (10ms-1s) and Non-RT RIC (>1s) hosting xApps/rApps that can run ML models alongside or replacing traditional SON algorithms.

SON ANR (TS 32.511): UE reports detected PCIs via measurement reports. eNB resolves PCI to ECGI and adds to Neighbor Relation Table (NRT). Simple, proven, automatic. AI ANR: ML predicts which neighbors should be in the NRT based on traffic patterns, handover history, and topology, even before a UE reports them. AI can also identify and remove stale neighbors (defined but never used) that waste UE measurement resources.

SON MRO (TS 32.521): Detects too-late, too-early, and wrong-cell handovers from RLF reports. Adjusts CIO (Cell Individual Offset) by +/-1 dB per cycle. Simple state machine. AI MRO: LSTM predicts UE trajectory, RL learns optimal per-UE handover parameters. Can adjust A3-Offset, TTT, Hysteresis, and CIO simultaneously — something SON cannot do without conflict resolution nightmares.

SON MLB (TS 32.522): When cell load exceeds threshold (e.g., PRB > 80%), increase CIO to push users to less-loaded neighbors. Problem: reactive — by the time it triggers, users already experience congestion. AI MLB: LSTM predicts traffic 1-4 hours ahead. Pre-adjusts CIO before congestion occurs. Multi-cell optimization ensures offloaded traffic does not overload the target cell.

SON CCO: Adjusts electrical tilt and TX power per cell based on coverage/capacity indicators. Problem: CCO and MLB can conflict (CCO reduces tilt for coverage, MLB increases load on the extended cell). SON resolves via priority. AI CCO: Treats the entire cluster as a single optimization problem. Uses RL or genetic algorithms to find globally optimal tilt/power settings across all cells simultaneously, respecting all KPI constraints.

The future is not "AI replacing SON" but Cognitive SON — using ML to enhance SON functions. SON provides the proven, safe framework (standardized, explainable, fast). AI provides the intelligence layer (prediction, multi-KPI optimization, anomaly detection). In Cognitive SON, ML models advise SON functions rather than bypassing them. The SON engine remains the actuator (safe, bounded parameter changes), while AI becomes the brain (what to optimize, when, and by how much).