pwc-archive/papers-with-abstracts huggingface dataset:
A User Study on Contrastive Explanations for Multi-Effector Temporal Planning with Non-Stationary Costs
Authors: Xiaowei Liu, Kevin McAreavey, Weiru Liu
Date: 2024-09-20 00:00:00
Tasks: Scheduling

In this paper, we adopt constrastive explanations within an end-user application for temporal planning of smart homes. In this application, users have requirements on the execution of appliance tasks, pay for energy according to dynamic energy tariffs, have access to high-capacity battery storage, and are able to sell energy to the grid. The concurrent scheduling of devices makes this a multi-effector planning problem, while the dynamic tariffs yield costs that are non-stationary (alternatively, costs that are stationary but depend on exogenous events). These characteristics are such that the planning problems are generally not supported by existing PDDL-based planners, so we instead design a custom domain-dependent planner that scales to reasonable appliance numbers and time horizons. We conduct a controlled user study with 128 participants using an online crowd-sourcing platform based on two user stories. Our results indicate that users provided with contrastive questions and explanations have higher levels of satisfaction, tend to gain improved understanding, and rate the helpfulness more favourably with the recommended AI schedule compared to those without access to these features.
Across-Game Engagement Modelling via Few-Shot Learning
Authors: Kosmas Pinitas, Konstantinos Makantasis, Georgios N. Yannakakis
Date: 2024-09-19 00:00:00
Tasks: Few-Shot Learning

Domain generalisation involves learning artificial intelligence (AI) models that can maintain high performance across diverse domains within a specific task. In video games, for instance, such AI models can supposedly learn to detect player actions across different games. Despite recent advancements in AI, domain generalisation for modelling the users' experience remains largely unexplored. While video games present unique challenges and opportunities for the analysis of user experience -- due to their dynamic and rich contextual nature -- modelling such experiences is limited by generally small datasets. As a result, conventional modelling methods often struggle to bridge the domain gap between users and games due to their reliance on large labelled training data and assumptions of common distributions of user experience. In this paper, we tackle this challenge by introducing a framework that decomposes the general domain-agnostic modelling of user experience into several domain-specific and game-dependent tasks that can be solved via few-shot learning. We test our framework on a variation of the publicly available GameVibe corpus, designed specifically to test a model's ability to predict user engagement across different first-person shooter games. Our findings demonstrate the superior performance of few-shot learners over traditional modelling methods and thus showcase the potential of few-shot learning for robust experience modelling in video games and beyond.
Low-code from frontend to backend: Connecting conversational user interfaces to backend services via a low-code IoT platform
Authors: Irene Weber
Date: 2024-09-13 00:00:00
Tasks: Chatbot

Current chatbot development platforms and frameworks facilitate setting up the language and dialog part of chatbots, while connecting it to backend services and business functions requires substantial manual coding effort and programming skills. This paper proposes an approach to overcome this situation. It proposes an architecture with a chatbot as frontend using an IoT (Internet of Things) platform as a middleware for connections to backend services. Specifically, it elaborates and demonstrates how to combine a chatbot developed on the open source development platform Rasa with the open source platform Node-RED, allowing low-code or no-code development of a transactional conversational user interface from frontend to backend.
What If We Had Used a Different App? Reliable Counterfactual KPI Analysis in Wireless Systems
Authors: Qiushuo Hou, Sangwoo Park, Matteo Zecchin, Yunlong Cai, Guanding Yu, Osvaldo Simeone
Date: 2024-09-30 00:00:00
Tasks: Conformal Prediction, counterfactual, Scheduling

In modern wireless network architectures, such as Open Radio Access Network (O-RAN), the operation of the radio access network (RAN) is managed by applications, or apps for short, deployed at intelligent controllers. These apps are selected from a given catalog based on current contextual information. For instance, a scheduling app may be selected on the basis of current traffic and network conditions. Once an app is chosen and run, it is no longer possible to directly test the key performance indicators (KPIs) that would have been obtained with another app. In other words, we can never simultaneously observe both the actual KPI, obtained by the selected app, and the counterfactual KPI, which would have been attained with another app, for the same network condition, making individual-level counterfactual KPIs analysis particularly challenging. This what-if analysis, however, would be valuable to monitor and optimize the network operation, e.g., to identify suboptimal app selection strategies. This paper addresses the problem of estimating the values of KPIs that would have been obtained if a different app had been implemented by the RAN. To this end, we propose a conformal-prediction-based counterfactual analysis method for wireless systems that provides reliable error bars for the estimated KPIs, despite the inherent covariate shift between logged and test data. Experimental results for medium access control-layer apps and for physical-layer apps demonstrate the merits of the proposed method.
A physics-compliant diagonal representation for wireless channels parametrized by beyond-diagonal reconfigurable intelligent surfaces
Authors: Philipp del Hougne
Date: 2024-09-30 00:00:00

The parametrization of wireless channels by so-called "beyond-diagonal reconfigurable intelligent surfaces" (BD-RIS) is mathematically characterized by a matrix whose off-diagonal entries are partially or fully populated. Physically, this corresponds to tunable coupling mechanisms between the RIS elements that originate from the RIS control circuit. Here, we derive a physics-compliant diagonal representation for BD-RIS-parametrized channels. We recognize that any RIS control circuit can always be separated into its static parts (SLC) and a set of tunable individual loads (IL). Therefore, a BD-RIS-parametrized channel results from the chain cascade of three systems: i) radio environment (RE), ii) SLC, and iii) IL. RE and SLC are static non-diagonal systems whose cascade K is terminated by the tunable diagonal system IL. This physics-compliant representation in terms of K and IL is directly analogous to that for conventional ("diagonal") RIS (D-RIS). Therefore, scenarios with BD-RIS can also readily be captured by the physics-compliant coupled-dipole model PhysFad, as we show. In addition, physics-compliant algorithms for system-level optimization with D-RIS can be directly applied to scenarios with BD-RIS. We demonstrate this important implication of our conceptual finding in a case study on end-to-end channel estimation and optimization in a BD-RIS-parametrized rich-scattering environment. Our case study is the first experimentally grounded system-level optimization for BD-RIS: We obtain the characteristics of RE and IL from experimental measurements and a commercial PIN diode, respectively. Altogether, our physics-compliant diagonal representation for BD-RIS enables a paradigm shift in how practitioners in wireless communications and signal processing implement system-level optimizations for BD-RIS because it enables them to directly apply existing physics-compliant D-RIS algorithms.
Distributed Channel Estimation and Optimization for 6D Movable Antenna: Unveiling Directional Sparsity
Authors: Xiaodan Shao, Rui Zhang, Qijun Jiang, Jihong Park, Tony Q. S. Quek, Robert Schober
Date: 2024-09-24 00:00:00
Tasks: CPU, Position

Six-dimensional movable antenna (6DMA) is an innovative technology to improve wireless network capacity by adjusting 3D positions and 3D rotations of antenna surfaces based on channel spatial distribution. However, the existing works on 6DMA have assumed a central processing unit (CPU) to jointly process the signals of all 6DMA surfaces to execute various tasks. This inevitably incurs prohibitively high processing cost for channel estimation. Therefore, we propose a distributed 6DMA processing architecture to reduce processing complexity of CPU by equipping each 6DMA surface with a local processing unit (LPU). In particular, we unveil for the first time a new \textbf{\textit{directional sparsity}} property of 6DMA channels, where each user has significant channel gains only for a (small) subset of 6DMA position-rotation pairs, which can receive direct/reflected signals from users. In addition, we propose a practical three-stage protocol for the 6DMA-equipped base station (BS) to conduct statistical CSI acquisition for all 6DMA candidate positions/rotations, 6DMA position/rotation optimization, and instantaneous channel estimation for user data transmission with optimized 6DMA positions/rotations. Specifically, the directional sparsity is leveraged to develop distributed algorithms for joint sparsity detection and channel power estimation, as well as for directional sparsity-aided instantaneous channel estimation. Using the estimated channel power, we develop a channel power-based optimization algorithm to maximize the ergodic sum rate of the users by optimizing the antenna positions/rotations. Simulation results show that our channel estimation algorithms are more accurate than benchmarks with lower pilot overhead, and our optimization outperforms fluid/movable antennas optimized only in two dimensions (2D), even when the latter have perfect instantaneous CSI.
User-Centric Cell-Free Massive MIMO With RIS-Integrated Antenna Arrays
Authors: Özlem Tuğfe Demir, Emil Björnson
Date: 2024-09-24 00:00:00

Cell-free massive MIMO (multiple-input multiple-output) is a promising network architecture for beyond 5G systems, which can particularly offer more uniform data rates across the coverage area. Recent works have shown how reconfigurable intelligent surfaces (RISs) can be used as relays in cell-free massive MIMO networks to improve data rates further. In this paper, we analyze an alternative architecture where an RIS is integrated into the antenna array at each access point and acts as an intelligent transmitting surface to expand the aperture area. This approach alleviates the multiplicative fading effect that normally makes RIS-aided systems inefficient and offers a cost-effective alternative to building large antenna arrays. We use a small number of antennas and a larger number of controllable RIS elements to match the performance of an antenna array whose size matches that of the RIS. In this paper, we explore this innovative transceiver architecture in the uplink of a cell-free massive MIMO system for the first time, demonstrating its potential benefits through analytic and numerical contributions. The simulation results validate the effectiveness of our proposed phase-shift configuration and highlight scenarios where the proposed architecture significantly enhances data rates.
UAV-Enabled Data Collection for IoT Networks via Rainbow Learning
Authors: Yingchao Jiao, Xuhui Zhang, Wenchao Liu, Yinyu Wu, Jinke Ren, Yanyan Shen, Bo Yang, Xinping Guan
Date: 2024-09-22 00:00:00
Tasks: Deep Reinforcement Learning, Fairness, Scheduling

Unmanned aerial vehicles (UAVs) enabled Internet of things (IoT) systems have become an important part of future wireless communications. To achieve higher communication rate, the joint design of UAV trajectory and resource allocation is crucial. In this paper, a multi-antenna UAV is dispatched to simultaneously collect data from multiple ground IoT nodes (GNs) within a time interval. To improve the sum data collection (SDC) volume from the GNs, the UAV trajectory, the UAV receive beamforming, the scheduling of the GNs, and the transmit power of the GNs are jointly optimized. Since the problem is non-convex and the variables are highly coupled, it is hard to be solved using traditional methods. To find a near-optimal solution, a double-loop structured optimization-driven deep reinforcement learning (DRL) algorithm, called rainbow learning based algorithm (RLA), and a fully DRL-based algorithm are proposed to solve the problem effectively. Specifically, the outer-loop of the RLA utilizes a fusion deep Q-network to optimize the UAV trajectory, GN scheduling, and power allocation, while the inner-loop optimizes receive beamforming by successive convex approximation. Simulation results verify that the proposed algorithms outperform two benchmarks with significant improvement in SDC volumes, energy efficiency, and fairness.
Rydberg Atomic Quantum Receivers for Classical Wireless Communication and Sensing
Authors: Tierui Gong, Aveek Chandra, Chau Yuen, Yong Liang Guan, Rainer Dumke, Chong Meng Samson See, Mérouane Debbah, Lajos Hanzo
Date: 2024-09-22 00:00:00

The Rydberg atomic quantum receivers (RAQR) are emerging quantum precision sensing platforms designed for receiving radio frequency (RF) signals. It relies on creation of Rydberg atoms from normal atoms by exciting one or more electrons to a very high energy level, thereby making the atom sensitive to RF signals. RAQRs realize RF-to-optical conversions based on light-atom interactions relying on the so called electromagnetically induced transparency (EIT) and Aulter-Townes splitting (ATS), so that the desired RF signal can be read out optically. The large dipole moments of Rydberg atoms associated with rich choices of Rydberg states and various modulation schemes facilitate an ultra-high sensitivity ($\sim$ nV/cm/$\sqrt{\text{Hz}}$) and an ultra-broadband tunability (direct-current to Terahertz). RAQRs also exhibit compelling scalability and lend themselves to the construction of innovative, compact receivers. Initial experimental studies have demonstrated their capabilities in classical wireless communications and sensing. To fully harness their potential in a wide variety of applications, we commence by outlining the underlying fundamentals of Rydberg atoms, followed by the principles and schemes of RAQRs. Then, we overview the state-of-the-art studies from both physics and communication societies. Furthermore, we conceive Rydberg atomic quantum single-input single-output (RAQ-SISO) and multiple-input multiple-output (RAQ-MIMO) schemes for facilitating the integration of RAQRs with classical wireless systems. Finally, we conclude with a set of potent research directions.
Reduced bit median quantization: A middle process for Efficient Image Compression
Authors: Fikresilase Wondmeneh Abebayew
Date: 2024-09-20 00:00:00
Tasks: Image Compression, Quantization

Image compression techniques have made remarkable progress when it comes to file size reduction with a tolerable quality reduction; nonetheless, they are facing some challenges when it comes to applying more compression with the same perceptible quality or in accounting for specific use cases such as deep archive files and more efficient image transfers. Previous techniques have tried to solve the former problem by applying one specific or a combination of different algorithms. However, none of these methods were able to achieve additional file size reduction beyond a certain compression. I introduce Reduced Bit Median Quantization (RBMQ), a middle-process image compression technique designed to enhance file size reduction so that it can be stored with already existing file extension formats. In RBMQ by applying only the first step in which the quantization of valued further file size reduction can be achieved without a noticeable decrease in the image quality. Furthermore, more size reduction can be achieved by reducing the representing bits for the quantized values which can be optimal for deep archival storage or big-size image transfer in which the image quality is not suitable for the human eye since it is dark and dim but can be much efficient to interact with network and storage components later to be decoded to get the only quantized value image that almost the same quality with the original one. RBMQ introduces redundancy to the pixel values to be taken advantage of by existing compression techniques furthermore it introduces bit reduction from 8 to 5 bits for image file extensions such as jpeg which substantially reduces the file size to be used for JPEG file transfers and deep archive storage.
MIMO Precoding Exploiting Extra Degrees of Freedom (DoF) in the Wavenumber Domain
Authors: Yuanbin Chen, Xufeng Guo, Tianqi Mao, Qingqing Wu, Zhaocheng Wang, Chau Yuen
Date: 2024-09-20 00:00:00

In this paper, we propose an emerging wavenumber-domain precoding scheme to break the limitations of rank-1 channels that merely supports single-stream transmission, enabling simultaneous transmission of multiple data streams. The proposed wavenumber-domain precoding scheme also breaks the Rayleigh distance demarcation, regardless of the far-field and near-field contexts. Specifically, by characterizing the channel response as the superposition of a series of Fourier harmonics specified by different wavenumbers, the degree of freedom (DoF) is dependent on the cardinality of the wavenumber support, based on which the extra DoF is presented. This representation is applicable for both far-field and near-field. Different wavenumber atoms, determined within this support, constitute the codebook for MIMO precoding, in which each atom allows for the transmission of a data stream. Then, to maximize the capacity, it is required to select the wavenumbers associated with the optimal transmission direction, and optimize its power allocation. Finally, our simulation results demonstrate the significant superiority in comparison to the conventional spatial division schemes, with the potential of approaching the theoretical performance upper bound achieved by singular value decomposition (SVD).
Causality-Driven Reinforcement Learning for Joint Communication and Sensing
Authors: Anik Roy, Serene Banerjee, Jishnu Sadasivan, Arnab Sarkar, Soumyajit Dey
Date: 2024-09-07 00:00:00
Tasks: Autonomous Driving, Causal Discovery, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The next-generation wireless network, 6G and beyond, envisions to integrate communication and sensing to overcome interference, improve spectrum efficiency, and reduce hardware and power consumption. Massive Multiple-Input Multiple Output (mMIMO)-based Joint Communication and Sensing (JCAS) systems realize this integration for 6G applications such as autonomous driving, as it requires accurate environmental sensing and time-critical communication with neighboring vehicles. Reinforcement Learning (RL) is used for mMIMO antenna beamforming in the existing literature. However, the huge search space for actions associated with antenna beamforming causes the learning process for the RL agent to be inefficient due to high beam training overhead. The learning process does not consider the causal relationship between action space and the reward, and gives all actions equal importance. In this work, we explore a causally-aware RL agent which can intervene and discover causal relationships for mMIMO-based JCAS environments, during the training phase. We use a state dependent action dimension selection strategy to realize causal discovery for RL-based JCAS. Evaluation of the causally-aware RL framework in different JCAS scenarios shows the benefit of our proposed framework over baseline methods in terms of the beamforming gain.
Joint LOS Identification and Data Association for 6G-Enabled Networked Device-Free Sensing
Authors: Qin Shi, Liang Liu
Date: 2024-09-05 00:00:00

This paper considers networked device-free sensing in an orthogonal frequency division multiplexing (OFDM) cellular system with multipath environment, where the passive targets reflect the downlink signals to the base stations (BSs) via non-line-of-sight (NLOS) paths and/or line-of-sight (LOS) paths, and the BSs share the sensing information extracted from their received echoes to jointly localize the targets. A two-phase localization protocol is considered. In Phase I, we design an efficient method that is able to accurately estimate the range of any path from a transmitting BS to a receiving BS via a target, even if the transmitting and receiving BSs are separated and not perfectly synchronized. In Phase II, we propose an effective method that is able to jointly identify the ranges of the LOS paths between the targets and the BSs as well as associate the ranges of LOS paths with the right targets, such that the number and the locations of the targets both can be accurately estimated. Numerical results verify that our proposed two-phase protocol can achieve high performance of networked sensing in the multipath environment.
OpenRANet: Neuralized Spectrum Access by Joint Subcarrier and Power Allocation with Optimization-based Deep Learning
Authors: Siya Chen, Chee Wei Tan, Xiangping Zhai, H. Vincent Poor
Date: 2024-08-31 00:00:00
Tasks: Computational Efficiency

The next-generation radio access network (RAN), known as Open RAN, is poised to feature an AI-native interface for wireless cellular networks, including emerging satellite-terrestrial systems, making deep learning integral to its operation. In this paper, we address the nonconvex optimization challenge of joint subcarrier and power allocation in Open RAN, with the objective of minimizing the total power consumption while ensuring users meet their transmission data rate requirements. We propose OpenRANet, an optimization-based deep learning model that integrates machine-learning techniques with iterative optimization algorithms. We start by transforming the original nonconvex problem into convex subproblems through decoupling, variable transformation, and relaxation techniques. These subproblems are then efficiently solved using iterative methods within the standard interference function framework, enabling the derivation of primal-dual solutions. These solutions integrate seamlessly as a convex optimization layer within OpenRANet, enhancing constraint adherence, solution accuracy, and computational efficiency by combining machine learning with convex analysis, as shown in numerical experiments. OpenRANet also serves as a foundation for designing resource-constrained AI-native wireless optimization strategies for broader scenarios like multi-cell systems, satellite-terrestrial networks, and future Open RAN deployments with complex power consumption requirements.
Safe Decentralized Multi-Agent Control using Black-Box Predictors, Conformal Decision Policies, and Control Barrier Functions
Authors: Sacha Huriot, Hussein Sibai
Date: 2024-09-27 00:00:00

We address the challenge of safe control in decentralized multi-agent robotic settings, where agents use uncertain black-box models to predict other agents' trajectories. We use the recently proposed conformal decision theory to adapt the restrictiveness of control barrier functions-based safety constraints based on observed prediction errors. We use these constraints to synthesize controllers that balance between the objectives of safety and task accomplishment, despite the prediction errors. We provide an upper bound on the average over time of the value of a monotonic function of the difference between the safety constraint based on the predicted trajectories and the constraint based on the ground truth ones. We validate our theory through experimental results showing the performance of our controllers when navigating a robot in the multi-agent scenes in the Stanford Drone Dataset.