Beitrag in einem Tagungsband
Adaptive Recursive Spatial Multiplexing (RSM) in interference-limited scenarios

Details zur Publikation
Mohamad, U.; Shah, I.; Hunziker, T.; Dahlhaus, D.
Wireless and Pervasive Computing (ISWPC), 2013 International Symposium on

Zusammenfassung, Abstract
Recursive Spatial Multiplexing (RSM) is a closed loop multiple-input multiple-output (MIMO) structure for achieving the capacity offered by MIMO channels with a low-complexity detector. We investigate how to make RSM able to deal with different interference scenarios. The interference at the receiver side is considered as a vector-valued stochastic process characterized by a covariance matrix which is to be estimated and used subsequently for defining the retransmission subspace identifier to be fed back to the transmitter. We consider both the sample covariance matrix (SCM) estimator and an empirical Bayesian (EB) scheme as well as different probability distribution functions and correlation properties of the interference vectors. It turns out that the proposed RSM modification substantially improves the bit-error rate performance in the presence of interference where EB and SCM perform comparably due to the conditions for the covariance matrix estimation in RSM with limited frame length. Moreover, adaptive RSM leads to a performance being independent of the correlation coefficient of the interference vector.

adaptive recursive spatial multiplexing, bit error rate, Bit error rate, closed loop multiple-input multiple-output structure, Correlation, covariance matrices, Covariance matrices, empirical Bayesian scheme, error statistics, Interference, interference vectors, low-complexity detector, MIMO, MIMO channels, MIMO communication, multiplexing, probability, probability distribution functions, radiofrequency interference, radio receiver, radio receivers, radio transmitter, radio transmitters, Receivers, retransmission subspace identifier, RSM, sample covariance matrix estimation, SCM, stochastic processes, Vectors, vector-valued stochastic process

Zuletzt aktualisiert 2019-25-07 um 16:36