Cellular network planning, which includes coverage planning and network dimensioning is essential for designing and deploying mobile networks. The Key Performance Indicators (KPI)s, such as average Cell Spectral Efficiency (CSE), user throughput, etc., are used to evaluate the design of cellular systems. These metrics are primarily dependent on area-averaged Signal-to-Interference-plus-Noise- Ratio (SINR) distribution of the users of the cell. In this work, we have developed a general semi-analytical framework for area-averaged SINR distribution and evaluated system level KPIs such as average CSE for Orthogonal Frequency Division Multiple Access (OFDMA)-Frequency Division Duplex (FDD) system based single frequency network. In our model, we consider International Telecommunication Union-Radiocommunication (ITU-R) specified channel path-loss models for Non-Line- of-Sight (NLoS) and Line-of-Sight (LoS) scenarios, directional antennas for base stations (BSs) with ITU-R specified antenna gains, shadowing correlation between desired and interfering signals, Nakagami-$\shapeparam$ fading channels to characterize NLoS and LoS conditions and, Bernoulli random variables to represent load conditions of interfering BSs, to model area- averaged SINR distribution. We have demonstrated that our model is in close agreement with Monte- Carlo simulations. Using our model, the average CSE is evaluated for different channel conditions and verified with simulations.

Link adaptation (LA) schemes maximize average spectral efficiency (ASE) using accurate channel state information (CSI) and knowledge of signal to noise ratio (SNR) distribution. In practical systems, partial CSI is available at the transmitter through feedback of modulation and coding scheme index, often known as channel quality indicator (CQI). However, parameters of SNR distribution (${\Theta }$) are not known or only assumed to be known. We propose two LA schemes, for execution at the receiver and transmitter, respectively, each without prior knowledge about${\Theta }$. In the receiver centric schemes, parametric estimation of SNR distribution is performed using unquantized SNR samples. In the transmitter centric scheme, we estimate${\Theta }$from CQI samples using an iterative joint quantization-estimation algorithm. These parametric estimates form long-term SNR distribution, which is used to compute SNR switching thresholds. Using these thresholds, rate and power adaptation decisions are made based on instantaneous SNR. We have derived the maximum-likelihood estimator and Cramer–Rao lower bound for the proposed estimator using CQI feedback for Nakagami-$m$fading channels and demonstrate that near optimal ASE can be achieved while using the proposed schemes as in an ideal scenario, where perfect knowledge about CSI and SNR distribution are availablea priori.