PER Prediction in MIMO-OFDM Systems With Approximate Demodulation: A GMI-Based Method

Abstract

In multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, the symbols in a packet may undergo different signal-to-interference-plus-noise ratio (SINR) levels, making packet error rate (PER) prediction a challenging task. Effective SINR mapping (ESM) simplifies this problem by mapping the SINRs on different symbols to a single packet-level effective SINR. A novel ESM method, referred to as mean generalized mutual information mapping (MGMIM), is put forward in this paper. The proposed method transforms the SINR of each symbol to the generalized mutual information (GMI) and then derives an effective SINR from the mean GMI measure over different symbols. The derived effective SINR summarizes the effect of all the SINRs on the packet, which would produce the same PER performance under an additive white Gaussian noise (AWGN) channel. Compared to existing techniques, MGMIM does not require MCS-dependent tuning parameters but delivers competitive or even better PER prediction accuracy. More importantly, MGMIM allows reliable PER prediction when the receiver employs approximate demodulation, which boosts its applicability in practice. These features are demonstrated by comparative numerical simulations.