Analytical Performance Evaluation of Cooperative and Multi-Radio Concepts in Emerging Wireless Networks

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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. Copyright may be transferred without further notice after which this version may become non-availabl

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