pwc-archive/papers-with-abstracts huggingface dataset:
Improved OFDM Signal Cancellation through Window Estimation
Authors: Daniel Chew, Samuel Berhanu, Chris Baumgart, A. Brinton Cooper
Date: 2023-03-22 00:00:00

The ability to cancel an OFDM signal is important to many wireless communication systems including Power-Domain Non-orthogonal Multiple Access (PD-NOMA), Rate-Splitting Multiple Access (RSMA), and spectrum underlay for dynamic spectrum access. In this paper, we show that estimating the windowing applied at the transmitter is important to that cancellation. Windowing at the transmitter is a popular means to control the bandwidth of an Orthogonal Frequency Division Multiplexed (OFDM) symbol and is overlooked in most literature on OFDM signal cancellation. We show the limitation to the amount of cancellation that can be achieved without knowledge of OFDM windowing. We show that the window can be estimated from received samples alone, and that window estimate can be used to improve the signal cancellation. The window is estimated in the presence of noise and imperfect estimates of the center frequency offset (CFO) and the channel. We conclude with results using synthetic and over-the-air data where we demonstrate a 5.3 dB improvement to OFDM signal cancellation over existing methods in an over-the-air experiment.
Spatial Path Index Modulation in mmWave/THz-Band Integrated Sensing and Communications
Authors: Ahmet M. Elbir, Kumar Vijay Mishra, Asmaa Abdallah, Abdulkadir Celik, Ahmed M. Eltawil
Date: 2023-03-22 00:00:00
Tasks: ISAC

As the demand for wireless connectivity continues to soar, the fifth generation and beyond wireless networks are exploring new ways to efficiently utilize the wireless spectrum and reduce hardware costs. One such approach is the integration of sensing and communications (ISAC) paradigms to jointly access the spectrum. Recent ISAC studies have focused on upper millimeter-wave and low terahertz bands to exploit ultrawide bandwidths. At these frequencies, hybrid beamformers that employ fewer radio-frequency chains are employed to offset expensive hardware but at the cost of lower multiplexing gains. Wideband hybrid beamforming also suffers from the beam-split effect arising from the subcarrier-independent (SI) analog beamformers. To overcome these limitations, this paper introduces a spatial path index modulation (SPIM) ISAC architecture, which transmits additional information bits via modulating the spatial paths between the base station and communications users. We design the SPIM-ISAC beamformers by first estimating both radar and communications parameters by developing beam-split-aware algorithms. Then, we propose to employ a family of hybrid beamforming techniques such as hybrid, SI, and subcarrier-dependent analog-only, and beam-split-aware beamformers. Numerical experiments demonstrate that the proposed SPIM-ISAC approach exhibits significantly improved spectral efficiency performance in the presence of beam-split than that of even fully digital non-SPIM beamformers.
Robust Millimeter Beamforming via Self-Supervised Hybrid Deep Learning
Authors: Fenghao Zhu, Bohao Wang, Zhaohui Yang, Chongwen Huang, Zhaoyang Zhang, George C. Alexandropoulos, Chau Yuen, Merouane Debbah
Date: 2023-03-09 00:00:00
Tasks: Deep Learning

Beamforming with large-scale antenna arrays has been widely used in recent years, which is acknowledged as an important part in 5G and incoming 6G. Thus, various techniques are leveraged to improve its performance, e.g., deep learning, advanced optimization algorithms, etc. Although its performance in many previous research scenarios with deep learning is quite attractive, usually it drops rapidly when the environment or dataset is changed. Therefore, designing effective beamforming network with strong robustness is an open issue for the intelligent wireless communications. In this paper, we propose a robust beamforming self-supervised network, and verify it in two kinds of different datasets with various scenarios. Simulation results show that the proposed self-supervised network with hybrid learning performs well in both classic DeepMIMO and new WAIR-D dataset with the strong robustness under the various environments. Also, we present the principle to explain the rationality of this kind of hybrid learning, which is instructive to apply with more kinds of datasets.
Few-shot Multimodal Multitask Multilingual Learning
Authors: Aman Chadha, Vinija Jain
Date: 2023-02-19 00:00:00
Tasks: Few-Shot Learning, In-Context Learning, Natural Language Understanding, NER, Prompt Engineering, QNLI, QQP, Question Answering, SST-2, Transfer Learning, Visual Entailment, Visual Question Answering, Visual Question Answering (VQA)

While few-shot learning as a transfer learning paradigm has gained significant traction for scenarios with limited data, it has primarily been explored in the context of building unimodal and unilingual models. Furthermore, a significant part of the existing literature in the domain of few-shot multitask learning perform in-context learning which requires manually generated prompts as the input, yielding varying outcomes depending on the level of manual prompt-engineering. In addition, in-context learning suffers from substantial computational, memory, and storage costs which eventually leads to high inference latency because it involves running all of the prompt's examples through the model every time a prediction is made. In contrast, methods based on the transfer learning via the fine-tuning paradigm avoid the aforementioned issues at a one-time cost of fine-tuning weights on a per-task basis. However, such methods lack exposure to few-shot multimodal multitask learning. In this paper, we propose few-shot learning for a multimodal multitask multilingual (FM3) setting by adapting pre-trained vision and language models using task-specific hypernetworks and contrastively fine-tuning them to enable few-shot learning. FM3's architecture combines the best of both worlds of in-context and fine-tuning based learning and consists of three major components: (i) multimodal contrastive fine-tuning to enable few-shot learning, (ii) hypernetwork task adaptation to perform multitask learning, and (iii) task-specific output heads to cater to a plethora of diverse tasks. FM3 learns the most prominent tasks in the vision and language domains along with their intersections, namely visual entailment (VE), visual question answering (VQA), and natural language understanding (NLU) tasks such as neural entity recognition (NER) and the GLUE benchmark including QNLI, MNLI, QQP, and SST-2.
EZtune: A Package for Automated Hyperparameter Tuning in R
Authors: Jill Lundell
Date: 2023-03-03 00:00:00

Statistical learning models have been growing in popularity in recent years. Many of these models have hyperparameters that must be tuned for models to perform well. Tuning these parameters is not trivial. EZtune is an R package with a simple user interface that can tune support vector machines, adaboost, gradient boosting machines, and elastic net. We first provide a brief summary of the the models that EZtune can tune, including a discussion of each of their hyperparameters. We then compare the ease of using EZtune, caret, and tidymodels. This is followed with a comparison of the accuracy and computation times for models tuned with EZtune and tidymodels. We conclude with a demonstration of how how EZtune can be used to help select a final model with optimal predictive power. Our comparison shows that EZtune can tune support vector machines and gradient boosting machines with EZtune also provides a user interface that is easy to use for a novice to statistical learning models or R.
Body-UAV Near-Ground LoRa Links through a Mediterranean Forest
Authors: Giulio Maria Bianco, Gaetano Marrocco
Date: 2023-03-17 00:00:00

LoRa low-power wide-area network protocol has recently gained attention for deploying ad-hoc search and rescue (SaR) systems. They could be empowered by exploiting body-UAV links that enable communications between a body-worn radio and a UAV-mounted one. However, to employ UAVs effectively, knowledge of the signal's propagation in the environment is required. Otherwise, communications and localization could be hindered. The radio range, the packet delivery ratio (PDR), and the large- and small-scale fading of body-UAV LoRa links at 868 MHz when the radio wearer is in a Mediterranean forest are here characterized for the first time with a near-ground UAV having a maximum flying height of 30 m. A log-distance model accounting for the body shadowing and the wearer's movements is derived. Over the full LoRa radio range of about 600 m, the new model predicts the path loss (PL) better than the state-of-the-art ones, with a reduction of the median error even by 10 dB. The observed small-scale fading is severe and follows a Nakagami-m distribution. Extensions of the model for similar scenarios can be drawn through appropriate corrective factors.
Provably Correct Sensor-driven Path-following for Unicycles using Monotonic Score Functions
Authors: Benton Clark, Varun Hariprasad, Hasan A. Poonawala
Date: 2023-03-21 00:00:00
Tasks: State Estimation

This paper develops a provably stable sensor-driven controller for path-following applications of robots with unicycle kinematics, one specific class of which is the wheeled mobile robot (WMR). The sensor measurement is converted to a scalar value (the score) through some mapping (the score function); the latter may be designed or learned. The score is then mapped to forward and angular velocities using a simple rule with three parameters. The key contribution is that the correctness of this controller only relies on the score function satisfying monotonicity conditions with respect to the underlying state -- local path coordinates -- instead of achieving specific values at all states. The monotonicity conditions may be checked online by moving the WMR, without state estimation, or offline using a generative model of measurements such as in a simulator. Our approach provides both the practicality of a purely measurement-based control and the correctness of state-based guarantees. We demonstrate the effectiveness of this path-following approach on both a simulated and a physical WMR that use a learned score function derived from a binary classifier trained on real depth images.
Distributed Safe Control Design and Probabilistic Safety Verification for Multi-Agent Systems
Authors: Han Wang, Antonis Papachristodoulou, Kostas Margellos
Date: 2023-03-22 00:00:00
Tasks: Collision Avoidance, Continuous Control

We propose distributed iterative algorithms for safe control design and safety verification for networked multi-agent systems. These algorithms rely on distributing a control barrier function (CBF) related quadratic programming (QP) problem assuming the existence of CBFs. The proposed distributed algorithm addresses infeasibility issues of existing schemes via a cooperation mechanism between agents. The resulting control input is guaranteed to be optimal, and satisfies CBF constraints of all agents. Furthermore, a truncated algorithm is proposed to facilitate computational implementation. The performance of the truncated algorithm is evaluated using a distributed safety verification algorithm. The algorithm quantifies safety for multi-agent systems probabilistically by means of CBFs. Both upper and lower bounds on the probability of safety are obtained using the so called scenario approach. Both the scenario sampling and safety verification procedures are fully distributed. The efficacy of our algorithms is demonstrated by an example on multi-robot collision avoidance.
Driveability Constrained Models for Optimal Control of Hybrid Electric Vehicles
Authors: Federico Miretti, Daniela Misul
Date: 2023-03-22 00:00:00
Tasks: energy management, Management

This work investigates the effect of three different driveability constraints on the optimal energy management strategy for a p2 parallel hybrid. Two of these constraints are used to prevent frequent gear shifting and engine start/stops, while the third is used to increase the sportiness of the vehicle by maximizing the available torque reserve at all times. The constraints are imposed by reformulating them as penalty terms to be added to the base running cost of the control strategy, which is fuel consumption. Dynamic programming, a popular optimal control technique, is then used to design the energy management strategy that minimizes the total cost. A case study is developed for a p2 parallel hybrid and simulated on a combination of the Artemis driving cycles. The impact of each driveability constraint is analyzed with respect to a set of relevant features of the control strategy, such as the choice of engine operating points and the gear shift pattern. The resulting discussion provides some useful insight for the design of real-time, rule-based control strategies.
Distributed Two-tier DRL Framework for Cell-Free Network: Association, Beamforming and Power Allocation
Authors: Kaiwen Yu, Chonghao Zhao, Gang Wu, Geoffrey Ye Li
Date: 2023-03-22 00:00:00
Tasks: Deep Reinforcement Learning

Intelligent wireless networks have long been expected to have self-configuration and self-optimization capabilities to adapt to various environments and demands. In this paper, we develop a novel distributed hierarchical deep reinforcement learning (DHDRL) framework with two-tier control networks in different timescales to optimize the long-term spectrum efficiency (SE) of the downlink cell-free multiple-input single-output (MISO) network, consisting of multiple distributed access points (AP) and user terminals (UT). To realize the proposed two-tier control strategy, we decompose the optimization problem into two sub-problems, AP-UT association (AUA) as well as beamforming and power allocation (BPA), resulting in a Markov decision process (MDP) and Partially Observable MDP (POMDP). The proposed method consists of two neural networks. At the system level, a distributed high-level neural network is introduced to optimize wireless network structure on a large timescale. While at the link level, a distributed low-level neural network is proposed to mitigate inter-AP interference and improve the transmission performance on a small timescale. Numerical results show that our method is effective for high-dimensional problems, in terms of spectrum efficiency, signaling overhead as well as satisfaction probability, and generalize well to diverse multi-object problems.
Indoor experimental validation of MPC-based trajectory tracking for a quadcopter via a flat mapping approach
Authors: Huu-Thinh Do, Ionela Prodan
Date: 2023-03-22 00:00:00

Differential flatness has been used to provide diffeomorphic transformations for non-linear dynamics to become a linear controllable system. This greatly simplifies the control synthesis since in the flat output space, the dynamics appear in canonical form (as chains of integrators). The caveat is that mapping from the original to the flat output space often leads to nonlinear constraints. In particular, the alteration of the feasible input set greatly hinders the subsequent calculations. In this paper, we particularize the problem for the case of the quadcopter dynamics and investigate the deformed input constraint set. An optimization-based procedure will achieve a non-conservative, linear, inner-approximation of the non-convex, flat-output derived, input constraints. Consequently, a receding horizon problem (linear in the flat output space) is easily solved and, via the inverse flat mapping, provides a feasible input to the original, nonlinear, dynamics. Experimental validation and comparisons confirm the benefits of the proposed approach and show promise for other class of flat systems.
Chance Constrained Stochastic Optimal Control for Arbitrarily Disturbed LTI Systems Via the One-Sided Vysochanskij-Petunin Inequality
Authors: Shawn Priore, Meeko Oishi
Date: 2023-03-22 00:00:00
Tasks: Collision Avoidance, Gaussian Processes

While many techniques have been developed for chance constrained stochastic optimal control with Gaussian disturbance processes, far less is known about computationally efficient methods to handle non-Gaussian processes. In this paper, we develop a method for solving chance constrained stochastic optimal control problems for linear time-invariant systems with general additive disturbances with finite moments and unimodal chance constraints. We propose an open-loop control scheme for multi-vehicle planning, with both target sets and collision avoidance constraints. Our method relies on the one-sided Vysochanskij-Petunin inequality, a tool from statistics used to bound tail probabilities of unimodal random variables. Using the one-sided Vysochanskij-Petunin inequality, we reformulate each chance constraint in terms of the expectation and standard deviation. While the reformulated bounds are conservative with respect to the original bounds, they have a simple and closed form, and are amenable to difference of convex optimization techniques. We demonstrate our approach on a multi-satellite rendezvous problem.
Zero-Sum Games between Large-Population Teams: Reachability-based Analysis under Mean-Field Sharing
Authors: Yue Guan, Mohammad Afshari, Panagiotis Tsiotras
Date: 2023-03-22 00:00:00

This work studies the behaviors of two large-population teams competing in a discrete environment. The team-level interactions are modeled as a zero-sum game while the agent dynamics within each team is formulated as a collaborative mean-field team problem. Drawing inspiration from the mean-field literature, we first approximate the large-population team game with its infinite-population limit. Subsequently, we construct a fictitious centralized system and transform the infinite-population game to an equivalent zero-sum game between two coordinators. We study the optimal coordination strategies for each team via a novel reachability analysis and later translate them back to decentralized strategies that the original agents deploy. We prove that the strategies are $\epsilon$-optimal for the original finite-population team game, and we further show that the suboptimality diminishes when team size approaches infinity. The theoretical guarantees are verified by numerical examples.
Learning for Online Mixed-Integer Model Predictive Control with Parametric Optimality Certificates
Authors: Luigi Russo, Siddharth H. Nair, Luigi Glielmo, Francesco Borrelli
Date: 2023-03-21 00:00:00
Tasks: Model Predictive Control, Motion Planning

We propose a supervised learning framework for computing solutions of multi-parametric Mixed Integer Linear Programs (MILPs) that arise in Model Predictive Control. Our approach also quantifies sub-optimality for the computed solutions. Inspired by Branch-and-Bound techniques, the key idea is to train a Neural Network/Random Forest, which for a given parameter, predicts a strategy consisting of (1) a set of Linear Programs (LPs) such that their feasible sets form a partition of the feasible set of the MILP and (2) a candidate integer solution. For control computation and sub-optimality quantification, we solve a set of LPs online in parallel. We demonstrate our approach for a motion planning example and compare against various commercial and open-source mixed-integer programming solvers.
Robust Output-Lifted Learning Model Predictive Control
Authors: Siddharth H. Nair, Francesco Borrelli
Date: 2023-03-21 00:00:00
Tasks: model, Model Predictive Control

We propose an iterative approach for designing Robust Learning Model Predictive Control (LMPC) policies for a class of nonlinear systems with additive, unmodelled dynamics. The nominal dynamics are assumed to be difference flat, i.e., the state and input can be reconstructed using flat output sequences. For the considered class of systems, we synthesize Robust MPC policies and show how to use historical trajectory data collected during iterative tasks to 1) obtain bounds on the unmodelled dynamics and 2) construct a convex value function approximation along with a convex safe set in the space of output sequences for designing terminal components in the Robust MPC design. We show that the proposed strategy guarantees robust constraint satisfaction, asymptotic convergence to a desired subset of the state space, and non-decreasing closed-loop performance at each policy update. Finally, simulation results demonstrate the effectiveness of the proposed strategy on a minimum time control problem using a constrained nonlinear and uncertain vehicle model.
System-specific parameter optimization for non-polarizable and polarizable force fields
Authors: Xiaojuan Hu, Kazi S. Amin, Markus Schneider, Carmay Lim, Dennis Salahub, Carsten Baldauf
Date: 2023-03-22 00:00:00

The accuracy of classical force fields (FFs) has been shown to be limited for the simulation of cation-protein systems despite their importance in understanding the processes of life. Improvements can result from optimizing the parameters of classical FFs or by extending the FF formulation by terms describing charge transfer and polarization effects. In this work, we introduce our implementation of the CTPOL model in OpenMM, which extends the classical additive FF formula by adding charge transfer (CT) and polarization (POL). Furthermore, we present an open-source parameterization tool, called FFAFFURR that enables the (system specific) parameterization of OPLS-AA and CTPOL models. The performance of our workflow was evaluated by its ability to reproduce quantum chemistry energies and by molecular dynamics simulations of a Zinc finger protein.
Volatile Memory Motifs: Minimal Spiking Neural Networks
Authors: Fabio Schittler Neves, Marc Timme
Date: 2023-03-21 00:00:00

How spiking neuronal networks encode memories in their different time and spatial scales constitute a fundamental topic in neuroscience and neuro-inspired engineering. Much attention has been paid to large networks and long-term memory, for example in models of associative memory. Smaller circuit motifs may play an important complementary role on shorter time scales, where broader network effects may be of less relevance. Yet, compact computational models of spiking neural networks that exhibit short-term volatile memory and actively hold information until their energy source is switched off, seem not fully understood. Here we propose that small spiking neural circuit motifs may act as volatile memory components. A minimal motif consists of only two interconnected neurons -- one self-connected excitatory neuron and one inhibitory neuron -- and realizes a single-bit volatile memory. An excitatory, delayed self-connection promotes a bistable circuit in which a self-sustained periodic orbit generating spike trains co-exists with the quiescent state of no neuron spiking. Transient external inputs may straightforwardly induce switching between those states. Moreover, the inhibitory neuron may act as an autonomous turn-off switch. It integrates incoming excitatory pulses until a threshold is reached after which the inhibitory neuron emits a spike that then inhibits further spikes in the excitatory neuron, terminating the memory. Our results show how external bits of information (excitatory signal), can be actively held in memory for a pre-defined amount of time. We show that such memory operations are robust against parameter variations and exemplify how sequences of multidimensional input signals may control the dynamics of a many-bits memory circuit in a desired way.