An experimental study of the performance of IEEE 802.11ad in smartphones

Aggarwal, Shivang; Ghoshal, Moinak; Banerjee, Piyali; Koutsonikolas, Dimitrios

doi:10.1016/j.comcom.2021.01.006

Cited by 7 publications

(9 citation statements)

References 32 publications

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“…Building on [10], a more comprehensive performance assessment of IEEE 802.11ad on smartphones is provided in [11]. The experimental study presented in this work considers the following three experimental setups.…”

Section: Experimental Studies and Wireless Vr Testbedsmentioning

confidence: 99%

“…In [12], an extensive measurement study is reported focusing on the predictability of throughput of an IEEE 802.11ad system on the downlink to an IEEE 802.11ad-enabled mobile device. In this study, using a similar experimental setup as in [10,11], the mobility patterns and orientations of the mobile device were varied. It was shown that throughput may be predicted in real time using a small neural network model with good accuracy at varying timescales.…”

Section: Experimental Studies and Wireless Vr Testbedsmentioning

confidence: 99%

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Video Freeze Assessment of TPCAST Wireless Virtual Reality: An Experimental Study

et al. 2022

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Wireless virtual reality (VR) offers a seamless user experience but has to cope with higher sensitivity to temporal impairments induced on the wireless link. Apart from bandwidth dynamics and latency, video freezes and their lengths are important temporal performance indicators that impact on the quality of experience (QoE) of networked VR applications and services. This paper reports an experimental study that focuses on the VR video frame freeze length characteristics of a wireless VR solution. A comprehensive measurement campaign using a commercial TPCAST wireless VR solution with an HTC Vive head-mounted display was conducted to obtain real VR video traces. The number of detected freezes and freeze intensities are reported both accumulated over four room quadrants as well as for each of the four quadrants subject to six transmitter-receiver distances. The statistical analysis of the VR video traces of the different experiments includes histograms of the freeze lengths and cumulative complementary histograms of the freeze length. The results of this analysis offer insights into the density of the underlying distributions of the measured data, illustrate the impact of the room topology on the freeze characteristics, and suggest the statistical modeling of the freeze characteristics as exponential and geometric distributions. The statistical models of the freeze characteristics may be included in wireless VR simulators supporting the development of physical layer, medium access layer, and higher layer functionalities. They also may serve as network-disturbance models for VR QoE studies, e.g., generating realistic freeze events in wireless VR stimuli.

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Section: Experimental Studies and Wireless Vr Testbedsmentioning

confidence: 99%

Section: Experimental Studies and Wireless Vr Testbedsmentioning

confidence: 99%

Video Freeze Assessment of TPCAST Wireless Virtual Reality: An Experimental Study

et al. 2022

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“…Selecting the right radio is challenging because mmWave and sub-6 GHz radios have very different features including supported data rates, range, link stability, reliability, and power consumption. First, mmWave technologies (e.g., 802.11ad) support multi-Gbps data rates but only at ranges up to a few 10s of ft [1,23]. Second, the data rate of mmWave links fluctuates rapidly, even under static, line-of-sight (LOS) conditions, whereas the data rate of sub-6 GHz WiFi (e.g., 802.11ac/ax) is much much more stable [1,23,28].…”

Section: Challenge 3: Using Mmwave With Other Network Modalitiesmentioning

confidence: 99%

“…First, mmWave technologies (e.g., 802.11ad) support multi-Gbps data rates but only at ranges up to a few 10s of ft [1,23]. Second, the data rate of mmWave links fluctuates rapidly, even under static, line-of-sight (LOS) conditions, whereas the data rate of sub-6 GHz WiFi (e.g., 802.11ac/ax) is much much more stable [1,23,28]. Hence, for applications with strict timing guarantees it may be preferable to use sub-6 GHz WiFi for communication even at the cost of reduced throughput.…”

Section: Challenge 3: Using Mmwave With Other Network Modalitiesmentioning

confidence: 99%

“…Hence, for applications with strict timing guarantees it may be preferable to use sub-6 GHz WiFi for communication even at the cost of reduced throughput. Third, mmWave signals are easily blocked by objects and human bodies [1,28,29], resulting in temporary link outages. Given that even optimized blockage detection and interface switching solutions [28] can take up to 100 ms in the worst case and algorithms on COTS devices take several seconds [22,28], simultaneously utilizing both interfaces might be preferable for high-priority streams.…”

Section: Challenge 3: Using Mmwave With Other Network Modalitiesmentioning

confidence: 99%

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Ramps

Shrivastav

Koutsonikolas

Bagchi

2021

Proceedings of the Fifth Workshop on Distributed Infrastructures for Deep Learning (DIDL) 2021

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Real time IoT analytics remains a challenging problem due to the distributed nature of the analytics platform (comprising sensors, edge server(s), and actuators), which raises three fundamental challenges of (i) how to map computations to a distributed and heterogeneous compute fabric, (ii) how to communicate multi-Gbps of data wirelessly between sensors and edge servers for high analytics accuracy, and (iii) how to effectively share the communication channel between multiple sensor-edge network streams. To meet these challenges, we envision an analytics platform that will tightly couple the application stack, the network stack, and emerging networking technologies, namely mmWave wireless and programmable switches, to meet both the computation and communication demands for real time IoT analytics.

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