Self-optimized Coverage Coordination in Femtocell Networks

This paper proposes a self-optimized coverage coordination scheme for two-tier femtocell networks, in which a femtocell base station adjusts the transmit power based on the statistics of the signal and the interference power that is measured at a femtocell downlink. Furthermore, an analytic expression is derived for the coverage leakage probability that a femtocell coverage area leaks into an outdoor macrocell. The coverage analysis is verified by simulation, which shows that the proposed scheme provides sufficient indoor femtocell coverage and that the femtocell coverage does not leak into an outdoor macrocell.

💡 Research Summary

The paper addresses a critical challenge in two‑tier heterogeneous networks: how to ensure adequate indoor coverage from femtocell base stations (FBSs) while preventing the femtocell’s signal from leaking into the outdoor macrocell area and causing interference. Existing solutions typically rely on static power settings or centralized coordination, both of which struggle to adapt to rapid changes in user distribution, traffic load, and indoor propagation conditions. To overcome these limitations, the authors propose a self‑optimized coverage coordination scheme in which each FBS autonomously adjusts its downlink transmit power based on real‑time statistics of the received signal strength (RSSI) and the interference power measured at the associated user equipment (UE).

System Model
The study considers a single macrocell overlaid with multiple femtocells deployed on indoor ceilings. The indoor propagation model includes a path‑loss exponent (n_{\text{in}}) and a wall attenuation factor (L_{\text{wall}}); the macrocell uses a separate outdoor path‑loss exponent (n_{\text{out}}). UEs periodically report their average RSSI and the interference they observe from the macrocell to their serving FBS. These reports are aggregated, filtered with a moving‑average filter, and used to compute reliable statistical estimates (\mu_{\text{RSSI}}, \sigma_{\text{RSSI}}, \mu_{I}, \sigma_{I}).

Power‑Optimization Algorithm

1. Measurement Collection – Each FBS gathers RSSI and interference reports from all connected UEs.
2. Statistical Processing – Outliers are removed and the data are smoothed to obtain mean and variance values.
3. Target SINR Definition – A service‑quality threshold (\gamma_{\text{target}}) (e.g., 12 dB) is set.
4. Power Update Rule – The current transmit power (P_t) is compared with the measured average SINR. If the SINR falls below (\gamma_{\text{target}}), the power is increased by (\Delta P_{\uparrow}= \min(\Delta P_{\max}, K(\gamma_{\text{target}}-\text{SINR}))); if it exceeds the target, the power is decreased by (\Delta P_{\downarrow}= \min(\Delta P_{\max}, K(\text{SINR}-\gamma_{\text{target}}))). The constant (K) translates SINR deviations into power adjustments.
5. Constraints – Power is bounded by (P_{\min}) and (P_{\max}), and the step size (\Delta P_{\max}) limits abrupt changes, ensuring stability.

Leakage‑Probability Analysis
A key contribution is an analytical expression for the probability that the femtocell’s coverage “leaks” into the macrocell. The authors model the received power at the femtocell boundary (d_{\text{boundary}}) as a log‑normal random variable:
\