In theory, the capacity of a wireless network grows linearly with the number of users and antennas equipped at the communication devices, and hence large-scale MU-MIMO can scale up the network throughput. However, three main challenges are impeding the implementation of this promising technology in the state-of-the-art WLANs. Firstly, the current large-scale MU-MIMO technology demands a large number of high-priced RF chains. Secondly, the wireless access points (APs) are overwhelmed by channel state information (CSI) feedback for nulling multi-user and -antenna interference. Thirdly, the lack of scalable user selection scheme limits the capability of APs to serve a large user population. To address these problems, we design BUSH, a large-scale MU-MIMO prototype that performs scalable beam user selection with hybrid beamforming for phased-array antennas in legacy WLANs. We design a low complexity algorithm that assigns each pair of RF chain and analog beam to the users to effectively reduce channel correlation and cross-talk interference without instantaneous CSI feedbacks. As a prerequisite of user selection, BUSH presents a low-overhead probing scheme in multi-carrier WLANs and designs a highly accurate blind Power Azimuth Spectrum (PAS) estimation algorithm using a single RF chain. For reducing the number of RF-chains used, the phased-array antennas use analog beamforming to steer beams toward each selected downlink user, and multiple RF chains use beamforming to further mitigate the interference among users. We implement BUSH on a software-defined radio platform and evaluate its performance in more than 30 indoor scenarios. The experimental results reveal that for throughput, BUSH outperforms the legacy 802.11ac by 2.08×, and an alternative benchmark system by 1.22× on average.

Owing to great potential in smart home and human-computer interactive applications, WiFi indoor localization has attracted extensive attentions in the past several years. The state-of-the-art systems have successfully achieved decimeter-level accuracies. However, the high accuracy is acquired at the cost of dense access point (AP) deployment, employing large size of frequency bandwidths or special-purpose radar signals which are not compatible with existing WiFi protocol, limiting their practical deployments. This paper presents the design and implementation of AWL, an accurate indoor localization system that enables a single WiFi AP to achieve decimeter-level accuracy with only one channel hopping. The key enabler of the system is we novelly employ channel hopping to create virtual antennas, without the need of adding more antennas or physically move the antennas' positions for a larger antenna array. We successfully utilize the widely known "bad" spatial aliasing to improve the AoA estimation accuracy. A novel multipath suppression scheme is also proposed to combat the severe multipath issue indoors. We build a prototype of AWL on WARP software-defined radio platform. Comprehensive experiments manifest that AWL achieves a median localization accuracy of 38 cm in a rich multipath indoor environment with only a single AP equipped with 6 antennas. In a small scale area, AWL is able to accurately track a moving device's trajectory, enabling applications such as writing/drawing in the air.

Large-scale MU-MIMO is a promising technology to scale network capacity and the capacity gain grows linearly with the numbers of antennas and users in theory. However, its practical deployment faces three critical challenges in the state-of-the-art WLANs: i) the demand of a large number of expensive RF chains; ii) the linear growth of feedback overheads with the number of antennas; iii) the lack of scalable user selection scheme for a large user population. In this paper, we design BUSH, a large-scale MU-MIMO prototype that performs scalable beam user selection with hybrid beamforming for phased-array antennas in legacy WLANs. The architecture of BUSH consists of three components. Firstly, a low complexity algorithm assigns each pair of RF chain and analog beam to the users to effectively reduce channel correlation and cross-talk interference without instantaneous CSI feedbacks. Secondly, as a prerequisite of user selection, BUSH presents a low-overhead probing scheme in multi-carrier WLANs, and designs a highly accurate blind Power Azimuth Spectrum (PAS) estimation algorithm using a single RF chain. Thirdly, the phased-array antennas use analog beamforming to steer spatial beams toward each selected downlink user, and the finite number of RF chains use beamforming to further mitigate the interference among users. We implement BUSH on the WARPv3 boards and evaluate its performance in more than 30 indoor scenarios. The experimental results show that in terms of total throughput BUSH outperforms the legacy 802.11ac by 2.08×, and an alternative benchmark system by 1.22× on average.

Multiview video is essential for various mobile three-dimensional (3D) and immersive applications that can capture scenes from multiple angles for better user experience. However, robust transmission of multiview video is very challenging in wireless networks due to high bandwidth requirement and time-varying channel quality. Though the up-to-date 802.11 system enables spatial multiplexing MIMO to enhance transmission capacity, it is still agnostic to 3D source coding structure in the transmission. In this paper, we study the optimal resource allocation problem in MIMO systems that deliver 3D content with multiview video coding. The basic idea is to exploit the channel diversity of multiple antennas and the source coding characteristics so as to achieve unequal error protection against channel errors. To achieve this goal, we develop a nonlinear mixed integer programming framework to perform antenna selection and power allocation, and propose low-complexity algorithms to assign these resources. We implement a proof-of-concept system, namely MuVi, on the software-defined-radio platform, WARP, to evaluate the proposed algorithms. MuVi is the practical system to tackle 3D multiview streaming in the latest Wi-Fi networks such as IEEE 802.11ac under realistic channel conditions. Extensive experimental results demonstrate that the peak signal-to-noise-ratio of MuVi significantly outperforms that of the conventional power allocation scheme in a variety of indoor environments.

Multi-view video streaming is essential for various mobile 3D and immersive applications that can capture the same scene from multiple angles. However, the large traffic volume of multi-view streaming will drain the battery power quickly. This paper studies the power efficient delivery of 3D content with multi-view video coding (MVC) in the emerging 802.11-like MIMO wireless systems, and the purpose is to minimize the power consumption with the video quality guarantee. We propose an efficient algorithm to perform antenna assignment and transmission power allocation, by exploiting both the source coding characteristics of MVC and channel diversity of multiple antennas. A proof-of-concept system, namely PoM, is designed on the software radio platform and is evaluated in realistic indoor environments. To the best of our knowledge, this is the first practical system for energy efficient multi-view video streaming. Experimental results show that PoM can significantly save energy in the transmission by 12% ~ 65% on average when the required PSNR decreases from 45dB to 35dB.

Shortest-path queries on weighted graphs are an essential operation in computer networks. The performance of such algorithms has become a critical challenge in emerging software-defined networks (SDN), since SDN controllers need to perform a shortest-path query for every flow. Unlike classic solutions (e.g., Dijkstra's algorithm), high-performance shortest-path query algorithms include two stages: preprocessing and query answering. Despite the improved query answering time, existing two-stage algorithms are still extremely time-consuming in preprocessing large-scale graphs. In this paper, we propose an efficient shortest-path query algorithm, called BBQ, which reduces the running time of both stages via tree decomposition. BBQ constructs a distance oracle in a bottom-top-bottom manner, which significantly reduces preprocessing time over existing algorithms. In addition, BBQ can answer batch queries in bulk by traversing the decomposed tree instead of executing separate queries. Our experimental results show that BBQ outperforms state-of-the-art approaches by orders of magnitude for the running time of the preprocessing stage. Meanwhile, BBQ also offers remarkable acceleration for answering batches of queries. As a result, SDN controllers that use BBQ can sustain 1.1-27.9 times higher connection request rates.