50 research outputs found
Multi-Channel Random Access with Replications
This paper considers a class of multi-channel random access algorithms, where
contending devices may send multiple copies (replicas) of their messages to the
central base station. We first develop a hypothetical algorithm that delivers a
lower estimate for the access delay performance within this class. Further, we
propose a feasible access control algorithm achieving low access delay by
sending multiple message replicas, which approaches the performance of the
hypothetical algorithm. The resulting performance is readily approximated by a
simple lower bound, which is derived for a large number of channels.Comment: 5 pages, 2 figures, accepted by ISIT 201
System-Level Dynamics of Highly Directional Distributed Networks
While highly directional communications may offer considerable improvements
in the link data rate and over-the-air latency of high-end wearable devices,
the system-level capacity trade-offs call for separate studies with respect to
the employed multiple access procedures and the network dynamics in general.
This letter proposes a framework for estimating the system-level area
throughput in dynamic distributed networks of highly-directional paired
devices. We provide numerical expressions for the steady-state distribution of
the number of actively communicating pairs and the probability of successful
session initialization as well as derive the corresponding closed-form
approximation for dense deployments.Comment: Accepted to IEEE Wireless Communications Letters on April 5, 2021.
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Resource-Efficient Federated Hyperdimensional Computing
In conventional federated hyperdimensional computing (HDC), training larger
models usually results in higher predictive performance but also requires more
computational, communication, and energy resources. If the system resources are
limited, one may have to sacrifice the predictive performance by reducing the
size of the HDC model. The proposed resource-efficient federated
hyperdimensional computing (RE-FHDC) framework alleviates such constraints by
training multiple smaller independent HDC sub-models and refining the
concatenated HDC model using the proposed dropout-inspired procedure. Our
numerical comparison demonstrates that the proposed framework achieves a
comparable or higher predictive performance while consuming less computational
and wireless resources than the baseline federated HDC implementation.Comment: Accepted to Federated Learning Systems (FLSys) workshop, in
Conjunction with the 6th MLSys Conference (MLSys 2023
A Concise Review of 5G New Radio Capabilities for Directional Access at mmWave Frequencies
In this work, we briefly outline the core 5G air interface improvements
introduced by the latest New Radio (NR) specifications, as well as elaborate on
the unique features of initial access in 5G NR with a particular emphasis on
millimeter-wave (mmWave) frequency range. The highly directional nature of 5G
mmWave cellular systems poses a variety of fundamental differences and research
problem formulations, and a holistic understanding of the key system design
principles behind the 5G NR is essential. Here, we condense the relevant
information collected from a wide diversity of 5G NR standardization documents
(based on 3GPP Release 15) to distill the essentials of directional access in
5G mmWave cellular, which becomes the foundation for any corresponding
system-level analysis.Comment: 14 pages, 6 figures, 4 tables, published in proceedings of
International Conference on Next Generation Wired/Wireless Networking, NEW2AN
2018, St. Petersburg, Russi
Path Loss Characterization for Intra-Vehicle Wearable Deployments at 60 GHz
In this work, we present the results of a wideband measurement campaign at 60
GHz conducted inside a Linkker electric city bus. Targeting prospective
millimeter-wave (mmWave) public transportation wearable scenarios, we mimic a
typical deployment of mobile high-end consumer devices in a dense environment.
Specifically, our intra-vehicle deployment includes one receiver and multiple
transmitters corresponding to a mmWave access point and passengers' wearable
and hand-held devices. While the receiver is located in the front part of the
bus, the transmitters repeat realistic locations of personal devices (i) at the
seat level (e.g., a hand-held device) and (ii) at a height 70 cm above the seat
(e.g., a wearable device: augmented reality glasses or a head-mounted display).
Based on the measured received power, we construct a logarithmic model for the
distance-dependent path loss. The parametrized models developed in the course
of this study have the potential to become an attractive ground for the link
budget estimation and interference footprint studies in crowded public
transportation scenarios.Comment: 4 pages, 8 figures, 1 table, accepted to EuCAP 201
Interplay of User Behavior, Communication, and Computing in Immersive Reality 6G Applications
Emerging extended reality (XR) services and applications that submerge users into a virtual universe pave the way towards ubiquitous contextualized experiences. Immersive interactions on-the-go not only bring new use cases but also distract users from the real world and modify their behavior and motion, which in turn may affect the operation of communication networks. This article explores the effects of XR user motion from the communication and computing perspectives. To this end, we offer a review of mobility patterns in XR and a detailed simulation study on the impact of interaction-dependent gait patterns on the delay and resource utilization. The results confirm the uniqueness of XR applications in terms of the user behavior patterns, which calls for novel application-centric algorithms, protocols, and mechanisms to facilitate high-performance connectivity under demanding XR requirements.acceptedVersionPeer reviewe
ML-Assisted Beam Selection via Digital Twins for Time-Sensitive Industrial IoT
In this article, we propose a machine learning (ML)-assisted beam selection framework that leverages the availability of digital twins to reduce beam training overheads and thus facilitate the efficient operation of time-sensitive IoT applications in dynamic industrial environments. Our approach employs a digital twin of the environment to create an accurate map-based channel model and train a beam predictor that narrows the beam search space to a set of candidate configurations. To verify the proposed concept, we perform shooting-and-bouncing ray (SBR) modeling for a reconstructed 3D model of an industrial vehicle calibrated using the real-world millimeter-wave (mmWave) propagation data collected during a measurement campaign. We confirm that lightweight ML models are capable of predicting the optimal beam configuration while enjoying considerably smaller size compared to the map-based channel model.acceptedVersionPeer reviewe
Coded Distributed Gaussian Process Regression
In this letter, we propose a coded load balancing method for distributed Gaussian process regression over heterogeneous wireless networks, where users with diverse computational and communications capabilities may offload excessive training data onto a computationally stronger central server to reduce collaborative processing times. The offloaded data are transformed using random Fourier feature mapping and encoded with a random orthogonal matrix to prevent transmission of raw data. The proposed method is particularly applicable to compute-intensive applications, where users operate with large datasets.acceptedVersionPeer reviewe