Parametric Estimation of SINR Distribution using Quantized SINR Samples for Maximizing Average Spectral Efficiency

Spectrally efficient wireless communication systems are designed to dynamically adapt transmission rate and power by comparing the instantaneous signal to interference plus noise ratio (SINR) samples against SINR switching thresholds, which can be designed a priori using perfect knowledge of SINR distribution. Nevertheless, a priori perfect knowledge of SINR distribution is hardly feasible in any practical operating system for the following reasons. The operating condition is not stationary owing to mobility, while it is impossible to have prior knowledge of all possible operating conditions. Even if the set of operating conditions is defined, identifying the current operating scenario is not a trivial task either. Considering the above challenges, dynamic estimation of SINR distribution is one possible way out. The challenge encountered in such estimation is that only quantized values of SINR are available. Leveraging the well-accepted log-normal approximation of the signal to interference plus noise ratio (SINR) distribution, we develop a mechanism to obtain parametric estimates of the distribution of SINR using quantized data in this work. The proposed method can be used at the transmitter and the receiver in the same manner with appropriate modifications to signalling protocols and algorithm parameter values. We demonstrate through numerical analysis that the proposed method can help achieve near-ideal average spectral efficiency (ASE).