Abstract-This paper presents a novel stochastic channel model for multiple-input multiple-output (MIMO) wireless radio channels. In contrast to state-of-the-art stochastic MIMO channel models, the spatial correlation properties of the channel are not divided into separate contributions from transmitter and receiver. Instead, the joint correlation properties are modeled by describing the average coupling between the eigenmodes of the two link ends. The necessary and sufficient condition for the proposed model to hold is that the eigenbasis at the receiver is independent of the transmit weights, and vice versa. The authors discuss the mathematical elements of the model, which can be easily extracted from measurements, from a radio propagation point of view and explain the underlying assumption of the model in physical terms. The validation of the proposed model by means of measured data obtained from two completely different measurement campaigns reveals its ability to better predict capacity and spatial channel structure than other popular stochastic channel models.

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Despite many valuable contributions to the theory and practice of MIMO communication systems from various scientific fields, we want to emphasize the outstanding importance of propagation aspects when dealing with MIMO systems. Radio propagation forms the basis for any radio channel including MIMO systems. On the one hand, popular mathematical models and commonly applied statistical assumptions sometimes turn out to neglect important properties of MIMO radio channels. On the other hand, detailed knowledge and investigations of MIMO specific phenomena (e.g. keyholes) do not imply practical relevance. By means of four specific examples we argue that studying propagation is indispensable in order to stay in touch with real MIMO channels.

The best-known decomposition schemes of multiclass learning problems are one per class coding (OPC) and error-correcting output coding (ECOC). Both methods perform a prior decomposition, that is, before training of the classifier takes place. The impact of output codes on the inferred decision rules can be experienced only after learning. Therefore, we present a novel algorithm for the code design of multiclass learning problems. This algorithm applies a maximum-likelihood objective function in conjunction with the expectation-maximization (EM) algorithm. Minimizing the augmented objective function yields the optimal decomposition of the multiclass learning problem in two-class problems. Experimental results show the potential gain of the optimized output codes over OPC or ECOC methods.