pwc-archive/papers-with-abstracts huggingface dataset:
A Fast Multitaper Power Spectrum Estimation in Nonuniformly Sampled Time Series
Authors: Jie Cui, Benjamin H. Brinkmann, Gregory A. Worrell
Date: 2024-07-02 00:00:00
Tasks: Time Series

Nonuniformly sampled signals are prevalent in real-world applications, but their power spectra estimation, usually from a finite number of samples of a single realization, presents a significant challenge. The optimal solution, which uses Bronez Generalized Prolate Spheroidal Sequence (GPSS), is computationally demanding and often impractical for large datasets. This paper describes a fast nonparametric method, the MultiTaper NonUniform Fast Fourier Transform (MTNUFFT), capable of estimating power spectra with lower computational burden. The method first derives a set of optimal tapers through cubic spline interpolation on a nominal analysis band. These tapers are subsequently shifted to other analysis bands using the NonUniform FFT (NUFFT). The estimated spectral power within the band is the average power at the outputs of the taper set. This algorithm eliminates the needs for time-consuming computation to solve the Generalized Eigenvalue Problem (GEP), thus reducing the computational load from $O(N^4)$ to $O(N \log N + N \log(1/\epsilon))$, which is comparable with the NUFFT. The statistical properties of the estimator are assessed using Bronez GPSS theory, revealing that the bias of estimates and variance bound of the MTNUFFT estimator are identical to those of the optimal estimator. Furthermore, the degradation of bias bound may serve as a measure of the deviation from optimality. The performance of the estimator is evaluated using both simulation and real-world data, demonstrating its practical applicability. The code of the proposed fast algorithm is available on GitHub (https://github.com/jiecui/mtnufft).
Relay-Assisted Carrier Aggregation (RACA) Uplink System for Enhancing Data Rate of Extended Reality (XR)
Authors: Chi-Wei Chen, Wen-Chiao Tsai, Lung-Sheng Tsai, An-Yeu, Wu
Date: 2024-07-02 00:00:00

In Extended Reality (XR) applications, high data rates and low latency are crucial for immersive experiences. Uplink transmission in XR is challenging due to the limited antennas and power of lightweight XR devices. To improve data transmission rates, we investigate a relay-assisted carrier aggregation (RACA) system. The XR device simultaneously transmits data to an access point (AP) and a relay in proximity over low-frequency and high-frequency bands, respectively. Then, the relay down-converts and amplifies the signals to the AP, effectively acting as an additional transmit antenna for the XR device. In this paper, we propose two algorithms to maximize the data rate of the XR device in their respective protocols. In the centralized protocol, the rate maximization problem is equivalently transformed as a weighted mean square error minimization (WMMSE) problem which can be solved iteratively by alternative optimization. In the distributed protocol, the rate maximization problem is decomposed into two independent sub-problems where the rate of the direct link and the rate of the relay link are maximized by singular value decomposition (SVD)-based methods with water-filling (WF). Simulation results show that the rate of the RACA system is improved by $32\%$ compared to that of the conventional carrier aggregation scheme.
Joint State and Parameter Estimation Using the Partial Errors-in-Variables Principle
Authors: Peng Liu, Kailai Li, Gustaf Hendeby, Fredrik Gustafsson
Date: 2024-07-01 00:00:00
Tasks: parameter estimation, regression

This letter proposes a new method for joint state and parameter estimation in uncertain dynamical systems. We exploit the partial errors-in-variables (PEIV) principle and formulate a regression problem in the sense of weighted total least squares, where the uncertainty in the parameter prior is explicitly considered. Based thereon, the PEIV regression can be solved iteratively through the Kalman smoothing and the regularized least squares for estimating the state and the parameter, respectively. The simulations demonstrate improved accuracy of the proposed method compared to existing approaches, including the joint maximum a posterior-maximum likelihood, the expectation maximisation, and the augmented state extended Kalman smoother.
Channel Characterization of Implantable Intrabody Communication through Experimental Measurements
Authors: Kayhan Ate, Anna Marcucci, Pietro Savazzi, Şükrü Özen, Fabio Dell'Acqua, Anna Vizziello
Date: 2024-07-01 00:00:00

Intrabody communication (IBC), is a promising technology that can be utilized for data transmission across the human body. In this study, a galvanic coupled (GC)-based IBC channel has been investigated for implantable configuration both theoretically and experimentally in the frequency range of 0 to 2.5 MHz. Theoretical studies were performed by using finite element method (FEM) based simulation software, called Comsol Multiphysics. A cylindrical human arm was modeled with realistic values. Experimental studies were carried out with chicken breast tissue as a substitute for human tissue. The pseudorandom noise (PN) sequences were transmitted to investigate the correlative channel sounder of tissue model. Results showed that the frequency affects signal propagation through the tissue model. Additionally, it is crucial to cancel common-mode noise in the IBC channel to enhance communication quality.
A comparative analysis of preamble sequences for Galvanic Coupling Intra-Body Communications
Authors: Farzana Kulsoom, Pietro Savazzi, Fabio Dell'Acqua, Hassan Nazeer Chaudhry, Anna Vizziello
Date: 2024-07-01 00:00:00

Galvanic coupled-intra-body communication (GC-IBC) is an innovative research area contributing to transform personalized medicine by enabling seamless connectivity and communication among implanted devices. To establish a reliable communication link between implanted devices, the preambles play a crucial role by e.g. conveying syncronization information or supporting channel response estimation. The preambles are carefully designed to ensure that they are mutually orthogonal, to minimize self-interference and maximize separability. For that purpose, many permeable sequences are proposed in the literature for 5G and sensor networks. Golay code, Constant Amplitude Zero Auto Correlation (CAZAC) and Zadoff-Chu (Z-Chu) sequences are among the most popular ones. In this work, we performed a comparative analysis of these sequences to determine their suitability for the GC-IBC system. We evaluated the effectiveness of the preamble sequences on the basis of their correlation properties and probability of error.
Prediction of Rare Channel Conditions using Bayesian Statistics and Extreme Value Theory
Authors: Tobias Kallehauge, Anders E. Kalør, Pablo Ramírez-Espinosa, Christophe Biscio, Petar Popovski
Date: 2024-07-01 00:00:00

Estimating the probability of rare channel conditions is a central challenge in ultra-reliable wireless communication, where random events, such as deep fades, can cause sudden variations in the channel quality. This paper proposes a sample-efficient framework for predicting the statistics of such events by utilizing spatial dependency between channel measurements acquired from various locations. The proposed framework combines radio maps with non-parametric models and extreme value theory (EVT) to estimate rare-event channel statistics under a Bayesian formulation. The framework can be applied to a wide range of problems in wireless communication and is exemplified by rate selection in ultra-reliable communications. Notably, besides simulated data, the proposed framework is also validated with experimental measurements. The results in both cases show that the Bayesian formulation provides significantly better results in terms of throughput compared to baselines that do not leverage measurements from surrounding locations. It is also observed that the models based on EVT are generally more accurate in predicting rare-event statistics than non-parametric models, especially when only a limited number of channel samples are available. Overall, the proposed methods can significantly reduce the number of measurements required to predict rare channel conditions and guarantee reliability.
Terahertz Communication Multi-UAV-Assisted Mobile Edge Computing System
Authors: Heekang Song, Hyowoon Seo, Wan Choi
Date: 2024-07-01 00:00:00
Tasks: Edge-computing

Mobile edge computing (MEC) and terahertz (THz)enabled unmanned aerial vehicle (UAV) communication systems are gaining significant attention for improving user service delays in future mobile networks. This article introduces a novel multi-UAV-aided MEC system operating at THz frequencies to minimize expected user service delays, including communication and computation latency. We address this challenge by jointly optimizing UAV relay selection, power control, positioning, and user-resource association for task offloading and resource allocation. To tackle the problem's complexities, we decompose it into four subproblems, each solved optimally with our proposed algorithm. An iterative penalty dual decomposition (PDD) algorithm approximates the original problem's solution. Numerical results demonstrate that our PDD-based approach outperforms baseline algorithms in terms of expected user service delay.
A Note On the Clark Conjecture On Time-Warped Bandlimited Signals
Authors: Xiang-Gen Xia
Date: 2024-07-01 00:00:00

In this note, a result of a previous paper on the Clark conjecture on time-warped bandlimited signals is extended to a more general class of the time warping functions, which includes most of the common functions in practice.
Multi-Functional Beamforming Design for Integrated Sensing, Communication, and Computation
Authors: Yapeng Zhao, Qingqing Wu, Wen Chen, Yong Zeng, Ruiqi Liu, Weidong Mei, Fen Hou, Shaodan Ma
Date: 2024-07-01 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) systems may face a heavy computation burden since the sensory data needs to be further processed. This paper studies a novel system that integrates sensing, communication, and computation, aiming to provide services for different objectives efficiently. This system consists of a multi-antenna multi-functional base station (BS), an edge server, a target, and multiple singleantenna communication users. The BS needs to allocate the available resources to efficiently provide sensing, communication, and computation services. Due to the heavy service burden and limited power budget, the BS can partially offload the tasks to the nearby edge server instead of computing them locally. We consider the estimation of the target response matrix, a general problem in radar sensing, and utilize Cramer-Rao bound (CRB) as the corresponding performance metric. To tackle the non-convex optimization problem, we propose both semidefinite relaxation (SDR)-based alternating optimization and SDR-based successive convex approximation (SCA) algorithms to minimize the CRB of radar sensing while meeting the requirement of communication users and the need for task computing. Furthermore, we demonstrate that the optimal rankone solutions of both the alternating and SCA algorithms can be directly obtained via the solver or further constructed even when dealing with multiple functionalities. Simulation results show that the proposed algorithms can provide higher target estimation performance than state-of-the-art benchmarks while satisfying the communication and computation constraints.
Time Index Modulation-Driven Standalone RIS Mechanism for Symbiotic Radio
Authors: M. Ertug Pihtili, Mehmet C. Ilter, Ertugrul Basar
Date: 2024-06-30 00:00:00

The rising demand for energy and spectrum resources in next-generation Internet-of-things (IoT) systems accounts for innovative modes of information and power transfer. One potential solution is to harness the active transmission capability of devices to facilitate data transmission and wireless energy harvesting (WEH) for backscatter communication so as to form a symbiotic radio (SR) environment in a mutualistic manner. Additionally, incorporating reconfigurable intelligent surfaces (RISs) into the SR environment can provide an additional link and enhance the reliability of backscatter communication, thereby reinforcing the symbiotic relationships between active and passive devices. This paper proposes a novel SR system where a standalone RIS sustains its functions through WEH based on a low-power RIS structure and establishes mutualistic symbiosis by utilizing a signal conveyed by the primary transmitter (PTx) to assist ongoing transmissions and convey information to the primary receiver (PRx). The PTx employs time index modulation (TIM) to transmit information to the PRx and power to the RIS and energy harvester (EH). A log-likelihood ratio (LLR)-based detector is presented to address challenges in the TIM scheme. Finally, the performance of the proposed scheme is investigated in terms of harvested direct current (DC) power at the RIS and EH, as well as the bit error rate (BER) at the PRx.
MIMO-NOMA Enabled Sectorized Cylindrical Massive Antenna Array for HAPS with Spatially Correlated Channels
Authors: Rozita Shafie, Mohammad Javad Omidi, Omid Abbasi, Halim Yanikomeroglu
Date: 2024-06-30 00:00:00

The high altitude platform station (HAPS) technology is garnering significant interest as a viable technology for serving as base stations in communication networks. However, HAPS faces the challenge of high spatial correlation among adjacent users' channel gains which is due to the dominant line-of-sight (LoS) path between HAPS and terrestrial users. Furthermore, there is a spatial correlation among antenna elements of HAPS that depends on the propagation environment and the distance between elements of the antenna array. This paper presents an antenna architecture for HAPS and considers the mentioned issues by characterizing the channel gain and the spatial correlation matrix of the HAPS. We propose a cylindrical antenna for HAPS that utilizes vertical uniform linear array (ULA) sectors. Moreover, to address the issue of high spatial correlation among users, the non-orthogonal multiple access (NOMA) clustering method is proposed. An algorithm is also developed to allocate power among users to maximize both spectral efficiency and energy efficiency while meeting quality of service (QoS) and successive interference cancellation (SIC) conditions. Finally, simulation results indicate that the spatial correlation has a significant impact on spectral efficiency and energy efficiency in multiple antenna HAPS systems.
Machine-Type Communication Waveforms: An Exploration of New Dimensions
Authors: Michael Wang, Lei Wang, Xiaohu You
Date: 2024-06-29 00:00:00

This paper derives a generalized class of waveforms with an application to machine-type communication (MTC) while studying its underlying structural characteristics in relation to conventional modulation waveforms. First, a canonical waveform of frequency-error tolerance is identified for a unified preamble and traffic signal design, ideal for MTC use as a composite waveform, commonly known as a transmission burst. It is shown that the most widely used modulation schemes for mIoT traffic signals, e.g., FSK and LoRa modulation, are simply subsets of the canonical waveform. The intrinsic characteristics and degrees of freedom the waveform offers are then explored. Most significantly, a new waveform dimension is uncovered and exploited as additional degrees of freedom for satisfying the MTC requirements, i.e., energy and resource efficiency and robustness. The corresponding benefits are evaluated analytically and numerically in AWGN, frequency-flat, and selective channels. We demonstrate that neither FSK nor LoRa can fully address the mIoT requirements since neither fully exploits the degrees of freedom from the perspective of the generalized waveform class. Finally, a solution is devised to optimize energy and resource efficiency under various deployment environments and practical constraints while maintaining the low-complexity property.
Multi-Satellite MIMO Systems for Direct User-Satellite Communications: A Survey
Authors: Zohre Mashayekh Bakhsh, Yasaman Omid, Gaojie Chen, Farbod Kayhan, Yi Ma, Rahim Tafazolli
Date: 2024-06-28 00:00:00
Tasks: Survey

Advancements in satellite technology have made direct-to-device connectivity a viable solution for ensuring global access. This method is designed to provide internet connectivity to remote, rural, or underserved areas where traditional cellular or broadband networks are lacking or insufficient. This paper is a survey providing an in-depth review of multi-satellite Multiple Input Multiple Output (MIMO) systems as a potential solution for addressing the link budget challenge in direct user-satellite communication. Special attention is given to works considering multi-satellite MIMO systems, both with and without satellite collaboration. In this context, collaboration refers to sharing data between satellites to improve the performance of the system. This survey begins by explaining several fundamental aspects of satellite communications (SatComs), which are vital prerequisites before investigating the multi-satellite MIMO systems. These aspects encompass satellite orbits, the structure of satellite systems, SatCom links, including the inter-satellite links (ISL) which facilitate satellite cooperation, satellite frequency bands, satellite antenna design, and satellite channel models, which should be known or estimated for effective data transmission to and from multiple satellites. Furthermore, this survey distinguishes itself by providing more comprehensive insights in comparison to other surveys. It specifically delves into the Orthogonal Time Frequency Space (OTFS) within the channel model section. It goes into detail about ISL noise and channel models, and it extends the ISL section by thoroughly investigating hybrid FSO/RF ISLs. Furthermore, analytical comparisons of simulation results from these works are presented to highlight the advantages of employing multi-satellite MIMO systems.
Examining the impact of forcing function inputs on structural identifiability
Authors: Jessica R Conrad, Marisa C Eisenberg
Date: 2024-07-03 00:00:00
Tasks: Mathematical Proofs

For mathematical and experimental ease, models with time varying parameters are often simplified to assume constant parameters. However, this simplification can potentially lead to identifiability issues (lack of uniqueness of parameter estimates). Methods have been developed to algebraically and numerically determine the identifiability of a model, as well as resolve identifiability issues. This specific type of simplification presents an alternate opportunity to instead use this information to resolve the unidentifiability. Given that re-parameterizing, collecting more data, and adding inputs can be potentially costly or impractical, this could present new alternatives. We present a method for resolving unidentifiability in a system by introducing a new data stream correlated with a parameter of interest. First, we demonstrate how and when non-constant input data can be introduced into any rational function ODE system without worsening the model identifiability. Then, we prove when these input functions improve structural and potentially also practical identifiability for a given model and relevant data. By utilizing pre-existing data streams, these methods can potentially reduce experimental costs, while still answering key questions. By connecting mathematical proofs to application, our framework removes guesswork from when, where, and how researchers can best introduce new data to improve model outcomes.
Robust optimal investment and consumption strategies with portfolio constraints and stochastic environment
Authors: Len Patrick Dominic M. Garces, Yang shen
Date: 2024-07-03 00:00:00

We investigate a continuous-time investment-consumption problem with model uncertainty in a general diffusion-based market with random model coefficients. We assume that a power utility investor is ambiguity-averse, with the preference to robustness captured by the homothetic multiplier robust specification, and the investor's investment and consumption strategies are constrained to closed convex sets. To solve this constrained robust control problem, we employ the stochastic Hamilton-Jacobi-Bellman-Isaacs equations, backward stochastic differential equations, and bounded mean oscillation martingale theory. Furthermore, we show the investor incurs (non-negative) utility loss, i.e. the loss in welfare, if model uncertainty is ignored. When the model coefficients are deterministic, we establish formally the relationship between the investor's robustness preference and the robust optimal investment-consumption strategy and the value function, and the impact of investment and consumption constraints on the investor's robust optimal investment-consumption strategy and value function. Extensive numerical experiments highlight the significant impact of ambiguity aversion, consumption and investment constraints, on the investor's robust optimal investment-consumption strategy, utility loss, and value function. Key findings include: 1) short-selling restriction always reduces the investor's utility loss when model uncertainty is ignored; 2) the effect of consumption constraints on utility loss is more delicate and relies on the investor's risk aversion level.
Portfolio optimisation: bridging the gap between theory and practice
Authors: Cristiano Arbex Valle
Date: 2024-07-01 00:00:00

Portfolio optimisation is essential in quantitative investing, but its implementation faces several practical difficulties. One particular challenge is converting optimal portfolio weights into real-life trades in the presence of realistic features, such as transaction costs and integral lots. This is especially important in automated trading, where the entire process happens without human intervention. Several works in literature have extended portfolio optimisation models to account for these features. In this paper, we highlight and illustrate difficulties faced when employing the existing literature in a practical setting, such as computational intractability, numerical imprecision and modelling trade-offs. We then propose a two-stage framework as an alternative approach to address this issue. Its goal is to optimise portfolio weights in the first stage and to generate realistic trades in the second. Through extensive computational experiments, we show that our approach not only mitigates the difficulties discussed above but also can be successfully employed in a realistic scenario. By splitting the problem in two, we are able to incorporate new features without adding too much complexity to any single model. With this in mind we model two novel features that are critical to many investment strategies: first, we integrate two classes of assets, futures contracts and equities, into a single framework, with an example illustrating how this can help portfolio managers in enhancing investment strategies. Second, we account for borrowing costs in short positions, which have so far been neglected in literature but which significantly impact profits in long/short strategies. Even with these new features, our two-stage approach still effectively converts optimal portfolios into actionable trades.