VR is expected to be one of the killer applications in 5G networks. However, many technical bottlenecks and challenges need to be overcome to facilitate its wide adoption. In particular, VR requirements in terms of high throughput, low latency, and reliable communication call for innovative solutions and fundamental research cutting across several disciplines. In view of the above, this article discusses the challenges and enablers for ultra-reliable and low-latency VR. Furthermore, in an interactive VR gaming arcade case study, we show that a smart network design that leverages the use of mmWave communication, edge computing, and proactive caching can achieve the future vision of VR over wireless.

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Toward Low-Latency and Ultra-Reliable Virtual Reality

While customers rivet their eyes on Wi-Fi 6, in the bowels of the IEEE 802.11 Working Group that creates Wi-Fi standards, the next generation Wi-Fi is being developed. At the very first sight, the new IEEE 802.11be amendment to the Wi-Fi standard is nothing but scaled 11ax with doubled bandwidth and the increased number of spatial streams, which together provide data rates as high as 40 Gbps. A bit deeper dive into the 802.11 activities reveals that 11be will support real-time applications. In reality, 11be introduces many more revolutionary changes to Wi-Fi, which will form a basement for further Wi-Fi evolution. Although by now (May 2020), the development process is at the very early phase without any draft specification, the analysis of the discussion in the 802.11 Working Group gives insights into the main innovations of 11be. In addition to the ones above, they include native multi-link operation, channel sounding optimization that opens the door for massive MIMO, advanced PHY and MAC techniques, the cooperation of various access points. The paper analyzes hundreds of features proposed for the new technology, focusing on the open problems that can be solved by the researchers who want to contribute to the development of 802.11be.

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Current Status and Directions of IEEE 802.11be, the Future Wi-Fi 7

Currently, new technologies have enabled the design of smart applications that are used as decision-making tools in the problems of daily life. The key issue in designing such an application is the increasing level of user interaction. Mixed reality (MR) is an emerging technology that deals with maximum user interaction in the real world compared to other similar technologies. Developing an MR application is complicated, and depends on the different components that have been addressed in previous literature. In addition to the extraction of such components, a comprehensive study that presents a generic framework comprising all components required to develop MR applications needs to be performed. This review studies intensive research to obtain a comprehensive framework for MR applications. The suggested framework comprises five layers: the first layer considers system components; the second and third layers focus on architectural issues for component integration; the fourth layer is the application layer that executes the architecture; and the fifth layer is the user interface layer that enables user interaction. The merits of this study are as follows: this review can act as a proper resource for MR basic concepts, and it introduces MR development steps and analytical models, a simulation toolkit, system types, and architecture types, in addition to practical issues for stakeholders such as considering MR different domains.

A Review on Mixed Reality: Current Trends, Challenges and Prospects

The past years have witnessed the rapid conceptualization and development of wireless sensing based on Channel State Information (CSI) with commodity WiFi devices. Recent studies have demonstrated the vast potential of WiFi sensing in detection, recognition, and estimation applications. However, the widespread deployment of WiFi sensing systems still faces a significant challenge: how to ensure the sensing performance when exposing a pre-trained sensing system to new domains, such as new environments, different configurations, and unseen users, without data collection and system retraining. This survey provides a comprehensive review of recent research efforts on cross-domain WiFi Sensing. We first introduce the mathematical model of CSI and explore the impact of different domains on CSI. Then we present a general workflow of cross-domain WiFi sensing systems, which consists of signal processing and cross-domain sensing. Five cross-domain sensing algorithms, including domain-invariant feature extraction, virtual sample generation, transfer learning, few-shot learning and big data solution, are summarized to show how they achieve high sensing accuracy when encountering new domains. The advantages and limitations of each algorithm are also summarized and the performance comparison is made based on different applications. Finally, we discuss the remaining challenges to further promote the practical usability of cross-domain WiFi sensing systems.

Cross-Domain WiFi Sensing with Channel State Information: A Survey

Immersive virtual reality (VR) applications require ultra-high data rate and low-latency for smooth operation. Hence in this paper, aiming to improve VR experience in multi-user VR wireless video streaming, a deep-learning aided scheme for maximizing the quality of the delivered video chunks with low-latency is proposed. Therein the correlations in the predicted field of view (FoV) and locations of viewers watching 360° HD VR videos are capitalized on to realize a proactive FoV-centric millimeter wave (mmWave) physical-layer multicast transmission. The problem is cast as a frame quality maximization problem subject to tight latency constraints and network stability. The problem is then decoupled into an HD frame request admission and scheduling subproblems and a matching theory game is formulated to solve the scheduling subproblem by associating requests from clusters of users to mmWave small cell base stations (SBSs) for their unicast/multicast transmission. Furthermore, for realistic modeling and simulation purposes, a real VR head-tracking dataset and a deep recurrent neural network (DRNN) based on gated recurrent units (GRUs) are leveraged. Extensive simulation results show how the content-reuse for clusters of users with highly overlapping FoVs brought in by multicasting reduces the VR frame delay in 12%. This reduction is further boosted by proactiveness that cuts by half the average delays of both reactive unicast and multicast baselines while preserving HD delivery rates above 98%. Finally, enforcing tight latency bounds shortens the delay-tail as evinced by 13% lower delays in the 99th percentile.

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Taming the Latency in Multi-User VR 360°: A QoE-Aware Deep Learning-Aided Multicast Framework

In recent years, Channel State Information (CSI) measured by WiFi is widely used for human activity recognition In this article, we propose a deep learning design for location- and person-independent activity recognition with WiFi The proposed design consists of three Deep Neural Networks (DNNs): a 2D Convolutional Neural Network (CNN) as the recognition algorithm, a 1D CNN as the state machine, and a reinforcement learning agent for neural architecture search The recognition algorithm learns location- and person-independent features from different perspectives of CSI data The state machine learns temporal dependency information from history classification results The reinforcement learning agent optimizes the neural architecture of the recognition algorithm using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) The proposed design is evaluated in a lab environment with different WiFi device locations, antenna orientations, sitting/standing/walking locations/orientations, and multiple persons The proposed design has 97% average accuracy when testing devices and persons are not seen during training The proposed design is also evaluated by two public datasets with accuracy of 80% and 83% The proposed design needs very little human efforts for ground truth labeling, feature engineering, signal processing, and tuning of learning parameters and hyperparameters

Location- and Person-Independent Activity Recognition with WiFi, Deep Neural Networks, and Reinforcement Learning

Mixed reality services promise to enhance our lives by providing us with real-time immersive media and contextual information delivered through lightweight wireless head mounted displays. We introduce a perception based mixed reality video streaming platform that reduces the data rate requirements for such services from several Gbps to 100 Mbps over a network that supports a 10 ms Round-Trip-Time. We expect mixed reality services first to be delivered through indoor and campus area networks and later through ubiquitous 5G networks. Our simulation and measurement results show that massive MIMO and millimeter wave transmissions can provide the required aggregate multi-Gigabit indoor data rates to support mixed reality services.

On wireless networks for the era of mixed reality

This paper explores the feasibility of leveraging deep reinforcement learning (DRL) to enable dynamic resource management in Wi-Fi networks implementing distributed multi-user MIMO (D-MIMO). D-MIMO is a technique by which a set of wireless access points are synchronized and grouped together to jointly serve multiple users simultaneously. This paper addresses two dynamic resource management problems germane to D-MIMO Wi-Fi networks: (i) channel assignment of D-MIMO groups, and (ii) deciding how to cluster access points to form D-MIMO groups, in order to maximize user throughput performance. These problems are known to be NP-Hard and only heuristic solutions exist in literature. We construct a DRL framework through which a learning agent interacts with a D-MIMO Wi-Fi network, learns about the network environment, and successfully converges to policies which address the aforementioned problems. Through extensive simulations and on-line training based on D-MIMO Wi-Fi networks, this paper demonstrates the efficacy of DRL agents in achieving an improvement of 20% in user throughput performance compared to heuristic solutions, particularly when network conditions are dynamic. This work also showcases the effectiveness of DRL agents in meeting multiple network objectives simultaneously, for instance, maximizing throughput of users as well as fairness of throughput among them.

Optimizing Throughput Performance in Distributed MIMO Wi-Fi Networks Using Deep Reinforcement Learning

This paper explores the feasibility of leveraging concepts from deep reinforcement learning (DRL) to enable dynamic resource management in Wi-Fi networks implementing distributed multi-user MIMO (D-MIMO). D-MIMO is a technique by which a set of wireless access points are synchronized and grouped together to jointly serve multiple users simultaneously. This paper addresses two dynamic resource management problems pertaining to D-MIMO Wi-Fi networks: (i) channel assignment of D-MIMO groups, and (ii) deciding how to cluster access points to form D-MIMO groups, in order to maximize user throughput performance. These problems are known to be NP-Hard and only heuristic solutions exist in literature. We construct a DRL framework through which a learning agent interacts with a D-MIMO Wi-Fi network, learns about the network environment, and is successful in converging to policies which address the aforementioned problems. Through extensive simulations and on-line training based on D-MIMO Wi-Fi networks, this paper demonstrates the efficacy of DRL in achieving an improvement of 20% in user throughput performance compared to heuristic solutions, particularly when network conditions are dynamic. This work also showcases the effectiveness of DRL in meeting multiple network objectives simultaneously, for instance, maximizing throughput of users as well as fairness of throughput among them.

Optimizing Throughput Performance in Distributed MIMO Wi-Fi Networks using Deep Reinforcement Learning

We present D-MIMOO, a distributed MIMO Wi-Fi architecture that boosts average network throughput compared to state-of-the-art access points with co-located antennas (baseline configuration). Designing a distributed MIMO system invites us to redesign fundamental Wi-Fi concepts, such as carrier sensing multiple access (CSMA) which governs channel access among Wi-Fi access points. We also propose a novel way of using channel reciprocity and the network topology to select downlink MU-MIMO recipients. The proposed solutions are standards compliant, do not require modifications at the user equipment and hence will work with legacy 802.11ac devices. We compare the performance of the D-MIMOO architecture to a baseline dense enterprise Wi-Fi deployment, and observe 2.5× improvement in median and 104% increase in average downlink per-stream throughput.

Eric Torkildson

Papers

Location- and Person-Independent Activity Recognition with WiFi, Deep Neural Networks, and Reinforcement Learning

On wireless networks for the era of mixed reality

Optimizing Throughput Performance in Distributed MIMO Wi-Fi Networks Using Deep Reinforcement Learning

Optimizing Throughput Performance in Distributed MIMO Wi-Fi Networks using Deep Reinforcement Learning

D-MIMOO – Distributed MIMO for Office Wi-Fi Networks