29 research outputs found
Towards Ultra-Low-Latency mmWave Wi-Fi for Multi-User Interactive Virtual Reality
The need for cables with high-fidelity Virtual Reality (VR) headsets remains
a stumbling block on the path towards interactive multi-user VR. Due to strict
latency constraints, designing fully wireless headsets is challenging, with the
few commercially available solutions being expensive. These solutions use
proprietary millimeter wave (mmWave) communications technologies, as extremely
high frequencies are needed to meet the throughput and latency requirements of
VR applications. In this work, we investigate whether such a system could be
built using specification-compliant IEEE 802.11ad hardware, which would
significantly reduce the cost of wireless mmWave VR solutions. We present a
theoretical framework to calculate attainable live VR video bitrates for
different IEEE 802.11ad channel access methods, using 1 or more head-mounted
displays connected to a single Access Point (AP). Using the ns-3 simulator, we
validate our theoretical framework, and demonstrate that a properly configured
IEEE 802.11ad AP can support at least 8 headsets receiving a 4K video stream
for each eye, with transmission latency under 1 millisecond.Comment: Published at 2020 IEEE Global Communications Conference (GLOBECOM
Short-Term Trajectory Prediction for Full-Immersive Multiuser Virtual Reality with Redirected Walking
Full-immersive multiuser Virtual Reality (VR) envisions supporting
unconstrained mobility of the users in the virtual worlds, while at the same
time constraining their physical movements inside VR setups through redirected
walking. For enabling delivery of high data rate video content in real-time,
the supporting wireless networks will leverage highly directional communication
links that will "track" the users for maintaining the Line-of-Sight (LoS)
connectivity. Recurrent Neural Networks (RNNs) and in particular Long
Short-Term Memory (LSTM) networks have historically presented themselves as a
suitable candidate for near-term movement trajectory prediction for natural
human mobility, and have also recently been shown as applicable in predicting
VR users' mobility under the constraints of redirected walking. In this work,
we extend these initial findings by showing that Gated Recurrent Unit (GRU)
networks, another candidate from the RNN family, generally outperform the
traditionally utilized LSTMs. Second, we show that context from a virtual world
can enhance the accuracy of the prediction if used as an additional input
feature in comparison to the more traditional utilization of solely the
historical physical movements of the VR users. Finally, we show that the
prediction system trained on a static number of coexisting VR users be scaled
to a multi-user system without significant accuracy degradation.Comment: 7 pages, 9 figures, 2 table
Graph Neural Network-enabled Terahertz-based Flow-guided Nanoscale Localization
Scientific advancements in nanotechnology and advanced materials are paving
the way toward nanoscale devices for in-body precision medicine; comprising
integrated sensing, computing, communication, data and energy storage
capabilities. In the human cardiovascular system, such devices are envisioned
to be passively flowing and continuously sensing for detecting events of
diagnostic interest. The diagnostic value of detecting such events can be
enhanced by assigning to them their physical locations (e.g., body region),
which is the main proposition of flow-guided localization. Current flow-guided
localization approaches suffer from low localization accuracy and they are
by-design unable to localize events within the entire cardiovascular system.
Toward addressing this issue, we propose the utilization of Graph Neural
Networks (GNNs) for this purpose, and demonstrate localization accuracy and
coverage enhancements of our proposal over the existing State of the Art (SotA)
approaches. Based on our evaluation, we provide several design guidelines for
GNN-enabled flow-guided localization.Comment: 6 pages, 5 figures, 1 table, 15 references. arXiv admin note: text
overlap with arXiv:2305.1849
Insights from the Design Space Exploration of Flow-Guided Nanoscale Localization
Nanodevices with Terahertz (THz)-based wireless communication capabilities
are providing a primer for flow-guided localization within the human
bloodstreams. Such localization is allowing for assigning the locations of
sensed events with the events themselves, providing benefits in precision
medicine along the lines of early and precise diagnostics, and reduced costs
and invasiveness. Flow-guided localization is still in a rudimentary phase,
with only a handful of works targeting the problem. Nonetheless, the
performance assessments of the proposed solutions are already carried out in a
non-standardized way, usually along a single performance metric, and ignoring
various aspects that are relevant at such a scale (e.g., nanodevices' limited
energy) and for such a challenging environment (e.g., extreme attenuation of
in-body THz propagation). As such, these assessments feature low levels of
realism and cannot be compared in an objective way. Toward addressing this
issue, we account for the environmental and scale-related peculiarities of the
scenario and assess the performance of two state-of-the-art flow-guided
localization approaches along a set of heterogeneous performance metrics such
as the accuracy and reliability of localization.Comment: 6 pages, 4 figures, 2 table
An AI-based incumbent protection system for collaborative intelligent radio networks
Since the early days of wireless communication, wireless spectrum has been allocated according to a static frequency plan, whereby most of the spectrum is licensed for exclusive use by specific services or radio technologies. While some spectrum bands are overcrowded, many other bands are heavily underutilized. As a result, there is a shortage of available spectrum to deploy emerging technologies that require high demands on data like 5G. Several global efforts address this problem by providing multi-tier spectrum sharing frameworks, for example, the Citizens Broadband Radio Service (CBRS) and Licensed Shared Access (LSA) models, to increase spectrum reuse. In these frameworks, the incumbent (i.e., the technology that used the spectrum exclusively in the past) has to be protected against service disruptions caused by the transmissions of the new technologies that start using the same spectrum. However, these approaches suffer from two main problems. First, spectrum re-allocation to new uses is a slow process that may take years. Second, they do not scale fast since it requires a centralized infrastructure to protect the incumbent and coordinate and grant access to the shared spectrum. As a solution, the Spectrum Collaboration Challenge (SC2) has shown that the collaborative intelligent radio networks (CIRNs) -- artificial intelligence (AI)-based autonomous wireless networks that collaborate -- can share and reuse spectrum efficiently without any coordination and with the guarantee of incumbent protection. In this article, we present the architectural design and the experimental validation of an incumbent protection system for the next generation of spectrum sharing frameworks. The proposed system is a two-step AI-based algorithm that recognizes, learns, and proactively predicts the incumbent's transmission pattern with an accuracy above 95 percent in near real time (less than 300 ms). The proposed algorithm was validated in Colosseum, the RF channel emulator built for the SC2 competition, using up to two incumbents simultaneously with different transmission patterns and sharing spectrum with up to five additional CIRNs
Toward Standardized Performance Evaluation of Flow-guided Nanoscale Localization
Nanoscale devices featuring Terahertz (THz)-based wireless communication
capabilities are envisioned to be deployed within human bloodstreams. Such
devices are envisaged to enable fine-grained sensing-based applications for
detecting events for early indications of various health conditions, as well as
actuation-based ones such as the targeted drug delivery. Intuitively,
associating the locations of such events with the events themselves would
provide an additional utility for precision diagnostics and treatment. This
vision recently yielded a new class of in-body localization coined under the
term "flow-guided nanoscale localization". Such localization can be piggybacked
on THz-based communication for detecting body regions in which events were
observed based on the duration of one circulation of a nanodevice in the
bloodstream. From a decades-long research on objective benchmarking of
"traditional" indoor localization, as well as its eventual standardization
(e.g., ISO/IEC 18305:2016), we know that in early stages the reported
performance results were often incomplete (e.g., targeting a subset of relevant
metrics), carrying out benchmarking experiments in different evaluation
environments and scenarios, and utilizing inconsistent performance indicators.
To avoid such a "lock-in" in flow-guided localization, in this paper we discuss
a workflow for standardized evaluation of such localization. The workflow is
implemented in the form of an open-source framework that is able to jointly
account for the mobility of the nanodevices in the bloodstream, in-body THz
communication between the nanodevices and on-body anchors, and energy-related
and other technological constraints at the nanodevice level. Accounting for
these constraints, the framework is able to generate the raw data that can be
streamlined into different flow-guided solutions for generating standardized
performance benchmarks.Comment: 8 pages, 6 figures, 15 references, available at:
https://bitbucket.org/filip_lemic/flow-guided-localization-in-ns3/src/master