How TEMS Improves Performance: Case Studies and Best PracticesTEMS (Test Mobile System or more broadly, Test and Measurement Systems) refers to a family of tools and methodologies used to analyze, monitor, and optimize wireless networks and mobile device performance. In a world where connectivity underpins nearly every business process and consumer expectation, TEMS plays a critical role in ensuring networks deliver the throughput, latency, coverage, and reliability required. This article explores how TEMS improves performance through real-world case studies and actionable best practices for network engineers, site planners, and operations teams.
What TEMS Does and Why It Matters
TEMS solutions typically combine drive testing, walk testing, crowd-sourced data, and network trace analysis to produce detailed visibility into network behavior. Key capabilities include:
- Radio-frequency (RF) measurements: signal strength (e.g., RSRP/RSCP/RSRP/RSRQ), SINR, and interference.
- Throughput and latency testing: TCP/UDP throughput, packet loss, jitter, and round-trip time.
- Call and session analytics: call setup success rate (CSSR), call drop rate (CDR), handover success rate.
- Protocol and signaling traces: RRC, NAS, S1/X2/MME interactions for cellular networks.
- Coverage mapping and heatmaps: geolocated visualization of performance metrics.
- Root-cause analysis: correlating customer experience issues with RF, configuration, or core-network problems.
By combining objective measurements with contextual metadata (device model, software version, location, time-of-day), TEMS helps teams go beyond “users complain” to “here’s exactly where, when, and why the problem occurred,” enabling targeted corrective actions.
How TEMS Improves Performance — Mechanisms
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Targeted troubleshooting and faster MTTR
- TEMS provides precise location-stamped traces and KPIs that cut mean time to repair (MTTR). Engineers no longer need to chase vague complaints; they can reproduce issues, inspect signaling, and identify the responsible subsystem (radio, transport, or core).
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Evidence-driven optimization
- Drive test and crowd-sourced metrics quantify coverage gaps, capacity bottlenecks, and poor handover zones. This evidence supports investment decisions (e.g., new site builds, parameter tuning, small cell deployment).
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Capacity planning and congestion management
- Statistical analysis of throughput, user concurrency, and spectral efficiency guides dimensioning. TEMS helps simulate or validate the effects of adding carriers, MIMO layers, or spectrum refarming.
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Configuration validation and compliance
- After network upgrades (software releases, parameter changes), TEMS validates key KPIs and detects regressions rapidly, enabling rollback or targeted fixes.
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User-experience centric metrics
- TEMS correlates network KPIs with application-level performance (web load times, video streaming quality), allowing teams to prioritize fixes by user-perceived impact.
Case Study 1 — Urban Congestion Relief for a Tier-1 Operator
Background: A Tier-1 operator experienced frequent complaints about slow data speeds in the downtown core during business hours. Initial analytics showed high cell load but no clear single cause.
TEMS approach:
- Conducted targeted drive tests and crowd-sourced data collection during peak hours.
- Captured detailed RSRP/RSRQ/SINR distributions, PRB utilization, and TCP throughput per sector.
- Collected signaling traces to inspect scheduling, retransmission rates, and RLC/MAC behavior.
Findings:
- Severe uplink interference on several sectors due to asymmetrical traffic and improper uplink power control parameters.
- Inadequate scheduler tuning: long RLC retransmission queues reduced effective throughput for new flows.
- Handover failures at specific intersection corners caused mid-session throughput drops.
Actions taken:
- Adjusted uplink power control and interference coordination parameters.
- Tuned scheduler and RLC retransmission thresholds.
- Retuned handover margins and reduced neighbor list latency for affected cells.
Outcome:
- Average peak throughput in the downtown core increased by 32%.
- User complaints decreased by 58%, and handover success rate improved by 14%.
Case Study 2 — Rural Coverage Optimization for Emergency Services
Background: A regional provider needed to ensure reliable voice/data for emergency services across a sparsely populated area where site density was low and budget constrained.
TEMS approach:
- Performed systematic drive and walk tests across critical routes and service points (hospitals, fire stations).
- Mapped coverage holes and evaluated handover performance along highways.
- Collected device-specific behavior (different handset models used by responders).
Findings:
- Coverage gaps in valley areas due to terrain shadowing and incorrect antenna downtilt settings.
- Some legacy site configurations used outdated neighbor lists causing failed handovers when responders moved between municipalities.
- Certain device models exhibited degraded uplink sensitivity affecting voice clarity.
Actions taken:
- Re-optimized antenna tilts and azimuth at select sites and added low-cost repeaters in critical valleys.
- Updated neighbor lists and handover parameters across regional clusters.
- Worked with device vendors to apply handset firmware updates improving uplink sensitivity.
Outcome:
- Measurable increase in service availability along critical routes to 99.6% during tests.
- Call drop rate reduced by 76% for emergency-service devices.
Case Study 3 — Validating a Major Software Upgrade
Background: A mobile operator planned a network-wide software upgrade to enable new RAN features (e.g., massive MIMO, carrier aggregation). They needed to ensure no regression in KPIs.
TEMS approach:
- Baseline measurements collected pre-upgrade across representative urban, suburban, and rural cells.
- Post-upgrade verification tests ran the same scenarios with automated TEMS scripts: throughput, handover, signaling trace comparisons.
- A/B testing in parallel clusters allowed direct comparison.
Findings:
- Most KPIs improved as expected, but a subset of suburban sectors showed intermittent RRC re-establishment events after configuration changes.
- Signaling traces pointed to timer mismatch between eNodeB and core elements.
Actions taken:
- Adjusted RRC timers and patched the eNodeB configuration templates.
- Re-ran targeted validation tests.
Outcome:
- Upgrade validated with KPIs meeting or exceeding baselines. RRC-related re-establishments dropped to baseline levels, avoiding wider rollback.
Best Practices for Using TEMS Effectively
- Define clear success metrics before testing: pick KPIs aligned with user experience (e.g., page load time, video MOS, CSSR) and business goals.
- Combine active drive/walk tests with passive and crowd-sourced measurements to get both controlled and real-world views.
- Automate repetitive test scenarios and use geofenced scheduling (time-of-day) to capture peak, off-peak, and special-event behavior.
- Correlate multi-layer data: RF KPIs, transport/backhaul stats, and core signaling traces — problems often span layers.
- Use device-aware testing: include a representative mix of popular handset models and OS versions.
- Keep a baseline repository and change log: baseline tests before upgrades make regressions obvious and save costly rollbacks.
- Prioritize fixes by impact: focus on changes that improve user-visible metrics first (throughput for heavy data users, latency for gaming/VoIP).
- Invest in visualization: heatmaps, sector timelines, and KPI trend dashboards accelerate root-cause identification.
- Train field teams on consistent data collection and tagging (weather, events, test scripts used) to improve repeatability.
- Engage cross-functional teams early: RF, transport, core, OSS, and product owners should collaborate using the same TEMS evidence.
Tools and Integration Tips
- Integrate TEMS outputs with OSS/BSS and ticketing systems so measured problems automatically generate prioritized tickets with evidence.
- Use APIs to feed TEMS-derived KPIs into capacity-planning models and automatic alarm systems.
- Combine TEMS with network simulators to model the impact of proposed changes before deployment.
- Regularly update device and protocol decoders in TEMS tools to support new RAN features and handset behaviours.
Measuring ROI from TEMS Activities
Quantifiable ROI examples:
- Reduced MTTR leading to fewer SLA breaches and lower operational costs.
- Fewer customer complaints and churn reductions after targeted fixes.
- Delaying capital spend by better identifying where small configuration changes or small-cell additions solve problems vs. full site builds.
- Faster, safer upgrades with A/B validation reduce costly rollbacks.
To estimate ROI: track pre/post KPIs (throughput, drop rate, complaint volume), calculate associated business impact (ARPU preservation, SLA penalties avoided), and compare against TEMS program costs (tools, manpower, test drives).
Common Pitfalls and How to Avoid Them
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Pitfall: Testing only in controlled conditions.
Avoidance: Always supplement with crowd-sourced or passive measurements for real-world validation. -
Pitfall: Not including diverse device models.
Avoidance: Maintain a device lab or rotating device list reflecting subscriber base. -
Pitfall: Data overload without actionable insights.
Avoidance: Define prioritized KPIs and use automated analytics to flag anomalies and probable root causes. -
Pitfall: Isolated fixes without cross-layer checks.
Avoidance: Correlate RF changes with transport and core metrics; coordinate across teams.
Conclusion
TEMS accelerates troubleshooting, validates upgrades, guides capacity planning, and ties technical KPIs to user experience. The case studies above show concrete gains — higher throughput, fewer drops, and measurable improvements in service availability — when TEMS is used systematically. Adopt clear success metrics, combine multiple data sources, automate validation, and make TEMS evidence the backbone of network operations decisions to realize the full performance benefits.
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