Robust data rate estimation with stochastic SINR modeling in multi-interference OFDMA networks

To meet the rapidly growing requirement for universal coverage of high-speed mobile services, wireless cellular networks have been moving towards increasing density. Inter-cell cooperation in such densely deployed networks is becoming more significant than ever before. Among others, data rate estimation is a fundamental issue for inter-cell resource management and optimization. In this paper, we focus on the data rate estimation problem based on stochastic SINR models. We derive a closed-form solution of user SINR distribution in multi-interference networks. We calculate upper and lower bounds of the SINR distribution and extend them to a weighted sum SINR model to achieve more accurate estimation of data rate. The simulation results reveal that our designed model can guarantee the accuracy of data rate estimation in diverse wireless network environments such as urban and suburban scenarios. It decreases the error of estimation and the ratio of high-error users even with very small signaling overhead fed back per user. Various factors, such as the number of reported cells, low-SINR effect, propagation environments and inaccuracy of channel measurement, which influence the estimation performance are analyzed and evaluated as well. The weighted sum model is verified to have great resistance to the influence of these factors and achieve accurate estimation.