Resilient multi-user beamforming WLANs: Mobility, interference, and imperfect CSI

Bejarano, Oscar; Hoefel, Roger Pierre Fabris; Knightly, Edward W.

doi:10.1109/infocom.2016.7524573

Cited by 9 publications

(7 citation statements)

References 21 publications

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“…To address the issues of inaccurate CSI, dynamic sounding algorithms [5,6] that adapt the time between consecutive soundings of a client have been developed to enhance MU-MIMO eciency under client motion and minimize sounding overhead.…”

Section: Mu-mimo Under User Mobilitymentioning

confidence: 99%

“…In [6], a sounding inhibition scheme based on experimental results is designed to minimize sounding overhead when repeated CSI measurements are not needed. The authors of [5] create a rate adaptation and retransmission algorithm that increases MU-MIMO eciency in the presence of client mobility and incomplete CSI. In [29], the authors create an algorithm that calculates the network's global sounding period and only includes clients with channel coherence times higher than the sounding period.…”

Section: Mu-mimo Under User Mobilitymentioning

confidence: 99%

See 1 more Smart Citation

Learning-driven MU-MIMO Grouping for Multi-User Multimedia Applications Over Commodity WiFi

Pasandi

Amirpour

Nadeem

et al. 2021

Proceedings of the Workshop on Design, Deployment, and Evaluation of Network-Assisted Video Streaming

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Over the last decade, the bandwidth expansion and MU-MIMO spectral eciency have promised to increase data throughput by allowing concurrent communication between one Access Point and multiple users. However, we are still a long way from enjoying such MU-MIMO MAC protocol improvements for bandwidth hungry applications such as video streaming in practical WiFi network settings due to heterogeneous channel conditions and devices, unreliable transmissions, and lack of useful feedback exchange among the lower and upper layers' requirements. This paper introduces MuViS, a novel dual-phase optimization framework that proposes a Quality of Experience (QoE) aware MU-MIMO optimization for multi-user video streaming over IEEE 802.11ac. MuViS rst employs reinforcement learning to optimize the MU-MIMO user group and mode selection for users based on their PHY/MAC layer characteristics. The video bitrate is then optimized based on the user's mode (Multi-User (MU) or Single-User (SU)). We present our design and its evaluation on smartphones and laptops using 802.11ac WiFi. Our experimental results in various indoor environments and congurations show a scalable framework that can support a large number of users with streaming at high video rates and satisfying QoE requirements.

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Section: Mu-mimo Under User Mobilitymentioning

confidence: 99%

Section: Mu-mimo Under User Mobilitymentioning

confidence: 99%

Learning-driven MU-MIMO Grouping for Multi-User Multimedia Applications Over Commodity WiFi

Pasandi

Amirpour

Nadeem

et al. 2021

Proceedings of the Workshop on Design, Deployment, and Evaluation of Network-Assisted Video Streaming

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“…Finally, we proposed a channel aware adaptive MCS scheduling scheme based on the RBIR metric. The PHY layer abstraction technique investigated in this paper has also been used to reduce the computational burden when over the air experiments are used to design MU beamforming protocols that increase the resilience against downlink interference due to imperfect channel estimation [25].…”

Section: Link Level Adaptation Using Rbir Esmmentioning

confidence: 99%

On Application of PHY Layer Abstraction Techniques for System Level Simulation and Adaptive Modulation in IEEE 802.11ac/ax Systems

Hoefel

Bejarano

2016

JCIS

Self Cite

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This paper investigates the application in IEEE 802.11ac/ax systems of the received bit information rate (RBIR) technique in order to estimate the effective signal-tointerference-plus-noise ratio used to abstract the physical layer (PHY) performance in system level simulations. The RBIR technique is evaluated based on simulation results of IEEE 802.11ac PHY operating over canonical flat fading and spatial-correlated frequency selective single-user and multiuser multiple input multiple output channels. We have concluded that the RBIR PHY abstraction methodology is accurate enough to provide first order insights on system level performance and design options with reduced computational complexity. Finally, the application of RBIR PHY abstraction scheme to schedule the modulation and code schemes on the flight to achieve a target quality-ofservice is also described.

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“…The columns of contains the columns of that correspond to the UL MIMO channel between the uth STA and the AP. Incorporating the effects of precoding, then (4) can be rewritten as ෩ = ൣ ෩ , ⋯ , ෩ , ⋯ , ෩ ൧ = ሾ , ⋯ , , ⋯ , ሿ, (5) where ෩ has size nr by nss,u.…”

Section: B Mmse Mu-mimo Detectorsmentioning

confidence: 99%

“…The optional implementation of downlink (DL) __________________________________________________ Roger Pierre Fabris Hoefel, Department of Electrical Engineering, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, Rio Grande do Sul (RS), Brazil; E-mail: roger.hoefel@ufrgs.br. multi-user (MU) MIMO techniques allows improving the network throughput when the system is loaded with stations (STAs) that have a lesser number of antennas than the number of antennas deployed at the access points (AP) due to the real world form factor and cost constraints [5].…”

Section: Introductionmentioning

confidence: 99%

IEEE 802.11ax: Performance of OFDM Uplink Multi-User MIMO over Non-Ideal Scenarios

Hoefel¹

2016

Anais De XXXIV Simpósio Brasileiro De Telecomunicações

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We first present the motivations, challenges and issues that have been driven intensive research and design activities toward the specification of IEEE 802.11ax amendment, the sixth generation of wireless local networks (WLANs). Next, we summarize the main medium access control (MAC) protocols and physical layer (PHY) techniques that have been researched and developed in the scope of the IEEE Task Group (TG) 802.11ax. In the following, we derive a mathematical model for both uplink multiuser multiple input multiple output (UL MU-MIMO) transmission scheme and mean squared error (MMSE) MU-MIMO detector. After presenting a first order validation of an IEEE 802.11ax simulator, we show preliminary results on the performance of IEEE 802.11ax UL MU-MIMO transceivers. We conclude that reserving degrees of freedom at receiver side to allow diversity gains can mitigate the negative effects of imperfect channel state information (CSI), providing expressive power gains. We also have concluded that it is necessary to implement sophisticated channel estimation schemes and advance MIMO detectors to cope with the interference generated in uplink channels loaded with a large number of clients.

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Resilient multi-user beamforming WLANs: Mobility, interference, and imperfect CSI

Cited by 9 publications

References 21 publications

Learning-driven MU-MIMO Grouping for Multi-User Multimedia Applications Over Commodity WiFi

Learning-driven MU-MIMO Grouping for Multi-User Multimedia Applications Over Commodity WiFi

On Application of PHY Layer Abstraction Techniques for System Level Simulation and Adaptive Modulation in IEEE 802.11ac/ax Systems

IEEE 802.11ax: Performance of OFDM Uplink Multi-User MIMO over Non-Ideal Scenarios

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