Autonomous robots are artifacts that comprise a significant number of heterogeneous hardware and software components and interact with dynamic environments. Therefore, there is always a chance of faults at run-time that negatively affect the reliability of the system. In this paper we present a novel diagnosis and repair architecture for ROS-based robot systems. It is an extension to the existing ROS diagnostics stack and follows a model-based diagnosis and repair approach. In the paper we discuss the integrated diagnosis and repair architecture in detail. Moreover, we show its application to an example robot system and report first experimental results. The presented work provides three major contributions: a combination of diagnosis and repair, the integration of hardware and software, and the integration into ROS.

In this paper we consider the vector perturbation (VP) precoding scheme for the multiuser MISO broadcast channel proposed by Hochwald et al. under the practical assumption that the receivers have limited dynamic range. In this case, VP precoding is shown to suffer from an error floor at high signal-to-noise ratio (SNR). As an alternative, we propose precoding with restricted VP (RVP), which takes the limited dynamic range of the receivers explicitly into account by restricting to a finite set of possible perturbation vectors at the transmitter side. We derive the diversity order of this RVP scheme and show that no error floor occurs and that the performance is superior to VP for the entire range of SNRs.

In this paper, we consider MIMO spatial multiplexing systems and elaborate the impact of the channel condition number on the performance of ML and ZF detection. In particular, we show that for channels with bounded condition number ZF detection achieves the same diversity as ML detection. Motivated by this, we propose a novel threshold receiver that uses simple ZF detection for well-conditioned channels and ML detection for poorly conditioned channels. We show that this receiver achieves full diversity and we provide an upper bound on its SNR gap to ML detection. We further investigate cost-reduced versions of the threshold receiver and examine their performance in terms of simulation results.