Abstract

This paper proposes the use of Shewharttest to reduce the number of data-transmissions in IoT networks. It is shown to outperform the widely-used least mean square (LMS) based data reduction method in terms of the number of data-transmissions, implementation complexity and mean square error (MSE) in prediction of time-series data at the sink node based on the partial transmissions of the measured time-series data from the sensor node. The paper also proposes the use of piggy-backing and interpolation to further reduce the MSE of the estimated time-series data at the sink node without increasing the number of packet transmissions. The time-series data used for the comparison of data reduction algorithms is a set of measured temperature values in indoor and outdoor scenarios for four days using custom-designed wireless sensor nodes. To express the effectiveness of the piggybacked transmissions on battery lifetime, the total current consumption of the sensor node is measured for different number of piggybacks and corresponding battery lifetime is estimated. It is shown that the proposed piggyback approach significantly reduces the MSE at the cost of slight decrease in battery-lifetime

Abstract

In this paper, sum link capacity expressions for successive interference cancellation (SIC) and regarding interference as noise (IAN) in reverse time-division duplexing (RTDD) heterogeneous cellular network are derived. The considered RTDD network always operates in a synchronized fashion such that if the macro tier is in the uplink (UL), then the small tier will be in the downlink (DL) and vice-versa. Rate and power throttling are used in the uplink (UL) for both IAN and SIC to consider an asymmetric traffic network (DL≫ UL). Systemlevel simulations are performed to compare the overall system throughput of IAN and SIC for different DL/UL ratios. It is observed that rate or power-throttled SIC performs better than rate-throttled IAN and worse than power-throttled IAN.

Abstract

Sparse arrays such as co-prime and nested arrays can identify more sources than the number of sensors. This is because their difference co-arrays contain a uniformly spaced virtual array with more elements than the number of sensors in the array. In this paper we demonstrate this using two dimensional co-prime and nested sparse arrays combined with sparse Bayesian learning (SBL) for 2D beamforming in azimuth and elevation. SBL can directly process the sparse array data and significantly outperform conventional beam-forming and MUSIC as seen from simulations.

Abstract

Sparse Bayesian learning (SBL) has emerged as a fast and competitive method to perform sparse processing. The SBL algorithm, which is developed using a Bayesian framework, iteratively solves a non-convex optimization problem using fixed point updates. It provides comparable performance and is significantly faster than convex optimization techniques used in sparse processing. We propose a multi-dictionary SBL algorithm that simultaneously can process observations generated by different underlying dictionaries sharing the same sparsity profile. Two algorithms are proposed and corresponding fixed point update equations are derived. Noise variances are estimated using stochastic maximum likelihood. The multi-dictionary SBL has many practical applications. We demonstrate this using direction-of-arrival (DOA) estimation. The first example uses the proposed multi-dictionary SBL to process multi …

Abstract

The paper considers direction of arrival (DOA) estimation from long-term observations in a very noisy environment. The concern is to derive methods obtaining reasonable DOAs at very low SNR. The noise is assumed zero-mean Gaussian and its variance varies in time and space, causing stationary data models to fit poorly over long observation times. Therefore a heteroscedastic Gaussian noise model is introduced where the variance varies across observations and sensors. The source amplitudes are assumed independent zero-mean complex Gaussian distributed with unknown variances (i.e. the source powers), inspiring stochastic maximum likelihood (ML) DOA estimation. The DOAs of plane waves are estimated from multi-snapshot sensor array data using sparse Bayesian learning (SBL) where the noise is estimated across both sensors and snapshots. This SBL approach is more flexible and performs better …

Abstract

Page 1. Multi-frequency sparse Bayesian learning with noise models Kay L. Gemba1, Santosh Nannuru, and Peter Gerstoft ASA SD FALL 2019 Marine Physical Laboratory of the Scripps Institution of Oceanography University of California at San Diego 1gemba@ucsd.edu 1 Page 2. Presentation objectives We investigate SBL performance and present results for the MFP and beamforming applications using 3 noise models: 1. SBL behaves similarly to an adaptive processor and displays robustness to modest model-data mismatch. SBL performance is compared to the white noise gain constraint (WNGC), MUSIC, and Bartlett processors. 2. Compare SBL performance, implemented with 3 noise models: 1. SBL1: stationary noise, 2. SBL2: non-stationary noise, which is useful when the noise variance evolves across snapshots, 3. SBL3: non-stationary noise in a spatially, non-homogeneous field (example: surface noise) …

Abstract

In this paper, we consider the weighted sum-rate (WSR) maximization problem in a cognitive relaying network under imperfect channel state information (CSI). The secondary network employs cooperative nonorthogonal multiple access (NOMA). We investigate the optimal power allocation and relay precoder design for the secondary network that shares spectrum with the primary network while keeping interference to the primary users below a threshold. We exploit the relation between the WSR and weighted mean-square error (WMSE) to solve the equivalent WSR maximization problem. We consider a practical scenario, where the available CSI of all the links are imperfect. The performance of the proposed solution is robust to the errors in the CSI. The optimization problem is intractable in its original form; however, we reformulate it as a tractable, equivalent optimization problem via certain transformations and approximations. We illustrate the robust performance of the proposed design via numerical simulation results.

Abstract

This paper discusses the concept and parameter design of a Robust Stair Climbing Compliant Modular Robot, capable of tackling stairs with overhangs. Modifying the geometry of the periphery of the wheels of our robot helps in tackling overhangs. Along with establishing a concept design, robust design parameters are set to minimize performance variation. The Grey-based Taguchi Method is adopted for providing an optimal setting for the design parameters of the robot. The robot prototype is shown to have successfully scaled stairs of varying dimensions, with overhang, thus corroborating the analysis performed.

Abstract

Autonomous explorative robots frequently encounter scenarios where multiple future trajectories can be pursued. Often these are cases with multiple paths around an obstacle or trajectory options towards various frontiers. Humans in such situations can inherently perceive and reason about the surrounding environment to identify several possibilities of either manoeuvring around the obstacles or moving towards various frontiers. In this work, we propose a 2 stage Convolutional Neural Network architecture which mimics such an ability to map the perceived surroundings to multiple trajectories that a robot can choose to traverse. The first stage is a Trajectory Proposal Network which suggests diverse regions in the environment which can be occupied in the future. The second stage is a Trajectory Sampling network which provides a finegrained trajectory over the regions proposed by Trajectory Proposal Network. We evaluate our framework in diverse and complicated real life settings. For the outdoor case, we use the KITTI dataset and our own outdoor driving dataset. In the indoor setting, we use an autonomous drone to navigate various scenarios and also a ground robot which can explore the environment using the trajectories proposed by our framework. Our experiments suggest that the framework is able to develop a semantic understanding of the obstacles, open regions and identify diverse trajectories that a robot can traverse. Our comparisons portray the performance gain of the proposed architecture over a diverse set of methods against which it is compared.

Abstract

Planning frameworks for autonomous vehicles must be robust and computationally efficient for real time realization. At the same time, they should accommodate the unpredictable behavior of the other participants and produce safe trajectories. In this paper, we present a computationally efficient hierarchical planning framework for autonomous vehicles that can generate safe trajectories in complex driving scenarios, which are commonly encountered in urban traffic settings. The first level of the proposed framework constructs a Model Predictive Control(MPC) routine using an efficient difference of convex programming approach, that generates smooth and collision-free trajectories. The constraints on curvature and road boundaries are seamlessly integrated into this optimization routine. The second layer is mainly responsible to handle the unpredictable behaviors that are typically exhibited by the other participants of traffic. It is built along the lines of time scaled collision cone(TSCC) which optimize for the velocities along the trajectory to handle such disturbances. We additionally show that our framework maintains optimal balance between temporal and path deviations while executing safe trajectories. To demonstrate the efficacy of the presented framework we validated it in extensive simulations in different driving scenarios like over taking, lane merging and jaywalking among many dynamic and static obstacles.