Pilot Beam Sequence Design for Channel Estimation in Millimeter-Wave MIMO Systems: A POMDP Framework

In this paper, adaptive pilot beam sequence design for channel estimation in large millimeter-wave (mmWave) MIMO systems is considered. By exploiting the sparsity of mmWave MIMO channels with the virtual channel representation and imposing a Markovian random walk assumption on the physical movement of the line-of-sight (LOS) and reflection clusters, it is shown that the sparse channel estimation problem in large mmWave MIMO systems reduces to a sequential detection problem that finds the locations and values of the non-zero-valued bins in a two-dimensional rectangular grid, and the optimal adaptive pilot design problem can be cast into the framework of a partially observable Markov decision process (POMDP). Under the POMDP framework, an optimal adaptive pilot beam sequence design method is obtained to maximize the accumulated transmission data rate for a given period of time. Numerical results are provided to validate our pilot signal design method and they show that the proposed method yields good performance.Comment: 6 pages, 6 figures, submitted to IEEE ICC 201

RAMP: A Risk-Aware Mapping and Planning Pipeline for Fast Off-Road Ground Robot Navigation

A key challenge in fast ground robot navigation in 3D terrain is balancing robot speed and safety. Recent work has shown that 2.5D maps (2D representations with additional 3D information) are ideal for real-time safe and fast planning. However, the prevalent approach of generating 2D occupancy grids through raytracing makes the generated map unsafe to plan in, due to inaccurate representation of unknown space. Additionally, existing planners such as MPPI do not consider speeds in known free and unknown space separately, leading to slower overall plans. The RAMP pipeline proposed here solves these issues using new mapping and planning methods. This work first presents ground point inflation with persistent spatial memory as a way to generate accurate occupancy grid maps from classified pointclouds. Then we present an MPPI-based planner with embedded variability in horizon, to maximize speed in known free space while retaining cautionary penetration into unknown space. Finally, we integrate this mapping and planning pipeline with risk constraints arising from 3D terrain, and verify that it enables fast and safe navigation using simulations and hardware demonstrations.Comment: 7 pages submitted to ICRA 202

Reverse Signaling of Tumor Necrosis Factor Superfamily Proteins in Macrophages and Microglia: Superfamily Portrait in the Neuroimmune Interface

The tumor necrosis factor (TNF) superfamily (TNFSF) is a protein superfamily of type II transmembrane proteins commonly containing the TNF homology domain. The superfamily contains more than 20 protein members, which can be released from the cell membrane by proteolytic cleavage. Members of the TNFSF function as cytokines and regulate diverse biological processes, including immune responses, proliferation, differentiation, apoptosis, and embryogenesis, by binding to TNFSF receptors. Many TNFSF proteins are also known to be responsible for the regulation of innate immunity and inflammation. Both receptor-mediated forward signaling and ligand-mediated reverse signaling play important roles in these processes. In this review, we discuss the functional expression and roles of various reverse signaling molecules and pathways of TNFSF members in macrophages and microglia in the central nervous system (CNS). A thorough understanding of the roles of TNFSF ligands and receptors in the activation of macrophages and microglia may improve the treatment of inflammatory diseases in the brain and periphery. In particular, TNFSF reverse signaling in microglia can be exploited to gain further insights into the functions of the neuroimmune interface in physiological and pathological processes in the CNS

A Forward Reachability Perspective on Robust Control Invariance and Discount Factors in Reachability Analysis

Control invariant sets are crucial for various methods that aim to design safe control policies for systems whose state constraints must be satisfied over an indefinite time horizon. In this article, we explore the connections among reachability, control invariance, and Control Barrier Functions (CBFs) by examining the forward reachability problem associated with control invariant sets. We present the notion of an "inevitable Forward Reachable Tube" (FRT) as a tool for analyzing control invariant sets. Our findings show that the inevitable FRT of a robust control invariant set with a differentiable boundary is the set itself. We highlight the role of the differentiability of the boundary in shaping the FRTs of the sets through numerical examples. We also formulate a zero-sum differential game between the control and disturbance, where the inevitable FRT is characterized by the zero-superlevel set of the value function. By incorporating a discount factor in the cost function of the game, the barrier constraint of the CBF naturally arises as the constraint that is imposed on the optimal control policy. As a result, the value function of our FRT formulation serves as a CBF-like function, which has not been previously realized in reachability studies. Conversely, any valid CBF is also a forward reachability value function inside the control invariant set, thereby revealing the inverse optimality of the CBF. As such, our work establishes a strong link between reachability, control invariance, and CBFs, filling a gap that prior formulations based on backward reachability were unable to bridge.Comment: The first two authors contributed equally to this wor