Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.36196

People effects on IoT indoor wireless channel characterization

Millena Michely de Medeiros Campos·Mateus de Oliveira e Mattos·Rafael da Silva Macedo·Alvaro Augusto Machado de Medeiros·Wellerson Viana de Oliveira·Vicente Angelo de Sousa Junior
View original source ↗Read source document ↗Export metadata ↓

Abstract

Wireless communication under 1 GHz is suitable for Internet of Things (IoT) applications due to larger coverage capability with less power consumption. Bearing in mind that people and elements contained in the environment can cause variations in the channel, this paper aims to evaluate the effect of the presence of people on a 900-MHz indoor narrowband wireless channel, as we characterize the small-scale phenomena. With the increase in the number of people, a greater variation in the communication channel was noticed, which is reflected in the parameters of the probability distributions used in the characterization of the random part of the signal. In addition, second-order statistics were used to analyze the data and an adherence test was applied to confirm the behavior of the signal in relation to the distributions.

Explore related subjects

cs.NI

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Millena Michely de Medeiros Campos, Mateus de Oliveira e Mattos, Rafael da Silva Macedo, Alvaro Augusto Machado de Medeiros, Wellerson Viana de Oliveira, Vicente Angelo de Sousa Junior. 2026-09-28. People effects on IoT indoor wireless channel characterization. https://doi.org/10.18265/1517-0306a2020v1n53p141-149

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

SpraySense: Sensing Gray Failures through Packet Spraying Discrepancies

Distributed machine learning (ML) training has become a dominant workload in modern data center networks, operating at massive scale with clusters comprising tens to hundreds of thousands of GPUs. The scale of these networks makes failures, and particularly gray failures, inevitable. Gray failures can significantly degrade both network and application performance, yet they are notoriously difficult to detect, localize, and debug. To meet the performance demands of ML workloads, adaptive routing is widely deployed to maximize network utilization by dynamically spreading traffic across many paths. While adaptive routing increases network utilization, it also greatly intensifies the effect of gray failures. Prior work has either dismissed gray failures as negligible or proposed detection mechanisms that fail to scale, rendering these approaches increasingly impractical for large-scale clusters. We present SpraySense, a passive gray failure detection system that leverages the statistical properties of adaptive routing and network load balancing. By combining these properties with flow-level information, SpraySense can identify failures before they significantly impact application performance, enabling preemptive rerouting and improving overall performance. Importantly, this is achieved through passive observation of traffic spraying, without introducing additional load on the network. We evaluate SpraySense and show that it can detect and localize a single-link packet-drop-rate $1.5\%$ within a single iteration and as little as $0.5\%$ within 5 training iterations of Llama-3 70B in a 64 spine topology.

cs.NI↗
arXiv2026

An Experimental Assessment of the Spatial and Frequency Selectivity of Reconfigurable Intelligent Surfaces

This work investigates the impact of reconfigurable intelligent surfaces (RIS) on radio links other than the one for which the RIS configuration is optimized. We consider three different scenarios in which a secondary communication link could be affected by a RIS whose configuration is optimized for a primary communication link operating in the vicinity, on the same or on different frequencies. This question is investigated experimentally in the FR1 band, using the CorteXlab radio testbed and a Greenerwave RIS. We show that the impact, in terms of received power and impact on the channel phase of the secondary link, is significant even outside of the nominal frequency range of the RIS, and is not mitigated by carrier frequency separation between the two communication links.

cs.NI↗
arXiv2026

Integrated Sensing and Communication in 3GPP: Evolution from 5G-Advanced to 6G

Integrated sensing and communication (ISAC) extends mobile networks from information transfer toward perception of passive objects and environments. By exploiting propagation delay, Doppler, angle, and temporal variation, a mobile network can support detection and tracking while reusing licensed spectrum, infrastructure, and edge computing at network scale and under operator control. Unlike conventional radar, ISAC must additionally address multi-application access, heterogeneous sensing entities, uncertainty, privacy, trust, and integration with communication services. This article reviews the evolution of ISAC in 3GPP from 5G-Advanced to 6G. It explains Release-19 ISAC service requirements and channel models, the Release-20 sensing function architecture and reporting abstractions, and the 5G monostatic baseline for drone detection and tracking. It also examines 6G-native sensing, including passive object sensing, communication assistance, multiple sensing modes, and multi-source data integration. The article makes the evolution explicit through a comparison of sensing capabilities and KPIs, an assessment of reusable functions and deployment costs, and a quantitative UAV case study linking service targets to sensing configuration, reporting overhead, and delivery latency.

cs.NI↗