Modeling System-Level Dynamics of Direct XR Sessions over mmWave Links

To improve the quality of experience (QoE) and prolong the battery life, high-end wearable devices may offload their computations – partially or fully – to a paired computing device. One of the promising connectivity solutions, due to heavy load, is millimeter-wave (mmWave) technologies, which offer wide bandwidth and promise to provide extreme throughput and low latency. The features of the mmWave access and the use of sophisticated beamforming techniques have posed a whole new set of problem formulations related to directionality. Over the past decade, stochastic geometry has been extensively used to study directional mmWave connectivity in static deployments; however, there remains a research gap of employing directionality in highly dynamic scenarios. To bridge this gap, in this paper, we analyze the effects of mmWave directionality for non-static device-to-device (D2D) links, typical for high-end wearable applications. We propose a queueing-theoretical approach to capturing the dynamics of the representative mmWave D2D scenario and derive approximations for the key system-level metrics of interest. Our numerical results yield important insights on the role that the directivity has in changing the interference footprint in dynamic D2D systems.

[1]  Olga Galinina,et al.  Analyzing Assisted Offloading of Cellular User Sessions onto D2D Links in Unlicensed Bands , 2015, IEEE Journal on Selected Areas in Communications.

[2]  Mehdi Bennis,et al.  Toward Low-Latency and Ultra-Reliable Virtual Reality , 2018, IEEE Network.

[3]  Ji Yang,et al.  Offloading Guidelines for Augmented Reality Applications on Wearable Devices , 2015, ACM Multimedia.

[4]  Robert W. Heath,et al.  Device-to-Device Millimeter Wave Communications: Interference, Coverage, Rate, and Finite Topologies , 2015, IEEE Transactions on Wireless Communications.

[5]  Melike Erol-Kantarci,et al.  Caching and Computing at the Edge for Mobile Augmented Reality and Virtual Reality (AR/VR) in 5G , 2017, ADHOCNETS.

[6]  Robert W. Heath,et al.  Interference in finite-sized highly dense millimeter wave networks , 2015, 2015 Information Theory and Applications Workshop (ITA).

[7]  Olga Galinina,et al.  Analyzing Effects of Directional Deafness on mmWave Channel Access in Unlicensed Bands , 2017, 2017 IEEE Globecom Workshops (GC Wkshps).

[8]  Olga Galinina,et al.  A Concise Review of 5G New Radio Capabilities for Directional Access at mmWave Frequencies , 2018, NEW2AN.

[9]  Lin Han,et al.  Problem Statement: Transport Support for Augmented and Virtual Reality Applications , 2017 .

[10]  Olga Galinina,et al.  Capturing Spatial Randomness of Heterogeneous Cellular/WLAN Deployments With Dynamic Traffic , 2014, IEEE Journal on Selected Areas in Communications.

[11]  Rodrigo Roman,et al.  Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges , 2016, Future Gener. Comput. Syst..

[12]  Ehab Mahmoud Mohamed,et al.  An Efficient Paradigm for Multiband WiGig D2D Networks , 2019, IEEE Access.

[13]  Amitabha Ghosh,et al.  5G Evolution: A View on 5G Cellular Technology Beyond 3GPP Release 15 , 2019, IEEE Access.

[14]  June Andrews,et al.  Keeping it real. , 2003, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[15]  Robert W. Heath,et al.  Swift-Link: A Compressive Beam Alignment Algorithm for Practical mmWave Radios , 2018, IEEE Transactions on Signal Processing.