Abstract-It is well known that suboptimal detection schemes for multiple-input multiple-output (MIMO) spatial multiplexing systems (equalization-based schemes as well as nulling-and-cancelling schemes) are unable to exploit all of the available diversity, and thus, their performance is inferior to ML detection. Motivated by experimental evidence that this inferior performance is primarily caused by the inability of suboptimal schemes to deal with "bad" (i.e., poorly conditioned) channel realizations, we study the decision regions of suboptimal schemes for bad channels. Based on a simplified model for bad channels, we then develop two computationally efficient detection algorithms that are robust to bad channels. In particular, the novel sphere-projection algorithm (SPA) is a simple add-on to standard suboptimal detectors that is able to achieve near-ML performance and significantly increased diversity gains. The SPA's computational complexity is comparable with that of nulling-and-cancelling detectors and only a fraction of that of the Fincke-Phost sphere-decoding algorithm for ML detection.

Abstract-We propose two methods for the estimation of scattering functions of random time-varying channels. In contrast to existing methods, our methods exploit the underspread property of these channels to achieve good estimation performance and low computational complexity. The first method uses a dedicated sounding to measure the channel. The second method uses the data signal of an ongoing data transmission as sounding signal and thus allows estimation without dedicated sounding. Both methods are effectively unbiased and can be implemented efficiently using the Zak transform. The performance of our scattering function estimators is studied both analytically by means of variance bounds and experimentally through numerical simulation, and their superiority over existing methods is demonstrated.

We discuss and compare the most important detection techniques for MIMO spatial multiplexing wireless systems, focusing on their performance and computational complexity. Our analysis shows that the limited performance of conventional suboptimal detection techniques is primarily caused by their inability to cope with poorly conditioned channels. The recently proposed sphere projection algorithm is better suited to these channels and can achieve near-optimal performance.

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