Exponential Effective SNR Mapping (EESM) based link error prediction is widely used for adaptive MIMO-OFDM in system evaluations to simplify simulation complexity. In MIMO system, EESM prediction method is more sophisticated due to specific MIMO detection techniques and spatial selectivity of multiple antennas. Especially for maximum likelihood (ML) detection it is hard to determine post-detected SNR due to non-linearity of ML detection, so penalty method based on MMSE detection has been proposed for EESM mapping. Simulations show EESM based effective SNR for ML receiver has obtained high fitting precision compared with AWGN performance in QPSK or 16QAM modulation, convolution coding with rate 1/2 in 2×2 MIMO-OFDM system. This method can be naturally generalized into other modulation and coding rates, higher level of MIMO modes, and other fading channels with frequency selectivity, time selectivity or spatial selectivity.Index Terms-MIMO-OFDM, EESM, link error prediction, ML detection, effective SNR
In this article, we investigate the performance of dual-hop amplify-and-forward multiple-input multiple-output relaying system with orthogonal space-time block code transmissions over doubly-correlated Nakagami-m fading channel, where the source, relay, and destination terminals are all equipped with multiple antennas. For two different CSI-assisted relaying schemes, which could be encompassed by a unified model, we provide the compact closed-form expressions for cumulative distribution function, probability density function, moment generating function, and generalized moment (GM) of the instantaneous end-to-end SNR. Besides, the exact analytical expressions for the outage probability (OP) and symbol error rate (SER) and approximate expression for ergodic capacity are also derived. Furthermore, we present the asymptotic expressions for OP and SER in the high SNR regime, from which we gain an insight into the system performance and derive the achievable diversity order and array gain. The analytical expressions are validated by Monte-Carlo simulations.
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