60 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
Sensing Integrated DFT-Spread OFDM Waveform and Deep Learning-powered Receiver Design for Terahertz Integrated Sensing and Communication Systems
Terahertz (THz) communications are envisioned as a key technology of
next-generation wireless systems due to its ultra-broad bandwidth. One step
forward, THz integrated sensing and communication (ISAC) system can realize
both unprecedented data rates and millimeter-level accurate sensing. However,
THz ISAC meets stringent challenges on waveform and receiver design to fully
exploit the peculiarities of THz channel and transceivers. In this work, a
sensing integrated discrete Fourier transform spread orthogonal frequency
division multiplexing (SI-DFT-s-OFDM) system is proposed for THz ISAC, which
can provide lower peak-to-average power ratio than OFDM and is adaptive to
flexible delay spread of the THz channel. Without compromising communication
capabilities, the proposed SI-DFT-s-OFDM realizes millimeter-level range
estimation and decimeter-per-second-level velocity estimation accuracy. In
addition, the bit error rate (BER) performance is improved by 5 dB gain at the
BER level compared with OFDM. At the receiver, a deep learning based
ISAC receiver with two neural networks is developed to recover transmitted data
and estimate target range and velocity, while mitigating the imperfections and
non-linearities of THz systems. Extensive simulation results demonstrate that
the proposed deep learning methods can realize mutually enhanced performance
for communication and sensing, and is robust against Doppler effects, phase
noise, and multi-target estimation
Intelligent Reflective Surface vs. Mobile Relay-supported NLoS Avoidance in Indoor mmWave Networks
The 6th generation of wireless communication (6G) is envisioned to give rise
to various technologies for improving the end-to-end communication performance,
where the communication is envisioned to utilize wireless signals in the
millimeter wave (mmWave) frequencies and above. Among others, these
technologies comprise Intelligent Reflective Surfaces (IRSs) and Mobile Relays
(MRs), whose envisaged roles include mitigating the negative effects of
Non-Line-of-Sight (NLoS) connectivity, in particular at mmWave and higher
frequencies. The core idea behind these technologies is to use cooperative
networking where the source sends a signal to a repeater, in this case the IRS
or the MR, which is upon reception forwarded to the destination. When comparing
the two technologies, it is important to realize that the IRSs are primarily
envisioned to be static entities attached to various objects in the environment
such as walls and furniture. In contrast, the MRs will feature a higher degree
of freedom, as they will be able to position themselves seamlessly in the
environment. Based on the above assumptions, we derive an approach for
determining the optimal position of the IRS and MR in indoor environments,
i.e., the one that maximizes the end-to-end link quality between the source and
the destination. We follow by capturing the communication quality indicators
for both IRS- and MR-supported NLoS avoidance in indoor mmWave communication in
a number of scenarios. Our results show that, from the end-to-end link quality
perspective, the MRs generally outperform the IRSs, suggesting their
utilization potential for throughput-optimized NLoS avoidance scenarios.Comment: 6 pages, 5 figure
Analytical Modelling of Raw Data for Flow-Guided In-body Nanoscale Localization
Advancements in nanotechnology and material science are paving the way toward
nanoscale devices that combine sensing, computing, data and energy storage, and
wireless communication. In precision medicine, these nanodevices show promise
for disease diagnostics, treatment, and monitoring from within the patients'
bloodstreams. Assigning the location of a sensed biological event with the
event itself, which is the main proposition of flow-guided in-body nanoscale
localization, would be immensely beneficial from the perspective of precision
medicine. The nanoscale nature of the nanodevices and the challenging
environment that the bloodstream represents, result in current flow-guided
localization approaches being constrained in their communication and
energy-related capabilities. The communication and energy constraints of the
nanodevices result in different features of raw data for flow-guided
localization, in turn affecting its performance. An analytical modeling of the
effects of imperfect communication and constrained energy causing intermittent
operation of the nanodevices on the raw data produced by the nanodevices would
be beneficial. Hence, we propose an analytical model of raw data for
flow-guided localization, where the raw data is modeled as a function of
communication and energy-related capabilities of the nanodevice. We evaluate
the model by comparing its output with the one obtained through the utilization
of a simulator for objective evaluation of flow-guided localization, featuring
comparably higher level of realism. Our results across a number of scenarios
and heterogeneous performance metrics indicate high similarity between the
model and simulator-generated raw datasets.Comment: 6 pages, 7 figures, 4 tables, 16 reference
Toward Energy Efficient Multiuser IRS-Assisted URLLC Systems: A Novel Rank Relaxation Method
This paper proposes an energy efficient resource allocation design algorithm
for an intelligent reflecting surface (IRS)-assisted downlink ultra-reliable
low-latency communication (URLLC) network. This setup features a multi-antenna
base station (BS) transmitting data traffic to a group of URLLC users with
short packet lengths. We maximize the total network's energy efficiency (EE)
through the optimization of active beamformers at the BS and passive
beamformers (a.k.a. phase shifts) at the IRS. The main non-convex problem is
divided into two sub-problems. An alternating optimization (AO) approach is
then used to solve the problem. Through the use of the successive convex
approximation (SCA) with a novel iterative rank relaxation method, we construct
a concave-convex objective function for each sub-problem. The first sub-problem
is a fractional program that is solved using the Dinkelbach method and a
penalty-based approach. The second sub-problem is then solved based on
semi-definite programming (SDP) and the penalty-based approach. The iterative
solution gradually approaches the rank-one for both the active beamforming and
unit modulus IRS phase-shift sub-problems. Our results demonstrate the efficacy
of the proposed solution compared to existing benchmarks
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
Real-time generation of 3-dimensional representations of static objects using small unmanned aerial vehicles
Recent advances in robotics and nanotechnology resulted in a set of miniaturized Unmanned Aerial Vehicles (UAVs). Such small UAVs are envisioned to operate in hard-to-reach areas for enabling applications such as structural monitoring or content capturing. Towards showcasing this vision, we demonstrate a small UAV-supported setup for real-time autonomous generation of 3-Dimensional (3D) representations of static objects. In the setup, a small UAV (i.e., CrazyFlie 2.1) is envisioned to visit a set of locations, acting as a carrier and power source of a camera sensor. At each location, the sensor is expected to take a picture of the object and report it to the station. The station implements a pipeline for 3D reconstruction based on the pictures taken by the UAV.This work was funded by the MCIN/AEI/10.13039/01100011033/ FEDER/EU HoloMit 2.0 (PID2021-126551OB-C21) and MCIN/AEI/ 10.13039/501100011033 TRAINER-A (PID2020-118011GB-C21).Peer ReviewedPostprint (author's final draft
ViFi: virtual fingerprinting WiFi-based indoor positioning via multi-wall multi-floor propagation model
Widespread adoption of indoor positioning systems based on WiFi fingerprinting is at present hindered by the large efforts required for measurements collection during the offline phase. Two approaches were recently proposed to address such issue: crowdsourcing and RSS radiomap prediction, based on either interpolation or propagation channel model fitting from a small set of measurements. RSS prediction promises better positioning accuracy when compared to crowdsourcing, but no systematic analysis of the impact of system parameters on positioning accuracy is available. This paper fills this gap by introducing ViFi, an indoor positioning system that relies on RSS prediction based on Multi-Wall Multi-Floor (MWMF) propagation model to generate a discrete RSS radiomap (virtual fingerprints). Extensive experimental results, obtained in multiple independent testbeds, show that ViFi outperforms virtual fingerprinting systems adopting simpler propagation models in terms of accuracy, and allows a sevenfold reduction in the number of measurements to be collected, while achieving the same accuracy of a traditional fingerprinting system deployed in the same environment. Finally, a set of guidelines for the implementation of ViFi in a generic environment, that saves the effort of collecting additional measurements for system testing and fine tuning, is proposed
- …