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A strong signal is not the same as good performance.

 GSM / early WCDMA  ·  field observations

That took time to accept. Many of the worst-performing clusters had excellent RSSI, clear dominance, and coverage plots that looked textbook clean. Yet they produced persistent call drops, handover failures, and poor voice quality day after day.

The issues were rarely coverage gaps. They came from how the radio system behaved under real traffic load. That distinction is not obvious until you spend enough time correlating drive data against busy-hour OSS counters and watch the two tell completely different stories.

Static neighbor planning

Neighbor lists were typically planned once during rollout and rarely touched afterward. Over months, traffic patterns shifted. New sites were added. Antenna adjustments changed dominance areas. The original neighbors became outdated, but the configuration stayed the same.

Symptom in OSS (busy hour): HO failure rate elevated in cluster.   No RXLEV degradation in drive data.  Root cause: Dominant cell shifted post-rollout.   Originally planned neighbor no longer the strongest candidate,   HO attempted to suboptimal target, poor RXQUAL drop before RLF timeout.  Drive test (off-peak, low traffic): No failure observed,   problem invisible outside busy-hour OSS counters

Handovers were sent to poor targets. Drops happened that drive tests in quiet conditions never caught. The fix required pulling OSS neighbor performance counters, cross-referencing with measurement reports, identifying actual dominant candidates under load, and rebuilding the neighbor set accordingly.

Interference from aggressive frequency reuse

Aggressive frequency reuse improved capacity metrics on paper. The interference effects were load-dependent and invisible in idle mode. They only appeared when multiple TRXs were active simultaneously, which meant post-launch drive tests done at low traffic hours missed the problem entirely.

Off-peak (1-2 TRXs active): RXLEV: -63 dBm strong RXQUAL: 0-1 clean C/I: ~14 dB above threshold Busy hour (full TRX load): RXLEV: -63 dBm unchanged, signal intact RXQUAL: 5-6 degraded C/I: ~7 dB below 9 dB GSM threshold Interference only surfaced when reading: HO failure counters (OSS) Congestion stats per TRX RXQUAL distribution, not just averages correlated together, not individually
Parameter inconsistency within clusters

Small differences in configuration between adjacent cells were rarely flagged during commissioning. Power control thresholds, handover margins, and timing advance settings that looked reasonable in isolation produced unpredictable behavior when cells interacted under load.

Asymmetric HO margins between adjacent cells: Cell A to Cell B: margin = 6 dB,   Cell B to Cell A: margin = 10 dB.   HO triggered in one direction, blocked in the other,   ping-pong under mobility, increased SDCCH load, congestion

The problem was that cells were audited individually. The interaction between adjacent cells under load was never modeled. Fixing this required cluster-level parameter audits, comparing every adjacent pair, not reviewing each cell in isolation.

Field data and OSS counters used together

The most effective troubleshooting combined drive-test measurements with OSS counters. The drive data showed where problems occurred. OSS counters showed when and under what load. Neither was sufficient alone. A drop appearing in both sources, correlated by location and time of day, was far more actionable than either finding in isolation.

Drive test alone: shows WHERE signal degraded cannot show WHEN or under what load OSS counters alone: show WHEN failures occurred, at what rate cannot show WHERE at UE level or WHY physically Combined: location + timing + load correlation identifies whether root cause is coverage/interference/neighbor/parameter

Only patterns confirmed in both sources were acted on. Single-source findings were treated as hypotheses, not conclusions.

A strong signal is not the same as good performance. Real networks succeed or fail based on how they behave under traffic, not on how they look on a coverage map. Coverage, interference, neighbor relations, and mobility parameters are a living system. They need to be analyzed together and revisited regularly. That understanding became the foundation for the more disciplined, data-driven optimization that followed in LTE and later generations.
GSM  ·  WCDMA  ·  RAN Optimization  ·  Interference Management  ·  OSS Analytics  ·  Neighbor Planning  ·  RF Engineering  ·  Telecommunications

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